case data updated to include 22 jan 2020; we did not use cases reported after this period as cases were reported at the province level hereafter, and large scale control interventions were initiated on 23 jan 2020; improved likelihood function, better accounting for first 41 confirmed cases, and now using all infections (rather than just cases detected) in wuhan for prediction of infection in international travellers; improved characterization of uncertainty in parameters, and calculation of epidemic trajectory confidence intervals using a more statistically rigorous method; extended range of latent period in sensitivity analysis to reflect reports of up to 6 day incubation period in household clusters; removed travel restriction analysis, as different modelling approaches (e g stochastic transmission, rather than deterministic transmission) are more appropriate to such analyses [SEP]",
"target": "entailment"
},
{
"instance_id": "R108239xR108316",
"template_id": "R108239",
"paper_id": "R108316",
"premise": "km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development",
"hypothesis": "analyzing knowledge transfer effectiveness an agent oriented modeling approach facilitating the transfer of knowledge between knowledge workers represents one of the main challenges of knowledge management knowledge transfer instruments, such as the experience factory concept, represent means for facilitating knowledge transfer in organizations as past research has shown, effectiveness of knowledge transfer instruments strongly depends on their situational context, on the stakeholders involved in knowledge transfer, and on their acceptance, motivation and goals in this paper, we introduce an agent oriented modeling approach for analyzing the effectiveness of knowledge transfer instruments in the light of (potentially conflicting) stakeholders' goals we apply this intentional approach to the experience factory concept and analyze under which conditions it can fail, and how adaptations to the experience factory can be explored in a structured way",
"sequence": "[CLS] km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development [SEP] analyzing knowledge transfer effectiveness an agent oriented modeling approach facilitating the transfer of knowledge between knowledge workers represents one of the main challenges of knowledge management knowledge transfer instruments, such as the experience factory concept, represent means for facilitating knowledge transfer in organizations as past research has shown, effectiveness of knowledge transfer instruments strongly depends on their situational context, on the stakeholders involved in knowledge transfer, and on their acceptance, motivation and goals in this paper, we introduce an agent oriented modeling approach for analyzing the effectiveness of knowledge transfer instruments in the light of (potentially conflicting) stakeholders' goals we apply this intentional approach to the experience factory concept and analyze under which conditions it can fail, and how adaptations to the experience factory can be explored in a structured way [SEP]",
"target": "entailment"
},
{
"instance_id": "R35087xR44776",
"template_id": "R35087",
"paper_id": "R44776",
"premise": "time interval has beginning has end",
"hypothesis": "estimating the generation interval for covid 19 based on symptom onset data abstract background estimating key infectious disease parameters from the covid 19 outbreak is quintessential for modelling studies and guiding intervention strategies whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for covid 19 have not been provided methods we used outbreak data from clusters in singapore and tianjin, china to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network from those estimates we obtained the proportions pre symptomatic transmission and reproduction numbers results the mean generation interval was 5 20 (95%ci 3 78 6 78) days for singapore and 3 95 (95%ci 3 01 4 91) days for tianjin, china when relying on a previously reported incubation period with mean 5 2 and sd 2 8 days the proportion of pre symptomatic transmission was 48% (95%ci 32 67%) for singapore and 62% (95%ci 50 76%) for tianjin, china estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution conclusions estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network detailed contact tracing information is essential for correctly estimating these quantities",
"sequence": "[CLS] time interval has beginning has end [SEP] estimating the generation interval for covid 19 based on symptom onset data abstract background estimating key infectious disease parameters from the covid 19 outbreak is quintessential for modelling studies and guiding intervention strategies whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for covid 19 have not been provided methods we used outbreak data from clusters in singapore and tianjin, china to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network from those estimates we obtained the proportions pre symptomatic transmission and reproduction numbers results the mean generation interval was 5 20 (95%ci 3 78 6 78) days for singapore and 3 95 (95%ci 3 01 4 91) days for tianjin, china when relying on a previously reported incubation period with mean 5 2 and sd 2 8 days the proportion of pre symptomatic transmission was 48% (95%ci 32 67%) for singapore and 62% (95%ci 50 76%) for tianjin, china estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution conclusions estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network detailed contact tracing information is essential for correctly estimating these quantities [SEP]",
"target": "entailment"
},
{
"instance_id": "R108239xR108301",
"template_id": "R108239",
"paper_id": "R108301",
"premise": "km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development",
"hypothesis": "a notation for knowledge intensive processes business process modeling has become essential for managing organizational knowledge artifacts however, this is not an easy task, especially when it comes to the so called knowledge intensive processes (kips) a kip comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents' knowledge the previously developed knowledge intensive process ontology (kipo) structures all the concepts (and relationships among them) to make a kip explicit nevertheless, kipo does not include a graphical notation, which is crucial for kip stakeholders to reach a common understanding about it this paper proposes the knowledge intensive process notation (kipn), a notation for building knowledge intensive processes graphical models",
"sequence": "[CLS] km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development [SEP] a notation for knowledge intensive processes business process modeling has become essential for managing organizational knowledge artifacts however, this is not an easy task, especially when it comes to the so called knowledge intensive processes (kips) a kip comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents' knowledge the previously developed knowledge intensive process ontology (kipo) structures all the concepts (and relationships among them) to make a kip explicit nevertheless, kipo does not include a graphical notation, which is crucial for kip stakeholders to reach a common understanding about it this paper proposes the knowledge intensive process notation (kipn), a notation for building knowledge intensive processes graphical models [SEP]",
"target": "entailment"
},
{
"instance_id": "R48214xR48303",
"template_id": "R48214",
"paper_id": "R48303",
"premise": "global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period",
"hypothesis": "a probabilistic approach to 21st century regional sea level projections using rcp and high end scenarios sea level change is an integrated climate system response due to changes in radiative forcing, anthropogenic land water use and land motion projecting sea level at a global and regional scale requires a subset of projections one for each sea level component given a particular climate change scenario we construct relative sea level projections through the 21st century for rcp 4 5, rcp 8 5 and high end (rcp 8 5 with increased ice sheet contribution) scenarios by aggregating spatial projections of individual sea level components in a probabilistic manner most of the global oceans adhere to the projected global average sea level change within 5 cm throughout the century for all scenarios; however coastal regions experience localised effects due to the non uniform spatial patterns of individual components this can result in local projections that are 10\u2032s of centimetres different from the global average by 2100 early in the century, rsl projections are consistent across all scenarios, however from the middle of the century the patterns of rsl for rcp scenarios deviate from the high end where the contribution from antarctica dominates similarly, the uncertainty in projected sea level is dominated by an uncertain antarctic fate we also explore the effect upon projections of, treating cmip5 model ensembles as normally distributed when they might not be, correcting cmip5 model output for internal variability using different polynomials and using different unloading patterns of ice for the greenland and antarctic ice sheets",
"sequence": "[CLS] global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period [SEP] a probabilistic approach to 21st century regional sea level projections using rcp and high end scenarios sea level change is an integrated climate system response due to changes in radiative forcing, anthropogenic land water use and land motion projecting sea level at a global and regional scale requires a subset of projections one for each sea level component given a particular climate change scenario we construct relative sea level projections through the 21st century for rcp 4 5, rcp 8 5 and high end (rcp 8 5 with increased ice sheet contribution) scenarios by aggregating spatial projections of individual sea level components in a probabilistic manner most of the global oceans adhere to the projected global average sea level change within 5 cm throughout the century for all scenarios; however coastal regions experience localised effects due to the non uniform spatial patterns of individual components this can result in local projections that are 10\u2032s of centimetres different from the global average by 2100 early in the century, rsl projections are consistent across all scenarios, however from the middle of the century the patterns of rsl for rcp scenarios deviate from the high end where the contribution from antarctica dominates similarly, the uncertainty in projected sea level is dominated by an uncertain antarctic fate we also explore the effect upon projections of, treating cmip5 model ensembles as normally distributed when they might not be, correcting cmip5 model output for internal variability using different polynomials and using different unloading patterns of ice for the greenland and antarctic ice sheets [SEP]",
"target": "entailment"
},
{
"instance_id": "R76209xR38225",
"template_id": "R76209",
"paper_id": "R38225",
"premise": "team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in",
"hypothesis": "joint extraction of entities and relations based on a novel tagging scheme joint extraction of entities and relations is an important task in information extraction to tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem then, based on our tagging scheme, we study different end to end models to extract entities and their relations directly, without identifying entities and relations separately we conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods what's more, the end to end model proposed in this paper, achieves the best results on the public dataset",
"sequence": "[CLS] team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in [SEP] joint extraction of entities and relations based on a novel tagging scheme joint extraction of entities and relations is an important task in information extraction to tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem then, based on our tagging scheme, we study different end to end models to extract entities and their relations directly, without identifying entities and relations separately we conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods what's more, the end to end model proposed in this paper, achieves the best results on the public dataset [SEP]",
"target": "entailment"
},
{
"instance_id": "R38147xR71679",
"template_id": "R38147",
"paper_id": "R71679",
"premise": "comparisons with existing machine learning (ml) models outperforms",
"hypothesis": "linking physicians to medical research results via knowledge graph embeddings and twitter informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients meanwhile, social media is an infrastructure that allows public instant sharing of information, thus it has recently become popular in medical applications in this study, we apply multi distance knowledge graph embeddings (mde) to link physicians and surgeons to the latest medical breakthroughs that are shared as the research results on twitter our study shows that using this method physicians can be informed about the new findings in their field given that they have an account dedicated to their profession",
"sequence": "[CLS] comparisons with existing machine learning (ml) models outperforms [SEP] linking physicians to medical research results via knowledge graph embeddings and twitter informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients meanwhile, social media is an infrastructure that allows public instant sharing of information, thus it has recently become popular in medical applications in this study, we apply multi distance knowledge graph embeddings (mde) to link physicians and surgeons to the latest medical breakthroughs that are shared as the research results on twitter our study shows that using this method physicians can be informed about the new findings in their field given that they have an account dedicated to their profession [SEP]",
"target": "entailment"
}
],
"contradictions": [
{
"instance_id": "R107684xR48315",
"template_id": "R107684",
"correct_template_id": "R48214",
"paper_id": "R48315",
"premise": "health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample",
"hypothesis": "sea level projections representing the deeply uncertain contribution of the west antarctic ice sheet there is a growing awareness that uncertainties surrounding future sea level projections may be much larger than typically perceived recently published projections appear widely divergent and highly sensitive to non trivial model choices moreover, the west antarctic ice sheet (wais) may be much less stable than previous believed, enabling a rapid disintegration here, we present a set of probabilistic sea level projections that approximates the deeply uncertain wais contributions the projections aim to inform robust decisions by clarifying the sensitivity to non trivial or controversial assumptions we show that the deeply uncertain wais contribution can dominate other uncertainties within decades these deep uncertainties call for the development of robust adaptive strategies these decision making needs, in turn, require mission oriented basic science, for example about potential signposts and the maximum rate of wais induced sea level changes",
"sequence": "[CLS] health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample [SEP] sea level projections representing the deeply uncertain contribution of the west antarctic ice sheet there is a growing awareness that uncertainties surrounding future sea level projections may be much larger than typically perceived recently published projections appear widely divergent and highly sensitive to non trivial model choices moreover, the west antarctic ice sheet (wais) may be much less stable than previous believed, enabling a rapid disintegration here, we present a set of probabilistic sea level projections that approximates the deeply uncertain wais contributions the projections aim to inform robust decisions by clarifying the sensitivity to non trivial or controversial assumptions we show that the deeply uncertain wais contribution can dominate other uncertainties within decades these deep uncertainties call for the development of robust adaptive strategies these decision making needs, in turn, require mission oriented basic science, for example about potential signposts and the maximum rate of wais induced sea level changes [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108008xR135546",
"template_id": "R108008",
"correct_template_id": null,
"paper_id": "R135546",
"premise": "business architecture available tools metamodel operations methodology best practices / reference models metamodel maturity metamodel revenue model & performance integration with other architectures methodology management ba initiatives methodology use case scenarios approach name metamodel business network methodology structured procedure model metamodel strategy and structure methodology maturity methodology development of ba model",
"hypothesis": "acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks although automated acute lymphoblastic leukemia (all) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells the traditional all classification strategy is arduous, time consuming, often suffers inter observer variations, and necessitates experienced pathologists this article has automated the all detection task, employing deep convolutional neural networks (cnns) we explore the weighted ensemble of deep cnns to recommend a better all cell classifier the weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, f1 score, auc, and kappa values various data augmentations and pre processing are incorporated for achieving a better generalization of the network we train and evaluate the proposed model utilizing the publicly available c nmc 2019 all dataset our proposed weighted ensemble model has outputted a weighted f1 score of 88 6%, a balanced accuracy of 86 2%, and an auc of 0 941 in the preliminary test set the qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region in contrast, the ensemble candidate models, such as xception, vgg 16, densenet 121, mobilenet, and inceptionresnet v2, separately produce coarse and scatter learned areas for most example cases since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications",
"sequence": "[CLS] business architecture available tools metamodel operations methodology best practices / reference models metamodel maturity metamodel revenue model & performance integration with other architectures methodology management ba initiatives methodology use case scenarios approach name metamodel business network methodology structured procedure model metamodel strategy and structure methodology maturity methodology development of ba model [SEP] acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks although automated acute lymphoblastic leukemia (all) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells the traditional all classification strategy is arduous, time consuming, often suffers inter observer variations, and necessitates experienced pathologists this article has automated the all detection task, employing deep convolutional neural networks (cnns) we explore the weighted ensemble of deep cnns to recommend a better all cell classifier the weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, f1 score, auc, and kappa values various data augmentations and pre processing are incorporated for achieving a better generalization of the network we train and evaluate the proposed model utilizing the publicly available c nmc 2019 all dataset our proposed weighted ensemble model has outputted a weighted f1 score of 88 6%, a balanced accuracy of 86 2%, and an auc of 0 941 in the preliminary test set the qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region in contrast, the ensemble candidate models, such as xception, vgg 16, densenet 121, mobilenet, and inceptionresnet v2, separately produce coarse and scatter learned areas for most example cases since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108555xR110638",
"template_id": "R108555",
"correct_template_id": "R108008",
"paper_id": "R110638",
"premise": "machine learning experiment has dataset has method has result classification category has feature",
"hypothesis": "enterprise ontology if one thing catches the eye in almost all literature about (re)designing or (re)engineering of enterprises, it is the lack of a well founded theory about their construction and operation often even the most basic notions like \"action\" or \"process\" are not precisely defined next, in order to master the diversity and the complexity of contemporary enterprises, theories are needed that separate the stable essence of an enterprise from the variable way in which it is realized and implemented such a theory and a matching methodology, which has passed the test of practical experience, constitute the contents of this book the enterprise ontology, as developed by dietz, is the starting point for profoundly understanding the organization of an enterprise and subsequently for analyzing, (re)designing, and (re)engineering it the approach covers numerous issues in an integrated way: business processes, in and outsourcing, information systems, management control, staffing etc researchers and students in enterprise engineering or related fields will discover in this book a revolutionary new way of thinking about business and organization in addition, it provides managers, business analysts, and enterprise information system designers for the first time with a solid and integrated insight into their daily work",
"sequence": "[CLS] machine learning experiment has dataset has method has result classification category has feature [SEP] enterprise ontology if one thing catches the eye in almost all literature about (re)designing or (re)engineering of enterprises, it is the lack of a well founded theory about their construction and operation often even the most basic notions like \"action\" or \"process\" are not precisely defined next, in order to master the diversity and the complexity of contemporary enterprises, theories are needed that separate the stable essence of an enterprise from the variable way in which it is realized and implemented such a theory and a matching methodology, which has passed the test of practical experience, constitute the contents of this book the enterprise ontology, as developed by dietz, is the starting point for profoundly understanding the organization of an enterprise and subsequently for analyzing, (re)designing, and (re)engineering it the approach covers numerous issues in an integrated way: business processes, in and outsourcing, information systems, management control, staffing etc researchers and students in enterprise engineering or related fields will discover in this book a revolutionary new way of thinking about business and organization in addition, it provides managers, business analysts, and enterprise information system designers for the first time with a solid and integrated insight into their daily work [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR4133",
"template_id": "R77101",
"correct_template_id": null,
"paper_id": "R4133",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "selected approaches ranking contextual term for the bioasq multi label classification (task6a and 7a) mesh annotations are attached to the medline abstracts to improve retrieval and this service is provided from the curators at the national library of medicine (nlm) efforts to automatically assign such headings to medline abstracts have proven difficult, on the other side, such approaches would increase throughput and efficiency trained solutions, i e machine learning solutions, achieve promising results, however these advancements do not fully explain, which features from the text would suit best the identification of mesh headings from the abstracts this manuscript describes new approaches for the identification of contextual features for automatic mesh annotations, which is a multi label classification (bioasq task6a): more specifically, different approaches for the identification of compound terms have been tested and evaluated the described system has then been extended to better rank selected labels and has been tested in the bioasq task7a challenge the tests show that our recall measures (see task6a) have improved and in the second challenge, both the performance for precision and recall were boosted our work improves our understanding how contextual features from the text help reduce the performance gap given between purely trained solutions and feature based solutions (possibly including trained solutions) in addition, we have to point out that the lexical features given from the mesh thesaurus come with a significant and high discrepancy towards the actual annotations of mesh headings attributed by human curators, which also hinders improvements to the automatic annotation of medline abstracts with mesh headings",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] selected approaches ranking contextual term for the bioasq multi label classification (task6a and 7a) mesh annotations are attached to the medline abstracts to improve retrieval and this service is provided from the curators at the national library of medicine (nlm) efforts to automatically assign such headings to medline abstracts have proven difficult, on the other side, such approaches would increase throughput and efficiency trained solutions, i e machine learning solutions, achieve promising results, however these advancements do not fully explain, which features from the text would suit best the identification of mesh headings from the abstracts this manuscript describes new approaches for the identification of contextual features for automatic mesh annotations, which is a multi label classification (bioasq task6a): more specifically, different approaches for the identification of compound terms have been tested and evaluated the described system has then been extended to better rank selected labels and has been tested in the bioasq task7a challenge the tests show that our recall measures (see task6a) have improved and in the second challenge, both the performance for precision and recall were boosted our work improves our understanding how contextual features from the text help reduce the performance gap given between purely trained solutions and feature based solutions (possibly including trained solutions) in addition, we have to point out that the lexical features given from the mesh thesaurus come with a significant and high discrepancy towards the actual annotations of mesh headings attributed by human curators, which also hinders improvements to the automatic annotation of medline abstracts with mesh headings [SEP]",
"target": "contradiction"
},
{
"instance_id": "R40006xR25623",
"template_id": "R40006",
"correct_template_id": null,
"paper_id": "R25623",
"premise": "basic reproduction number estimate basic reproduction number time period location",
"hypothesis": "drivers of agile software development use: dialectic interplay between benefits and hindrances information and software technology context: agile software development with its emphasis on producing working code through frequent releases, extensive client interactions and iterative development has emerged as an alternative to traditional plan based software development methods while a number of case studies have provided insights into the use and consequences of agile, few empirical studies have examined the factors that drive the adoption and use of agile objective: we draw on intention based theories and a dialectic perspective to identify factors driving the use of agile practices among adopters of this software development methodology method: data for the study was gathered through an anonymous online survey of software development professionals we requested participation from members of a selected list of online discussion groups, and received 98 responses results: our analyses reveal that subjective norm and training play a significant role in influencing software developers' use of agile processes and methods, while perceived benefits and perceived limitations are not primary drivers of agile use among adopters interestingly, perceived benefit emerges as a significant predictor of agile use only if adopters face hindrances to their agile practices conclusion: we conclude that research in the adoption of software development innovations should examine the effects of both enabling and detracting factors and the interactions between them since training, subjective norm, and the interplay between perceived benefits and perceived hindrances appear to be key factors influencing the adoption of agile methods, researchers can focus on how to (a) perform training on agile methods more effectively, (b) facilitate the dialog between developers and managers about perceived benefits and hindrances, and (c) capitalize on subjective norm to publicize the benefits of agile methods within an organization further, when managing the transition to new software development methods, we recommend that practitioners adapt their strategies and tactics contingent on the extent of perceived hindrances to the change",
"sequence": "[CLS] basic reproduction number estimate basic reproduction number time period location [SEP] drivers of agile software development use: dialectic interplay between benefits and hindrances information and software technology context: agile software development with its emphasis on producing working code through frequent releases, extensive client interactions and iterative development has emerged as an alternative to traditional plan based software development methods while a number of case studies have provided insights into the use and consequences of agile, few empirical studies have examined the factors that drive the adoption and use of agile objective: we draw on intention based theories and a dialectic perspective to identify factors driving the use of agile practices among adopters of this software development methodology method: data for the study was gathered through an anonymous online survey of software development professionals we requested participation from members of a selected list of online discussion groups, and received 98 responses results: our analyses reveal that subjective norm and training play a significant role in influencing software developers' use of agile processes and methods, while perceived benefits and perceived limitations are not primary drivers of agile use among adopters interestingly, perceived benefit emerges as a significant predictor of agile use only if adopters face hindrances to their agile practices conclusion: we conclude that research in the adoption of software development innovations should examine the effects of both enabling and detracting factors and the interactions between them since training, subjective norm, and the interplay between perceived benefits and perceived hindrances appear to be key factors influencing the adoption of agile methods, researchers can focus on how to (a) perform training on agile methods more effectively, (b) facilitate the dialog between developers and managers about perceived benefits and hindrances, and (c) capitalize on subjective norm to publicize the benefits of agile methods within an organization further, when managing the transition to new software development methods, we recommend that practitioners adapt their strategies and tactics contingent on the extent of perceived hindrances to the change [SEP]",
"target": "contradiction"
},
{
"instance_id": "R54009xR109872",
"template_id": "R54009",
"correct_template_id": null,
"paper_id": "R109872",
"premise": "climate sensitivity has unit has evaluation data used has ecs result",
"hypothesis": "pagerank related methods for analyzing citation networks a central question in citation analysis is how the most important or most prominent nodes in a citation network can be identified many different approaches have been proposed to address this question in this chapter, we focus on approaches that assess the importance of a node in a citation network based not just on the local structure of the network but instead on the network\u2019s global structure for instance, rather than just counting the number of citations a journal has received, these approaches also take into account from which journals the citations originate and how often these citing journals have been cited themselves the methods that we study are closely related to the well known pagerank method for ranking web pages we therefore start by discussing the pagerank method, and we then review the work that has been done in the field of citation analysis on similar types of methods in the second part of the chapter, we provide a tutorial in which we demonstrate how pagerank calculations can be performed for citation networks constructed based on data from the web of science database the sci2 tool is used to construct citation networks, and matlab is used to perform pagerank calculations",
"sequence": "[CLS] climate sensitivity has unit has evaluation data used has ecs result [SEP] pagerank related methods for analyzing citation networks a central question in citation analysis is how the most important or most prominent nodes in a citation network can be identified many different approaches have been proposed to address this question in this chapter, we focus on approaches that assess the importance of a node in a citation network based not just on the local structure of the network but instead on the network\u2019s global structure for instance, rather than just counting the number of citations a journal has received, these approaches also take into account from which journals the citations originate and how often these citing journals have been cited themselves the methods that we study are closely related to the well known pagerank method for ranking web pages we therefore start by discussing the pagerank method, and we then review the work that has been done in the field of citation analysis on similar types of methods in the second part of the chapter, we provide a tutorial in which we demonstrate how pagerank calculations can be performed for citation networks constructed based on data from the web of science database the sci2 tool is used to construct citation networks, and matlab is used to perform pagerank calculations [SEP]",
"target": "contradiction"
},
{
"instance_id": "R74758xR36128",
"template_id": "R74758",
"correct_template_id": null,
"paper_id": "R36128",
"premise": "experiment has study design has independent variable has experimental procedure has material has subject selection method has hypotheses has dependent variables data analysis has rewards",
"hypothesis": "risk estimation and prediction by modeling the transmission of the novel coronavirus (covid 19) in mainland china excluding hubei province abstract background in december 2019, an outbreak of coronavirus disease (covid 19) was identified in wuhan, china and, later on, detected in other parts of china our aim is to evaluate the effectiveness of the evolution of interventions and self protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in mainland china excluding hubei province based on the published data and a novel mathematical model methods a novel covid 19 transmission dynamic model incorporating the intervention measures implemented in china is proposed covid 19 daily data of mainland china excluding hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases for the period january 20th march 3rd, 2020, were archived from the national health commission of china (nhcc) we parameterize the model by using the markov chain monte carlo (mcmc) method and estimate the control reproduction number r c , as well as the effective daily reproduction ratio r e ( t ), of the disease transmission in mainland china excluding hubei province results the estimation outcomes indicate that r c is 3 36 (95% ci 3 20 3 64) and r e ( t ) has dropped below 1 since january 31st, 2020, which implies that the containment strategies implemented by the chinese government in mainland china excluding hubei province are indeed effective and magnificently suppressed covid 19 transmission moreover, our results show that relieving personal protection too early may lead to the spread of disease for a longer time and more people would be infected, and may even cause epidemic or outbreak again by calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by april, 2020 conclusions to ensure the epidemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, etc people should be fully aware of the real time epidemic situation and keep sufficient personal protection until april if all the above conditions are met, the outbreak is expected to be ended by april in mainland china apart from hubei province",
