nils-herrmann/clean_scirepeval huggingface dataset:
Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers

Citation screening is time consuming and inefficient. We sought to evaluate the performance of Abstrackr, a semi-automated online tool for predictive title and abstract screening.



Four systematic reviews (aHUS, dietary fibre, ECHO, rituximab) were used to evaluate Abstrackr. Citations from electronic searches of biomedical databases were imported into Abstrackr, and titles and abstracts were screened and included or excluded according to the entry criteria. This process was continued until Abstrackr predicted and classified the remaining unscreened citations as relevant or irrelevant. These classification predictions were checked for accuracy against the original review decisions. Sensitivity analyses were performed to assess the effects of including case reports in the aHUS dataset whilst screening and the effects of using larger imbalanced datasets with the ECHO dataset. The performance of Abstrackr was calculated according to the number of relevant studies missed, the workload saving, the false negative rate, and the precision of the algorithm to correctly predict relevant studies for inclusion, i.e. further full text inspection.



Of the unscreened citations, Abstrackr's prediction algorithm correctly identified all relevant citations for the rituximab and dietary fibre reviews. However, one relevant citation in both the aHUS and ECHO reviews was incorrectly predicted as not relevant. The workload saving achieved with Abstrackr varied depending on the complexity and size of the reviews (9 % rituximab, 40 % dietary fibre, 67 % aHUS, and 57 % ECHO). The proportion of citations predicted as relevant, and therefore, warranting further full text inspection (i.e. the precision of the prediction) ranged from 16 % (aHUS) to 45 % (rituximab) and was affected by the complexity of the reviews. The false negative rate ranged from 2.4 to 21.7 %. Sensitivity analysis performed on the aHUS dataset increased the precision from 16 to 25 % and increased the workload saving by 10 % but increased the number of relevant studies missed. Sensitivity analysis performed with the larger ECHO dataset increased the workload saving (80 %) but reduced the precision (6.8 %) and increased the number of missed citations.



Semi-automated title and abstract screening with Abstrackr has the potential to save time and reduce research waste.
Bilingualism does not alter cognitive decline or dementia risk among Spanish-speaking immigrants.

Clinic-based studies suggest that dementia is diagnosed at older ages in bilinguals compared with monolinguals. The current study sought to test this hypothesis in a large, prospective, community-based study of initially nondemented Hispanic immigrants living in a Spanish-speaking enclave of northern Manhattan.



Participants included 1,067 participants in the Washington/Hamilton Heights Inwood Columbia Aging Project (WHICAP) who were tested in Spanish and followed at 18-24 month intervals for up to 23 years. Spanish-English bilingualism was estimated via both self-report and an objective measure of English reading level. Multilevel models for change estimated the independent effects of bilingualism on cognitive decline in 4 domains: episodic memory, language, executive function, and speed. Over the course of the study, 282 participants developed dementia. Cox regression was used to estimate the independent effect of bilingualism on dementia conversion. Covariates included country of origin, gender, education, time spent in the United States, recruitment cohort, and age at enrollment.



Independent of the covariates, bilingualism was associated with better memory and executive function at baseline. However, bilingualism was not independently associated with rates of cognitive decline or dementia conversion. Results were similar whether bilingualism was measured via self-report or an objective test of reading level.



This study does not support a protective effect of bilingualism on age-related cognitive decline or the development of dementia. In this sample of Hispanic immigrants, bilingualism is related to higher initial scores on cognitive tests and higher educational attainment and may not represent a unique source of cognitive reserve.
EFFECTS OF A SECURE ATTACHMENT RELATIONSHIP ON RIGHT BRAIN DEVELOPMENT , AFFECT REGULATION , AND INFANT MENTAL HEALTH