"sequence": "[CLS] experiment has study design has independent variable has experimental procedure has material has subject selection method has hypotheses has dependent variables data analysis has rewards [SEP] risk estimation and prediction by modeling the transmission of the novel coronavirus (covid 19) in mainland china excluding hubei province abstract background in december 2019, an outbreak of coronavirus disease (covid 19) was identified in wuhan, china and, later on, detected in other parts of china our aim is to evaluate the effectiveness of the evolution of interventions and self protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in mainland china excluding hubei province based on the published data and a novel mathematical model methods a novel covid 19 transmission dynamic model incorporating the intervention measures implemented in china is proposed covid 19 daily data of mainland china excluding hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases for the period january 20th march 3rd, 2020, were archived from the national health commission of china (nhcc) we parameterize the model by using the markov chain monte carlo (mcmc) method and estimate the control reproduction number r c , as well as the effective daily reproduction ratio r e ( t ), of the disease transmission in mainland china excluding hubei province results the estimation outcomes indicate that r c is 3 36 (95% ci 3 20 3 64) and r e ( t ) has dropped below 1 since january 31st, 2020, which implies that the containment strategies implemented by the chinese government in mainland china excluding hubei province are indeed effective and magnificently suppressed covid 19 transmission moreover, our results show that relieving personal protection too early may lead to the spread of disease for a longer time and more people would be infected, and may even cause epidemic or outbreak again by calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by april, 2020 conclusions to ensure the epidemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, etc people should be fully aware of the real time epidemic situation and keep sufficient personal protection until april if all the above conditions are met, the outbreak is expected to be ended by april in mainland china apart from hubei province [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR30343",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R30343",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "the impact of trade openness on global carbon dioxide emissions: evidence from the top ten emitters among developing countries abstract this study aims to analyze the relationship between carbon dioxide (co 2 ) emissions, trade openness, real income and energy consumption in the top ten co 2 emitters among the developing countries; namely china, india, south korea, brazil, mexico, indonesia, south africa, turkey, thailand and malaysia over the period of 1971\u20132011 in addition, the possible presence of the ekc hypothesis is investigated for the analyzed countries the zivot\u2013andrews unit root test with structural break, the bounds testing for cointegration in the presence of structural break and the vecm granger causality method are employed the empirical results indicate that (i) the analyzed variables are co integrated for thailand, turkey, india, brazil, china, indonesia and korea, (ii) real income, energy consumption and trade openness are the main determinants of carbon emissions in the long run, (iii) there exists a number of causal relations between the analyzed variables, (iv) the ekc hypothesis is validated for turkey, india, china and korea robust policy implications can be derived from this study since the estimated models pass several diagnostic and stability tests",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] the impact of trade openness on global carbon dioxide emissions: evidence from the top ten emitters among developing countries abstract this study aims to analyze the relationship between carbon dioxide (co 2 ) emissions, trade openness, real income and energy consumption in the top ten co 2 emitters among the developing countries; namely china, india, south korea, brazil, mexico, indonesia, south africa, turkey, thailand and malaysia over the period of 1971\u20132011 in addition, the possible presence of the ekc hypothesis is investigated for the analyzed countries the zivot\u2013andrews unit root test with structural break, the bounds testing for cointegration in the presence of structural break and the vecm granger causality method are employed the empirical results indicate that (i) the analyzed variables are co integrated for thailand, turkey, india, brazil, china, indonesia and korea, (ii) real income, energy consumption and trade openness are the main determinants of carbon emissions in the long run, (iii) there exists a number of causal relations between the analyzed variables, (iv) the ekc hypothesis is validated for turkey, india, china and korea robust policy implications can be derived from this study since the estimated models pass several diagnostic and stability tests [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR46651",
"template_id": "R35087",
"correct_template_id": null,
"paper_id": "R46651",
"premise": "time interval has beginning has end",
"hypothesis": "latent semantics in named entity recognition language independent named entity recognition system novel features based on latent semantics experiments on multiple languages english, spanish, dutch, czech state of the art results in this paper, we propose new features for named entity recognition (ner) based on latent semantics furthermore, we explore the effect of unsupervised morphological information on these methods and on the ner system in general the newly created ner system is fully language independent thanks to the unsupervised nature of the proposed features we evaluate the system on english, spanish, dutch and czech corpora and study the difference between weakly and highly inflectional languages our system achieves the same or even better results than state of the art language dependent systems the proposed features proved to be very useful and are the main reason of our promising results",
"sequence": "[CLS] time interval has beginning has end [SEP] latent semantics in named entity recognition language independent named entity recognition system novel features based on latent semantics experiments on multiple languages english, spanish, dutch, czech state of the art results in this paper, we propose new features for named entity recognition (ner) based on latent semantics furthermore, we explore the effect of unsupervised morphological information on these methods and on the ner system in general the newly created ner system is fully language independent thanks to the unsupervised nature of the proposed features we evaluate the system on english, spanish, dutch and czech corpora and study the difference between weakly and highly inflectional languages our system achieves the same or even better results than state of the art language dependent systems the proposed features proved to be very useful and are the main reason of our promising results [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR75915",
"template_id": "R46273",
"correct_template_id": null,
"paper_id": "R75915",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "virtex: learning visual representations from textual annotations the de facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on imagenet recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images in contrast, we aim to learn high quality visual representations from fewer images to this end, we revisit supervised pretraining, and seek data efficient alternatives to classification based pretraining we propose virtex a pretraining approach using semantically dense captions to learn visual representations we train convolutional networks from scratch on coco captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation on all tasks, virtex yields features that match or exceed those learned on imagenet supervised or unsupervised despite using up to ten times fewer images",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] virtex: learning visual representations from textual annotations the de facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on imagenet recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images in contrast, we aim to learn high quality visual representations from fewer images to this end, we revisit supervised pretraining, and seek data efficient alternatives to classification based pretraining we propose virtex a pretraining approach using semantically dense captions to learn visual representations we train convolutional networks from scratch on coco captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation on all tasks, virtex yields features that match or exceed those learned on imagenet supervised or unsupervised despite using up to ten times fewer images [SEP]",
"target": "contradiction"
},
{
"instance_id": "R69198xR6325",
"template_id": "R69198",
"correct_template_id": null,
"paper_id": "R6325",
"premise": "scientific concept extraction dataset text coverage concept types inter coder agreement",
"hypothesis": "natural language question answering over rdf rdf question/answering (q/a) allows users to ask questions in natural languages over a knowledge base represented by rdf to answer a national language question, the existing work takes a two stage approach: question understanding and query evaluation their focus is on question understanding to deal with the disambiguation of the natural language phrases the most common technique is the joint disambiguation, which has the exponential search space in this paper, we propose a systematic framework to answer natural language questions over rdf repository (rdf q/a) from a graph data driven perspective we propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, rdf q/a is reduced to subgraph matching problem more importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found the cost of disambiguation is saved if there are no matching found we compare our method with some state of the art rdf q/a systems in the benchmark dataset extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly",
"sequence": "[CLS] scientific concept extraction dataset text coverage concept types inter coder agreement [SEP] natural language question answering over rdf rdf question/answering (q/a) allows users to ask questions in natural languages over a knowledge base represented by rdf to answer a national language question, the existing work takes a two stage approach: question understanding and query evaluation their focus is on question understanding to deal with the disambiguation of the natural language phrases the most common technique is the joint disambiguation, which has the exponential search space in this paper, we propose a systematic framework to answer natural language questions over rdf repository (rdf q/a) from a graph data driven perspective we propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, rdf q/a is reduced to subgraph matching problem more importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found the cost of disambiguation is saved if there are no matching found we compare our method with some state of the art rdf q/a systems in the benchmark dataset extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR109366",
"template_id": "R46273",
"correct_template_id": null,
"paper_id": "R109366",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "cyber resilience in firms, organizations and societies abstract cyber resilience involves most societal actors, i e organizations, individuals, threat actors, governments, insurers, etc , at most levels of organization actors are embedded within each other and choose strategies based on beliefs and preferences which impact and is impacted by cyber resilience the article reviews the literature, attempting to capture the core ingredients of cyber resilience non threat actors seeking to obtain cyber resilience are distinguished from threat actors actors have resources, competence, technology, and tools they make choices that impact the cyber resilience for all actors, including themselves cyber resilience relates to cyber insurance through entry requirements or preconditions for cyber contracts, need for various services such as incident response, data gathering, and cover limitations cyber resilience is linked to the internet of things which in the future can be expected to simplify life through artificial intelligence and machine learning, while being vulnerable through a large attack surface, insufficient technology, challenging handling of data, possible high trust in computers and software, and ethics",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] cyber resilience in firms, organizations and societies abstract cyber resilience involves most societal actors, i e organizations, individuals, threat actors, governments, insurers, etc , at most levels of organization actors are embedded within each other and choose strategies based on beliefs and preferences which impact and is impacted by cyber resilience the article reviews the literature, attempting to capture the core ingredients of cyber resilience non threat actors seeking to obtain cyber resilience are distinguished from threat actors actors have resources, competence, technology, and tools they make choices that impact the cyber resilience for all actors, including themselves cyber resilience relates to cyber insurance through entry requirements or preconditions for cyber contracts, need for various services such as incident response, data gathering, and cover limitations cyber resilience is linked to the internet of things which in the future can be expected to simplify life through artificial intelligence and machine learning, while being vulnerable through a large attack surface, insufficient technology, challenging handling of data, possible high trust in computers and software, and ethics [SEP]",
"target": "contradiction"
},
{
"instance_id": "R54009xR41005",
"template_id": "R54009",
"correct_template_id": null,
"paper_id": "R41005",
"premise": "climate sensitivity has unit has evaluation data used has ecs result",
"hypothesis": "mechanistic statistical sir modelling for early estimation of the actual number of cases and mortality rate from covid 19 the first cases of covid 19 in france were detected on january 24, 2020 the number of screening tests carried out and the methodology used to target the patients tested do not allow for a direct computation of the real number of cases and the mortality this http url this report, we develop a 'mechanistic statistical' approach coupling a sir ode model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method the objective of this model is not to make forecasts but to estimate the real number of people infected with covid 19 during the observation window in france and to deduce the mortality rate associated with the epidemic main results the actual number of infected cases in france is probably much higher than the observations: we find here a factor x 15 (95% ci: 1 5 11 7), which leads to a 5 2/1000 mortality rate (95% ci: 1 5 / 1000 11 7/ 1000) at the end of the observation period we find a r0 of 4 8, a high value which may be linked to the long viral shedding period of 20 days",
"sequence": "[CLS] climate sensitivity has unit has evaluation data used has ecs result [SEP] mechanistic statistical sir modelling for early estimation of the actual number of cases and mortality rate from covid 19 the first cases of covid 19 in france were detected on january 24, 2020 the number of screening tests carried out and the methodology used to target the patients tested do not allow for a direct computation of the real number of cases and the mortality this http url this report, we develop a 'mechanistic statistical' approach coupling a sir ode model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method the objective of this model is not to make forecasts but to estimate the real number of people infected with covid 19 during the observation window in france and to deduce the mortality rate associated with the epidemic main results the actual number of infected cases in france is probably much higher than the observations: we find here a factor x 15 (95% ci: 1 5 11 7), which leads to a 5 2/1000 mortality rate (95% ci: 1 5 / 1000 11 7/ 1000) at the end of the observation period we find a r0 of 4 8, a high value which may be linked to the long viral shedding period of 20 days [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR76792",
"template_id": "R35087",
"correct_template_id": "R108555",
"paper_id": "R76792",
"premise": "time interval has beginning has end",
"hypothesis": "mining twitter feeds for software user requirements twitter enables large populations of end users of software to publicly share their experiences and concerns about software systems in the form of micro blogs such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems however, automatically capturing, classifying, and presenting useful tweets is not a trivial task challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam motivated by these challenges, this paper reports on a three fold study that is aimed at leveraging twitter as a main source of software user requirements the main objective is to enable a responsive, interactive, and adaptive data driven requirements engineering process our analysis is conducted using 4,000 tweets collected from the twitter feeds of 10 software systems sampled from a broad range of application domains the results reveal that around 50% of collected tweets contain useful technical information the results also show that text classifiers such as support vector machines and naive bayes can be very effective in capturing and categorizing technically informative tweets additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software relevant tweets",
"sequence": "[CLS] time interval has beginning has end [SEP] mining twitter feeds for software user requirements twitter enables large populations of end users of software to publicly share their experiences and concerns about software systems in the form of micro blogs such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems however, automatically capturing, classifying, and presenting useful tweets is not a trivial task challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam motivated by these challenges, this paper reports on a three fold study that is aimed at leveraging twitter as a main source of software user requirements the main objective is to enable a responsive, interactive, and adaptive data driven requirements engineering process our analysis is conducted using 4,000 tweets collected from the twitter feeds of 10 software systems sampled from a broad range of application domains the results reveal that around 50% of collected tweets contain useful technical information the results also show that text classifiers such as support vector machines and naive bayes can be very effective in capturing and categorizing technically informative tweets additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software relevant tweets [SEP]",
"target": "contradiction"
},
{
"instance_id": "R138668xR38225",
"template_id": "R138668",
"correct_template_id": "R38248",
"paper_id": "R38225",
"premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data",
"hypothesis": "joint extraction of entities and relations based on a novel tagging scheme joint extraction of entities and relations is an important task in information extraction to tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem then, based on our tagging scheme, we study different end to end models to extract entities and their relations directly, without identifying entities and relations separately we conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods what's more, the end to end model proposed in this paper, achieves the best results on the public dataset",
"sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] joint extraction of entities and relations based on a novel tagging scheme joint extraction of entities and relations is an important task in information extraction to tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem then, based on our tagging scheme, we study different end to end models to extract entities and their relations directly, without identifying entities and relations separately we conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods what's more, the end to end model proposed in this paper, achieves the best results on the public dataset [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76795xR43000",
"template_id": "R76795",
"correct_template_id": null,
"paper_id": "R43000",
"premise": "crowd intelligence utilities in crowdre re activities with crowd involvement",
"hypothesis": "pattern based model reuse using colored petri nets colored petri net (cpn) is a graphical modeling language for simulation and modeling and for verification of discrete event systems cpn allows developers to define a model in the form of reusable components a model component is an independent element, which is specified using a formalized description, can conform to a certain component standard, has a well defined interface, and encapsulates certain behavior modern components can help the developer reuse existing models according to their requirement as it reduces the cost and time of development composability is the capability to select and integrate various components to fulfill user requirements composability provides the means to achieve reusability where \"reuse\" is the ability of a simulation component to be reclaimed for various applications we propose a verification framework for developers to select and assemble cpn based components and verify their composability the goal of this paper is to provide a pattern which helps developer in making models of concurrent systems we present a case study of a restaurant model as proof of concept a verified composition affirms reuse of model components in a meaningful manner by satisfying given requirement specifications",
"sequence": "[CLS] crowd intelligence utilities in crowdre re activities with crowd involvement [SEP] pattern based model reuse using colored petri nets colored petri net (cpn) is a graphical modeling language for simulation and modeling and for verification of discrete event systems cpn allows developers to define a model in the form of reusable components a model component is an independent element, which is specified using a formalized description, can conform to a certain component standard, has a well defined interface, and encapsulates certain behavior modern components can help the developer reuse existing models according to their requirement as it reduces the cost and time of development composability is the capability to select and integrate various components to fulfill user requirements composability provides the means to achieve reusability where \"reuse\" is the ability of a simulation component to be reclaimed for various applications we propose a verification framework for developers to select and assemble cpn based components and verify their composability the goal of this paper is to provide a pattern which helps developer in making models of concurrent systems we present a case study of a restaurant model as proof of concept a verified composition affirms reuse of model components in a meaningful manner by satisfying given requirement specifications [SEP]",
"target": "contradiction"
},
{
"instance_id": "R107684xR109012",
"template_id": "R107684",
"correct_template_id": null,
"paper_id": "R109012",
"premise": "health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample",
"hypothesis": "drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network interference between pharmacological substances can cause serious medical injuries correctly predicting so called drug drug interactions (ddi) does not only reduce these cases but can also result in a reduction of drug development cost presently, most drug related knowledge is the result of clinical evaluations and post marketing surveillance; resulting in a limited amount of information existing data driven prediction approaches for ddis typically rely on a single source of information, while using information from multiple sources would help improve predictions machine learning (ml) techniques are used, but the techniques are often unable to deal with skewness in the data hence, we propose a new ml approach for predicting ddis based on multiple data sources for this task, we use 12,000 drug features from drugbank, pharmgkb, and kegg drugs, which are integrated using knowledge graphs (kgs) to train our prediction model, we first embed the nodes in the graph using various embedding approaches we found that the best performing combination was a complex embedding method creating using pytorch biggraph (pbg) with a convolutional lstm network and classic machine learning based prediction models the model averaging ensemble method of three best classifiers yields up to 0 94, 0 92, 0 80 for aupr, f1 f1 score, and mcc, respectively during 5 fold cross validation tests",
"sequence": "[CLS] health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample [SEP] drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network interference between pharmacological substances can cause serious medical injuries correctly predicting so called drug drug interactions (ddi) does not only reduce these cases but can also result in a reduction of drug development cost presently, most drug related knowledge is the result of clinical evaluations and post marketing surveillance; resulting in a limited amount of information existing data driven prediction approaches for ddis typically rely on a single source of information, while using information from multiple sources would help improve predictions machine learning (ml) techniques are used, but the techniques are often unable to deal with skewness in the data hence, we propose a new ml approach for predicting ddis based on multiple data sources for this task, we use 12,000 drug features from drugbank, pharmgkb, and kegg drugs, which are integrated using knowledge graphs (kgs) to train our prediction model, we first embed the nodes in the graph using various embedding approaches we found that the best performing combination was a complex embedding method creating using pytorch biggraph (pbg) with a convolutional lstm network and classic machine learning based prediction models the model averaging ensemble method of three best classifiers yields up to 0 94, 0 92, 0 80 for aupr, f1 f1 score, and mcc, respectively during 5 fold cross validation tests [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108239xR49115",
"template_id": "R108239",
"correct_template_id": null,
"paper_id": "R49115",
"premise": "km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development",
"hypothesis": "extreme sea level implications of 1 5 \u00b0c, 2 0 \u00b0c, and 2 5 \u00b0c temperature stabilization targets in the 21st and 22nd centuries sea level rise (slr) is magnifying the frequency and severity of coastal flooding the rate and amount of global mean sea level (gmsl) rise is a function of the trajectory of global mean surface temperature (gmst) therefore, temperature stabilization targets (e g , 1 5 {\\deg}c and 2 0 {\\deg}c of warming above pre industrial levels, as from the paris agreement) have important implications for coastal flood risk here, we assess differences in the return periods of coastal floods at a global network of tide gauges between scenarios that stabilize gmst warming at 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c above pre industrial levels we employ probabilistic, localized slr projections and long term hourly tide gauge records to construct estimates of the return levels of current and future flood heights for the 21st and 22nd centuries by 2100, under 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c gmst stabilization, median gmsl is projected to rise 47 cm with a very likely range of 28 82 cm (90% probability), 55 cm (very likely 30 94 cm), and 58 cm (very likely 36 93 cm), respectively as an independent comparison, a semi empirical sea level model calibrated to temperature and gmsl over the past two millennia estimates median gmsl will rise within < 13% of these projections by 2150, relative to the 2 0 {\\deg}c scenario, gmst stabilization of 1 5 {\\deg}c inundates roughly 5 million fewer inhabitants that currently occupy lands, including 40,000 fewer individuals currently residing in small island developing states relative to a 2 0 {\\deg}c scenario, the reduction in the amplification of the frequency of the 100 yr flood arising from a 1 5 {\\deg}c gmst stabilization is greatest in the eastern united states and in europe, with flood frequency amplification being reduced by about half",
"sequence": "[CLS] km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development [SEP] extreme sea level implications of 1 5 \u00b0c, 2 0 \u00b0c, and 2 5 \u00b0c temperature stabilization targets in the 21st and 22nd centuries sea level rise (slr) is magnifying the frequency and severity of coastal flooding the rate and amount of global mean sea level (gmsl) rise is a function of the trajectory of global mean surface temperature (gmst) therefore, temperature stabilization targets (e g , 1 5 {\\deg}c and 2 0 {\\deg}c of warming above pre industrial levels, as from the paris agreement) have important implications for coastal flood risk here, we assess differences in the return periods of coastal floods at a global network of tide gauges between scenarios that stabilize gmst warming at 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c above pre industrial levels we employ probabilistic, localized slr projections and long term hourly tide gauge records to construct estimates of the return levels of current and future flood heights for the 21st and 22nd centuries by 2100, under 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c gmst stabilization, median gmsl is projected to rise 47 cm with a very likely range of 28 82 cm (90% probability), 55 cm (very likely 30 94 cm), and 58 cm (very likely 36 93 cm), respectively as an independent comparison, a semi empirical sea level model calibrated to temperature and gmsl over the past two millennia estimates median gmsl will rise within < 13% of these projections by 2150, relative to the 2 0 {\\deg}c scenario, gmst stabilization of 1 5 {\\deg}c inundates roughly 5 million fewer inhabitants that currently occupy lands, including 40,000 fewer individuals currently residing in small island developing states relative to a 2 0 {\\deg}c scenario, the reduction in the amplification of the frequency of the 100 yr flood arising from a 1 5 {\\deg}c gmst stabilization is greatest in the eastern united states and in europe, with flood frequency amplification being reduced by about half [SEP]",
"target": "contradiction"
},
{
"instance_id": "R54875xR29340",
"template_id": "R54875",
"correct_template_id": null,
"paper_id": "R29340",
"premise": "climate response has unit has evaluation data used has tcr result",
"hypothesis": "taxonomy of cost of quality (coq) across the enterprise resource planning (erp) implementation phases\u201d companies declare that quality or customer satisfaction is their top priority in order to keep and attract more business in an increasingly competitive marketplace the cost of quality (coq) is a tool which can help determine the optimal level of quality investment coq analysis enables organizations to identify measure and control the consequences of poor quality this study attempts to identify the coq elements across the enterprise resource planning (erp) implementation phases for the erp implementation services of consultancy companies the findings provide guidance to project managers on how best to utilize their limited resources in summary, we suggest that project teams should focus on \u201cvalue added\u201d activities and minimize the cost of \u201cnon value added\u201d activities at each phase of the erp implementation project key words: services, erp implementation services, quality standard, service quality standard, cost of quality, project management, project quality management, project financial management",
"sequence": "[CLS] climate response has unit has evaluation data used has tcr result [SEP] taxonomy of cost of quality (coq) across the enterprise resource planning (erp) implementation phases\u201d companies declare that quality or customer satisfaction is their top priority in order to keep and attract more business in an increasingly competitive marketplace the cost of quality (coq) is a tool which can help determine the optimal level of quality investment coq analysis enables organizations to identify measure and control the consequences of poor quality this study attempts to identify the coq elements across the enterprise resource planning (erp) implementation phases for the erp implementation services of consultancy companies the findings provide guidance to project managers on how best to utilize their limited resources in summary, we suggest that project teams should focus on \u201cvalue added\u201d activities and minimize the cost of \u201cnon value added\u201d activities at each phase of the erp implementation project key words: services, erp implementation services, quality standard, service quality standard, cost of quality, project management, project quality management, project financial management [SEP]",
"target": "contradiction"
},
{
"instance_id": "R51438xR110714",
"template_id": "R51438",
"correct_template_id": null,
"paper_id": "R110714",
"premise": "common template has result has hypothesis has methodology",
"hypothesis": "fisetin and rutin as 3c protease inhibitors of enterovirus a71 enterovirus a71 (ev a71) causes severe complications: encephalitis, pulmonary edema, and death no effective drug has been approved for clinical use this study investigated the antiviral effects of flavonoids against ev a71 an in vitro inhibitor screening assay using recombinant ev a71 3c protease (3cpro) demonstrated fisetin and rutin inhibiting 3cpro enzymatic activity in a dose dependent manner cell based fluorescence resonance energy transfer (fret) assay with an ev a71 3cpro cleavage motif probe also confirmed that fisetin and rutin inhibited the replication of ev a71 in cells a virus replication assay indicated that fisetin and rutin reduced significantly the ev a71 induced cytopathic effect and viral plaque titers in rd cells culture the ic(50) values of plaque reduction against ev a71 were 85 \u03bcm for fisetin and 110 \u03bcm for rutin therapeutic indices (cc50/ic50 of plaque reduction assays) of fisetin and rutin exceeded 10 the study suggests that fisetin and rutin inhibit the replication of ev a71",
"sequence": "[CLS] common template has result has hypothesis has methodology [SEP] fisetin and rutin as 3c protease inhibitors of enterovirus a71 enterovirus a71 (ev a71) causes severe complications: encephalitis, pulmonary edema, and death no effective drug has been approved for clinical use this study investigated the antiviral effects of flavonoids against ev a71 an in vitro inhibitor screening assay using recombinant ev a71 3c protease (3cpro) demonstrated fisetin and rutin inhibiting 3cpro enzymatic activity in a dose dependent manner cell based fluorescence resonance energy transfer (fret) assay with an ev a71 3cpro cleavage motif probe also confirmed that fisetin and rutin inhibited the replication of ev a71 in cells a virus replication assay indicated that fisetin and rutin reduced significantly the ev a71 induced cytopathic effect and viral plaque titers in rd cells culture the ic(50) values of plaque reduction against ev a71 were 85 \u03bcm for fisetin and 110 \u03bcm for rutin therapeutic indices (cc50/ic50 of plaque reduction assays) of fisetin and rutin exceeded 10 the study suggests that fisetin and rutin inhibit the replication of ev a71 [SEP]",
"target": "contradiction"
},
{
"instance_id": "R54009xR28179",
"template_id": "R54009",
"correct_template_id": null,
"paper_id": "R28179",
"premise": "climate sensitivity has unit has evaluation data used has ecs result",
"hypothesis": "an optimal containership slot allocation for liner shipping revenue management in the competitive liner shipping market, carriers may utilize revenue management systems to increase profits by using slot allocation and pricing in this paper, related research on revenue management for transportation industries is reviewed a conceptual model for liner shipping revenue management (lsrm) is proposed and a slot allocation model is formulated through mathematical programming to maximize freight contribution we illustrate this slot allocation model with a case study of a taiwan liner shipping company and the results show the applicability and better performances than the previous allocation used in practice",
"sequence": "[CLS] climate sensitivity has unit has evaluation data used has ecs result [SEP] an optimal containership slot allocation for liner shipping revenue management in the competitive liner shipping market, carriers may utilize revenue management systems to increase profits by using slot allocation and pricing in this paper, related research on revenue management for transportation industries is reviewed a conceptual model for liner shipping revenue management (lsrm) is proposed and a slot allocation model is formulated through mathematical programming to maximize freight contribution we illustrate this slot allocation model with a case study of a taiwan liner shipping company and the results show the applicability and better performances than the previous allocation used in practice [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR68601",
"template_id": "R46273",
"correct_template_id": null,
"paper_id": "R68601",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "work in progress: collaboration for quality: a librarian faculty partnership to assess students\u2019 information literacy in freshman engineering work in progress: collaboration for quality: a librarian faculty partnership to assess students\u2019 information literacy in freshman engineering ms alison bradley, university of north carolina, charlotte alison bradley graduated with a bachelor of arts degree from mcgill university and a master\u2019s of science in library science from the university of north carolina at chapel hill she is the interim head of research and information services, stem librarian, and assistant professor at unc charlotte r daniel latta, university of north carolina, charlotte r daniel latta is lecturer and academic advisor in the office of student development and success at the william states lee college of engineering latta is a licensed professional engineer in nc, sc and va and professional land surveyor in nc mrs meg harkins, university of north carolina, charlotte meg harkins is lecturer, freshman engineering advisor and director of the engineering freshman learning community at university of north carolina, charlotte\u2019s william states lee college of engineering she is a licensed professional engineer in pennsylvania",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] work in progress: collaboration for quality: a librarian faculty partnership to assess students\u2019 information literacy in freshman engineering work in progress: collaboration for quality: a librarian faculty partnership to assess students\u2019 information literacy in freshman engineering ms alison bradley, university of north carolina, charlotte alison bradley graduated with a bachelor of arts degree from mcgill university and a master\u2019s of science in library science from the university of north carolina at chapel hill she is the interim head of research and information services, stem librarian, and assistant professor at unc charlotte r daniel latta, university of north carolina, charlotte r daniel latta is lecturer and academic advisor in the office of student development and success at the william states lee college of engineering latta is a licensed professional engineer in nc, sc and va and professional land surveyor in nc mrs meg harkins, university of north carolina, charlotte meg harkins is lecturer, freshman engineering advisor and director of the engineering freshman learning community at university of north carolina, charlotte\u2019s william states lee college of engineering she is a licensed professional engineer in pennsylvania [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108555xR75832",
"template_id": "R108555",
"correct_template_id": null,
"paper_id": "R75832",
"premise": "machine learning experiment has dataset has method has result classification category has feature",
"hypothesis": "the coordination\u2010information bubble in humanitarian response: theoretical foundations and empirical investigations humanitarian disasters are highly dynamic and uncertain the shifting situation, volatility of information, and the emergence of decision processes and coordination structures require humanitarian organizations to continuously adapt their operations in this study, we aim to make headway in understanding adaptive decision making in a dynamic interplay between changing situation, volatile information, and emerging coordination structures starting from theories of sensemaking, coordination, and decision making, we present two case studies that represent the response to two different humanitarian disasters: typhoon haiyan in the philippines, and the syria crisis, one of the most prominent ongoing conflicts for both, we highlight how volatile information and the urge to respond via sensemaking lead to fragmentation and misalignment of emergent coordination structures and decisions, which, in turn, slow down adaptation based on the case studies, we derive propositions and the need to continuously align laterally between different regions and hierarchically between operational and strategic levels to avoid persistence of coordination information bubbles we discuss the implications of our findings for the development of methods and theory to ensure that humanitarian operations management captures the critical role of information as a driver of emergent coordination and adaptive decisions",
"sequence": "[CLS] machine learning experiment has dataset has method has result classification category has feature [SEP] the coordination\u2010information bubble in humanitarian response: theoretical foundations and empirical investigations humanitarian disasters are highly dynamic and uncertain the shifting situation, volatility of information, and the emergence of decision processes and coordination structures require humanitarian organizations to continuously adapt their operations in this study, we aim to make headway in understanding adaptive decision making in a dynamic interplay between changing situation, volatile information, and emerging coordination structures starting from theories of sensemaking, coordination, and decision making, we present two case studies that represent the response to two different humanitarian disasters: typhoon haiyan in the philippines, and the syria crisis, one of the most prominent ongoing conflicts for both, we highlight how volatile information and the urge to respond via sensemaking lead to fragmentation and misalignment of emergent coordination structures and decisions, which, in turn, slow down adaptation based on the case studies, we derive propositions and the need to continuously align laterally between different regions and hierarchically between operational and strategic levels to avoid persistence of coordination information bubbles we discuss the implications of our findings for the development of methods and theory to ensure that humanitarian operations management captures the critical role of information as a driver of emergent coordination and adaptive decisions [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR38507",
"template_id": "R46273",
"correct_template_id": "R76209",
"paper_id": "R38507",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "domain independent extraction of scientific concepts from research articles we examine the novel task of domain independent scientific concept extraction from abstracts of scholarly articles and present two contributions first, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process this set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of science, technology and medicine at the phrasal level in a joint effort with domain experts the resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains second, we present a state of the art deep learning baseline further, we propose the active learning strategy for an optimal selection of instances from among the various domains in our data the experimental results show that (1) a substantial agreement is achievable by non experts after consultation with domain experts, (2) the baseline system achieves a fairly high f1 score, (3) active learning enables us to nearly halve the amount of required training data",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] domain independent extraction of scientific concepts from research articles we examine the novel task of domain independent scientific concept extraction from abstracts of scholarly articles and present two contributions first, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process this set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of science, technology and medicine at the phrasal level in a joint effort with domain experts the resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains second, we present a state of the art deep learning baseline further, we propose the active learning strategy for an optimal selection of instances from among the various domains in our data the experimental results show that (1) a substantial agreement is achievable by non experts after consultation with domain experts, (2) the baseline system achieves a fairly high f1 score, (3) active learning enables us to nearly halve the amount of required training data [SEP]",
"target": "contradiction"
},
{
"instance_id": "R38147xR109894",
"template_id": "R38147",
"correct_template_id": null,
"paper_id": "R109894",
"premise": "comparisons with existing machine learning (ml) models outperforms",
"hypothesis": "a hybrid approach toward research paper recommendation using centrality measures and author ranking the volume of research articles in digital repositories is increasing this spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries the problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories in the traditional recommendation approaches, the results of the query miss many high quality papers, in the related work section, which are either published recently or have low citation count to overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles many research paper recommendation approaches have been implemented which includes collaborative filtering based, content based, and citation analysis based techniques the collaborative filtering based approaches primarily use paper citation matrix for recommendations, whereas the content based approaches only consider the content of the paper the citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest it is therefore very difficult for a recommender system to recommend high quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information the proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors\u2019 collaboration network finds key authors from those papers the papers selected by this hybrid approach are then recommended to the user the results have shown that our proposed method performs exceedingly well as compared with the state of the art existing systems, such as google scholar and multilevel simultaneous citation network",
"sequence": "[CLS] comparisons with existing machine learning (ml) models outperforms [SEP] a hybrid approach toward research paper recommendation using centrality measures and author ranking the volume of research articles in digital repositories is increasing this spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries the problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories in the traditional recommendation approaches, the results of the query miss many high quality papers, in the related work section, which are either published recently or have low citation count to overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles many research paper recommendation approaches have been implemented which includes collaborative filtering based, content based, and citation analysis based techniques the collaborative filtering based approaches primarily use paper citation matrix for recommendations, whereas the content based approaches only consider the content of the paper the citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest it is therefore very difficult for a recommender system to recommend high quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information the proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors\u2019 collaboration network finds key authors from those papers the papers selected by this hybrid approach are then recommended to the user the results have shown that our proposed method performs exceedingly well as compared with the state of the art existing systems, such as google scholar and multilevel simultaneous citation network [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76795xR36143",
"template_id": "R76795",
"correct_template_id": null,
"paper_id": "R36143",
"premise": "crowd intelligence utilities in crowdre re activities with crowd involvement",
"hypothesis": "a cybernetics based dynamic infection model for analyzing sars cov 2 infection stability and predicting uncontrollable risks abstract since december 2019, covid 19 has raged in wuhan and subsequently all over china and the world we propose a cybernetics based dynamic infection model (cdim) to the dynamic infection process with a probability distributed incubation delay and feedback principle reproductive trends and the stability of the sars cov 2 infection in a city can then be analyzed, and the uncontrollable risks can be forecasted before they really happen the infection mechanism of a city is depicted using the philosophy of cybernetics and approaches of the control engineering distinguished with other epidemiological models, such as sir, seir, etc , that compute the theoretical number of infected people in a closed population, cdim considers the immigration and emigration population as system inputs, and administrative and medical resources as dynamic control variables the epidemic regulation can be simulated in the model to support the decision making for containing the outbreak city case studies are demonstrated for verification and validation",
"sequence": "[CLS] crowd intelligence utilities in crowdre re activities with crowd involvement [SEP] a cybernetics based dynamic infection model for analyzing sars cov 2 infection stability and predicting uncontrollable risks abstract since december 2019, covid 19 has raged in wuhan and subsequently all over china and the world we propose a cybernetics based dynamic infection model (cdim) to the dynamic infection process with a probability distributed incubation delay and feedback principle reproductive trends and the stability of the sars cov 2 infection in a city can then be analyzed, and the uncontrollable risks can be forecasted before they really happen the infection mechanism of a city is depicted using the philosophy of cybernetics and approaches of the control engineering distinguished with other epidemiological models, such as sir, seir, etc , that compute the theoretical number of infected people in a closed population, cdim considers the immigration and emigration population as system inputs, and administrative and medical resources as dynamic control variables the epidemic regulation can be simulated in the model to support the decision making for containing the outbreak city case studies are demonstrated for verification and validation [SEP]",
"target": "contradiction"
},
{
"instance_id": "R49194xR108235",
"template_id": "R49194",
"correct_template_id": null,
"paper_id": "R108235",
"premise": "occupant's perception and behaviour study category results experimental details",
"hypothesis": "suspended accounts in retrospect: an analysis of twitter spam in this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon to perform our analysis, we identify over 1 1 million accounts suspended by twitter for disruptive activities over the course of seven months in the process, we collect a dataset of 1 8 billion tweets, 80 million of which belong to spam accounts we use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide spread abuse of legitimate web services such as url shorteners and free web hosting we also identify an emerging marketplace of illegitimate programs operated by spammers that include twitter account sellers, ad based url shorteners, and spam affiliate programs that help enable underground market diversification our results show that 77% of spam accounts identified by twitter are suspended within on day of their first tweet because of these pressures, less than 9% of accounts form social relationships with regular twitter users instead, 17% of accounts rely on hijacking trends, while 52% of accounts use unsolicited mentions to reach an audience in spite of daily account attrition, we show how five spam campaigns controlling 145 thousand accounts combined are able to persist for months at a time, with each campaign enacting a unique spamming strategy surprisingly, three of these campaigns send spam directing visitors to reputable store fronts, blurring the line regarding what constitutes spam on social networks",
"sequence": "[CLS] occupant's perception and behaviour study category results experimental details [SEP] suspended accounts in retrospect: an analysis of twitter spam in this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon to perform our analysis, we identify over 1 1 million accounts suspended by twitter for disruptive activities over the course of seven months in the process, we collect a dataset of 1 8 billion tweets, 80 million of which belong to spam accounts we use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide spread abuse of legitimate web services such as url shorteners and free web hosting we also identify an emerging marketplace of illegitimate programs operated by spammers that include twitter account sellers, ad based url shorteners, and spam affiliate programs that help enable underground market diversification our results show that 77% of spam accounts identified by twitter are suspended within on day of their first tweet because of these pressures, less than 9% of accounts form social relationships with regular twitter users instead, 17% of accounts rely on hijacking trends, while 52% of accounts use unsolicited mentions to reach an audience in spite of daily account attrition, we show how five spam campaigns controlling 145 thousand accounts combined are able to persist for months at a time, with each campaign enacting a unique spamming strategy surprisingly, three of these campaigns send spam directing visitors to reputable store fronts, blurring the line regarding what constitutes spam on social networks [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR48034",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R48034",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "oba: an ontology based framework for creating rest apis for knowledge graphs in recent years, semantic web technologies have been increasingly adopted by researchers, industry and public institutions to describe and link data on the web, create web annotations and consume large knowledge graphs like wikidata and dbpedia however, there is still a knowledge gap between ontology engineers, who design, populate and create knowledge graphs; and web developers, who need to understand, access and query these knowledge graphs but are not familiar with ontologies, rdf or sparql in this paper we describe the ontology based apis framework (oba), our approach to automatically create rest apis from ontologies while following restful api best practices given an ontology (or ontology network) oba uses standard technologies familiar to web developers (openapi specification, json) and combines them with w3c standards (owl, json ld frames and sparql) to create maintainable apis with documentation, units tests, automated validation of resources and clients (in python, javascript, etc ) for non semantic web experts to access the contents of a target knowledge graph we showcase oba with three examples that illustrate the capabilities of the framework for different ontologies",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] oba: an ontology based framework for creating rest apis for knowledge graphs in recent years, semantic web technologies have been increasingly adopted by researchers, industry and public institutions to describe and link data on the web, create web annotations and consume large knowledge graphs like wikidata and dbpedia however, there is still a knowledge gap between ontology engineers, who design, populate and create knowledge graphs; and web developers, who need to understand, access and query these knowledge graphs but are not familiar with ontologies, rdf or sparql in this paper we describe the ontology based apis framework (oba), our approach to automatically create rest apis from ontologies while following restful api best practices given an ontology (or ontology network) oba uses standard technologies familiar to web developers (openapi specification, json) and combines them with w3c standards (owl, json ld frames and sparql) to create maintainable apis with documentation, units tests, automated validation of resources and clients (in python, javascript, etc ) for non semantic web experts to access the contents of a target knowledge graph we showcase oba with three examples that illustrate the capabilities of the framework for different ontologies [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76209xR69988",
"template_id": "R76209",
"correct_template_id": null,
"paper_id": "R69988",
"premise": "team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in",
"hypothesis": "screening of electrophilic compounds yields an aziridinyl peptide as new active site directed sars cov main protease inhibitor abstract the coronavirus main protease, mpro, is considered a major target for drugs suitable to combat coronavirus infections including the severe acute respiratory syndrome (sars) in this study, comprehensive hplc and fret substrate based screenings of various electrophilic compounds were performed to identify potential mpro inhibitors the data revealed that the coronaviral main protease is inhibited by aziridine and oxirane 2 carboxylates among the trans configured aziridine 2,3 dicarboxylates the gly gly containing peptide 2c was found to be the most potent inhibitor",
"sequence": "[CLS] team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in [SEP] screening of electrophilic compounds yields an aziridinyl peptide as new active site directed sars cov main protease inhibitor abstract the coronavirus main protease, mpro, is considered a major target for drugs suitable to combat coronavirus infections including the severe acute respiratory syndrome (sars) in this study, comprehensive hplc and fret substrate based screenings of various electrophilic compounds were performed to identify potential mpro inhibitors the data revealed that the coronaviral main protease is inhibited by aziridine and oxirane 2 carboxylates among the trans configured aziridine 2,3 dicarboxylates the gly gly containing peptide 2c was found to be the most potent inhibitor [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR36106",
"template_id": "R35087",
"correct_template_id": null,
"paper_id": "R36106",
"premise": "time interval has beginning has end",
"hypothesis": "characterizing the transmission and identifying the control strategy for covid 19 through epidemiological modeling abstract the outbreak of the novel coronavirus disease, covid 19, originating from wuhan, china in early december, has infected more than 70,000 people in china and other countries and has caused more than 2,000 deaths as the disease continues to spread, the biomedical society urgently began identifying effective approaches to prevent further outbreaks through rigorous epidemiological analysis, we characterized the fast transmission of covid 19 with a basic reproductive number 5 6 and proved a sole zoonotic source to originate in wuhan no changes in transmission have been noted across generations by evaluating different control strategies through predictive modeling and monte carlo simulations, a comprehensive quarantine in hospitals and quarantine stations has been found to be the most effective approach government action to immediately enforce this quarantine is highly recommended",
"sequence": "[CLS] time interval has beginning has end [SEP] characterizing the transmission and identifying the control strategy for covid 19 through epidemiological modeling abstract the outbreak of the novel coronavirus disease, covid 19, originating from wuhan, china in early december, has infected more than 70,000 people in china and other countries and has caused more than 2,000 deaths as the disease continues to spread, the biomedical society urgently began identifying effective approaches to prevent further outbreaks through rigorous epidemiological analysis, we characterized the fast transmission of covid 19 with a basic reproductive number 5 6 and proved a sole zoonotic source to originate in wuhan no changes in transmission have been noted across generations by evaluating different control strategies through predictive modeling and monte carlo simulations, a comprehensive quarantine in hospitals and quarantine stations has been found to be the most effective approach government action to immediately enforce this quarantine is highly recommended [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR111988",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R111988",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "a needle in a haystack: what do twitter users say about software? users of the twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs therefore, tweets have the potential to aid in the continuous evolution of software applications despite the existence of such relevant tweets, little is known about their number and content in this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] a needle in a haystack: what do twitter users say about software? users of the twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs therefore, tweets have the potential to aid in the continuous evolution of software applications despite the existence of such relevant tweets, little is known about their number and content in this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108239xR137377",
"template_id": "R108239",
"correct_template_id": null,
"paper_id": "R137377",
"premise": "km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development",
"hypothesis": "a dc non thermal atmospheric pressure plasma microjet a direct current (dc), non thermal, atmospheric pressure plasma microjet is generated with helium/oxygen gas mixture as working gas the electrical property is characterized as a function of the oxygen concentration and show distinctive regions of operation side on images of the jet were taken to analyze the mode of operation as well as the jet length a self pulsed mode is observed before the transition of the discharge to normal glow mode optical emission spectroscopy is employed from both end on and side on along the jet to analyze the reactive species generated in the plasma line emissions from atomic oxygen (at 777 4 nm) and helium (at 706 5 nm) were studied with respect to the oxygen volume percentage in the working gas, flow rate and discharge current optical emission intensities of cu and oh are found to depend heavily on the oxygen concentration in the working gas ozone concentration measured in a semi confined zone in front of the plasma jet is found to be from tens to ~120 ppm the results presented here demonstrate potential pathways for the adjustment and tuning of various plasma parameters such as reactive species selectivity and quantities or even ultraviolet emission intensities manipulation in an atmospheric pressure non thermal plasma source the possibilities of fine tuning these plasma species allows for enhanced applications in health and medical related areas",
"sequence": "[CLS] km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development [SEP] a dc non thermal atmospheric pressure plasma microjet a direct current (dc), non thermal, atmospheric pressure plasma microjet is generated with helium/oxygen gas mixture as working gas the electrical property is characterized as a function of the oxygen concentration and show distinctive regions of operation side on images of the jet were taken to analyze the mode of operation as well as the jet length a self pulsed mode is observed before the transition of the discharge to normal glow mode optical emission spectroscopy is employed from both end on and side on along the jet to analyze the reactive species generated in the plasma line emissions from atomic oxygen (at 777 4 nm) and helium (at 706 5 nm) were studied with respect to the oxygen volume percentage in the working gas, flow rate and discharge current optical emission intensities of cu and oh are found to depend heavily on the oxygen concentration in the working gas ozone concentration measured in a semi confined zone in front of the plasma jet is found to be from tens to ~120 ppm the results presented here demonstrate potential pathways for the adjustment and tuning of various plasma parameters such as reactive species selectivity and quantities or even ultraviolet emission intensities manipulation in an atmospheric pressure non thermal plasma source the possibilities of fine tuning these plasma species allows for enhanced applications in health and medical related areas [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108555xR109029",
"template_id": "R108555",
"correct_template_id": null,
"paper_id": "R109029",
"premise": "machine learning experiment has dataset has method has result classification category has feature",
"hypothesis": "deep learning improves prediction of drug\u2013drug and drug\u2013food interactions drug interactions, including drug\u2013drug interactions (ddis) and drug\u2013food constituent interactions (dfis), can trigger unexpected pharmacological effects, including adverse drug events (ades), with causal mechanisms often unknown several computational methods have been developed to better understand drug interactions, especially for ddis however, these methods do not provide sufficient details beyond the chance of ddi occurrence, or require detailed drug information often unavailable for ddi prediction here, we report development of a computational framework deepddi that uses names of drug\u2013drug or drug\u2013food constituent pairs and their structural information as inputs to accurately generate 86 important ddi types as outputs of human readable sentences deepddi uses deep neural network with its optimized prediction performance and predicts 86 ddi types with a mean accuracy of 92 4% using the drugbank gold standard ddi dataset covering 192,284 ddis contributed by 191,878 drug pairs deepddi is used to suggest potential causal mechanisms for the reported ades of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects furthermore, deepddi is applied to 3,288,157 drug\u2013food constituent pairs (2,159 approved drugs and 1,523 well characterized food constituents) to predict dfis the effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents are predicted these results suggest that deepddi can provide important information on drug prescription and even dietary suggestions while taking certain drugs and also guidelines during drug development",
"sequence": "[CLS] machine learning experiment has dataset has method has result classification category has feature [SEP] deep learning improves prediction of drug\u2013drug and drug\u2013food interactions drug interactions, including drug\u2013drug interactions (ddis) and drug\u2013food constituent interactions (dfis), can trigger unexpected pharmacological effects, including adverse drug events (ades), with causal mechanisms often unknown several computational methods have been developed to better understand drug interactions, especially for ddis however, these methods do not provide sufficient details beyond the chance of ddi occurrence, or require detailed drug information often unavailable for ddi prediction here, we report development of a computational framework deepddi that uses names of drug\u2013drug or drug\u2013food constituent pairs and their structural information as inputs to accurately generate 86 important ddi types as outputs of human readable sentences deepddi uses deep neural network with its optimized prediction performance and predicts 86 ddi types with a mean accuracy of 92 4% using the drugbank gold standard ddi dataset covering 192,284 ddis contributed by 191,878 drug pairs deepddi is used to suggest potential causal mechanisms for the reported ades of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects furthermore, deepddi is applied to 3,288,157 drug\u2013food constituent pairs (2,159 approved drugs and 1,523 well characterized food constituents) to predict dfis the effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents are predicted these results suggest that deepddi can provide important information on drug prescription and even dietary suggestions while taking certain drugs and also guidelines during drug development [SEP]",
"target": "contradiction"
},
{
"instance_id": "R69198xR75426",
"template_id": "R69198",
"correct_template_id": null,
"paper_id": "R75426",
"premise": "scientific concept extraction dataset text coverage concept types inter coder agreement",
"hypothesis": "security and cryptographic challenges for authentication based on biometrics data authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of the society, e g , smart city, internet of things (iot), cloud computing, big data in the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc due to its uniqueness, characteristic, biometrics systems will become more and more vulnerable, privacy being one of the most important challenges the classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy the current paper has several objectives the main objective consists in creating a framework based on cryptographic modules which can be applied in systems with biometric authentication methods the technologies used in creating the framework are: c#, java, c++, python, and haskell the wide range of technologies for developing the algorithms give the readers the possibility and not only, to choose the proper modules for their own research or business direction the cryptographic modules contain algorithms based on machine learning and modern cryptographic algorithms: aes (advanced encryption system), sha 256, rc4, rc5, rc6, mars, blowfish, twofish, threefish, rsa (rivest shamir adleman), elliptic curve, and diffie hellman as methods for implementing with success the cryptographic modules, we will propose a methodology which can be used as a how to guide the article will focus only on the first category, machine learning, and data clustering, algorithms with applicability in the cloud computing environment for tests we have used a virtual machine (virtual box) with apache hadoop and a biometric analysis tool the weakness of the algorithms and methods implemented within the framework will be evaluated and presented in order for the reader to acknowledge the latest status of the security analysis and the vulnerabilities founded in the mentioned algorithms another important result of the authors consists in creating a scheme for biometric enrollment (in results) the purpose of the scheme is to give a big overview on how to use it, step by step, in real life, and how to use the algorithms in the end, as a conclusion, the current work paper gives a comprehensive background on the most important and challenging aspects on how to design and implement an authentication system based on biometrics characteristics",
"sequence": "[CLS] scientific concept extraction dataset text coverage concept types inter coder agreement [SEP] security and cryptographic challenges for authentication based on biometrics data authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of the society, e g , smart city, internet of things (iot), cloud computing, big data in the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc due to its uniqueness, characteristic, biometrics systems will become more and more vulnerable, privacy being one of the most important challenges the classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy the current paper has several objectives the main objective consists in creating a framework based on cryptographic modules which can be applied in systems with biometric authentication methods the technologies used in creating the framework are: c#, java, c++, python, and haskell the wide range of technologies for developing the algorithms give the readers the possibility and not only, to choose the proper modules for their own research or business direction the cryptographic modules contain algorithms based on machine learning and modern cryptographic algorithms: aes (advanced encryption system), sha 256, rc4, rc5, rc6, mars, blowfish, twofish, threefish, rsa (rivest shamir adleman), elliptic curve, and diffie hellman as methods for implementing with success the cryptographic modules, we will propose a methodology which can be used as a how to guide the article will focus only on the first category, machine learning, and data clustering, algorithms with applicability in the cloud computing environment for tests we have used a virtual machine (virtual box) with apache hadoop and a biometric analysis tool the weakness of the algorithms and methods implemented within the framework will be evaluated and presented in order for the reader to acknowledge the latest status of the security analysis and the vulnerabilities founded in the mentioned algorithms another important result of the authors consists in creating a scheme for biometric enrollment (in results) the purpose of the scheme is to give a big overview on how to use it, step by step, in real life, and how to use the algorithms in the end, as a conclusion, the current work paper gives a comprehensive background on the most important and challenging aspects on how to design and implement an authentication system based on biometrics characteristics [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR111791",
"template_id": "R35087",
"correct_template_id": null,
"paper_id": "R111791",
"premise": "time interval has beginning has end",
"hypothesis": "automated detection of covid 19 cases using deep neural networks with x ray images abstract the novel coronavirus 2019 (covid 2019), which first appeared in wuhan city of china in december 2019, spread rapidly around the world and became a pandemic it has caused a devastating effect on both daily lives, public health, and the global economy it is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients the need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the covid 19 virus application of advanced artificial intelligence (ai) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages in this study, a new model for automatic covid 19 detection using raw chest x ray images is presented the proposed model is developed to provide accurate diagnostics for binary classification (covid vs no findings) and multi class classification (covid vs no findings vs pneumonia) our model produced a classification accuracy of 98 08% for binary classes and 87 02% for multi class cases the darknet model was used in our study as a classifier for the you only look once (yolo) real time object detection system we implemented 17 convolutional layers and introduced different filtering on each layer our model (available at (https://github com/muhammedtalo/covid 19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients",
"sequence": "[CLS] time interval has beginning has end [SEP] automated detection of covid 19 cases using deep neural networks with x ray images abstract the novel coronavirus 2019 (covid 2019), which first appeared in wuhan city of china in december 2019, spread rapidly around the world and became a pandemic it has caused a devastating effect on both daily lives, public health, and the global economy it is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients the need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the covid 19 virus application of advanced artificial intelligence (ai) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages in this study, a new model for automatic covid 19 detection using raw chest x ray images is presented the proposed model is developed to provide accurate diagnostics for binary classification (covid vs no findings) and multi class classification (covid vs no findings vs pneumonia) our model produced a classification accuracy of 98 08% for binary classes and 87 02% for multi class cases the darknet model was used in our study as a classifier for the you only look once (yolo) real time object detection system we implemented 17 convolutional layers and introduced different filtering on each layer our model (available at (https://github com/muhammedtalo/covid 19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR109529",
"template_id": "R35087",
"correct_template_id": null,
"paper_id": "R109529",
"premise": "time interval has beginning has end",