Over the last ten years the basic knowledge of brain structure and function has vastly expanded, and its incorporation into the developmental sciences is now allowing for more complex and heuristic models of human infancy. In a continuation of this effort, in this two-part work I integrate current interdisciplinary data from attachment studies on dyadic affective communications, neuroscience on the early developing right brain, psychophysiology on stress systems, and psychiatry on psychopathogenesis to provide a deeper understanding of the psychoneurobiological mechanisms that underlie infant mental health. In this article I detail the neurobiology of a secure attachment, an exemplar of adaptive infant mental health, and focus upon the primary caregiver’s psychobiological regulation of the infant’s maturing limbic system, the brain areas specialized for adapting to a rapidly changing environment. The infant’s early developing right hemisphere has deep connections into the limbic and autonomic nervous systems and is dominant for the human stress response, and in this manner the attachment relationship facilitates the expansion of the child’s coping capcities. This model suggests that adaptive infant mental health can be fundamentally defined as the earliest expression of flexible strategies for coping with the novelty and stress that is inherent in human interactions. This efficient right brain function is a resilience factor for optimal development over the later stages of the life cycle.  En los últimos diez an ̃os el conocimiento ba ́sico de la estructura y funcio ́n del cerebro se ha expandido considerablemente, y su incorporacio ́n mo parte de las ciencias del desarrollo permite ahora tener modelos de infancia humana ma ́s complejos y heurı ́sticos. Como una continuacio ́n a este esfuerzo, en este ensayo que contiene dos partes, se integra la actual informacio ́n interdisciplinaria que proviene de los estudios de la unio ́n afectiva en relacio ́n con comunicaciones afectivas en forma de dı ́adas, la neurociencia en el desarrollo inicial del lado derecho del cerebro, la sicofisiologı ́a de los sistemas de tensión emocional, ası ́ como la siquiatrı ́a en cuanto a la sicopatoge ́nesis, con el fin de presentar un conocimiento ma ́s profundo de los mecanismos siconeurobiolo ́gic s que sirven de base para la salud mental infantil. En este ensayo se explica con detalle la neurobiologı ́a de una relacio ́n afectiva segura, un modelo de salud mental infantil que se puede adaptar, y el enfoque del mismo se centra en la reglamentacio ́n sicobiológica que quien primariamente cuida del nin ̃o tiene del maduramiento del sistema lı́mbico del infante, o sea, las a ́reas del cerebro especialmente dedicadas a la adaptacio ́n un medio Direct correspondence to: Allan N. Schore, Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, 9817 Sylvia Avenue, Northridge, CA 91324; fax: (818) 349-4404;  anschore@aol.com. 8 ● A.N. Schore IMHJ (Wiley) LEFT INTERACTIVE
On Automating Basic Data Curation Tasks

Big data analytics is firmly recognized as a strategic priority for modern enterprises. At the heart of big data analytics lies the data curation process, consists of tasks that transform raw data (unstructured, semi-structured and structured data sources) into curated data, i.e. contextualized data and knowledge that is maintained and made available for use by end-users and applications. To achieve this, the data curation process may involve techniques and algorithms for extracting, classifying, linking, merging, enriching, sampling, and the summarization of data and knowledge. To facilitate the data curation process and enhance the productivity of researchers and developers, we identify and implement a set of basic data curation APIs and make them available as services to researchers and developers to assist them in transforming their raw data into curated data. The curation APIs enable developers to easily add features such as extracting keyword, part of speech, and named entities such as Persons, Locations, Organizations, Companies, Products, Diseases, Drugs, etc.; providing synonyms and stems for extracted information items leveraging lexical knowledge bases for the English language such as WordNet; linking extracted entities to external knowledge bases such as Google Knowledge Graph and Wikidata; discovering similarity among the extracted information items, such as calculating similarity between string and numbers; classifying, sorting and categorizing data into various types, forms or any other distinct class; and indexing structured and unstructured data into their data applications. These services can be accessed via a REST API, and the data is returned as a JSON file that can be integrated into data applications. The curation APIs are available as an open source project on GitHub.
Survey of Software Inspection Research

There is a great need to assure and improve the reliability and quality of software. Software inspections were introduced over 30 years ago as an answer for this need and they have inspired a lot of research covering many different kinds of aspects. There is a need for an up-to-date survey revealing the current state and the overall evolution of the most prominent research on the area. This paper presents a comprehensive survey focusing on the most relevant 16 international high-impact scientific publication series. There are 153 articles included in the survey covering both technical and management aspects. The main results include a description of the research trends during 1980-2008 and a description of the main results of the included studies. The description is organized based on a taxonomy of the inspection research as having emerged based on the survey. At general level the surveyed research provides clear evidence that inspections generally benefit software development and quality assurance. There are several proposed theoretical variations for the inspection process but also many empirical studies. Although the conducted research is relatively scattered, proper sciencebased understanding about some of the most studied issues has been achieved. Our main conclusion is that conducting empirical research needs to be continued in order to validate the effects of the different kinds of proposed theoretical constructs in practice. Empirical studies are needed especially in order to better understand the proper implementation and the actual impacts of applying inspections in different kinds of industrial and organizational settings.
Standardized Precipitation Evapotranspiration Index ( SPEI ) revisited : parameter fitting , evapotranspiration models , tools , datasets and drought monitoring