"hypothesis": "a markovian model for the analysis of age of information in iot networks age of information (aoi) is a critical metric in status update systems as these systems require the fresh updates this letter investigates the uplink of an internet of thing (iot) network where ${l}$ nodes transmit their information packets to a base station the effects of the arrival rate of packets at the nodes, the number of nodes in the system, and queue length of each node have been studied by devising a discrete time markov chain (mc) model this model helps in predicting the values of aoi and probability of packet drops in such systems the notion of first in first out is used for queuing, which transmits the oldest packet first, resulting in decreasing the overall aoi of the system the results show that aoi increases with the increase in queue length, number of nodes and arrival rate and we quantify the aforementioned metrics using the mc model the results found using the mc model are also validated using extensive simulations",
"sequence": "[CLS] time interval has beginning has end [SEP] a markovian model for the analysis of age of information in iot networks age of information (aoi) is a critical metric in status update systems as these systems require the fresh updates this letter investigates the uplink of an internet of thing (iot) network where ${l}$ nodes transmit their information packets to a base station the effects of the arrival rate of packets at the nodes, the number of nodes in the system, and queue length of each node have been studied by devising a discrete time markov chain (mc) model this model helps in predicting the values of aoi and probability of packet drops in such systems the notion of first in first out is used for queuing, which transmits the oldest packet first, resulting in decreasing the overall aoi of the system the results show that aoi increases with the increase in queue length, number of nodes and arrival rate and we quantify the aforementioned metrics using the mc model the results found using the mc model are also validated using extensive simulations [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR109373",
"template_id": "R46273",
"correct_template_id": null,
"paper_id": "R109373",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "progress in developing inhibitors of sars cov 2 3c like protease coronavirus disease 2019 (covid 19) is caused by severe acute respiratory syndrome coronavirus 2 (sars cov 2) the viral outbreak started in late 2019 and rapidly became a serious health threat to the global population covid 19 was declared a pandemic by the world health organization in march 2020 several therapeutic options have been adopted to prevent the spread of the virus although vaccines have been developed, antivirals are still needed to combat the infection of this virus sars cov 2 is an enveloped virus, and its genome encodes polyproteins that can be processed into structural and nonstructural proteins maturation of viral proteins requires cleavages by proteases therefore, the main protease (3 chymotrypsin like protease (3clpro) or mpro) encoded by the viral genome is an attractive drug target because it plays an important role in cleaving viral polyproteins into functional proteins inhibiting this enzyme is an efficient strategy to block viral replication structural studies provide valuable insight into the function of this protease and structural basis for rational inhibitor design in this review, we describe structural studies on the main protease of sars cov 2 the strategies applied in developing inhibitors of the main protease of sars cov 2 and currently available protein inhibitors are summarized due to the availability of high resolution structures, structure guided drug design will play an important role in developing antivirals the availability of high resolution structures, potent peptidic inhibitors, and diverse compound scaffolds indicate the feasibility of developing potent protease inhibitors as antivirals for covid 19",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] progress in developing inhibitors of sars cov 2 3c like protease coronavirus disease 2019 (covid 19) is caused by severe acute respiratory syndrome coronavirus 2 (sars cov 2) the viral outbreak started in late 2019 and rapidly became a serious health threat to the global population covid 19 was declared a pandemic by the world health organization in march 2020 several therapeutic options have been adopted to prevent the spread of the virus although vaccines have been developed, antivirals are still needed to combat the infection of this virus sars cov 2 is an enveloped virus, and its genome encodes polyproteins that can be processed into structural and nonstructural proteins maturation of viral proteins requires cleavages by proteases therefore, the main protease (3 chymotrypsin like protease (3clpro) or mpro) encoded by the viral genome is an attractive drug target because it plays an important role in cleaving viral polyproteins into functional proteins inhibiting this enzyme is an efficient strategy to block viral replication structural studies provide valuable insight into the function of this protease and structural basis for rational inhibitor design in this review, we describe structural studies on the main protease of sars cov 2 the strategies applied in developing inhibitors of the main protease of sars cov 2 and currently available protein inhibitors are summarized due to the availability of high resolution structures, structure guided drug design will play an important role in developing antivirals the availability of high resolution structures, potent peptidic inhibitors, and diverse compound scaffolds indicate the feasibility of developing potent protease inhibitors as antivirals for covid 19 [SEP]",
"target": "contradiction"
},
{
"instance_id": "R49194xR76672",
"template_id": "R49194",
"correct_template_id": null,
"paper_id": "R76672",
"premise": "occupant's perception and behaviour study category results experimental details",
"hypothesis": "robotic disassembly line balancing problem: a mathematical model and ant colony optimization approach abstract the use of robots is significantly increasing day by day in manufacturing systems, and especially improving the efficiency of the lines robots can be used to complete disassembly tasks, and each of the robots can need different operation times to perform the tasks in this paper, the balancing of the robotic disassembly line problem has been studied to develop efficient solution techniques firstly, a mixed integer linear mathematical model is proposed to determine and solve the problem optimally a case study from literature is addressed to assess and show the efficiency and effectiveness of the model to minimize cycle time secondly, a heuristic algorithm based on ant colony optimization is also proposed to discover a solution for especially the large size test problems due to the complexity of the problem the performance of the proposed heuristic algorithm is verified and compared with the different heuristic on data sets the computational results indicate that the proposed mathematical model and the algorithms are promising for the small and large size test problems, respectively finally, it should be stated that robots have great potential to use in the area of disassembly line and useful solutions provide according to test results",
"sequence": "[CLS] occupant's perception and behaviour study category results experimental details [SEP] robotic disassembly line balancing problem: a mathematical model and ant colony optimization approach abstract the use of robots is significantly increasing day by day in manufacturing systems, and especially improving the efficiency of the lines robots can be used to complete disassembly tasks, and each of the robots can need different operation times to perform the tasks in this paper, the balancing of the robotic disassembly line problem has been studied to develop efficient solution techniques firstly, a mixed integer linear mathematical model is proposed to determine and solve the problem optimally a case study from literature is addressed to assess and show the efficiency and effectiveness of the model to minimize cycle time secondly, a heuristic algorithm based on ant colony optimization is also proposed to discover a solution for especially the large size test problems due to the complexity of the problem the performance of the proposed heuristic algorithm is verified and compared with the different heuristic on data sets the computational results indicate that the proposed mathematical model and the algorithms are promising for the small and large size test problems, respectively finally, it should be stated that robots have great potential to use in the area of disassembly line and useful solutions provide according to test results [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR3099",
"template_id": "R77101",
"correct_template_id": null,
"paper_id": "R3099",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "towards a knowledge graph for science the document centric workflows in science have reached (or already exceeded) the limits of adequacy this is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis this presents an opportunity to rethink the dominant paradigm of document centric scholarly information communication and transform it into knowledge based information flows by representing and expressing information through semantically rich, interlinked knowledge graphs at the core of knowledge based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future by integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable this has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs furthermore, research results become directly comparable and easier to reuse as our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] towards a knowledge graph for science the document centric workflows in science have reached (or already exceeded) the limits of adequacy this is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis this presents an opportunity to rethink the dominant paradigm of document centric scholarly information communication and transform it into knowledge based information flows by representing and expressing information through semantically rich, interlinked knowledge graphs at the core of knowledge based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future by integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable this has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs furthermore, research results become directly comparable and easier to reuse as our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR2000",
"template_id": "R77101",
"correct_template_id": null,
"paper_id": "R2000",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "a semi automated, knime based workflow for biofilm assays backgrounda current focus of biofilm research is the chemical interaction between microorganisms within the biofilms prerequisites for this research are bioassay systems which integrate reliable tools for the planning of experiments with robot assisted measurements and with rapid data processing here, data structures that are both human and machine readable may be particularly useful resultsin this report, we present several simplification and robotisation options for an assay of bacteria induced biofilm formation by the freshwater diatom achnanthidium minutissimum we also tested several proof of concept robotisation methods for pipetting, as well as for measuring the biofilm absorbance directly in the multi well plates furthermore, we exemplify the implementation of an improved data processing workflow for this assay using the konstanz information miner (knime), a free and open source data analysis environment the workflow integrates experiment planning files and absorbance read out data, towards their automated processing for analysis conclusionsour workflow lead to a substantial reduction of the measurement and data processing workload, while still reproducing previously obtained results in the a minutissimum biofilm assay the methods, scripts and files we designed are described here, offering adaptable options for other medium throughput biofilm screenings",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] a semi automated, knime based workflow for biofilm assays backgrounda current focus of biofilm research is the chemical interaction between microorganisms within the biofilms prerequisites for this research are bioassay systems which integrate reliable tools for the planning of experiments with robot assisted measurements and with rapid data processing here, data structures that are both human and machine readable may be particularly useful resultsin this report, we present several simplification and robotisation options for an assay of bacteria induced biofilm formation by the freshwater diatom achnanthidium minutissimum we also tested several proof of concept robotisation methods for pipetting, as well as for measuring the biofilm absorbance directly in the multi well plates furthermore, we exemplify the implementation of an improved data processing workflow for this assay using the konstanz information miner (knime), a free and open source data analysis environment the workflow integrates experiment planning files and absorbance read out data, towards their automated processing for analysis conclusionsour workflow lead to a substantial reduction of the measurement and data processing workload, while still reproducing previously obtained results in the a minutissimum biofilm assay the methods, scripts and files we designed are described here, offering adaptable options for other medium throughput biofilm screenings [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108555xR36106",
"template_id": "R108555",
"correct_template_id": null,
"paper_id": "R36106",
"premise": "machine learning experiment has dataset has method has result classification category has feature",
"hypothesis": "characterizing the transmission and identifying the control strategy for covid 19 through epidemiological modeling abstract the outbreak of the novel coronavirus disease, covid 19, originating from wuhan, china in early december, has infected more than 70,000 people in china and other countries and has caused more than 2,000 deaths as the disease continues to spread, the biomedical society urgently began identifying effective approaches to prevent further outbreaks through rigorous epidemiological analysis, we characterized the fast transmission of covid 19 with a basic reproductive number 5 6 and proved a sole zoonotic source to originate in wuhan no changes in transmission have been noted across generations by evaluating different control strategies through predictive modeling and monte carlo simulations, a comprehensive quarantine in hospitals and quarantine stations has been found to be the most effective approach government action to immediately enforce this quarantine is highly recommended",
"sequence": "[CLS] machine learning experiment has dataset has method has result classification category has feature [SEP] characterizing the transmission and identifying the control strategy for covid 19 through epidemiological modeling abstract the outbreak of the novel coronavirus disease, covid 19, originating from wuhan, china in early december, has infected more than 70,000 people in china and other countries and has caused more than 2,000 deaths as the disease continues to spread, the biomedical society urgently began identifying effective approaches to prevent further outbreaks through rigorous epidemiological analysis, we characterized the fast transmission of covid 19 with a basic reproductive number 5 6 and proved a sole zoonotic source to originate in wuhan no changes in transmission have been noted across generations by evaluating different control strategies through predictive modeling and monte carlo simulations, a comprehensive quarantine in hospitals and quarantine stations has been found to be the most effective approach government action to immediately enforce this quarantine is highly recommended [SEP]",
"target": "contradiction"
},
{
"instance_id": "R138668xR49468",
"template_id": "R138668",
"correct_template_id": null,
"paper_id": "R49468",
"premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data",
"hypothesis": "retinal blood vessel segmentation using hybrid features and multi layer perceptron neural networks segmentation of retinal blood vessels is the first step for several computer aided diagnosis systems (cad), not only for ocular disease diagnosis such as diabetic retinopathy (dr) but also of non ocular disease, such as hypertension, stroke and cardiovascular diseases in this paper, a supervised learning based method, using a multi layer perceptron neural network and carefully selected vector of features, is proposed in particular, for each pixel of a retinal fundus image, we construct a 24 d feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, hessian, and difference of gaussian values a post processing technique depending on mathematical morphological operators is used to optimise the segmentation moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image the proposed method is tested on three known datasets: digital retinal image for extraction (drive), structure analysis of the retina (stare), and chased db1 datasets the experimental results, both visual and quantitative, testify to the robustness of the proposed method this proposed method achieved 0 9607, 0 7542, and 0 9843 in drive, 0 9632, 0 7806, and 0 9825 on stare, 0 9577, 0 7585 and 0 9846 in chase db1, with respectable accuracy, sensitivity, and specificity performance metrics furthermore, they testify that the method is superior to seven similar state of the art methods",
"sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] retinal blood vessel segmentation using hybrid features and multi layer perceptron neural networks segmentation of retinal blood vessels is the first step for several computer aided diagnosis systems (cad), not only for ocular disease diagnosis such as diabetic retinopathy (dr) but also of non ocular disease, such as hypertension, stroke and cardiovascular diseases in this paper, a supervised learning based method, using a multi layer perceptron neural network and carefully selected vector of features, is proposed in particular, for each pixel of a retinal fundus image, we construct a 24 d feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, hessian, and difference of gaussian values a post processing technique depending on mathematical morphological operators is used to optimise the segmentation moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image the proposed method is tested on three known datasets: digital retinal image for extraction (drive), structure analysis of the retina (stare), and chased db1 datasets the experimental results, both visual and quantitative, testify to the robustness of the proposed method this proposed method achieved 0 9607, 0 7542, and 0 9843 in drive, 0 9632, 0 7806, and 0 9825 on stare, 0 9577, 0 7585 and 0 9846 in chase db1, with respectable accuracy, sensitivity, and specificity performance metrics furthermore, they testify that the method is superior to seven similar state of the art methods [SEP]",
"target": "contradiction"
},
{
"instance_id": "R48000xR36118",
"template_id": "R48000",
"correct_template_id": null,
"paper_id": "R36118",
"premise": "problem description sub problem same as",
"hypothesis": "the novel coronavirus, 2019 ncov, is highly contagious and more infectious than initially estimated abstract the novel coronavirus (2019 ncov) is a recently emerged human pathogen that has spread widely since january 2020 initially, the basic reproductive number, r 0 , was estimated to be 2 2 to 2 7 here we provide a new estimate of this quantity we collected extensive individual case reports and estimated key epidemiology parameters, including the incubation period integrating these estimates and high resolution real time human travel and infection data with mathematical models, we estimated that the number of infected individuals during early epidemic double every 2 4 days, and the r 0 value is likely to be between 4 7 and 6 6 we further show that quarantine and contact tracing of symptomatic individuals alone may not be effective and early, strong control measures are needed to stop transmission of the virus one sentence summary by collecting and analyzing spatiotemporal data, we estimated the transmission potential for 2019 ncov",
"sequence": "[CLS] problem description sub problem same as [SEP] the novel coronavirus, 2019 ncov, is highly contagious and more infectious than initially estimated abstract the novel coronavirus (2019 ncov) is a recently emerged human pathogen that has spread widely since january 2020 initially, the basic reproductive number, r 0 , was estimated to be 2 2 to 2 7 here we provide a new estimate of this quantity we collected extensive individual case reports and estimated key epidemiology parameters, including the incubation period integrating these estimates and high resolution real time human travel and infection data with mathematical models, we estimated that the number of infected individuals during early epidemic double every 2 4 days, and the r 0 value is likely to be between 4 7 and 6 6 we further show that quarantine and contact tracing of symptomatic individuals alone may not be effective and early, strong control measures are needed to stop transmission of the virus one sentence summary by collecting and analyzing spatiotemporal data, we estimated the transmission potential for 2019 ncov [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76209xR111791",
"template_id": "R76209",
"correct_template_id": null,
"paper_id": "R111791",
"premise": "team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in",
"hypothesis": "automated detection of covid 19 cases using deep neural networks with x ray images abstract the novel coronavirus 2019 (covid 2019), which first appeared in wuhan city of china in december 2019, spread rapidly around the world and became a pandemic it has caused a devastating effect on both daily lives, public health, and the global economy it is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients the need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the covid 19 virus application of advanced artificial intelligence (ai) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages in this study, a new model for automatic covid 19 detection using raw chest x ray images is presented the proposed model is developed to provide accurate diagnostics for binary classification (covid vs no findings) and multi class classification (covid vs no findings vs pneumonia) our model produced a classification accuracy of 98 08% for binary classes and 87 02% for multi class cases the darknet model was used in our study as a classifier for the you only look once (yolo) real time object detection system we implemented 17 convolutional layers and introduced different filtering on each layer our model (available at (https://github com/muhammedtalo/covid 19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients",
"sequence": "[CLS] team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in [SEP] automated detection of covid 19 cases using deep neural networks with x ray images abstract the novel coronavirus 2019 (covid 2019), which first appeared in wuhan city of china in december 2019, spread rapidly around the world and became a pandemic it has caused a devastating effect on both daily lives, public health, and the global economy it is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients the need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the covid 19 virus application of advanced artificial intelligence (ai) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages in this study, a new model for automatic covid 19 detection using raw chest x ray images is presented the proposed model is developed to provide accurate diagnostics for binary classification (covid vs no findings) and multi class classification (covid vs no findings vs pneumonia) our model produced a classification accuracy of 98 08% for binary classes and 87 02% for multi class cases the darknet model was used in our study as a classifier for the you only look once (yolo) real time object detection system we implemented 17 convolutional layers and introduced different filtering on each layer our model (available at (https://github com/muhammedtalo/covid 19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108239xR109882",
"template_id": "R108239",
"correct_template_id": null,
"paper_id": "R109882",
"premise": "km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development",
"hypothesis": "predicting the research performance of early career scientists this paper examines how early career related factors can predict the future research performance of computer and information scientists although a few bibliometric studies have previously investigated multiple factors relating to early career scientists that significantly predict their future research performance, there have been limited studies on early career related factors affecting scientists in the fields of information science and computer science this study analyzes 4102 scientists whose publishing careers started in the same year the criteria used to quantify future research performance of the target scientists included the number of publications and citation counts of publications in a 4 year citation window to indicate future research productivity and research impact, respectively these criteria were regressed on 13 early career related factors the results showed that these factors accounted for about 27% and 23% of the future productivity of the target scientists in terms of journal articles and conference papers, respectively; these 13 factors were also responsible for 19% of the future impact of target scientists\u2019 journal articles and 19% of the future impact of their conference papers the factor that most contributed to explaining the future research performance (i e publication numbers) and future research impact (i e citation counts of publications) was the number of publications (both journal articles and conference papers) produced by the target scientists in their early career years",
"sequence": "[CLS] km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development [SEP] predicting the research performance of early career scientists this paper examines how early career related factors can predict the future research performance of computer and information scientists although a few bibliometric studies have previously investigated multiple factors relating to early career scientists that significantly predict their future research performance, there have been limited studies on early career related factors affecting scientists in the fields of information science and computer science this study analyzes 4102 scientists whose publishing careers started in the same year the criteria used to quantify future research performance of the target scientists included the number of publications and citation counts of publications in a 4 year citation window to indicate future research productivity and research impact, respectively these criteria were regressed on 13 early career related factors the results showed that these factors accounted for about 27% and 23% of the future productivity of the target scientists in terms of journal articles and conference papers, respectively; these 13 factors were also responsible for 19% of the future impact of target scientists\u2019 journal articles and 19% of the future impact of their conference papers the factor that most contributed to explaining the future research performance (i e publication numbers) and future research impact (i e citation counts of publications) was the number of publications (both journal articles and conference papers) produced by the target scientists in their early career years [SEP]",
"target": "contradiction"
},
{
"instance_id": "R138668xR41008",
"template_id": "R138668",
"correct_template_id": "R46269",
"paper_id": "R41008",
"premise": "psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data",
"hypothesis": "communicating the risk of death from novel coronavirus disease (covid 19) to understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases however, there are three technical obstacles that should be addressed to appropriately measure this risk first, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests in the ongoing covid 19 epidemic, health authorities must cope with the uncertainty in the risk of death from covid 19, and high risk individuals should be identified using approaches that can address the abovementioned three problems although covid 19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care",
"sequence": "[CLS] psychiatric disorders ai overview study cohort outcome assessment aims performance findings used models data [SEP] communicating the risk of death from novel coronavirus disease (covid 19) to understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases however, there are three technical obstacles that should be addressed to appropriately measure this risk first, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests in the ongoing covid 19 epidemic, health authorities must cope with the uncertainty in the risk of death from covid 19, and high risk individuals should be identified using approaches that can address the abovementioned three problems although covid 19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR41013",
"template_id": "R77101",
"correct_template_id": "R46273",
"paper_id": "R41013",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "transmission potential and severity of covid 19 in south korea abstract objectives since the first case of 2019 novel coronavirus (covid 19) identified on jan 20, 2020 in south korea, the number of cases rapidly increased, resulting in 6,284 cases including 42 deaths as of march 6, 2020 to examine the growth rate of the outbreak, we aimed to present the first study to report the reproduction number of covid 19 in south korea methods the daily confirmed cases of covid 19 in south korea were extracted from publicly available sources by using the empirical reporting delay distribution and simulating the generalized growth model, we estimated the effective reproduction number based on the discretized probability distribution of the generation interval results we identified four major clusters and estimated the reproduction number at 1 5 (95% ci: 1 4 1 6) in addition, the intrinsic growth rate was estimated at 0 6 (95% ci: 0 6, 0 7) and the scaling of growth parameter was estimated at 0 8 (95% ci: 0 7, 0 8), indicating sub exponential growth dynamics of covid 19 the crude case fatality rate is higher among males (1 1%) compared to females (0 4%) and increases with older age conclusions our results indicate early sustained transmission of covid 19 in south korea and support the implementation of social distancing measures to rapidly control the outbreak",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] transmission potential and severity of covid 19 in south korea abstract objectives since the first case of 2019 novel coronavirus (covid 19) identified on jan 20, 2020 in south korea, the number of cases rapidly increased, resulting in 6,284 cases including 42 deaths as of march 6, 2020 to examine the growth rate of the outbreak, we aimed to present the first study to report the reproduction number of covid 19 in south korea methods the daily confirmed cases of covid 19 in south korea were extracted from publicly available sources by using the empirical reporting delay distribution and simulating the generalized growth model, we estimated the effective reproduction number based on the discretized probability distribution of the generation interval results we identified four major clusters and estimated the reproduction number at 1 5 (95% ci: 1 4 1 6) in addition, the intrinsic growth rate was estimated at 0 6 (95% ci: 0 6, 0 7) and the scaling of growth parameter was estimated at 0 8 (95% ci: 0 7, 0 8), indicating sub exponential growth dynamics of covid 19 the crude case fatality rate is higher among males (1 1%) compared to females (0 4%) and increases with older age conclusions our results indicate early sustained transmission of covid 19 in south korea and support the implementation of social distancing measures to rapidly control the outbreak [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR36109",
"template_id": "R77101",
"correct_template_id": null,
"paper_id": "R36109",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "transmission interval estimates suggest pre symptomatic spread of covid 19 abstract background as the covid 19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic we determine the incubation period and serial interval distribution for transmission clusters in singapore and in tianjin we infer the basic reproduction number and identify the extent of pre symptomatic transmission methods we collected outbreak information from singapore and tianjin, china, reported from jan 19 feb 26 and jan 21 feb 27, respectively we estimated incubation periods and serial intervals in both populations results the mean incubation period was 7 1 (6 13, 8 25) days for singapore and 9 (7 92, 10 2) days for tianjin both datasets had shorter incubation periods for earlier occurring cases the mean serial interval was 4 56 (2 69, 6 42) days for singapore and 4 22 (3 43, 5 01) for tianjin we inferred that early in the outbreaks, infection was transmitted on average 2 55 and 2 89 days before symptom onset (singapore, tianjin) the estimated basic reproduction number for singapore was 1 97 (1 45, 2 48) secondary cases per infective; for tianjin it was 1 87 (1 65, 2 09) secondary cases per infective conclusions estimated serial intervals are shorter than incubation periods in both singapore and tianjin, suggesting that pre symptomatic transmission is occurring shorter serial intervals lead to lower estimates of r0, which suggest that half of all secondary infections should be prevented to control spread",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] transmission interval estimates suggest pre symptomatic spread of covid 19 abstract background as the covid 19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic we determine the incubation period and serial interval distribution for transmission clusters in singapore and in tianjin we infer the basic reproduction number and identify the extent of pre symptomatic transmission methods we collected outbreak information from singapore and tianjin, china, reported from jan 19 feb 26 and jan 21 feb 27, respectively we estimated incubation periods and serial intervals in both populations results the mean incubation period was 7 1 (6 13, 8 25) days for singapore and 9 (7 92, 10 2) days for tianjin both datasets had shorter incubation periods for earlier occurring cases the mean serial interval was 4 56 (2 69, 6 42) days for singapore and 4 22 (3 43, 5 01) for tianjin we inferred that early in the outbreaks, infection was transmitted on average 2 55 and 2 89 days before symptom onset (singapore, tianjin) the estimated basic reproduction number for singapore was 1 97 (1 45, 2 48) secondary cases per infective; for tianjin it was 1 87 (1 65, 2 09) secondary cases per infective conclusions estimated serial intervals are shorter than incubation periods in both singapore and tianjin, suggesting that pre symptomatic transmission is occurring shorter serial intervals lead to lower estimates of r0, which suggest that half of all secondary infections should be prevented to control spread [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR12223",
"template_id": "R35087",
"correct_template_id": null,
"paper_id": "R12223",
"premise": "time interval has beginning has end",
"hypothesis": "modelling the epidemic trend of the 2019 novel coronavirus outbreak in china we present a timely evaluation of the chinese 2019 ncov epidemic in its initial phase, where 2019 ncov demonstrates comparable transmissibility but lower fatality rates than sars and mers a quick diagnosis that leads to case isolation and integrated interventions will have a major impact on its future trend nevertheless, as china is facing its spring festival travel rush and the epidemic has spread beyond its borders, further investigation on its potential spatiotemporal transmission pattern and novel intervention strategies are warranted",
"sequence": "[CLS] time interval has beginning has end [SEP] modelling the epidemic trend of the 2019 novel coronavirus outbreak in china we present a timely evaluation of the chinese 2019 ncov epidemic in its initial phase, where 2019 ncov demonstrates comparable transmissibility but lower fatality rates than sars and mers a quick diagnosis that leads to case isolation and integrated interventions will have a major impact on its future trend nevertheless, as china is facing its spring festival travel rush and the epidemic has spread beyond its borders, further investigation on its potential spatiotemporal transmission pattern and novel intervention strategies are warranted [SEP]",
"target": "contradiction"
},
{
"instance_id": "R107684xR130451",
"template_id": "R107684",
"correct_template_id": null,
"paper_id": "R130451",
"premise": "health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample",
"hypothesis": "a hierarchical model for data to text generation transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as \u201cdata to text\u201d these structures generally regroup multiple elements, as well as their attributes most attempts rely on translation encoder decoder methods which linearize elements into a sequence this however loses most of the structure contained in the data in this work, we propose to overpass this limitation with a hierarchical model that encodes the data structure at the element level and the structure level evaluations on rotowire show the effectiveness of our model w r t qualitative and quantitative metrics",
"sequence": "[CLS] health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample [SEP] a hierarchical model for data to text generation transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as \u201cdata to text\u201d these structures generally regroup multiple elements, as well as their attributes most attempts rely on translation encoder decoder methods which linearize elements into a sequence this however loses most of the structure contained in the data in this work, we propose to overpass this limitation with a hierarchical model that encodes the data structure at the element level and the structure level evaluations on rotowire show the effectiveness of our model w r t qualitative and quantitative metrics [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76209xR110717",
"template_id": "R76209",
"correct_template_id": null,
"paper_id": "R110717",
"premise": "team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in",
"hypothesis": "identification of a flavonoid isolated from plum (prunus domestica) as a potent inhibitor of hepatitis c virus entry hepatitis c virus (hcv) infection is a major cause of chronic liver diseases that often requires liver transplantation the standard therapies are limited by severe side effects, resistance development, high expense and in a substantial proportion of cases, fail to clear the infection which bespeak the need for development of well tolerated antivirals since most of the drug development strategies target the replication stage of viral lifecycle, the identification of entry inhibitors might be crucial especially in case of liver transplant recipients in the present study we have evaluated fruits which are known for their hepatoprotective effects in order to screen for entry inhibitors we report the identification of a flavonoid, rutin, isolated from prunus domestica as a new hcv entry inhibitor characterization and confirmation of the chemical structure was done by lc esi ms, nmr and ir spectral analyses rutin significantly inhibited hcv lp binding to hepatoma cells and inhibited cell culture derived hcv (hcvcc) entry into hepatoma cells importantly, rutin was found to be non toxic to hepatoma cells furthermore, rutin inhibits the early entry stage of hcv lifecycle possibly by directly acting on the viral particle in conclusion, rutin is a promising candidate for development of anti hcv therapeutics in the management of hcv infection",
"sequence": "[CLS] team subtask contribution description pearson uncentered correlation has approach average accuracy accuracy bleu score f1 harmonic mean of spearman and pearson correlation between predicted scores and average manual scores code repositories performance ranks team name recall average spearman's rank correlation precision participated in [SEP] identification of a flavonoid isolated from plum (prunus domestica) as a potent inhibitor of hepatitis c virus entry hepatitis c virus (hcv) infection is a major cause of chronic liver diseases that often requires liver transplantation the standard therapies are limited by severe side effects, resistance development, high expense and in a substantial proportion of cases, fail to clear the infection which bespeak the need for development of well tolerated antivirals since most of the drug development strategies target the replication stage of viral lifecycle, the identification of entry inhibitors might be crucial especially in case of liver transplant recipients in the present study we have evaluated fruits which are known for their hepatoprotective effects in order to screen for entry inhibitors we report the identification of a flavonoid, rutin, isolated from prunus domestica as a new hcv entry inhibitor characterization and confirmation of the chemical structure was done by lc esi ms, nmr and ir spectral analyses rutin significantly inhibited hcv lp binding to hepatoma cells and inhibited cell culture derived hcv (hcvcc) entry into hepatoma cells importantly, rutin was found to be non toxic to hepatoma cells furthermore, rutin inhibits the early entry stage of hcv lifecycle possibly by directly acting on the viral particle in conclusion, rutin is a promising candidate for development of anti hcv therapeutics in the management of hcv infection [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108555xR25623",
"template_id": "R108555",
"correct_template_id": null,
"paper_id": "R25623",
"premise": "machine learning experiment has dataset has method has result classification category has feature",
"hypothesis": "drivers of agile software development use: dialectic interplay between benefits and hindrances information and software technology context: agile software development with its emphasis on producing working code through frequent releases, extensive client interactions and iterative development has emerged as an alternative to traditional plan based software development methods while a number of case studies have provided insights into the use and consequences of agile, few empirical studies have examined the factors that drive the adoption and use of agile objective: we draw on intention based theories and a dialectic perspective to identify factors driving the use of agile practices among adopters of this software development methodology method: data for the study was gathered through an anonymous online survey of software development professionals we requested participation from members of a selected list of online discussion groups, and received 98 responses results: our analyses reveal that subjective norm and training play a significant role in influencing software developers' use of agile processes and methods, while perceived benefits and perceived limitations are not primary drivers of agile use among adopters interestingly, perceived benefit emerges as a significant predictor of agile use only if adopters face hindrances to their agile practices conclusion: we conclude that research in the adoption of software development innovations should examine the effects of both enabling and detracting factors and the interactions between them since training, subjective norm, and the interplay between perceived benefits and perceived hindrances appear to be key factors influencing the adoption of agile methods, researchers can focus on how to (a) perform training on agile methods more effectively, (b) facilitate the dialog between developers and managers about perceived benefits and hindrances, and (c) capitalize on subjective norm to publicize the benefits of agile methods within an organization further, when managing the transition to new software development methods, we recommend that practitioners adapt their strategies and tactics contingent on the extent of perceived hindrances to the change",
"sequence": "[CLS] machine learning experiment has dataset has method has result classification category has feature [SEP] drivers of agile software development use: dialectic interplay between benefits and hindrances information and software technology context: agile software development with its emphasis on producing working code through frequent releases, extensive client interactions and iterative development has emerged as an alternative to traditional plan based software development methods while a number of case studies have provided insights into the use and consequences of agile, few empirical studies have examined the factors that drive the adoption and use of agile objective: we draw on intention based theories and a dialectic perspective to identify factors driving the use of agile practices among adopters of this software development methodology method: data for the study was gathered through an anonymous online survey of software development professionals we requested participation from members of a selected list of online discussion groups, and received 98 responses results: our analyses reveal that subjective norm and training play a significant role in influencing software developers' use of agile processes and methods, while perceived benefits and perceived limitations are not primary drivers of agile use among adopters interestingly, perceived benefit emerges as a significant predictor of agile use only if adopters face hindrances to their agile practices conclusion: we conclude that research in the adoption of software development innovations should examine the effects of both enabling and detracting factors and the interactions between them since training, subjective norm, and the interplay between perceived benefits and perceived hindrances appear to be key factors influencing the adoption of agile methods, researchers can focus on how to (a) perform training on agile methods more effectively, (b) facilitate the dialog between developers and managers about perceived benefits and hindrances, and (c) capitalize on subjective norm to publicize the benefits of agile methods within an organization further, when managing the transition to new software development methods, we recommend that practitioners adapt their strategies and tactics contingent on the extent of perceived hindrances to the change [SEP]",
"target": "contradiction"
},
{
"instance_id": "R54875xR78466",
"template_id": "R54875",
"correct_template_id": null,
"paper_id": "R78466",
"premise": "climate response has unit has evaluation data used has tcr result",
"hypothesis": "how can i improve my app? classifying user reviews for software maintenance and evolution app stores, such as google play or the apple store, allow users to provide feedback on apps by posting review comments and giving star ratings these platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks however, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task in this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) natural language processing, (2) text analysis and (3) sentiment analysis to automatically classify app reviews into the proposed categories we show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%)",
"sequence": "[CLS] climate response has unit has evaluation data used has tcr result [SEP] how can i improve my app? classifying user reviews for software maintenance and evolution app stores, such as google play or the apple store, allow users to provide feedback on apps by posting review comments and giving star ratings these platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks however, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task in this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) natural language processing, (2) text analysis and (3) sentiment analysis to automatically classify app reviews into the proposed categories we show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%) [SEP]",
"target": "contradiction"
},
{
"instance_id": "R49194xR37006",
"template_id": "R49194",
"correct_template_id": null,
"paper_id": "R37006",
"premise": "occupant's perception and behaviour study category results experimental details",
"hypothesis": "estimating the unreported number of novel coronavirus (2019 ncov) cases in china in the first half of january 2020: a data driven modelling analysis of the early outbreak background: in december 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019 ncov) emerged in wuhan, china and has swiftly spread to other parts of china and a number of foreign countries the 2019 ncov cases might have been under reported roughly from 1 to 15 january 2020, and thus we estimated the number of unreported cases and the basic reproduction number, r0, of 2019 ncov methods: we modelled the epidemic curve of 2019 ncov cases, in mainland china from 1 december 2019 to 24 january 2020 through the exponential growth the number of unreported cases was determined by the maximum likelihood estimation we used the serial intervals (si) of infection caused by two other well known coronaviruses (cov), severe acute respiratory syndrome (sars) and middle east respiratory syndrome (mers) covs, as approximations of the unknown si for 2019 ncov to estimate r0 results: we confirmed that the initial growth phase followed an exponential growth pattern the under reporting was likely to have resulted in 469 (95% ci: 403\u2013540) unreported cases from 1 to 15 january 2020 the reporting rate after 17 january 2020 was likely to have increased 21 fold (95% ci: 18\u201325) in comparison to the situation from 1 to 17 january 2020 on average we estimated the r0 of 2019 ncov at 2 56 (95% ci: 2 49\u20132 63) conclusion: the under reporting was likely to have occurred during the first half of january 2020 and should be considered in future investigation",
"sequence": "[CLS] occupant's perception and behaviour study category results experimental details [SEP] estimating the unreported number of novel coronavirus (2019 ncov) cases in china in the first half of january 2020: a data driven modelling analysis of the early outbreak background: in december 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019 ncov) emerged in wuhan, china and has swiftly spread to other parts of china and a number of foreign countries the 2019 ncov cases might have been under reported roughly from 1 to 15 january 2020, and thus we estimated the number of unreported cases and the basic reproduction number, r0, of 2019 ncov methods: we modelled the epidemic curve of 2019 ncov cases, in mainland china from 1 december 2019 to 24 january 2020 through the exponential growth the number of unreported cases was determined by the maximum likelihood estimation we used the serial intervals (si) of infection caused by two other well known coronaviruses (cov), severe acute respiratory syndrome (sars) and middle east respiratory syndrome (mers) covs, as approximations of the unknown si for 2019 ncov to estimate r0 results: we confirmed that the initial growth phase followed an exponential growth pattern the under reporting was likely to have resulted in 469 (95% ci: 403\u2013540) unreported cases from 1 to 15 january 2020 the reporting rate after 17 january 2020 was likely to have increased 21 fold (95% ci: 18\u201325) in comparison to the situation from 1 to 17 january 2020 on average we estimated the r0 of 2019 ncov at 2 56 (95% ci: 2 49\u20132 63) conclusion: the under reporting was likely to have occurred during the first half of january 2020 and should be considered in future investigation [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108555xR28965",
"template_id": "R108555",
"correct_template_id": null,
"paper_id": "R28965",
"premise": "machine learning experiment has dataset has method has result classification category has feature",
"hypothesis": "prioritizing clinical information system project risk factors: a delphi study identifying the risks associated with the implementation of clinical information systems (cis) in health care organizations can be a major challenge for managers, clinicians, and it specialists, as there are numerous ways in which they can be described and categorized risks vary in nature, severity, and consequence, so it is important that those considered to be high level risks be identified, understood, and managed this study addresses this issue by first reviewing the extant literature on it/cis project risks, and second conducting a delphi survey among 21 experts highly involved in cis projects in canada in addition to providing a comprehensive list of risk factors and their relative importance, this study is helpful in unifying the literature on it implementation and health informatics our risk factor oriented research actually confirmed many of the factors found to be important in both these streams",
"sequence": "[CLS] machine learning experiment has dataset has method has result classification category has feature [SEP] prioritizing clinical information system project risk factors: a delphi study identifying the risks associated with the implementation of clinical information systems (cis) in health care organizations can be a major challenge for managers, clinicians, and it specialists, as there are numerous ways in which they can be described and categorized risks vary in nature, severity, and consequence, so it is important that those considered to be high level risks be identified, understood, and managed this study addresses this issue by first reviewing the extant literature on it/cis project risks, and second conducting a delphi survey among 21 experts highly involved in cis projects in canada in addition to providing a comprehensive list of risk factors and their relative importance, this study is helpful in unifying the literature on it implementation and health informatics our risk factor oriented research actually confirmed many of the factors found to be important in both these streams [SEP]",
"target": "contradiction"
},
{
"instance_id": "R40006xR33251",
"template_id": "R40006",
"correct_template_id": null,
"paper_id": "R33251",
"premise": "basic reproduction number estimate basic reproduction number time period location",
"hypothesis": "critical factors of attracting supply chain network members to electronic marketplaces: the case of sunbooks ltd and the hungarian book trade
vertical electronic marketplaces often suffer from the low level of liquidity attracting members is critical, however, not even a sound and efficient it and logistic background is enough to convince both the supplier and the customer side in this paper the authors present the case study of sunbooks ltd this venture has started to transform the hungarian book trade market that suffers from serious deficiencies in field of information and material flow despite the vast investments and that the marketplace is prepared to serve the whole hungarian book industry, the market share started to grow very slowly the authors identify three contingency factors which can be accounted for the evolution dynamics of this virtual network they explain how the business model is subjected to the evolution of market characteristics, and how the third factor, the \u201csoft issues\u201d determine the evolution opportunities even in a supporting market situation",
"sequence": "[CLS] basic reproduction number estimate basic reproduction number time period location [SEP] critical factors of attracting supply chain network members to electronic marketplaces: the case of sunbooks ltd and the hungarian book trade
vertical electronic marketplaces often suffer from the low level of liquidity attracting members is critical, however, not even a sound and efficient it and logistic background is enough to convince both the supplier and the customer side in this paper the authors present the case study of sunbooks ltd this venture has started to transform the hungarian book trade market that suffers from serious deficiencies in field of information and material flow despite the vast investments and that the marketplace is prepared to serve the whole hungarian book industry, the market share started to grow very slowly the authors identify three contingency factors which can be accounted for the evolution dynamics of this virtual network they explain how the business model is subjected to the evolution of market characteristics, and how the third factor, the \u201csoft issues\u201d determine the evolution opportunities even in a supporting market situation [SEP]",
"target": "contradiction"
},
{
"instance_id": "R51438xR112015",
"template_id": "R51438",
"correct_template_id": null,
"paper_id": "R112015",
"premise": "common template has result has hypothesis has methodology",
"hypothesis": "a little bird told me: mining tweets for requirements and software evolution twitter is one of the most popular social networks previous research found that users employ twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution however, due to their large number in the range of thousands per day for popular applications a manual analysis is unfeasible in this work we present alertme, an approach to automatically classify, group and rank tweets about software applications we apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets we ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement our results show that alertme is an effective approach for filtering, summarizing and ranking tweets about software applications alertme enables the exploitation of twitter as a feedback channel for information relevant to software evolution, including end user requirements",
"sequence": "[CLS] common template has result has hypothesis has methodology [SEP] a little bird told me: mining tweets for requirements and software evolution twitter is one of the most popular social networks previous research found that users employ twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution however, due to their large number in the range of thousands per day for popular applications a manual analysis is unfeasible in this work we present alertme, an approach to automatically classify, group and rank tweets about software applications we apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets we ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement our results show that alertme is an effective approach for filtering, summarizing and ranking tweets about software applications alertme enables the exploitation of twitter as a feedback channel for information relevant to software evolution, including end user requirements [SEP]",
"target": "contradiction"
},
{
"instance_id": "R107684xR111748",
"template_id": "R107684",
"correct_template_id": null,
"paper_id": "R111748",
"premise": "health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample",
"hypothesis": "the coupled brains of captivated audiences: an investigation of the collective brain dynamics of an audience watching a suspenseful film abstract suspense not only creates a strong psychological tension within individuals, but it does so reliably across viewers who become collectively engaged with the story despite its prevalence in media psychology, limited work has examined suspense from a media neuroscience perspective, and thus the biological underpinnings of suspense remain unknown here we examine continuous brain responses of 494 viewers watching a suspenseful movie to create a time resolved measure of the degree to which a movie aligns audience wide brain responses, we computed dynamic inter subject correlations of functional magnetic resonance imaging (fmri) time series among all viewers using sliding window analysis in parallel, we captured in the moment reports of suspense in an independent sample via continuous response measurement (crm) we found that dynamic inter subject correlations over the course of the movie tracked well with the reported suspense in the crm sample, particularly in regions associated with emotional salience and higher cognitive processes these results are compatible with theoretical views on motivated attention and psychological tension the finding that fmri based audience response measurement relates to audience reports of suspense creates new opportunities for research on the mechanisms of suspense and other entertainment phenomena and has applied potential for measuring audience responses in a nonreactive and objective fashion",
"sequence": "[CLS] health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample [SEP] the coupled brains of captivated audiences: an investigation of the collective brain dynamics of an audience watching a suspenseful film abstract suspense not only creates a strong psychological tension within individuals, but it does so reliably across viewers who become collectively engaged with the story despite its prevalence in media psychology, limited work has examined suspense from a media neuroscience perspective, and thus the biological underpinnings of suspense remain unknown here we examine continuous brain responses of 494 viewers watching a suspenseful movie to create a time resolved measure of the degree to which a movie aligns audience wide brain responses, we computed dynamic inter subject correlations of functional magnetic resonance imaging (fmri) time series among all viewers using sliding window analysis in parallel, we captured in the moment reports of suspense in an independent sample via continuous response measurement (crm) we found that dynamic inter subject correlations over the course of the movie tracked well with the reported suspense in the crm sample, particularly in regions associated with emotional salience and higher cognitive processes these results are compatible with theoretical views on motivated attention and psychological tension the finding that fmri based audience response measurement relates to audience reports of suspense creates new opportunities for research on the mechanisms of suspense and other entertainment phenomena and has applied potential for measuring audience responses in a nonreactive and objective fashion [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76795xR57722",
"template_id": "R76795",
"correct_template_id": null,
"paper_id": "R57722",
"premise": "crowd intelligence utilities in crowdre re activities with crowd involvement",
"hypothesis": "novel host associations and habitats for senecio specialist herbivorous insects in auckland we studied the genusand species specialist monophagous herbivorous insects of senecio (asteraceae) in auckland, new zealand with the exception of the widespread s hispidulus, the eight native senecio species in mainland auckland (two endemic) are typically uncommon and restricted to less modified conservation land however, 11 naturalised senecio have established and are often widespread in urban and rural habitats three endemic senecio specialist herbivores \u2013 nyctemera annulata, patagoniodes farnaria, and tephritis fascigera \u2013 formed novel host associations with naturalised senecio species and spread into modified landscapes host associations for these species were not related to whether senecio species are naturalised or native however, the abundances of patagonoides farnaria and tephritis fascigera were significantly higher in wildland habitats than rural or urban habitats, and wildland senecio were on average 1 4 times more likely to experience >5% folivory than urban conspecifics",
"sequence": "[CLS] crowd intelligence utilities in crowdre re activities with crowd involvement [SEP] novel host associations and habitats for senecio specialist herbivorous insects in auckland we studied the genusand species specialist monophagous herbivorous insects of senecio (asteraceae) in auckland, new zealand with the exception of the widespread s hispidulus, the eight native senecio species in mainland auckland (two endemic) are typically uncommon and restricted to less modified conservation land however, 11 naturalised senecio have established and are often widespread in urban and rural habitats three endemic senecio specialist herbivores \u2013 nyctemera annulata, patagoniodes farnaria, and tephritis fascigera \u2013 formed novel host associations with naturalised senecio species and spread into modified landscapes host associations for these species were not related to whether senecio species are naturalised or native however, the abundances of patagonoides farnaria and tephritis fascigera were significantly higher in wildland habitats than rural or urban habitats, and wildland senecio were on average 1 4 times more likely to experience >5% folivory than urban conspecifics [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR49282",
"template_id": "R70247",
"correct_template_id": "R49194",
"paper_id": "R49282",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "the impact of thermal environment on occupant ieq perception and productivity abstract in this paper, the effects of thermal environment on occupant ieq perception and productivity were studied seven groups of experiments were carried out in a controlled office environment and the physical parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured in the experiments, indoor air temperature was the independent variable, which varied from 16 \u00b0c to 28 \u00b0c with a step of 2 \u00b0c, and other constant ieq parameters were the control variables the dependent variable would be human perception of various control variables and productivity subjects (9 females and 12 males) were recruited to participate every experiment for 2 h during each experiment, they voted their perceptions of thermal comfort, indoor air quality, lighting and acoustic environment, and performed simulated office tasks to evaluate the productivity the results showed that the variation of thermal environment not only affected thermal comfort but also had a \u201ccomparative\u201d impact on the perception of other ieq factors when thermal environment was unsatisfactory, it weakened the \u201ccomfort expectation\u201d of other ieq factors, which accordingly resulted in the less dissatisfaction with other ieq factors conversely, when thermal environment was quite satisfying, it raised \u201ccomfort expectation\u201d of other ieq factors, which lowered the evaluation of the real performance of other ieq factors retroactively the quantitative relationship between productivity and thermal environment was established the optimal productivity was obtained when people felt \u201cneutral\u201d or \u201cslightly cool\u201d, and the increase of thermal satisfaction had a positive effect on productivity",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] the impact of thermal environment on occupant ieq perception and productivity abstract in this paper, the effects of thermal environment on occupant ieq perception and productivity were studied seven groups of experiments were carried out in a controlled office environment and the physical parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured in the experiments, indoor air temperature was the independent variable, which varied from 16 \u00b0c to 28 \u00b0c with a step of 2 \u00b0c, and other constant ieq parameters were the control variables the dependent variable would be human perception of various control variables and productivity subjects (9 females and 12 males) were recruited to participate every experiment for 2 h during each experiment, they voted their perceptions of thermal comfort, indoor air quality, lighting and acoustic environment, and performed simulated office tasks to evaluate the productivity the results showed that the variation of thermal environment not only affected thermal comfort but also had a \u201ccomparative\u201d impact on the perception of other ieq factors when thermal environment was unsatisfactory, it weakened the \u201ccomfort expectation\u201d of other ieq factors, which accordingly resulted in the less dissatisfaction with other ieq factors conversely, when thermal environment was quite satisfying, it raised \u201ccomfort expectation\u201d of other ieq factors, which lowered the evaluation of the real performance of other ieq factors retroactively the quantitative relationship between productivity and thermal environment was established the optimal productivity was obtained when people felt \u201cneutral\u201d or \u201cslightly cool\u201d, and the increase of thermal satisfaction had a positive effect on productivity [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108239xR12245",
"template_id": "R108239",
"correct_template_id": null,
"paper_id": "R12245",
"premise": "km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development",
"hypothesis": "estimation of the transmission risk of 2019 ncov and its implication for public health interventions since the emergence of the first cases in wuhan, china, the novel coronavirus (2019 ncov) infection has been quickly spreading out to other provinces and neighboring countries estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity a deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures the estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6 47 (95% ci 5 71\u20137 23) sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by wuhan on 2019 ncov infection in beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value it is essential to assess how the expensive, resource intensive measures implemented by the chinese authorities can contribute to the prevention and control of the 2019 ncov infection, and how long they should be maintained under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 january 2020) with a significant low peak value with travel restriction (no imported exposed individuals to beijing), the number of infected individuals in seven days will decrease by 91 14% in beijing, compared with the scenario of no travel restriction",
"sequence": "[CLS] km oriented enterprise modeling approaches technology and infrastructure products and services, which requires knowledge processes (knowledge activities applied and embedded within organizational processes) types of knowledge flows governance approach name human capital (roles and accountabilities, km stakeholders) km objectives knowledge and its status activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation km culture organizational processes, which requires knowledge stages of knowledge development [SEP] estimation of the transmission risk of 2019 ncov and its implication for public health interventions since the emergence of the first cases in wuhan, china, the novel coronavirus (2019 ncov) infection has been quickly spreading out to other provinces and neighboring countries estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity a deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures the estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6 47 (95% ci 5 71\u20137 23) sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by wuhan on 2019 ncov infection in beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value it is essential to assess how the expensive, resource intensive measures implemented by the chinese authorities can contribute to the prevention and control of the 2019 ncov infection, and how long they should be maintained under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 january 2020) with a significant low peak value with travel restriction (no imported exposed individuals to beijing), the number of infected individuals in seven days will decrease by 91 14% in beijing, compared with the scenario of no travel restriction [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR77177",
"template_id": "R77101",
"correct_template_id": "R108555",
"paper_id": "R77177",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "mining user requirements from application store reviews using frame semantics context and motivation: research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years the majority of the proposed techniques rely on classifying the textual description of user reviews into different categories of technically informative user requirements and uninformative feedback question/problem: relying on the textual attributes of reviews often produces high dimensional models this increases the complexity of the classifier and can lead to overfitting problems principal ideas/results: we propose a novel semantic approach for app review classification the proposed approach is based on the notion of semantic role labeling, or characterizing the lexical meaning of text in terms of semantic frames semantic frames help to generalize from text (individual words) to more abstract scenarios (contexts) this reduces the dimensionality of the data and enhances the predictive capabilities of the classifier three datasets of user reviews are used to conduct our experimental analysis results show that semantic frames can be used to generate lower dimensional and more accurate models in comparison to text classification methods contribution: a novel semantic approach for extracting user requirements from app reviews the proposed approach enables a more efficient classification process and reduces the chance of overfitting",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] mining user requirements from application store reviews using frame semantics context and motivation: research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years the majority of the proposed techniques rely on classifying the textual description of user reviews into different categories of technically informative user requirements and uninformative feedback question/problem: relying on the textual attributes of reviews often produces high dimensional models this increases the complexity of the classifier and can lead to overfitting problems principal ideas/results: we propose a novel semantic approach for app review classification the proposed approach is based on the notion of semantic role labeling, or characterizing the lexical meaning of text in terms of semantic frames semantic frames help to generalize from text (individual words) to more abstract scenarios (contexts) this reduces the dimensionality of the data and enhances the predictive capabilities of the classifier three datasets of user reviews are used to conduct our experimental analysis results show that semantic frames can be used to generate lower dimensional and more accurate models in comparison to text classification methods contribution: a novel semantic approach for extracting user requirements from app reviews the proposed approach enables a more efficient classification process and reduces the chance of overfitting [SEP]",
"target": "contradiction"
},
{
"instance_id": "R107684xR135960",
"template_id": "R107684",
"correct_template_id": null,
"paper_id": "R135960",
"premise": "health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample",
"hypothesis": "knowledge extraction from web based application source code: an approach to database reverse engineering for ontology development this paper presents a novel approach for extracting knowledge from web based application source code in supplementing and assisting ontology development from database schemas the structure of web based application source code is defined in order to distinguish different kinds of knowledge within the source code for ontology development the connections between the relevant parts of web application source code and the backend database schema with their various forms are explicitly specified in detail a knowledge processing and integration model for extracting and integrating the knowledge embedded in the source code for ontology development is then proposed",
"sequence": "[CLS] health persuasion has sample size has independent variable operationalisation of dependent variable has kind of appeal has kind of messages has result has ressource type of sample [SEP] knowledge extraction from web based application source code: an approach to database reverse engineering for ontology development this paper presents a novel approach for extracting knowledge from web based application source code in supplementing and assisting ontology development from database schemas the structure of web based application source code is defined in order to distinguish different kinds of knowledge within the source code for ontology development the connections between the relevant parts of web application source code and the backend database schema with their various forms are explicitly specified in detail a knowledge processing and integration model for extracting and integrating the knowledge embedded in the source code for ontology development is then proposed [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR111716",
"template_id": "R77101",
"correct_template_id": null,
"paper_id": "R111716",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "engaged listeners: shared neural processing of powerful political speeches powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners what is happening in the brains of a captivated audience? here, we assess audience wide functional brain dynamics during listening to speeches of varying rhetorical quality the speeches were given by german politicians and evaluated as rhetorically powerful or weak listening to each of the speeches induced similar neural response time courses, as measured by inter subject correlation analysis, in widespread brain regions involved in spoken language processing crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses these patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] engaged listeners: shared neural processing of powerful political speeches powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners what is happening in the brains of a captivated audience? here, we assess audience wide functional brain dynamics during listening to speeches of varying rhetorical quality the speeches were given by german politicians and evaluated as rhetorically powerful or weak listening to each of the speeches induced similar neural response time courses, as measured by inter subject correlation analysis, in widespread brain regions involved in spoken language processing crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses these patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46269xR109894",
"template_id": "R46269",
"correct_template_id": null,
"paper_id": "R109894",
"premise": "case fatality rate estimate value specification has unit confidence interval (95%) has value",
"hypothesis": "a hybrid approach toward research paper recommendation using centrality measures and author ranking the volume of research articles in digital repositories is increasing this spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries the problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories in the traditional recommendation approaches, the results of the query miss many high quality papers, in the related work section, which are either published recently or have low citation count to overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles many research paper recommendation approaches have been implemented which includes collaborative filtering based, content based, and citation analysis based techniques the collaborative filtering based approaches primarily use paper citation matrix for recommendations, whereas the content based approaches only consider the content of the paper the citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest it is therefore very difficult for a recommender system to recommend high quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information the proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors\u2019 collaboration network finds key authors from those papers the papers selected by this hybrid approach are then recommended to the user the results have shown that our proposed method performs exceedingly well as compared with the state of the art existing systems, such as google scholar and multilevel simultaneous citation network",
"sequence": "[CLS] case fatality rate estimate value specification has unit confidence interval (95%) has value [SEP] a hybrid approach toward research paper recommendation using centrality measures and author ranking the volume of research articles in digital repositories is increasing this spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries the problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories in the traditional recommendation approaches, the results of the query miss many high quality papers, in the related work section, which are either published recently or have low citation count to overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles many research paper recommendation approaches have been implemented which includes collaborative filtering based, content based, and citation analysis based techniques the collaborative filtering based approaches primarily use paper citation matrix for recommendations, whereas the content based approaches only consider the content of the paper the citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest it is therefore very difficult for a recommender system to recommend high quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information the proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors\u2019 collaboration network finds key authors from those papers the papers selected by this hybrid approach are then recommended to the user the results have shown that our proposed method performs exceedingly well as compared with the state of the art existing systems, such as google scholar and multilevel simultaneous citation network [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR109347",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R109347",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "dual targeting of cytokine storm and viral replication in covid 19 by plant derived steroidal pregnanes: an in silico perspective the high morbidity and mortality rate of severe acute respiratory syndrome coronavirus 2 (sars cov 2) infection arises majorly from the acute respiratory distress syndrome and \u201ccytokine storm\u201d syndrome, which is sustained by an aberrant systemic inflammatory response and elevated pro inflammatory cytokines thus, phytocompounds with broad spectrum anti inflammatory activity that target multiple sars cov 2 proteins will enhance the development of effective drugs against the disease in this study, an in house library of 117 steroidal plant derived pregnanes (pdps) was docked in the active regions of human glucocorticoid receptors (hgrs) in a comparative molecular docking analysis based on the minimal binding energy and a comparative dexamethasone binding mode analysis, a list of top twenty ranked pdps docked in the agonist conformation of hgr, with binding energies ranging between 9 8 and 11 2 kcal/mol, was obtained and analyzed for possible interactions with the human janus kinases 1 and interleukins 6 and sars cov 2 3 chymotrypsin like protease, papain like protease and rna dependent rna polymerase for each target protein, the top three ranked pdps were selected eight pdps (bregenin, hirundigenin, anhydroholantogenin, atratogenin a, atratogenin b, glaucogenin a, glaucogenin c and glaucogenin d) with high binding tendencies to the catalytic residues of multiple targets were identified a high degree of structural stability was observed from the 100 ns molecular dynamics simulation analyses of glaucogenin c and hirundigenin complexes of hgr the selected top eight ranked pdps demonstrated high druggable potentials and favourable in silico admet properties thus, the therapeutic potentials of glaucogenin c and hirundigenin can be explored for further in vitro and in vivo studies",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] dual targeting of cytokine storm and viral replication in covid 19 by plant derived steroidal pregnanes: an in silico perspective the high morbidity and mortality rate of severe acute respiratory syndrome coronavirus 2 (sars cov 2) infection arises majorly from the acute respiratory distress syndrome and \u201ccytokine storm\u201d syndrome, which is sustained by an aberrant systemic inflammatory response and elevated pro inflammatory cytokines thus, phytocompounds with broad spectrum anti inflammatory activity that target multiple sars cov 2 proteins will enhance the development of effective drugs against the disease in this study, an in house library of 117 steroidal plant derived pregnanes (pdps) was docked in the active regions of human glucocorticoid receptors (hgrs) in a comparative molecular docking analysis based on the minimal binding energy and a comparative dexamethasone binding mode analysis, a list of top twenty ranked pdps docked in the agonist conformation of hgr, with binding energies ranging between 9 8 and 11 2 kcal/mol, was obtained and analyzed for possible interactions with the human janus kinases 1 and interleukins 6 and sars cov 2 3 chymotrypsin like protease, papain like protease and rna dependent rna polymerase for each target protein, the top three ranked pdps were selected eight pdps (bregenin, hirundigenin, anhydroholantogenin, atratogenin a, atratogenin b, glaucogenin a, glaucogenin c and glaucogenin d) with high binding tendencies to the catalytic residues of multiple targets were identified a high degree of structural stability was observed from the 100 ns molecular dynamics simulation analyses of glaucogenin c and hirundigenin complexes of hgr the selected top eight ranked pdps demonstrated high druggable potentials and favourable in silico admet properties thus, the therapeutic potentials of glaucogenin c and hirundigenin can be explored for further in vitro and in vivo studies [SEP]",
"target": "contradiction"
},
{
"instance_id": "R51438xR36132",
"template_id": "R51438",
"correct_template_id": null,
"paper_id": "R36132",
"premise": "common template has result has hypothesis has methodology",
"hypothesis": "lessons drawn from china and south korea for managing covid 19 epidemic: insights from a comparative modeling study abstract we conducted a comparative study of covid 19 epidemic in three different settings: mainland china, the guangdong province of china and south korea, by formulating two disease transmission dynamics models incorporating epidemic characteristics and setting specific interventions, and fitting the models to multi source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions we estimated the initial basic reproduction number for south korea, the guangdong province and mainland china as 2 6 (95% confidence interval (ci): (2 5, 2 7)), 3 0 (95%ci: (2 6, 3 3)) and 3 8 (95%ci: (3 5,4 2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days we found that the effective reproduction number for the guangdong province and mainland china has fallen below the threshold 1 since february 8 th and 18 th respectively, while the effective reproduction number for south korea remains high, suggesting that the interventions implemented need to be enhanced in order to halt further infections we also project the epidemic trend in south korea under different scenarios where a portion or the entirety of the integrated package of interventions in china is used we show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in china and specially its provinces outside its epicenter, and we show that this approach can also be effective to mitigate the burden of the covid 19 epidemic in south korea the experience of outbreak control in mainland china should be a guiding reference for the rest of the world including south korea",
"sequence": "[CLS] common template has result has hypothesis has methodology [SEP] lessons drawn from china and south korea for managing covid 19 epidemic: insights from a comparative modeling study abstract we conducted a comparative study of covid 19 epidemic in three different settings: mainland china, the guangdong province of china and south korea, by formulating two disease transmission dynamics models incorporating epidemic characteristics and setting specific interventions, and fitting the models to multi source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions we estimated the initial basic reproduction number for south korea, the guangdong province and mainland china as 2 6 (95% confidence interval (ci): (2 5, 2 7)), 3 0 (95%ci: (2 6, 3 3)) and 3 8 (95%ci: (3 5,4 2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days we found that the effective reproduction number for the guangdong province and mainland china has fallen below the threshold 1 since february 8 th and 18 th respectively, while the effective reproduction number for south korea remains high, suggesting that the interventions implemented need to be enhanced in order to halt further infections we also project the epidemic trend in south korea under different scenarios where a portion or the entirety of the integrated package of interventions in china is used we show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in china and specially its provinces outside its epicenter, and we show that this approach can also be effective to mitigate the burden of the covid 19 epidemic in south korea the experience of outbreak control in mainland china should be a guiding reference for the rest of the world including south korea [SEP]",
"target": "contradiction"
},
{
"instance_id": "R48214xR38466",
"template_id": "R48214",
"correct_template_id": null,
"paper_id": "R38466",
"premise": "global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period",
"hypothesis": "biotea 2 bioschemas, facilitating structured markup for semantically annotated scholarly publications the total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata schema org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results bioschemas is based on the standards of schema org, providing new types, properties and guidelines for metadata, i e , providing metadata profiles tailored to the life sciences domain here we present our proposed contribution to bioschemas (from the project \u201cbiotea\u201d), which supports metadata contributions for scholarly publications via profiles and web components biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our biotea model has been mapped to schema org following bioschemas standards",
"sequence": "[CLS] global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period [SEP] biotea 2 bioschemas, facilitating structured markup for semantically annotated scholarly publications the total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata schema org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results bioschemas is based on the standards of schema org, providing new types, properties and guidelines for metadata, i e , providing metadata profiles tailored to the life sciences domain here we present our proposed contribution to bioschemas (from the project \u201cbiotea\u201d), which supports metadata contributions for scholarly publications via profiles and web components biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our biotea model has been mapped to schema org following bioschemas standards [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76795xR44087",
"template_id": "R76795",
"correct_template_id": null,
"paper_id": "R44087",
"premise": "crowd intelligence utilities in crowdre re activities with crowd involvement",
"hypothesis": "modelling the potential health impact of the covid 19 pandemic on a hypothetical european country abstract a seir simulation model for the covid 19 pandemic was developed (http://covidsim eu) and applied to a hypothetical european country of 10 million population our results show which interventions potentially push the epidemic peak into the subsequent year (when vaccinations may be available) or which fail different levels of control (via contact reduction) resulted in 22% to 63% of the population sick, 0 2% to 0 6% hospitalised, and 0 07% to 0 28% dead (n=6,450 to 28,228)",
"sequence": "[CLS] crowd intelligence utilities in crowdre re activities with crowd involvement [SEP] modelling the potential health impact of the covid 19 pandemic on a hypothetical european country abstract a seir simulation model for the covid 19 pandemic was developed (http://covidsim eu) and applied to a hypothetical european country of 10 million population our results show which interventions potentially push the epidemic peak into the subsequent year (when vaccinations may be available) or which fail different levels of control (via contact reduction) resulted in 22% to 63% of the population sick, 0 2% to 0 6% hospitalised, and 0 07% to 0 28% dead (n=6,450 to 28,228) [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR49078",
"template_id": "R46273",
"correct_template_id": null,
"paper_id": "R49078",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "community climate simulations to assess avoided impacts in 1 5 and 2 \u00b0c futures abstract the paris agreement of december 2015 stated a goal to pursue efforts to keep global temperatures below 1 5 \u00b0c above preindustrial levels and well below 2 \u00b0c the ipcc was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments in this study, we produce a set of scenarios using a simple model designed to achieve long term 1 5 and 2 \u00b0c temperatures in a stable climate these scenarios are then used to produce century scale ensemble simulations using the community earth system model, providing impact relevant long term climate data for stabilization pathways at 1 5 and 2 \u00b0c levels and an overshoot 1 5 \u00b0c case, which are realized (for the 21st century) in the coupled model and are freely available to the community here we describe the design of the simulations and a brief overview of their impact relevant climate response exceedance of historical record temperature occurs with 60 % greater frequency in the 2 \u00b0c climate than in a 1 5 \u00b0c climate aggregated globally, and with twice the frequency in equatorial and arid regions extreme precipitation intensity is statistically significantly higher in a 2 0 \u00b0c climate than a 1 5 \u00b0c climate in some specific regions (but not all) the model exhibits large differences in the arctic, which is ice free with a frequency of 1 in 3 years in the 2 0 \u00b0c scenario, and 1 in 40 years in the 1 5 \u00b0c scenario significance of impact differences with respect to multi model variability is not assessed",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] community climate simulations to assess avoided impacts in 1 5 and 2 \u00b0c futures abstract the paris agreement of december 2015 stated a goal to pursue efforts to keep global temperatures below 1 5 \u00b0c above preindustrial levels and well below 2 \u00b0c the ipcc was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments in this study, we produce a set of scenarios using a simple model designed to achieve long term 1 5 and 2 \u00b0c temperatures in a stable climate these scenarios are then used to produce century scale ensemble simulations using the community earth system model, providing impact relevant long term climate data for stabilization pathways at 1 5 and 2 \u00b0c levels and an overshoot 1 5 \u00b0c case, which are realized (for the 21st century) in the coupled model and are freely available to the community here we describe the design of the simulations and a brief overview of their impact relevant climate response exceedance of historical record temperature occurs with 60 % greater frequency in the 2 \u00b0c climate than in a 1 5 \u00b0c climate aggregated globally, and with twice the frequency in equatorial and arid regions extreme precipitation intensity is statistically significantly higher in a 2 0 \u00b0c climate than a 1 5 \u00b0c climate in some specific regions (but not all) the model exhibits large differences in the arctic, which is ice free with a frequency of 1 in 3 years in the 2 0 \u00b0c scenario, and 1 in 40 years in the 1 5 \u00b0c scenario significance of impact differences with respect to multi model variability is not assessed [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR70030",
"template_id": "R35087",
"correct_template_id": "R70247",
"paper_id": "R70030",
"premise": "time interval has beginning has end",
"hypothesis": "synthesis and biological activity evaluation of 5 pyrazoline substituted 4 thiazolidinones abstract a series of novel 5 pyrazoline substituted 4 thiazolidinones have been synthesized target compounds were evaluated for their anticancer activity in vitro within dtp nci protocol among the tested compounds, the derivatives 4d and 4f were found to be the most active, which demonstrated certain sensitivity profile toward the leukemia subpanel cell lines with gi50 value ranges of 2 12\u20134 58 \u03bcm (4d) and 1 64\u20133 20 \u03bcm (4f) the screening of antitrypanosomal and antiviral activities of 5 (3 naphthalen 2 yl 5 aryl 4,5 dihydropyrazol 1 yl) thiazolidine 2,4 diones was carried out with the promising influence of the mentioned compounds on trypanosoma brucei, but minimal effect on sars coronavirus and influenza types a and b viruses",
"sequence": "[CLS] time interval has beginning has end [SEP] synthesis and biological activity evaluation of 5 pyrazoline substituted 4 thiazolidinones abstract a series of novel 5 pyrazoline substituted 4 thiazolidinones have been synthesized target compounds were evaluated for their anticancer activity in vitro within dtp nci protocol among the tested compounds, the derivatives 4d and 4f were found to be the most active, which demonstrated certain sensitivity profile toward the leukemia subpanel cell lines with gi50 value ranges of 2 12\u20134 58 \u03bcm (4d) and 1 64\u20133 20 \u03bcm (4f) the screening of antitrypanosomal and antiviral activities of 5 (3 naphthalen 2 yl 5 aryl 4,5 dihydropyrazol 1 yl) thiazolidine 2,4 diones was carried out with the promising influence of the mentioned compounds on trypanosoma brucei, but minimal effect on sars coronavirus and influenza types a and b viruses [SEP]",
"target": "contradiction"
},
{
"instance_id": "R77101xR139277",
"template_id": "R77101",
"correct_template_id": null,
"paper_id": "R139277",
"premise": "standard's template has author has organization has publication year has research field",
"hypothesis": "a highly sensitive glucose biosensor based on a micro disk array electrode design modified with carbon quantum dots and gold nanoparticles abstract a miniaturised biosensor for glucose was prepared by immobilizing glucose oxidase (gox) onto a carbon quantum dots (cqds) gold nanoparticles (aunps) nanohybrid material which was in turn attached to gold micro disk array electrodes the gold micro disk array electrodes (gdae) were microfabricated on si substrate using electronics standard lithography, deposition and etching techniques each microelectrode consisted of 85 gold disk electrodes with 20 \u03bcm diameter and 200 \u03bcm inter electrode distance and were located hexagonally the electrodes were studied by scanning electron microscopy (sem), atomic force microscopy (afm) and cyclic voltammetry (cv) cv of the bare electrodes showed a symmetrical sigmoidal voltammogram arising from their radial diffusion profile the electrodes developed were used to fabricate a miniaturised glucose biosensor the resulting biosensor exhibited a high sensitivity of 626 06 \u03bca mm\u22121 cm\u22122 towards glucose detection with excellent reproducibility and reusability the superior performance of the biosensor is discussed in relation to the use of the micron sized low density disk array electrodes",
"sequence": "[CLS] standard's template has author has organization has publication year has research field [SEP] a highly sensitive glucose biosensor based on a micro disk array electrode design modified with carbon quantum dots and gold nanoparticles abstract a miniaturised biosensor for glucose was prepared by immobilizing glucose oxidase (gox) onto a carbon quantum dots (cqds) gold nanoparticles (aunps) nanohybrid material which was in turn attached to gold micro disk array electrodes the gold micro disk array electrodes (gdae) were microfabricated on si substrate using electronics standard lithography, deposition and etching techniques each microelectrode consisted of 85 gold disk electrodes with 20 \u03bcm diameter and 200 \u03bcm inter electrode distance and were located hexagonally the electrodes were studied by scanning electron microscopy (sem), atomic force microscopy (afm) and cyclic voltammetry (cv) cv of the bare electrodes showed a symmetrical sigmoidal voltammogram arising from their radial diffusion profile the electrodes developed were used to fabricate a miniaturised glucose biosensor the resulting biosensor exhibited a high sensitivity of 626 06 \u03bca mm\u22121 cm\u22122 towards glucose detection with excellent reproducibility and reusability the superior performance of the biosensor is discussed in relation to the use of the micron sized low density disk array electrodes [SEP]",
"target": "contradiction"
},
{
"instance_id": "R48214xR44901",
"template_id": "R48214",
"correct_template_id": "R40006",
"paper_id": "R44901",
"premise": "global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period",
"hypothesis": "real time estimation of the risk of death from novel coronavirus (covid 19) infection: inference using exported cases the exported cases of 2019 novel coronavirus (covid 19) infection that were confirmed outside china provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (ccfr) in mainland china knowledge of the ccfr is critical to characterize the severity and understand the pandemic potential of covid 19 in the early stage of the epidemic using the exponential growth rate of the incidence, the present study statistically estimated the ccfr and the basic reproduction number\u2014the average number of secondary cases generated by a single primary case in a na\u00efve population we modeled epidemic growth either from a single index case with illness onset on 8 december 2019 (scenario 1), or using the growth rate fitted along with the other parameters (scenario 2) based on data from 20 exported cases reported by 24 january 2020 the cumulative incidence in china by 24 january was estimated at 6924 cases (95% confidence interval [ci]: 4885, 9211) and 19,289 cases (95% ci: 10,901, 30,158), respectively the latest estimated values of the ccfr were 5 3% (95% ci: 3 5%, 7 5%) for scenario 1 and 8 4% (95% ci: 5 3%, 12 3%) for scenario 2 the basic reproduction number was estimated to be 2 1 (95% ci: 2 0, 2 2) and 3 2 (95% ci: 2 7, 3 7) for scenarios 1 and 2, respectively based on these results, we argued that the current covid 19 epidemic has a substantial potential for causing a pandemic the proposed approach provides insights in early risk assessment using publicly available data",
"sequence": "[CLS] global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period [SEP] real time estimation of the risk of death from novel coronavirus (covid 19) infection: inference using exported cases the exported cases of 2019 novel coronavirus (covid 19) infection that were confirmed outside china provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (ccfr) in mainland china knowledge of the ccfr is critical to characterize the severity and understand the pandemic potential of covid 19 in the early stage of the epidemic using the exponential growth rate of the incidence, the present study statistically estimated the ccfr and the basic reproduction number\u2014the average number of secondary cases generated by a single primary case in a na\u00efve population we modeled epidemic growth either from a single index case with illness onset on 8 december 2019 (scenario 1), or using the growth rate fitted along with the other parameters (scenario 2) based on data from 20 exported cases reported by 24 january 2020 the cumulative incidence in china by 24 january was estimated at 6924 cases (95% confidence interval [ci]: 4885, 9211) and 19,289 cases (95% ci: 10,901, 30,158), respectively the latest estimated values of the ccfr were 5 3% (95% ci: 3 5%, 7 5%) for scenario 1 and 8 4% (95% ci: 5 3%, 12 3%) for scenario 2 the basic reproduction number was estimated to be 2 1 (95% ci: 2 0, 2 2) and 3 2 (95% ci: 2 7, 3 7) for scenarios 1 and 2, respectively based on these results, we argued that the current covid 19 epidemic has a substantial potential for causing a pandemic the proposed approach provides insights in early risk assessment using publicly available data [SEP]",
"target": "contradiction"
},
{
"instance_id": "R108008xR49115",
"template_id": "R108008",
"correct_template_id": null,
"paper_id": "R49115",
"premise": "business architecture available tools metamodel operations methodology best practices / reference models metamodel maturity metamodel revenue model & performance integration with other architectures methodology management ba initiatives methodology use case scenarios approach name metamodel business network methodology structured procedure model metamodel strategy and structure methodology maturity methodology development of ba model",
"hypothesis": "extreme sea level implications of 1 5 \u00b0c, 2 0 \u00b0c, and 2 5 \u00b0c temperature stabilization targets in the 21st and 22nd centuries sea level rise (slr) is magnifying the frequency and severity of coastal flooding the rate and amount of global mean sea level (gmsl) rise is a function of the trajectory of global mean surface temperature (gmst) therefore, temperature stabilization targets (e g , 1 5 {\\deg}c and 2 0 {\\deg}c of warming above pre industrial levels, as from the paris agreement) have important implications for coastal flood risk here, we assess differences in the return periods of coastal floods at a global network of tide gauges between scenarios that stabilize gmst warming at 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c above pre industrial levels we employ probabilistic, localized slr projections and long term hourly tide gauge records to construct estimates of the return levels of current and future flood heights for the 21st and 22nd centuries by 2100, under 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c gmst stabilization, median gmsl is projected to rise 47 cm with a very likely range of 28 82 cm (90% probability), 55 cm (very likely 30 94 cm), and 58 cm (very likely 36 93 cm), respectively as an independent comparison, a semi empirical sea level model calibrated to temperature and gmsl over the past two millennia estimates median gmsl will rise within < 13% of these projections by 2150, relative to the 2 0 {\\deg}c scenario, gmst stabilization of 1 5 {\\deg}c inundates roughly 5 million fewer inhabitants that currently occupy lands, including 40,000 fewer individuals currently residing in small island developing states relative to a 2 0 {\\deg}c scenario, the reduction in the amplification of the frequency of the 100 yr flood arising from a 1 5 {\\deg}c gmst stabilization is greatest in the eastern united states and in europe, with flood frequency amplification being reduced by about half",
"sequence": "[CLS] business architecture available tools metamodel operations methodology best practices / reference models metamodel maturity metamodel revenue model & performance integration with other architectures methodology management ba initiatives methodology use case scenarios approach name metamodel business network methodology structured procedure model metamodel strategy and structure methodology maturity methodology development of ba model [SEP] extreme sea level implications of 1 5 \u00b0c, 2 0 \u00b0c, and 2 5 \u00b0c temperature stabilization targets in the 21st and 22nd centuries sea level rise (slr) is magnifying the frequency and severity of coastal flooding the rate and amount of global mean sea level (gmsl) rise is a function of the trajectory of global mean surface temperature (gmst) therefore, temperature stabilization targets (e g , 1 5 {\\deg}c and 2 0 {\\deg}c of warming above pre industrial levels, as from the paris agreement) have important implications for coastal flood risk here, we assess differences in the return periods of coastal floods at a global network of tide gauges between scenarios that stabilize gmst warming at 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c above pre industrial levels we employ probabilistic, localized slr projections and long term hourly tide gauge records to construct estimates of the return levels of current and future flood heights for the 21st and 22nd centuries by 2100, under 1 5 {\\deg}c, 2 0 {\\deg}c, and 2 5 {\\deg}c gmst stabilization, median gmsl is projected to rise 47 cm with a very likely range of 28 82 cm (90% probability), 55 cm (very likely 30 94 cm), and 58 cm (very likely 36 93 cm), respectively as an independent comparison, a semi empirical sea level model calibrated to temperature and gmsl over the past two millennia estimates median gmsl will rise within < 13% of these projections by 2150, relative to the 2 0 {\\deg}c scenario, gmst stabilization of 1 5 {\\deg}c inundates roughly 5 million fewer inhabitants that currently occupy lands, including 40,000 fewer individuals currently residing in small island developing states relative to a 2 0 {\\deg}c scenario, the reduction in the amplification of the frequency of the 100 yr flood arising from a 1 5 {\\deg}c gmst stabilization is greatest in the eastern united states and in europe, with flood frequency amplification being reduced by about half [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR36114",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R36114",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "estimation of the epidemic properties of the 2019 novel coronavirus: a mathematical modeling study abstract background the 2019 novel coronavirus (covid 19) emerged in wuhan, china in december 2019 and has been spreading rapidly in china decisions about its pandemic threat and the appropriate level of public health response depend heavily on estimates of its basic reproduction number and assessments of interventions conducted in the early stages of the epidemic methods we conducted a mathematical modeling study using five independent methods to assess the basic reproduction number (r0) of covid 19, using data on confirmed cases obtained from the china national health commission for the period 10 th january \u2013 8 th february we analyzed the data for the period before the closure of wuhan city (10 th january \u2013 23 rd january) and the post closure period (23 rd january \u2013 8 th february) and for the whole period, to assess both the epidemic risk of the virus and the effectiveness of the closure of wuhan city on spread of covid 19 findings before the closure of wuhan city the basic reproduction number of covid 19 was 4 38 (95% ci: 3 63 \u2013 5 13), dropping to 3 41 (95% ci: 3 16 \u2013 3 65) after the closure of wuhan city over the entire epidemic period covid 19 had a basic reproduction number of 3 39 (95% ci: 3 09 \u2013 3 70), indicating it has a very high transmissibility interpretation covid 19 is a highly transmissible virus with a very high risk of epidemic outbreak once it emerges in metropolitan areas the closure of wuhan city was effective in reducing the severity of the epidemic, but even after closure of the city and the subsequent expansion of that closure to other parts of hubei the virus remained extremely infectious emergency planners in other cities should consider this high infectiousness when considering responses to this virus funding national natural science foundation of china, china medical board, national science and technology major project of china",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] estimation of the epidemic properties of the 2019 novel coronavirus: a mathematical modeling study abstract background the 2019 novel coronavirus (covid 19) emerged in wuhan, china in december 2019 and has been spreading rapidly in china decisions about its pandemic threat and the appropriate level of public health response depend heavily on estimates of its basic reproduction number and assessments of interventions conducted in the early stages of the epidemic methods we conducted a mathematical modeling study using five independent methods to assess the basic reproduction number (r0) of covid 19, using data on confirmed cases obtained from the china national health commission for the period 10 th january \u2013 8 th february we analyzed the data for the period before the closure of wuhan city (10 th january \u2013 23 rd january) and the post closure period (23 rd january \u2013 8 th february) and for the whole period, to assess both the epidemic risk of the virus and the effectiveness of the closure of wuhan city on spread of covid 19 findings before the closure of wuhan city the basic reproduction number of covid 19 was 4 38 (95% ci: 3 63 \u2013 5 13), dropping to 3 41 (95% ci: 3 16 \u2013 3 65) after the closure of wuhan city over the entire epidemic period covid 19 had a basic reproduction number of 3 39 (95% ci: 3 09 \u2013 3 70), indicating it has a very high transmissibility interpretation covid 19 is a highly transmissible virus with a very high risk of epidemic outbreak once it emerges in metropolitan areas the closure of wuhan city was effective in reducing the severity of the epidemic, but even after closure of the city and the subsequent expansion of that closure to other parts of hubei the virus remained extremely infectious emergency planners in other cities should consider this high infectiousness when considering responses to this virus funding national natural science foundation of china, china medical board, national science and technology major project of china [SEP]",
"target": "contradiction"
},
{
"instance_id": "R48214xR112044",
"template_id": "R48214",
"correct_template_id": null,
"paper_id": "R112044",
"premise": "global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period",
"hypothesis": "can app changelogs improve requirements classification from app reviews?: an exploratory study [background] recent research on mining app reviews for software evolution indicated that the elicitation and analysis of user requirements can benefit from supplementing user reviews by data from other sources however, only a few studies reported results of leveraging app changelogs together with app reviews [aims] motivated by those findings, this exploratory experimental study looks into the role of app changelogs in the classification of requirements derived from app reviews we aim at understanding if the use of app changelogs can lead to more accurate identification and classification of functional and non functional requirements from app reviews we also want to know which classification technique works better in this context [method] we did a case study on the effect of app changelogs on automatic classification of app reviews specifically, manual labeling, text preprocessing, and four supervised machine learning algorithms were applied to a series of experiments, varying in the number of app changelogs in the experimental data [results] we compared the accuracy of requirements classification from app reviews, by training the four classifiers with varying combinations of app reviews and changelogs among the four algorithms, na\u00efve bayes was found to be more accurate for categorizing app reviews [conclusions] the results show that official app changelogs did not contribute to more accurate identification and classification of requirements from app reviews in addition, na\u00efve bayes seems to be more suitable for our further research on this topic",
"sequence": "[CLS] global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period [SEP] can app changelogs improve requirements classification from app reviews?: an exploratory study [background] recent research on mining app reviews for software evolution indicated that the elicitation and analysis of user requirements can benefit from supplementing user reviews by data from other sources however, only a few studies reported results of leveraging app changelogs together with app reviews [aims] motivated by those findings, this exploratory experimental study looks into the role of app changelogs in the classification of requirements derived from app reviews we aim at understanding if the use of app changelogs can lead to more accurate identification and classification of functional and non functional requirements from app reviews we also want to know which classification technique works better in this context [method] we did a case study on the effect of app changelogs on automatic classification of app reviews specifically, manual labeling, text preprocessing, and four supervised machine learning algorithms were applied to a series of experiments, varying in the number of app changelogs in the experimental data [results] we compared the accuracy of requirements classification from app reviews, by training the four classifiers with varying combinations of app reviews and changelogs among the four algorithms, na\u00efve bayes was found to be more accurate for categorizing app reviews [conclusions] the results show that official app changelogs did not contribute to more accurate identification and classification of requirements from app reviews in addition, na\u00efve bayes seems to be more suitable for our further research on this topic [SEP]",
"target": "contradiction"
},
{
"instance_id": "R48214xR113181",
"template_id": "R48214",
"correct_template_id": "R76795",
"paper_id": "R113181",
"premise": "global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period",
"hypothesis": "towards crowd based requirements engineering a research preview [context and motivation] stakeholders who are highly distributed form a large, heterogeneous online group, the so called \u201ccrowd\u201d the rise of mobile, social and cloud apps has led to a stark increase in crowd based settings [question/problem] traditional requirements engineering (re) techniques face scalability issues and require the co presence of stakeholders and engineers, which cannot be realized in a crowd setting while different approaches have recently been introduced to partially automate re in this context, a multi method approach to (semi )automate all re activities is still needed [principal ideas/results] we propose \u201ccrowd based requirements engineering\u201d as an approach that integrates existing elicitation and analysis techniques and fills existing gaps by introducing new concepts it collects feedback through direct interactions and social collaboration, and by deploying mining techniques [contribution] this paper describes the initial state of the art of our approach, and previews our plans for further research",
"sequence": "[CLS] global mean sea level rise projections has lower limit for likely range has lower limit for 95% confidence interval has upper limit for likely range has start of period has upper limit for 95% confidence interval has unit climate scenario has value has end of period [SEP] towards crowd based requirements engineering a research preview [context and motivation] stakeholders who are highly distributed form a large, heterogeneous online group, the so called \u201ccrowd\u201d the rise of mobile, social and cloud apps has led to a stark increase in crowd based settings [question/problem] traditional requirements engineering (re) techniques face scalability issues and require the co presence of stakeholders and engineers, which cannot be realized in a crowd setting while different approaches have recently been introduced to partially automate re in this context, a multi method approach to (semi )automate all re activities is still needed [principal ideas/results] we propose \u201ccrowd based requirements engineering\u201d as an approach that integrates existing elicitation and analysis techniques and fills existing gaps by introducing new concepts it collects feedback through direct interactions and social collaboration, and by deploying mining techniques [contribution] this paper describes the initial state of the art of our approach, and previews our plans for further research [SEP]",
"target": "contradiction"
},
{
"instance_id": "R35087xR8245",
"template_id": "R35087",
"correct_template_id": null,
"paper_id": "R8245",
"premise": "time interval has beginning has end",
"hypothesis": "knowledge base shipping to the linked open data cloud popular knowledge bases that provide sparql endpoints for the web are usually experiencing a high number of requests, which often results in low availability of their interfaces a common approach to counter the availability issue is to run a local mirror of the knowledge base running a sparql endpoint is currently a complex task which requires a lot of effort and technical support for domain experts who just want to use the sparql interface with our approach of containerised knowledge base shipping we are introducing a simple to setup methodology for running a local mirror of an rdf knowledge base and sparql endpoint with interchangeable exploration components the flexibility of the presented approach further helps maintaining the publication infrastructure for dataset projects we are demonstrating and evaluating the presented methodology at the example of the dataset projects dbpedia, catalogus professorum lipsiensium and s\u00e4chsisches pfarrerbuch",
"sequence": "[CLS] time interval has beginning has end [SEP] knowledge base shipping to the linked open data cloud popular knowledge bases that provide sparql endpoints for the web are usually experiencing a high number of requests, which often results in low availability of their interfaces a common approach to counter the availability issue is to run a local mirror of the knowledge base running a sparql endpoint is currently a complex task which requires a lot of effort and technical support for domain experts who just want to use the sparql interface with our approach of containerised knowledge base shipping we are introducing a simple to setup methodology for running a local mirror of an rdf knowledge base and sparql endpoint with interchangeable exploration components the flexibility of the presented approach further helps maintaining the publication infrastructure for dataset projects we are demonstrating and evaluating the presented methodology at the example of the dataset projects dbpedia, catalogus professorum lipsiensium and s\u00e4chsisches pfarrerbuch [SEP]",
"target": "contradiction"
},
{
"instance_id": "R46273xR109331",
"template_id": "R46273",
"correct_template_id": null,
"paper_id": "R109331",
"premise": "case fatality rate estimate case fatality rate location time period",
"hypothesis": "potential inhibitors of coronavirus 3 chymotrypsin like protease (3clpro): an in silico screening of alkaloids and terpenoids from african medicinal plants abstract the novel coronavirus disease 2019 (covid 19) caused by sars cov 2 has raised myriad of global concerns there is currently no fda approved antiviral strategy to alleviate the disease burden the conserved 3 chymotrypsin like protease (3clpro), which controls coronavirus replication is a promising drug target for combating the coronavirus infection this study screens some african plants derived alkaloids and terpenoids as potential inhibitors of coronavirus 3clpro using in silico approach bioactive alkaloids (62) and terpenoids (100) of plants native to africa were docked to the 3clpro of the novel sars cov 2 the top twenty alkaloids and terpenoids with high binding affinities to the sars cov 2 3clpro were further docked to the 3clpro of sars cov and mers cov the docking scores were compared with 3clpro referenced inhibitors (lopinavir and ritonavir) the top docked compounds were further subjected to adem/tox and lipinski filtering analyses for drug likeness prediction analysis this ligand protein interaction study revealed that more than half of the top twenty alkaloids and terpenoids interacted favourably with the coronaviruses 3clpro, and had binding affinities that surpassed that of lopinavir and ritonavir also, a highly defined hit list of seven compounds (10 hydroxyusambarensine, cryptoquindoline, 6 oxoisoiguesterin, 22 hydroxyhopan 3 one, cryptospirolepine, isoiguesterin and 20 epibryonolic acid) were identified furthermore, four non toxic, druggable plant derived alkaloids (10 hydroxyusambarensine, and cryptoquindoline) and terpenoids (6 oxoisoiguesterin and 22 hydroxyhopan 3 one), that bind to the receptor binding site and catalytic dyad of sars cov 2 3clpro were identified from the predictive adme/tox and lipinski filter analysis however, further experimental analyses are required for developing these possible leads into natural anti covid 19 therapeutic agents for combating the pandemic communicated by ramaswamy h sarma",
"sequence": "[CLS] case fatality rate estimate case fatality rate location time period [SEP] potential inhibitors of coronavirus 3 chymotrypsin like protease (3clpro): an in silico screening of alkaloids and terpenoids from african medicinal plants abstract the novel coronavirus disease 2019 (covid 19) caused by sars cov 2 has raised myriad of global concerns there is currently no fda approved antiviral strategy to alleviate the disease burden the conserved 3 chymotrypsin like protease (3clpro), which controls coronavirus replication is a promising drug target for combating the coronavirus infection this study screens some african plants derived alkaloids and terpenoids as potential inhibitors of coronavirus 3clpro using in silico approach bioactive alkaloids (62) and terpenoids (100) of plants native to africa were docked to the 3clpro of the novel sars cov 2 the top twenty alkaloids and terpenoids with high binding affinities to the sars cov 2 3clpro were further docked to the 3clpro of sars cov and mers cov the docking scores were compared with 3clpro referenced inhibitors (lopinavir and ritonavir) the top docked compounds were further subjected to adem/tox and lipinski filtering analyses for drug likeness prediction analysis this ligand protein interaction study revealed that more than half of the top twenty alkaloids and terpenoids interacted favourably with the coronaviruses 3clpro, and had binding affinities that surpassed that of lopinavir and ritonavir also, a highly defined hit list of seven compounds (10 hydroxyusambarensine, cryptoquindoline, 6 oxoisoiguesterin, 22 hydroxyhopan 3 one, cryptospirolepine, isoiguesterin and 20 epibryonolic acid) were identified furthermore, four non toxic, druggable plant derived alkaloids (10 hydroxyusambarensine, and cryptoquindoline) and terpenoids (6 oxoisoiguesterin and 22 hydroxyhopan 3 one), that bind to the receptor binding site and catalytic dyad of sars cov 2 3clpro were identified from the predictive adme/tox and lipinski filter analysis however, further experimental analyses are required for developing these possible leads into natural anti covid 19 therapeutic agents for combating the pandemic communicated by ramaswamy h sarma [SEP]",
"target": "contradiction"
},
{
"instance_id": "R76795xR112015",
"template_id": "R76795",
"correct_template_id": null,
"paper_id": "R112015",
"premise": "crowd intelligence utilities in crowdre re activities with crowd involvement",
"hypothesis": "a little bird told me: mining tweets for requirements and software evolution twitter is one of the most popular social networks previous research found that users employ twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution however, due to their large number in the range of thousands per day for popular applications a manual analysis is unfeasible in this work we present alertme, an approach to automatically classify, group and rank tweets about software applications we apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets we ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement our results show that alertme is an effective approach for filtering, summarizing and ranking tweets about software applications alertme enables the exploitation of twitter as a feedback channel for information relevant to software evolution, including end user requirements",
"sequence": "[CLS] crowd intelligence utilities in crowdre re activities with crowd involvement [SEP] a little bird told me: mining tweets for requirements and software evolution twitter is one of the most popular social networks previous research found that users employ twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution however, due to their large number in the range of thousands per day for popular applications a manual analysis is unfeasible in this work we present alertme, an approach to automatically classify, group and rank tweets about software applications we apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets we ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement our results show that alertme is an effective approach for filtering, summarizing and ranking tweets about software applications alertme enables the exploitation of twitter as a feedback channel for information relevant to software evolution, including end user requirements [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR4857",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R4857",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "how are topics born? understanding the research dynamics preceding the emergence of new areas the ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies while the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers hence, detecting the emergence of a new research area at an embryonic stage , i e , before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge in this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics this study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the \u2018parents\u2019 of the new topic these initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in philosophy of science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross fertilise",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] how are topics born? understanding the research dynamics preceding the emergence of new areas the ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies while the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers hence, detecting the emergence of a new research area at an embryonic stage , i e , before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge in this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics this study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the \u2018parents\u2019 of the new topic these initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in philosophy of science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross fertilise [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR53034",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R53034",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "modeling safest and optimal emergency evacuation plan for large scale pedestrians environments large scale events are always vulnerable to natural disasters and man made chaos which poses great threat to crowd safety such events need an appropriate evacuation plan to alleviate the risk of causalities we propose a modeling framework for large scale evacuation of pedestrians during emergency situation proposed framework presents optimal and safest path evacuation for a hypothetical large scale crowd scenario the main aim is to provide the safest and nearest evacuation path because during disastrous situations there is possibility of exit gate blockade and directions of evacuees may have to be changed at run time for this purpose run time diversions are given to evacuees to ensure their quick and safest exit in this work, different evacuation algorithms are implemented and compared to determine the optimal solution in terms of evacuation time and crowd safety the recommended framework incorporates anylogic simulation environment to design complex spatial environment for large scale pedestrians as agents",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] modeling safest and optimal emergency evacuation plan for large scale pedestrians environments large scale events are always vulnerable to natural disasters and man made chaos which poses great threat to crowd safety such events need an appropriate evacuation plan to alleviate the risk of causalities we propose a modeling framework for large scale evacuation of pedestrians during emergency situation proposed framework presents optimal and safest path evacuation for a hypothetical large scale crowd scenario the main aim is to provide the safest and nearest evacuation path because during disastrous situations there is possibility of exit gate blockade and directions of evacuees may have to be changed at run time for this purpose run time diversions are given to evacuees to ensure their quick and safest exit in this work, different evacuation algorithms are implemented and compared to determine the optimal solution in terms of evacuation time and crowd safety the recommended framework incorporates anylogic simulation environment to design complex spatial environment for large scale pedestrians as agents [SEP]",
"target": "contradiction"
},
{
"instance_id": "R70247xR36138",
"template_id": "R70247",
"correct_template_id": null,
"paper_id": "R36138",
"premise": "bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method",
"hypothesis": "estimating the generation interval for covid 19 based on symptom onset data abstract background estimating key infectious disease parameters from the covid 19 outbreak is quintessential for modelling studies and guiding intervention strategies whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for covid 19 have not been provided methods we used outbreak data from clusters in singapore and tianjin, china to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network from those estimates we obtained the proportions pre symptomatic transmission and reproduction numbers results the mean generation interval was 5 20 (95%ci 3 78 6 78) days for singapore and 3 95 (95%ci 3 01 4 91) days for tianjin, china when relying on a previously reported incubation period with mean 5 2 and sd 2 8 days the proportion of pre symptomatic transmission was 48% (95%ci 32 67%) for singapore and 62% (95%ci 50 76%) for tianjin, china estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution conclusions estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network detailed contact tracing information is essential for correctly estimating these quantities",
"sequence": "[CLS] bioassay has assay title has confirmatory assay has role assay measurement type has participant has assay format has endpoint pubchemaid is bioassay type of has assay method [SEP] estimating the generation interval for covid 19 based on symptom onset data abstract background estimating key infectious disease parameters from the covid 19 outbreak is quintessential for modelling studies and guiding intervention strategies whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for covid 19 have not been provided methods we used outbreak data from clusters in singapore and tianjin, china to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network from those estimates we obtained the proportions pre symptomatic transmission and reproduction numbers results the mean generation interval was 5 20 (95%ci 3 78 6 78) days for singapore and 3 95 (95%ci 3 01 4 91) days for tianjin, china when relying on a previously reported incubation period with mean 5 2 and sd 2 8 days the proportion of pre symptomatic transmission was 48% (95%ci 32 67%) for singapore and 62% (95%ci 50 76%) for tianjin, china estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution conclusions estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network detailed contact tracing information is essential for correctly estimating these quantities [SEP]",
"target": "contradiction"
},
{
"instance_id": "R54875xR109012",
"template_id": "R54875",
"correct_template_id": null,
"paper_id": "R109012",
"premise": "climate response has unit has evaluation data used has tcr result",
"hypothesis": "drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network interference between pharmacological substances can cause serious medical injuries correctly predicting so called drug drug interactions (ddi) does not only reduce these cases but can also result in a reduction of drug development cost presently, most drug related knowledge is the result of clinical evaluations and post marketing surveillance; resulting in a limited amount of information existing data driven prediction approaches for ddis typically rely on a single source of information, while using information from multiple sources would help improve predictions machine learning (ml) techniques are used, but the techniques are often unable to deal with skewness in the data hence, we propose a new ml approach for predicting ddis based on multiple data sources for this task, we use 12,000 drug features from drugbank, pharmgkb, and kegg drugs, which are integrated using knowledge graphs (kgs) to train our prediction model, we first embed the nodes in the graph using various embedding approaches we found that the best performing combination was a complex embedding method creating using pytorch biggraph (pbg) with a convolutional lstm network and classic machine learning based prediction models the model averaging ensemble method of three best classifiers yields up to 0 94, 0 92, 0 80 for aupr, f1 f1 score, and mcc, respectively during 5 fold cross validation tests",
"sequence": "[CLS] climate response has unit has evaluation data used has tcr result [SEP] drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network interference between pharmacological substances can cause serious medical injuries correctly predicting so called drug drug interactions (ddi) does not only reduce these cases but can also result in a reduction of drug development cost presently, most drug related knowledge is the result of clinical evaluations and post marketing surveillance; resulting in a limited amount of information existing data driven prediction approaches for ddis typically rely on a single source of information, while using information from multiple sources would help improve predictions machine learning (ml) techniques are used, but the techniques are often unable to deal with skewness in the data hence, we propose a new ml approach for predicting ddis based on multiple data sources for this task, we use 12,000 drug features from drugbank, pharmgkb, and kegg drugs, which are integrated using knowledge graphs (kgs) to train our prediction model, we first embed the nodes in the graph using various embedding approaches we found that the best performing combination was a complex embedding method creating using pytorch biggraph (pbg) with a convolutional lstm network and classic machine learning based prediction models the model averaging ensemble method of three best classifiers yields up to 0 94, 0 92, 0 80 for aupr, f1 f1 score, and mcc, respectively during 5 fold cross validation tests [SEP]",
"target": "contradiction"
},
{
"instance_id": "R49194xR129926",
"template_id": "R49194",
"correct_template_id": null,
"paper_id": "R129926",
"premise": "occupant's perception and behaviour study category results experimental details",
"hypothesis": "generating long sequences with sparse transformers transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length in this paper we introduce sparse factorizations of the attention matrix which reduce this to $o(n \\sqrt{n})$ we also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training we call networks with these changes sparse transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers we use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of enwik8, cifar 10, and imagenet 64 we generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self attention to model sequences of length one million or more",
"sequence": "[CLS] occupant's perception and behaviour study category results experimental details [SEP] generating long sequences with sparse transformers transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length in this paper we introduce sparse factorizations of the attention matrix which reduce this to $o(n \\sqrt{n})$ we also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training we call networks with these changes sparse transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers we use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of enwik8, cifar 10, and imagenet 64 we generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self attention to model sequences of length one million or more [SEP]",
"target": "contradiction"
}
],
"neutrals": [
{
"instance_id": "R29340",
"template_id": null,
"paper_id": "R29340",
"premise": null,
"hypothesis": "taxonomy of cost of quality (coq) across the enterprise resource planning (erp) implementation phases\u201d companies declare that quality or customer satisfaction is their top priority in order to keep and attract more business in an increasingly competitive marketplace the cost of quality (coq) is a tool which can help determine the optimal level of quality investment coq analysis enables organizations to identify measure and control the consequences of poor quality this study attempts to identify the coq elements across the enterprise resource planning (erp) implementation phases for the erp implementation services of consultancy companies the findings provide guidance to project managers on how best to utilize their limited resources in summary, we suggest that project teams should focus on \u201cvalue added\u201d activities and minimize the cost of \u201cnon value added\u201d activities at each phase of the erp implementation project key words: services, erp implementation services, quality standard, service quality standard, cost of quality, project management, project quality management, project financial management",
"sequence": "[CLS] None [SEP] taxonomy of cost of quality (coq) across the enterprise resource planning (erp) implementation phases\u201d companies declare that quality or customer satisfaction is their top priority in order to keep and attract more business in an increasingly competitive marketplace the cost of quality (coq) is a tool which can help determine the optimal level of quality investment coq analysis enables organizations to identify measure and control the consequences of poor quality this study attempts to identify the coq elements across the enterprise resource planning (erp) implementation phases for the erp implementation services of consultancy companies the findings provide guidance to project managers on how best to utilize their limited resources in summary, we suggest that project teams should focus on \u201cvalue added\u201d activities and minimize the cost of \u201cnon value added\u201d activities at each phase of the erp implementation project key words: services, erp implementation services, quality standard, service quality standard, cost of quality, project management, project quality management, project financial management [SEP]",
"target": "neutral"
},
{
"instance_id": "R49096",
"template_id": null,
"paper_id": "R49096",
"premise": null,
"hypothesis": "stabilization of global temperature at 1 5\u00b0c and 2 0\u00b0c: implications for coastal areas the effectiveness of stringent climate stabilization scenarios for coastal areas in terms of reduction of impacts/adaptation needs and wider policy implications has received little attention here we use the warming acidification and sea level projector earth systems model to calculate large ensembles of global sea level rise (slr) and ocean ph projections to 2300 for 1 5\u00b0c and 2 0\u00b0c stabilization scenarios, and a reference unmitigated rcp8 5 scenario the potential consequences of these projections are then considered for global coastal flooding, small islands, deltas, coastal cities and coastal ecology under both stabilization scenarios, global mean ocean ph (and temperature) stabilize within a century this implies significant ecosystem impacts are avoided, but detailed quantification is lacking, reflecting scientific uncertainty by contrast, slr is only slowed and continues to 2300 (and beyond) hence, while coastal impacts due to slr are reduced significantly by climate stabilization, especially after 2100, potential impacts continue to grow for centuries slr in 2300 under both stabilization scenarios exceeds unmitigated slr in 2100 therefore, adaptation remains essential in densely populated and economically important coastal areas under climate stabilization given the multiple adaptation steps that this will require, an adaptation pathways approach has merits for coastal areas this article is part of the theme issue \u2018the paris agreement: understanding the physical and social challenges for a warming world of 1 5\u00b0c above pre industrial levels\u2019",
"sequence": "[CLS] None [SEP] stabilization of global temperature at 1 5\u00b0c and 2 0\u00b0c: implications for coastal areas the effectiveness of stringent climate stabilization scenarios for coastal areas in terms of reduction of impacts/adaptation needs and wider policy implications has received little attention here we use the warming acidification and sea level projector earth systems model to calculate large ensembles of global sea level rise (slr) and ocean ph projections to 2300 for 1 5\u00b0c and 2 0\u00b0c stabilization scenarios, and a reference unmitigated rcp8 5 scenario the potential consequences of these projections are then considered for global coastal flooding, small islands, deltas, coastal cities and coastal ecology under both stabilization scenarios, global mean ocean ph (and temperature) stabilize within a century this implies significant ecosystem impacts are avoided, but detailed quantification is lacking, reflecting scientific uncertainty by contrast, slr is only slowed and continues to 2300 (and beyond) hence, while coastal impacts due to slr are reduced significantly by climate stabilization, especially after 2100, potential impacts continue to grow for centuries slr in 2300 under both stabilization scenarios exceeds unmitigated slr in 2100 therefore, adaptation remains essential in densely populated and economically important coastal areas under climate stabilization given the multiple adaptation steps that this will require, an adaptation pathways approach has merits for coastal areas this article is part of the theme issue \u2018the paris agreement: understanding the physical and social challenges for a warming world of 1 5\u00b0c above pre industrial levels\u2019 [SEP]",
"target": "neutral"
},
{
"instance_id": "R138294",
"template_id": null,
"paper_id": "R138294",
"premise": null,
"hypothesis": "towards a flexible ict architecture for multi channel e government service provisioning the planning and subsequent nationwide implementation of e government service provisioning faces a number of challenges at the level of municipalities in the netherlands initiatives are confronted with a highly fragmented ict architecture that has been vertically organized around departments and with hardly any common horizontal functionality this situation is even further enforced by a defacto duopoly on the software market of information systems used by municipalities the provision of services over web based channels leads to a need for a more flexible, open ict architecture based on standardized elements the goal of the research presented in this paper is to determine the feasibility of a component based approach to meet the aforementioned challenge for a more flexible, open ict architecture the research consisted of two parts (1) the identification of opportunities for generic components in the ict architecture of municipalities and (2) supporting the evaluation of these opportunities using simulation",
"sequence": "[CLS] None [SEP] towards a flexible ict architecture for multi channel e government service provisioning the planning and subsequent nationwide implementation of e government service provisioning faces a number of challenges at the level of municipalities in the netherlands initiatives are confronted with a highly fragmented ict architecture that has been vertically organized around departments and with hardly any common horizontal functionality this situation is even further enforced by a defacto duopoly on the software market of information systems used by municipalities the provision of services over web based channels leads to a need for a more flexible, open ict architecture based on standardized elements the goal of the research presented in this paper is to determine the feasibility of a component based approach to meet the aforementioned challenge for a more flexible, open ict architecture the research consisted of two parts (1) the identification of opportunities for generic components in the ict architecture of municipalities and (2) supporting the evaluation of these opportunities using simulation [SEP]",
"target": "neutral"
},
{
"instance_id": "R111748",
"template_id": null,
"paper_id": "R111748",
"premise": null,
"hypothesis": "the coupled brains of captivated audiences: an investigation of the collective brain dynamics of an audience watching a suspenseful film abstract suspense not only creates a strong psychological tension within individuals, but it does so reliably across viewers who become collectively engaged with the story despite its prevalence in media psychology, limited work has examined suspense from a media neuroscience perspective, and thus the biological underpinnings of suspense remain unknown here we examine continuous brain responses of 494 viewers watching a suspenseful movie to create a time resolved measure of the degree to which a movie aligns audience wide brain responses, we computed dynamic inter subject correlations of functional magnetic resonance imaging (fmri) time series among all viewers using sliding window analysis in parallel, we captured in the moment reports of suspense in an independent sample via continuous response measurement (crm) we found that dynamic inter subject correlations over the course of the movie tracked well with the reported suspense in the crm sample, particularly in regions associated with emotional salience and higher cognitive processes these results are compatible with theoretical views on motivated attention and psychological tension the finding that fmri based audience response measurement relates to audience reports of suspense creates new opportunities for research on the mechanisms of suspense and other entertainment phenomena and has applied potential for measuring audience responses in a nonreactive and objective fashion",
"sequence": "[CLS] None [SEP] the coupled brains of captivated audiences: an investigation of the collective brain dynamics of an audience watching a suspenseful film abstract suspense not only creates a strong psychological tension within individuals, but it does so reliably across viewers who become collectively engaged with the story despite its prevalence in media psychology, limited work has examined suspense from a media neuroscience perspective, and thus the biological underpinnings of suspense remain unknown here we examine continuous brain responses of 494 viewers watching a suspenseful movie to create a time resolved measure of the degree to which a movie aligns audience wide brain responses, we computed dynamic inter subject correlations of functional magnetic resonance imaging (fmri) time series among all viewers using sliding window analysis in parallel, we captured in the moment reports of suspense in an independent sample via continuous response measurement (crm) we found that dynamic inter subject correlations over the course of the movie tracked well with the reported suspense in the crm sample, particularly in regions associated with emotional salience and higher cognitive processes these results are compatible with theoretical views on motivated attention and psychological tension the finding that fmri based audience response measurement relates to audience reports of suspense creates new opportunities for research on the mechanisms of suspense and other entertainment phenomena and has applied potential for measuring audience responses in a nonreactive and objective fashion [SEP]",
"target": "neutral"
},
{
"instance_id": "R39020",
"template_id": null,
"paper_id": "R39020",
"premise": null,
"hypothesis": "a data driven assessment of early travel restrictions related to the spreading of the novel covid 19 within mainland china two months after it was firstly reported, the novel coronavirus disease covid 19 has already spread worldwide however, the vast majority of reported infections have occurred in china to assess the effect of early travel restrictions adopted by the health authorities in china, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020 this allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban our findings indicate that i) travel restrictions are an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease the latter is due to the impossibility of removing the risk of seeding the disease to other regions our study also highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding",
"sequence": "[CLS] None [SEP] a data driven assessment of early travel restrictions related to the spreading of the novel covid 19 within mainland china two months after it was firstly reported, the novel coronavirus disease covid 19 has already spread worldwide however, the vast majority of reported infections have occurred in china to assess the effect of early travel restrictions adopted by the health authorities in china, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020 this allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban our findings indicate that i) travel restrictions are an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease the latter is due to the impossibility of removing the risk of seeding the disease to other regions our study also highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding [SEP]",
"target": "neutral"
},
{
"instance_id": "R74633",
"template_id": null,
"paper_id": "R74633",
"premise": null,
"hypothesis": "building iot based applications for smart cities: how can ontology catalogs help? the internet of things (iot) plays an ever increasing role in enabling smart city applications an ontology based semantic approach can help improve interoperability between a variety of iot generated as well as complementary data needed to drive these applications while multiple ontology catalogs exist, using them for iot and smart city applications require significant amount of work in this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? we consider four ontology catalogs that are relevant for iot and smart cities: 1) ready4smartcities; 2) linked open vocabulary (lov); 3) opensensingcity (osc); and 4) lovs for iot (lov4iot) to support semantic interoperability with the reuse of ontology based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by osc and lov4iot ontology catalogs researchers and developers have completed a survey based evaluation of the lov4iot catalog the usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance the quality of iot and smart city ontologies have been evaluated to improve the ontology catalog quality we also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools",
"sequence": "[CLS] None [SEP] building iot based applications for smart cities: how can ontology catalogs help? the internet of things (iot) plays an ever increasing role in enabling smart city applications an ontology based semantic approach can help improve interoperability between a variety of iot generated as well as complementary data needed to drive these applications while multiple ontology catalogs exist, using them for iot and smart city applications require significant amount of work in this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? we consider four ontology catalogs that are relevant for iot and smart cities: 1) ready4smartcities; 2) linked open vocabulary (lov); 3) opensensingcity (osc); and 4) lovs for iot (lov4iot) to support semantic interoperability with the reuse of ontology based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by osc and lov4iot ontology catalogs researchers and developers have completed a survey based evaluation of the lov4iot catalog the usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance the quality of iot and smart city ontologies have been evaluated to improve the ontology catalog quality we also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools [SEP]",
"target": "neutral"
},
{
"instance_id": "R41605",
"template_id": null,
"paper_id": "R41605",
"premise": null,
"hypothesis": "serological and molecular findings during sars cov 2 infection: the first case study in finland, january to february 2020 the first case of coronavirus disease (covid 19) in finland was confirmed on 29 january 2020 no secondary cases were detected we describe the clinical picture and laboratory findings 3\u201323 days since the first symptoms the sars cov 2/finland/1/2020 virus strain was isolated, the genome showing a single nucleotide substitution to the reference strain from wuhan neutralising antibody response appeared within 9 days along with specific igm and igg response, targeting particularly nucleocapsid and spike proteins",
"sequence": "[CLS] None [SEP] serological and molecular findings during sars cov 2 infection: the first case study in finland, january to february 2020 the first case of coronavirus disease (covid 19) in finland was confirmed on 29 january 2020 no secondary cases were detected we describe the clinical picture and laboratory findings 3\u201323 days since the first symptoms the sars cov 2/finland/1/2020 virus strain was isolated, the genome showing a single nucleotide substitution to the reference strain from wuhan neutralising antibody response appeared within 9 days along with specific igm and igg response, targeting particularly nucleocapsid and spike proteins [SEP]",
"target": "neutral"
},
{
"instance_id": "R57722",
"template_id": null,
"paper_id": "R57722",
"premise": null,
"hypothesis": "novel host associations and habitats for senecio specialist herbivorous insects in auckland we studied the genusand species specialist monophagous herbivorous insects of senecio (asteraceae) in auckland, new zealand with the exception of the widespread s hispidulus, the eight native senecio species in mainland auckland (two endemic) are typically uncommon and restricted to less modified conservation land however, 11 naturalised senecio have established and are often widespread in urban and rural habitats three endemic senecio specialist herbivores \u2013 nyctemera annulata, patagoniodes farnaria, and tephritis fascigera \u2013 formed novel host associations with naturalised senecio species and spread into modified landscapes host associations for these species were not related to whether senecio species are naturalised or native however, the abundances of patagonoides farnaria and tephritis fascigera were significantly higher in wildland habitats than rural or urban habitats, and wildland senecio were on average 1 4 times more likely to experience >5% folivory than urban conspecifics",
"sequence": "[CLS] None [SEP] novel host associations and habitats for senecio specialist herbivorous insects in auckland we studied the genusand species specialist monophagous herbivorous insects of senecio (asteraceae) in auckland, new zealand with the exception of the widespread s hispidulus, the eight native senecio species in mainland auckland (two endemic) are typically uncommon and restricted to less modified conservation land however, 11 naturalised senecio have established and are often widespread in urban and rural habitats three endemic senecio specialist herbivores \u2013 nyctemera annulata, patagoniodes farnaria, and tephritis fascigera \u2013 formed novel host associations with naturalised senecio species and spread into modified landscapes host associations for these species were not related to whether senecio species are naturalised or native however, the abundances of patagonoides farnaria and tephritis fascigera were significantly higher in wildland habitats than rural or urban habitats, and wildland senecio were on average 1 4 times more likely to experience >5% folivory than urban conspecifics [SEP]",
"target": "neutral"
},
{
"instance_id": "R136518",
"template_id": null,
"paper_id": "R136518",
"premise": null,
"hypothesis": "codeontology: rdf ization of source code in this paper, we leverage advances in the semantic web area, including data modeling (rdf), data management and querying (jena and sparql), to develop codeontology, a community shared software framework supporting expressive queries over source code the project consists of two main contributions: an ontology that provides a formal representation of object oriented programming languages, and a parser that is able to analyze java source code and serialize it into rdf triples the parser has been successfully applied to the source code of openjdk 8, gathering a structured dataset consisting of more than 2 million rdf triples codeontology allows to generate linked data from any java project, thereby enabling the execution of highly expressive queries over source code, by means of a powerful language like sparql",
"sequence": "[CLS] None [SEP] codeontology: rdf ization of source code in this paper, we leverage advances in the semantic web area, including data modeling (rdf), data management and querying (jena and sparql), to develop codeontology, a community shared software framework supporting expressive queries over source code the project consists of two main contributions: an ontology that provides a formal representation of object oriented programming languages, and a parser that is able to analyze java source code and serialize it into rdf triples the parser has been successfully applied to the source code of openjdk 8, gathering a structured dataset consisting of more than 2 million rdf triples codeontology allows to generate linked data from any java project, thereby enabling the execution of highly expressive queries over source code, by means of a powerful language like sparql [SEP]",
"target": "neutral"
},
{
"instance_id": "R112044",
"template_id": null,
"paper_id": "R112044",
"premise": null,
"hypothesis": "can app changelogs improve requirements classification from app reviews?: an exploratory study [background] recent research on mining app reviews for software evolution indicated that the elicitation and analysis of user requirements can benefit from supplementing user reviews by data from other sources however, only a few studies reported results of leveraging app changelogs together with app reviews [aims] motivated by those findings, this exploratory experimental study looks into the role of app changelogs in the classification of requirements derived from app reviews we aim at understanding if the use of app changelogs can lead to more accurate identification and classification of functional and non functional requirements from app reviews we also want to know which classification technique works better in this context [method] we did a case study on the effect of app changelogs on automatic classification of app reviews specifically, manual labeling, text preprocessing, and four supervised machine learning algorithms were applied to a series of experiments, varying in the number of app changelogs in the experimental data [results] we compared the accuracy of requirements classification from app reviews, by training the four classifiers with varying combinations of app reviews and changelogs among the four algorithms, na\u00efve bayes was found to be more accurate for categorizing app reviews [conclusions] the results show that official app changelogs did not contribute to more accurate identification and classification of requirements from app reviews in addition, na\u00efve bayes seems to be more suitable for our further research on this topic",
"sequence": "[CLS] None [SEP] can app changelogs improve requirements classification from app reviews?: an exploratory study [background] recent research on mining app reviews for software evolution indicated that the elicitation and analysis of user requirements can benefit from supplementing user reviews by data from other sources however, only a few studies reported results of leveraging app changelogs together with app reviews [aims] motivated by those findings, this exploratory experimental study looks into the role of app changelogs in the classification of requirements derived from app reviews we aim at understanding if the use of app changelogs can lead to more accurate identification and classification of functional and non functional requirements from app reviews we also want to know which classification technique works better in this context [method] we did a case study on the effect of app changelogs on automatic classification of app reviews specifically, manual labeling, text preprocessing, and four supervised machine learning algorithms were applied to a series of experiments, varying in the number of app changelogs in the experimental data [results] we compared the accuracy of requirements classification from app reviews, by training the four classifiers with varying combinations of app reviews and changelogs among the four algorithms, na\u00efve bayes was found to be more accurate for categorizing app reviews [conclusions] the results show that official app changelogs did not contribute to more accurate identification and classification of requirements from app reviews in addition, na\u00efve bayes seems to be more suitable for our further research on this topic [SEP]",
"target": "neutral"
},
{
"instance_id": "R37008",
"template_id": null,
"paper_id": "R37008",
"premise": null,
"hypothesis": "estimation of the transmission risk of the 2019 ncov and its implication for public health interventions since the emergence of the first cases in wuhan, china, the novel coronavirus (2019 ncov) infection has been quickly spreading out to other provinces and neighboring countries estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity a deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures the estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6 47 (95% ci 5 71\u20137 23) sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by wuhan on 2019 ncov infection in beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value it is essential to assess how the expensive, resource intensive measures implemented by the chinese authorities can contribute to the prevention and control of the 2019 ncov infection, and how long they should be maintained under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 january 2020) with a significant low peak value with travel restriction (no imported exposed individuals to beijing), the number of infected individuals in seven days will decrease by 91 14% in beijing, compared with the scenario of no travel restriction",
"sequence": "[CLS] None [SEP] estimation of the transmission risk of the 2019 ncov and its implication for public health interventions since the emergence of the first cases in wuhan, china, the novel coronavirus (2019 ncov) infection has been quickly spreading out to other provinces and neighboring countries estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity a deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures the estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6 47 (95% ci 5 71\u20137 23) sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by wuhan on 2019 ncov infection in beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value it is essential to assess how the expensive, resource intensive measures implemented by the chinese authorities can contribute to the prevention and control of the 2019 ncov infection, and how long they should be maintained under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 january 2020) with a significant low peak value with travel restriction (no imported exposed individuals to beijing), the number of infected individuals in seven days will decrease by 91 14% in beijing, compared with the scenario of no travel restriction [SEP]",
"target": "neutral"
},
{
"instance_id": "R42003",
"template_id": null,
"paper_id": "R42003",
"premise": null,
"hypothesis": "virus isolation from the first patient with sars cov 2 in korea novel coronavirus (sars cov 2) is found to cause a large outbreak started from wuhan since december 2019 in china and sars cov 2 infections have been reported with epidemiological linkage to china in 25 countries until now we isolated sars cov 2 from the oropharyngeal sample obtained from the patient with the first laboratory confirmed sars cov 2 infection in korea cytopathic effects of sars cov 2 in the vero cell cultures were confluent 3 days after the first blind passage of the sample coronavirus was confirmed with spherical particle having a fringe reminiscent of crown on transmission electron microscopy phylogenetic analyses of whole genome sequences showed that it clustered with other sars cov 2 reported from wuhan",
"sequence": "[CLS] None [SEP] virus isolation from the first patient with sars cov 2 in korea novel coronavirus (sars cov 2) is found to cause a large outbreak started from wuhan since december 2019 in china and sars cov 2 infections have been reported with epidemiological linkage to china in 25 countries until now we isolated sars cov 2 from the oropharyngeal sample obtained from the patient with the first laboratory confirmed sars cov 2 infection in korea cytopathic effects of sars cov 2 in the vero cell cultures were confluent 3 days after the first blind passage of the sample coronavirus was confirmed with spherical particle having a fringe reminiscent of crown on transmission electron microscopy phylogenetic analyses of whole genome sequences showed that it clustered with other sars cov 2 reported from wuhan [SEP]",
"target": "neutral"
},
{
"instance_id": "R110705",
"template_id": null,
"paper_id": "R110705",
"premise": null,
"hypothesis": "in vitro antiviral activity of the anthraquinone chrysophanic acid against poliovirus chrysophanic acid (1,8 dihydroxy 3 methylanthraquinone), isolated from the australian aboriginal medicinal plant dianella longifolia, has been found to inhibit the replication of poliovirus types 2 and 3 (picornaviridae) in vitro the compound inhibited poliovirus induced cytopathic effects in bgm (buffalo green monkey) kidney cells at a 50% effective concentration of 0 21 and 0 02 microgram/ml for poliovirus types 2 and 3, respectively the compound inhibited an early stage in the viral replication cycle, but did not have an irreversible virucidal effect on poliovirus particles chrysophanic acid did not have significant antiviral activity against five other viruses tested: coxsackievirus types a21 and b4, human rhinovirus type 2 (picornaviridae), and the enveloped viruses ross river virus (togaviridae) and herpes simplex virus type 1 (herpesviridae) four structurally related anthraquinones rhein, 1,8 dihydroxyanthraquinone, emodin and aloe emodin were also tested for activity against poliovirus type 3 none of the four compounds was as active as chrysophanic acid against the virus the results suggested that two hydrophobic positions on the chrysophanic acid molecule (c 6 and the methyl group attached to c 3) were important for the compound's activity against poliovirus",
"sequence": "[CLS] None [SEP] in vitro antiviral activity of the anthraquinone chrysophanic acid against poliovirus chrysophanic acid (1,8 dihydroxy 3 methylanthraquinone), isolated from the australian aboriginal medicinal plant dianella longifolia, has been found to inhibit the replication of poliovirus types 2 and 3 (picornaviridae) in vitro the compound inhibited poliovirus induced cytopathic effects in bgm (buffalo green monkey) kidney cells at a 50% effective concentration of 0 21 and 0 02 microgram/ml for poliovirus types 2 and 3, respectively the compound inhibited an early stage in the viral replication cycle, but did not have an irreversible virucidal effect on poliovirus particles chrysophanic acid did not have significant antiviral activity against five other viruses tested: coxsackievirus types a21 and b4, human rhinovirus type 2 (picornaviridae), and the enveloped viruses ross river virus (togaviridae) and herpes simplex virus type 1 (herpesviridae) four structurally related anthraquinones rhein, 1,8 dihydroxyanthraquinone, emodin and aloe emodin were also tested for activity against poliovirus type 3 none of the four compounds was as active as chrysophanic acid against the virus the results suggested that two hydrophobic positions on the chrysophanic acid molecule (c 6 and the methyl group attached to c 3) were important for the compound's activity against poliovirus [SEP]",
"target": "neutral"
},
{
"instance_id": "R38498",
"template_id": null,
"paper_id": "R38498",
"premise": null,
"hypothesis": "an analytics tool for exploring scientific software and related publications scientific software is one of the key elements for reproducible research however, classic publications and related scientific software are typically not (sufficiently) linked, and tools are missing to jointly explore these artefacts in this paper, we report on our work on developing the analytics tool scisoftx (https://labs tib eu/info/projekt/scisoftx/) for jointly exploring software and publications the presented prototype, a concept for automatic code discovery, and two use cases demonstrate the feasibility and usefulness of the proposal",
"sequence": "[CLS] None [SEP] an analytics tool for exploring scientific software and related publications scientific software is one of the key elements for reproducible research however, classic publications and related scientific software are typically not (sufficiently) linked, and tools are missing to jointly explore these artefacts in this paper, we report on our work on developing the analytics tool scisoftx (https://labs tib eu/info/projekt/scisoftx/) for jointly exploring software and publications the presented prototype, a concept for automatic code discovery, and two use cases demonstrate the feasibility and usefulness of the proposal [SEP]",
"target": "neutral"
},
{
"instance_id": "R135602",
"template_id": null,
"paper_id": "R135602",
"premise": null,
"hypothesis": "model driven architecture based software development for epidemiological surveillance systems epidemiological surveillance systems enable collection, analysis and dissemination of information on the monitored disease to different stakeholders it may be done manually or using a software given the poor performances of manual systems, the software approach is generally adopted epidemiological surveillance systems are based on existing softwares, softwares developed from scratch given the specifications or softwares provided by a vendor these solutions are not always suitable because epidemiological surveillance systems evolve quickly (new drugs, new treatment protocols, etc ), leading to software updates, which can take time (while waiting for a new version) and be expensive in this article, we present the use of the model driven architecture (mda) approach to model and generate epidemiological surveillance systems the result is a complete mda based methodology and tool to develop epidemiological surveillance systems the tool was used to model and generate softwares that are now used for epidemiological surveillance of tuberculosis in cameroon",
"sequence": "[CLS] None [SEP] model driven architecture based software development for epidemiological surveillance systems epidemiological surveillance systems enable collection, analysis and dissemination of information on the monitored disease to different stakeholders it may be done manually or using a software given the poor performances of manual systems, the software approach is generally adopted epidemiological surveillance systems are based on existing softwares, softwares developed from scratch given the specifications or softwares provided by a vendor these solutions are not always suitable because epidemiological surveillance systems evolve quickly (new drugs, new treatment protocols, etc ), leading to software updates, which can take time (while waiting for a new version) and be expensive in this article, we present the use of the model driven architecture (mda) approach to model and generate epidemiological surveillance systems the result is a complete mda based methodology and tool to develop epidemiological surveillance systems the tool was used to model and generate softwares that are now used for epidemiological surveillance of tuberculosis in cameroon [SEP]",
"target": "neutral"
},
{
"instance_id": "R36151",
"template_id": null,
"paper_id": "R36151",
"premise": null,
"hypothesis": "effects of voluntary event cancellation and school closure as countermeasures against covid 19 outbreak in japan abstract background to control the covid 19 outbreak in japan, sports and entertainment events were canceled and schools were closed throughout japan from february 26 through march 19 that policy has been designated as voluntary event cancellation and school closure (vecsc) object this study assesses vecsc effectiveness based on predicted outcomes method: a simple susceptible\u2013infected\u2013recovery model was applied to data of patients with symptoms in japan during january 14 through march 25 the respective reproduction numbers were estimated before vecsc (r), during vecsc (r e ), and after vecsc (r a ) results results suggest r before vecsc as 1 987 [1 908, 2 055], r e during vecsc as 1 122 [0 980, 1 260], and r a after vecsc as 3 086 [2 529, 3 739] discussion and conclusion results demonstrated that vecsc can reduce covid 19 infectiousness considerably, but the value of r rose to exceed 2 5 after vecsc",
"sequence": "[CLS] None [SEP] effects of voluntary event cancellation and school closure as countermeasures against covid 19 outbreak in japan abstract background to control the covid 19 outbreak in japan, sports and entertainment events were canceled and schools were closed throughout japan from february 26 through march 19 that policy has been designated as voluntary event cancellation and school closure (vecsc) object this study assesses vecsc effectiveness based on predicted outcomes method: a simple susceptible\u2013infected\u2013recovery model was applied to data of patients with symptoms in japan during january 14 through march 25 the respective reproduction numbers were estimated before vecsc (r), during vecsc (r e ), and after vecsc (r a ) results results suggest r before vecsc as 1 987 [1 908, 2 055], r e during vecsc as 1 122 [0 980, 1 260], and r a after vecsc as 3 086 [2 529, 3 739] discussion and conclusion results demonstrated that vecsc can reduce covid 19 infectiousness considerably, but the value of r rose to exceed 2 5 after vecsc [SEP]",
"target": "neutral"
},
{
"instance_id": "R109894",
"template_id": null,
"paper_id": "R109894",
"premise": null,
"hypothesis": "a hybrid approach toward research paper recommendation using centrality measures and author ranking the volume of research articles in digital repositories is increasing this spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries the problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories in the traditional recommendation approaches, the results of the query miss many high quality papers, in the related work section, which are either published recently or have low citation count to overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles many research paper recommendation approaches have been implemented which includes collaborative filtering based, content based, and citation analysis based techniques the collaborative filtering based approaches primarily use paper citation matrix for recommendations, whereas the content based approaches only consider the content of the paper the citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest it is therefore very difficult for a recommender system to recommend high quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information the proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors\u2019 collaboration network finds key authors from those papers the papers selected by this hybrid approach are then recommended to the user the results have shown that our proposed method performs exceedingly well as compared with the state of the art existing systems, such as google scholar and multilevel simultaneous citation network",
"sequence": "[CLS] None [SEP] a hybrid approach toward research paper recommendation using centrality measures and author ranking the volume of research articles in digital repositories is increasing this spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries the problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories in the traditional recommendation approaches, the results of the query miss many high quality papers, in the related work section, which are either published recently or have low citation count to overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles many research paper recommendation approaches have been implemented which includes collaborative filtering based, content based, and citation analysis based techniques the collaborative filtering based approaches primarily use paper citation matrix for recommendations, whereas the content based approaches only consider the content of the paper the citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest it is therefore very difficult for a recommender system to recommend high quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information the proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors\u2019 collaboration network finds key authors from those papers the papers selected by this hybrid approach are then recommended to the user the results have shown that our proposed method performs exceedingly well as compared with the state of the art existing systems, such as google scholar and multilevel simultaneous citation network [SEP]",
"target": "neutral"
},
{
"instance_id": "R44530",
"template_id": null,
"paper_id": "R44530",
"premise": null,
"hypothesis": "social media analytics \u2013 challenges in topic discovery, data collection, and data preparation abstract since an ever increasing part of the population makes use of social media in their day to day lives, social media data is being analysed in many different disciplines the social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis while there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation to address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed the literature search revealed that the volume of data was most often cited as a challenge by researchers in contrast, other categories have received less attention based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions the findings are used to extend an existing framework on social media analytics the article provides benefits for researchers and practitioners who wish to collect and analyse social media data",
"sequence": "[CLS] None [SEP] social media analytics \u2013 challenges in topic discovery, data collection, and data preparation abstract since an ever increasing part of the population makes use of social media in their day to day lives, social media data is being analysed in many different disciplines the social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis while there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation to address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed the literature search revealed that the volume of data was most often cited as a challenge by researchers in contrast, other categories have received less attention based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions the findings are used to extend an existing framework on social media analytics the article provides benefits for researchers and practitioners who wish to collect and analyse social media data [SEP]",
"target": "neutral"
},
{
"instance_id": "R32995",
"template_id": null,
"paper_id": "R32995",
"premise": null,
"hypothesis": "chromosomal abnormalities in untreated patients with non hodgkin\u2019s lymphoma: associations with histology, clinical characteristics, and treatment outcome the nebraska lymphoma study group abstract we describe the chromosomal abnormalities found in 104 previously untreated patients with non hodgkin's lymphoma (nhl) and the correlations of these abnormalities with disease characteristics the cytogenetic method used was a 24 to 48 hour culture, followed by g banding several significant associations were discovered a trisomy 3 was correlated with high grade nhl in the patients with an immunoblastic nhl, an abnormal chromosome no 3 or 6 was found significantly more frequently as previously described, a t(14;18) was significantly correlated with a follicular growth pattern abnormalities on chromosome no 17 were correlated with a diffuse histology and a shorter survival a shorter survival was also correlated with a +5, +6, +18, all abnormalities on chromosome no 5, or involvement of breakpoint 14q11\u201312 in a multivariate analysis, these chromosomal abnormalities appeared to be independent prognostic factors and correlated with survival more strongly than any traditional prognostic variable patients with a t(11;14)(q13;q32) had an elevated lactate dehydrogenase (ldh) skin infiltration was correlated with abnormalities on 2p abnormalities involving breakpoints 6q11\u201316 were correlated with b symptoms patients with abnormalities involving breakpoints 3q21\u201325 and 13q21\u201324 had more frequent bulky disease the correlations of certain clinical findings with specific chromosomal abnormalities might help unveil the pathogenetic mechanisms of nhl and tailor treatment regimens",
"sequence": "[CLS] None [SEP] chromosomal abnormalities in untreated patients with non hodgkin\u2019s lymphoma: associations with histology, clinical characteristics, and treatment outcome the nebraska lymphoma study group abstract we describe the chromosomal abnormalities found in 104 previously untreated patients with non hodgkin's lymphoma (nhl) and the correlations of these abnormalities with disease characteristics the cytogenetic method used was a 24 to 48 hour culture, followed by g banding several significant associations were discovered a trisomy 3 was correlated with high grade nhl in the patients with an immunoblastic nhl, an abnormal chromosome no 3 or 6 was found significantly more frequently as previously described, a t(14;18) was significantly correlated with a follicular growth pattern abnormalities on chromosome no 17 were correlated with a diffuse histology and a shorter survival a shorter survival was also correlated with a +5, +6, +18, all abnormalities on chromosome no 5, or involvement of breakpoint 14q11\u201312 in a multivariate analysis, these chromosomal abnormalities appeared to be independent prognostic factors and correlated with survival more strongly than any traditional prognostic variable patients with a t(11;14)(q13;q32) had an elevated lactate dehydrogenase (ldh) skin infiltration was correlated with abnormalities on 2p abnormalities involving breakpoints 6q11\u201316 were correlated with b symptoms patients with abnormalities involving breakpoints 3q21\u201325 and 13q21\u201324 had more frequent bulky disease the correlations of certain clinical findings with specific chromosomal abnormalities might help unveil the pathogenetic mechanisms of nhl and tailor treatment regimens [SEP]",
"target": "neutral"
},
{
"instance_id": "R36128",
"template_id": null,
"paper_id": "R36128",
"premise": null,
"hypothesis": "risk estimation and prediction by modeling the transmission of the novel coronavirus (covid 19) in mainland china excluding hubei province abstract background in december 2019, an outbreak of coronavirus disease (covid 19) was identified in wuhan, china and, later on, detected in other parts of china our aim is to evaluate the effectiveness of the evolution of interventions and self protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in mainland china excluding hubei province based on the published data and a novel mathematical model methods a novel covid 19 transmission dynamic model incorporating the intervention measures implemented in china is proposed covid 19 daily data of mainland china excluding hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases for the period january 20th march 3rd, 2020, were archived from the national health commission of china (nhcc) we parameterize the model by using the markov chain monte carlo (mcmc) method and estimate the control reproduction number r c , as well as the effective daily reproduction ratio r e ( t ), of the disease transmission in mainland china excluding hubei province results the estimation outcomes indicate that r c is 3 36 (95% ci 3 20 3 64) and r e ( t ) has dropped below 1 since january 31st, 2020, which implies that the containment strategies implemented by the chinese government in mainland china excluding hubei province are indeed effective and magnificently suppressed covid 19 transmission moreover, our results show that relieving personal protection too early may lead to the spread of disease for a longer time and more people would be infected, and may even cause epidemic or outbreak again by calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by april, 2020 conclusions to ensure the epidemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, etc people should be fully aware of the real time epidemic situation and keep sufficient personal protection until april if all the above conditions are met, the outbreak is expected to be ended by april in mainland china apart from hubei province",
"sequence": "[CLS] None [SEP] risk estimation and prediction by modeling the transmission of the novel coronavirus (covid 19) in mainland china excluding hubei province abstract background in december 2019, an outbreak of coronavirus disease (covid 19) was identified in wuhan, china and, later on, detected in other parts of china our aim is to evaluate the effectiveness of the evolution of interventions and self protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in mainland china excluding hubei province based on the published data and a novel mathematical model methods a novel covid 19 transmission dynamic model incorporating the intervention measures implemented in china is proposed covid 19 daily data of mainland china excluding hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases for the period january 20th march 3rd, 2020, were archived from the national health commission of china (nhcc) we parameterize the model by using the markov chain monte carlo (mcmc) method and estimate the control reproduction number r c , as well as the effective daily reproduction ratio r e ( t ), of the disease transmission in mainland china excluding hubei province results the estimation outcomes indicate that r c is 3 36 (95% ci 3 20 3 64) and r e ( t ) has dropped below 1 since january 31st, 2020, which implies that the containment strategies implemented by the chinese government in mainland china excluding hubei province are indeed effective and magnificently suppressed covid 19 transmission moreover, our results show that relieving personal protection too early may lead to the spread of disease for a longer time and more people would be infected, and may even cause epidemic or outbreak again by calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by april, 2020 conclusions to ensure the epidemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, etc people should be fully aware of the real time epidemic situation and keep sufficient personal protection until april if all the above conditions are met, the outbreak is expected to be ended by april in mainland china apart from hubei province [SEP]",
"target": "neutral"
}
]
}