The Standardized Precipitation Evapotranspiration Index (SPEI) was developed in 2010 and has been used in an increasing number of climatology and hydrology studies. The objective of this article is to describe computing options that provide flexible and robust use of the SPEI. In particular, we present methods for estimating the parameters of the log-logistic distribution for obtaining standardized values, methods for computing reference evapotranspiration (ET0), and weighting kernels used for calculation of the SPEI at different time scales. We discuss the use of alternative ET0 and actual evapotranspiration (ETa) methods and different options on the resulting SPEI series by use of observational and global gridded data. The results indicate that the equation used to calculate ET0 can have a significant effect on the SPEI in some regions of the world. Although the original formulation of the SPEI was based on plotting-positions Probability Weighted Moment (PWM), we now recommend use of unbiased PWM for model fitting. Finally, we present new software tools for computation and analysis of SPEI series, an updated global gridded database, and a realtime drought-monitoring system.
Fingerprint enhancement for direct grayscale minutiae extraction by combining MFRAT and Gabor filters

Minutiae are important features in the fingerprints matching. The effective of minutiae extraction depends greatly on the results of fingerprint enhancement. This paper proposes a novel fingerprint enhancement method for direct gray scale extracting minutiae based on combining Gabor filters with the Adaptive Modified Finite Radon Transform (AMFRAT) filters. First, the proposed method uses Gabor filters as band-pass filters for deleting the noise and clarifying ridges. Next, AMFRAT filters are applied for connecting broken ridges together, filling the created holes and clarifying linear symmetry of ridges quickly. AMFRAT is the MFRAT filter, the window size of which is adaptively adjusted according to the coherence values. The small window size is for high curvature ridge areas (small coherence value), and vice versa. As the result, the ridges are the linear symmetry areas, and more suitable for direct gray scale minutiae extraction. Finally, linear symmetry filter is only used for locating minutiae in an inverse model, as "lack of linear symmetry" occurs at minutiae points. Experimental results on FVC2004 databases DB4 (set A) shows that the proposed method is capable of improving the goodness index (GI).
How Good Is My GAN?

Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification—GANtrain and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two measures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.
Comparaison de performance entre OpenStack et OpenNebula et les architectures multi-Cloud : Application à la cosmologie .

With the increasing numbers of Cloud Service Providers and the migration of the Grids to the Cloud paradigm, it is necessary to be able to leverage these new resources. Moreover, a large class of High Performance Computing (hpc) applications can run these resources without (or with minor) modifications. But using these resources come with the cost of being able to interact with these new resource providers. In this paper we introduce the design of a hpc middleware that is able to use resources coming from an environment that compose of multiple Clouds as well as classical hpc resources. Using the Diet middleware, we are able to deploy a large-scale, distributed hpc platform that spans across a large pool of resources aggregated from different providers. Furthermore, we hide to the end users the difficulty and complexity of selecting and using these new resources even when new Cloud Service Providers are added to the pool. Finally, we validate the architecture concept through cosmological simulation ramses. Thus we give a comparison of 2 well-known Cloud Computing Software: OpenStack and OpenNebula.  Cloud, IaaS, OpenNebula, Multi-Clouds, DIET, OpenStack, RAMSES, cosmology ∗ ENS de Lyon, France,  FirstName.LastName@ens-lyon.fr † ENSI de Bourges, France,  France,  Comparison on OpenStack and OpenNebula performance to improve multi-Cloud architecture on cosmological simulation use case Résumé : Avec l’augmentation du nombre de fournisseurs de service Cloud et la migration des applications depuis les grilles de calcul vers le Cloud, il est ncessaire de pouvoir tirer parti de ces nouvelles ressources. De plus, une large classe des applications de calcul haute performance peuvent s’excuter sur ces ressources sans modifications (ou avec des modifications mineures). Mais utiliser ces ressources vient avec le cot d’tre capable d’intragir avec des nouveaux fournisseurs de ressources. Dans ce papier, nous introduisons la conception d’un nouveau intergiciel hpc qui permet d’utiliser les ressources qui proviennent d’un environement compos de plusieurs Clouds comme des ressources classiques. En utilisant l’intergiciel Diet, nous sommes capable de dployer une plateforme hpc distribue et large chelle qui s’tend sur un large ensemble de ressources aggrges entre plusieurs fournisseurs Cloud. De plus, nous cachons l’utilisateur final la difficult et la complexit de slectionner et d’utiliser ces nouvelles ressources quand un nouveau fournisseur de service Cloud est ajout dans l’ensemble. Finalement, nous validons notre concept d’architecture via une application de simulation cosmologique ramses. Et nous fournissons une comparaison entre 2 intergiciels de Cloud: OpenStack et OpenNebula. Mots-clés : Cloud, IaaS, OpenNebula, Multi-Clouds, DIET, OpenStack, RAMSES, cosmologie Comparaison de performance entre OpenStack et OpenNebula et les architectures multi-Cloud: Application la
Extracting Cybersecurity Related Linked Data from Text

The Web is typically our first source of information about new software vulnerabilities, exploits and cyber-attacks. Information is found in semi-structured vulnerability databases as well as in text from security bulletins, news reports, cyber security blogs and Internet chat rooms. It can be useful to cyber security systems if there is a way to recognize and extract relevant information and represent it as easily shared and integrated semantic data. We describe such an automatic framework that generates and publishes a RDF linked data representation of cyber security concepts and vulnerability descriptions extracted from the National Vulnerability Database and from text sources. A CRF-based system is used to identify cybersecurity-relatedentities, concepts and relations in text, which are then represented using custom ontologies for the cyber security domain and also mapped to objects in the DBpedia knowledge base. The resulting cyber security linked data collection can be used for many purposes, including automating early vulnerability identification, mitigation and prevention efforts.
Figurative language and the semantics-pragmatics distinction

This paper aims at demonstrating that the cognitive mechanisms underlying certain tropes (e.g. metaphor or metonymy) may assume variable degrees of conventionalisation, thereby giving rise to a range of phenomena along either side of the semantics/ pragmatics distinction. Examining specifically cases of metonymy, I propose a pragmatic account of creative, one-off metonymic expressions using the framework of relevance theory; my main argument is that metonymy is a variety of the interpretive use of language. I further look at degrees of conventionalisation that a given metonymy may go through until it becomes fully semanticised, thus bringing about semantic change. My discussion should have farreaching implications for lexical semantics and the relevance-theoretic distinction between descriptive and interpretive use.
The ethical "elephant" in the death penalty "room".

The United States Supreme Court recently ruled that execution by a commonly used protocol of drug administration does not represent cruel or unusual punishment. Various medical journals have editorialized on this drug protocol, the death penalty in general and the role that physicians play. Many physicians, and societies of physicians, express the opinion that it is unethical for doctors to participate in executions. This Target Article explores the harm that occurs to murder victims' relatives when an execution is delayed or indefinitely postponed. By using established principles in psychiatry and the science of the brain, it is shown that victims' relatives can suffer brain damage when justice is not done. Conversely, adequate justice can reverse some of those changes in the brain. Thus, physician opposition to capital punishment may be contributing to significant harm. In this context, the ethics of physician involvement in lethal injection is complex.
Evaluating the FIWARE Platform

This paper describes the result of a thorough analysis and evaluation of the so-called FIWARE platform from a smart application development point of view. FIWARE is the result of a series of wellfunded EU projects that is currently intensively promoted throughout public agencies in Europe and world-wide. The goal was to figure out how services provided by FIWARE facilitate the development of smart applications. It was conducted first by an analysis of the central components that make up the service stack, followed by the implementation of a pilot project that aimed on using as many of these services as possible.
Overview , Strengths , and Limitations of Systematic Reviews and Meta-Analyses

The support of medical decisions comes from several sources. These include individual physician experience, pathophysiological constructs, pivotal clinical trials, qualitative reviews of the literature, and, increasingly, meta-analyses. Historically, the first of these four sources of knowledge largely informed medical and dental decision makers. Meta-analysis came on the scene around the 1970s and has received much attention. What is meta-analysis? It is the process of combining the quantitative results of separate (but similar) studies by means of formal statistical methods. Statistically, the purpose is to increase the precision with which the treatment effect of an intervention can be estimated. Stated in another way, one can say that meta-analysis combines the results of several studies with the purpose of addressing a set of related research hypotheses. The underlying studies can come in the form of published literature, raw data from individual clinical studies, or summary statistics in reports or abstracts. More broadly, a meta-analysis arises from a systematic review. There are three major components to a systematic review and meta-analysis. The systematic review starts with the formulation of the research question and hypotheses. Clinical or substantive insight about the particular domain of research often identifies not only the unmet investigative needs, but helps prepare for the systematic review by defining the necessary initial parameters. These include the hypotheses, endpoints, important covariates, and exposures or treatments of interest. Like any basic or clinical research endeavor, a prospectively defined and clear study plan enhances the expected utility and applicability of the final results for ultimately influencing practice or policy. After this foundational preparation, the second component, a systematic review, commences. The systematic review proceeds with an explicit and reproducible protocol to locate and evaluate the available data. The collection, abstraction, and compilation of the data follow a more rigorous and prospectively defined objective process. The definitions, structure, and methodologies of the underlying studies must be critically appraised. Hence, both “the content” and “the infrastructure” of the underlying data are analyzed, evaluated, and systematically recorded. Unlike an informal review of the literature, this systematic disciplined approach is intended to reduce the potential for subjectivity or bias in the subsequent findings. Typically, a literature search of an online database is the starting point for gathering the data. The most common sources are MEDLINE (United States Library of Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses