nils-herrmann/clean_scirepeval huggingface dataset:
Validity and reliability of the Cohen 10-item Perceived Stress Scale in patients with chronic headache: Persian version.

The Cohen Perceived Stress Scale is being used widely in various countries. The present study evaluated the validity and reliability of the Cohen 10-item Perceived Stress Scale (PSS-10) in assessing tension headache, migraine, and stress-related diseases in Iran.



This study is a methodological and cross-sectional descriptive investigation of 100 patients with chronic headache admitted to the pain clinic of Baqiyatallah Educational and Therapeutic Center. Convenience sampling was used for subject selection. PSS psychometric properties were evaluated in two stages. First, the standard scale was translated. Then, the face validity, content, and construct of the translated version were determined.



The average age of participants was 38 years with a standard deviation (SD) of 13.2. As for stress levels, 12% were within the normal range, 36% had an intermediate level, and 52% had a high level of stress. The face validity and scale content were remarkable, and the KMO coefficient was 0.82. Bartlett's test yielded 0.327 which was statistically significant (p<0.0001) representing the quality of the sample. In factor analysis of the scale, the two elements of "coping" and "distress" were determined. A Cronbach's Alpha coefficient of 0.72 was obtained. This confirmed the remarkable internal consistency and stability of the scale through repeated measure tests (0.93).



The Persian PSS-10 has good internal consistency and reliability. The availability of a validated Persian PSS-10 would indicate a link between stress and chronic headache.
The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing

Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion, we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied in NLP research in a statistically sound manner1.
Understanding intention to continuously share information on weblogs

Weblogs, or blogs, have been a significant new development in recent years. Many businesses have begun using blogs to stimulate discussions, garner ideas, and provide further visibility. Thus, blog usage as well as individual motives behind continued usage is an important area of research. This research aims to examine behavioral motivations underlying individual intention to keep using blogs.  Based on social cognitive theory and related technology adoption literature, this study considered knowledge self-efficacy, subjective norms, feedback, and personal outcome expectations as the determinants of continuing to share information on blogs. The proposed model was empirically evaluated using randomized survey data collected from 155 users of a popular web site in Taiwan (WRETCH blog).  The results revealed that although both self-efficacy and personal outcome expectations affected directly the intention of sharing information on blogs, personal outcome expectations had stronger impacts on behavioral intention. In addition, social persuasion has no direct but indirect impact via self-efficacy and personal outcome expectations on the intention of sharing information.  This study contributes to a theoretical understanding of the factors that promote the usage of weblogs. For web log service providers, the results help them better develop tools to enhance people’s intention to share information.  The paper verifies the effect of knowledge self-efficacy, personal outcome expectations, and social persuasion on the behavior of blog users. Furthermore, it verifies the determinants of knowledge self-efficacy.
Specific reading disability (dyslexia): what have we learned in the past four decades?

We summarize some of the most important findings from research evaluating the hypothesized causes of specific reading disability ('dyslexia') over the past four decades. After outlining components of reading ability, we discuss manifest causes of reading difficulties, in terms of deficiencies in component reading skills that might lead to such difficulties. The evidence suggests that inadequate facility in word identification due, in most cases, to more basic deficits in alphabetic coding is the basic cause of difficulties in learning to read. We next discuss hypothesized deficiencies in reading-related cognitive abilities as underlying causes of deficiencies in component reading skills. The evidence in these areas suggests that, in most cases, phonological skills deficiencies associated with phonological coding deficits are the probable causes of the disorder rather than visual, semantic, or syntactic deficits, although reading difficulties in some children may be associated with general language deficits. Hypothesized deficits in general learning abilities (e.g., attention, association learning, cross-modal transfer etc.) and low-level sensory deficits have weak validity as causal factors in specific reading disability. These inferences are, by and large, supported by research evaluating the biological foundations of dyslexia. Finally, evidence is presented in support of the idea that many poor readers are impaired because of inadequate instruction or other experiential factors. This does not mean that biological factors are not relevant, because the brain and environment interact to produce the neural networks that support reading acquisition. We conclude with a discussion of the clinical implications of the research findings, focusing on the need for enhanced instruction.
Tabu Search - Part I

This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning, and more recently has demonstrated its value in treating classical problems such as the traveling salesman and graph coloring problems. Nevertheless, the approach is still in its infancy, and a good deal remains to be discovered about its most effective forms of implementation and about the range of problems for which it is best suited. This paper undertakes to present the major ideas and findings to date, and to indicate challenges for future research. Part I of this study indicates the basic principles, ranging from the short-term memory process at the core of the search to the intermediate and long term memory processes for intensifying and diversifying the search. Included are illustrative data structures for implementing the tabu conditions (and associated aspiration criteria) that underlie these processes. Part I concludes with a discussion of probabilistic tabu search and a summary of computational experience for a variety of applications. Part I1 of this study (to appear in a subsequent issue) examines more advanced considerations, applying the basic ideas to special settings and outlining a dynamic move structure to insure finiteness. Part I1 also describes tabu search methods for solving mixed integer programming problems and gives a brief summary of additional practical experience, including the use of tabu search to guide other types of processes, such as those of neural networks. T a b u search is a strategy for solving combinatorial optimization problems whose applications range from graph theory and matroid settings to general pure and mixed integer programming problems. It is an adaptive procedure with the ability to make use of many other methods, such as linear programming algorithms and specialized heuristics, which it directs to overcome the limitations of local optimality. Tabu search has its origins in combinatorial procedures applied to nonlinear covering problems in the late 1970~,[~1 and subsequently applied to a diverse collection of problems ranging from scheduling and computer channel balancing to cluster analysis and space planning.'3.4,6.71 Latest research and computational comparisons involving traveling salesman, graph coloring, job shop flow sequencing, integrated circuit design and time tabling problems have likewise disclosed the ability of tabu search to obtain high quality solutions with modest computational effort, generally dominating alternative …
Circle of Emotions in Life: Emotion Mapping in 2Dimensions

Understanding human emotions is a complex problem. Various tools and techniques have been employed by researchers across various domains to understand and infer human emotions. With the advent of smart machines, the problem took a different angle to try and machines themselves make to understand the emotions of its user (s). Human emotions had to be labeled and then were organized with inverse emotions being placed opposite to each other. Various continuous as well as discrete emotion-mapping models were proposed to identify and quantify human emotions. This paper is the result of studying around fifteen such models and comparing them with each other to come to a more concrete viewpoint towards understand human emotions.
Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.
Accurate and efficient pulse measurement from facial videos on smartphones

Non-contact measurement of cardiac pulse signals has attracted high interests due to its convenience and cost effectiveness. However, extracting pulse signals on mobile handheld devices (e.g. smartphones) based on face videos captured by mobile cameras usually suffers from low measurement accuracy due to misalignment errors in face tracking and inevitable illumination changes in a mobile scenario, and low efficiency due to a handheld's limited computing power. We propose two techniques to address these limitations: 1) an accurate and efficient face tracking method based on an Active Shape Model (ASM) and the LDB (Local Difference Binary) feature description; 2) an adaptive temporal filtering method which can detect, and in turn denoise, sharp intensity changes in the source trace. Experimental results demonstrate that the proposed solution can achieve a speedup of 6.2X and is robust to noises in common mobile scenarios.
Fitting Parameterized Three-Dimensional Models to Images

Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The LevenbergMarquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects. This paper has been published in  (May 1991), pp. 441–450.
Gut feelings: the emerging biology of gut–brain communication

The concept that the gut and the brain are closely connected, and that this interaction plays an important part not only in gastrointestinal function but also in certain feeling states and in intuitive decision making, is deeply rooted in our language. Recent neurobiological insights into this gut–brain crosstalk have revealed a complex, bidirectional communication system that not only ensures the proper maintenance of gastrointestinal homeostasis and digestion but is likely to have multiple effects on affect, motivation and higher cognitive functions, including intuitive decision making. Moreover, disturbances of this system have been implicated in a wide range of disorders, including functional and inflammatory gastrointestinal disorders, obesity and eating disorders.
Service Quality ( Servqual ) and its Effect on Customer Satisfaction in Retailing

Service Quality within retail units is pivotal for satisfying customers, retaining them and creating loyalty amongst customers. This research uses SERVQUAL to analyze the gap between perceptions and expectations of the customer, concerning with the service at retail units in the South Indian state of Andhra Pradesh. Customer Satisfaction level is assessed for the services offered at select retail units in the city of Hyderabad. Five dimensions in service quality (servqual), tangibility, reliability, responsiveness, empathy, and assurance (Parasuraman, Zeithaml, &Berry, 1985) have been considered for this empirical research. General purpose of this research to know some factors that impact customer satisfaction. The purpose are (1) to describe applied of service quality (servqual) dimension in retail Business (2) to know service quality (servqual) dimensions that make customers satisfied, and (3) to know service quality (servqual) dimensions that are dominant in influencing customer satisfaction. The research methodology was carried out in a survey crosssectional applied to 369 respondents. The data obtained was analyzed by using reliability method, correlation and regression. Result of research showed that services offered by retail units have positive impact and are significant in building customer satisfaction. Findings of this empirical research reiterate the point of view that Service Quality dimensions are crucial for customer satisfaction in retailing – a burgeoning sector with high growth potential and opportunities in fast growing economies like India’s.
Modeling and control of a hexacopter with a rotating seesaw

This paper presents a novel multicomputer type with a unique structure. This structure allows decoupling angular motion from translational motion along one of the body axes, and by that, five of the body degrees of freedom (DoF) can be controlled independently. The proposed aerial vehicle is a hexacopter with six propellers, such that four propellers are attached to the vehicle body and the other two attached to a seesaw. The seesaw is free to rotate around a single axis that is fixed in the body frame. Compared to a standard hexacopter, this unique design improves the maneuverability of the aerial vehicle, without the price of additional actuators. As opposed to tilting-rotor aerial-vehicle, in the proposed design all actuators generate lift. The paper describes the dynamical model of the novel vehicle and suggests a nonlinear controller to track desired trajectories along five of its DoF. The performances of the design are demonstrated numerically.
High Performance Methods for Linked Open Data Connectivity Analytics

The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or “slices” (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.
Genre classification for million song dataset using confidence-based classifiers combination

We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.
Predicting and analyzing secondary education placement-test scores: A data mining approach

Understanding the factors that lead to success (or failure) of students at placement tests is an interesting and challenging problem. Since the centralized placement tests and future academic achievements are considered to be related concepts, analysis of the success factors behind placement tests may help understand and potentially improve academic achievement. In this study using a large and feature rich dataset from Secondary Education Transition System in Turkey we developed models to predict secondary education placement test results, and using sensitivity analysis on those prediction models we identified the most important predictors. The results showed that C5 decision tree algorithm is the best predictor with 95% accuracy on hold-out sample, followed by support vector machines (with an accuracy of 91%) and artificial neural networks (with an accuracy of 89%). Logistic regression models came out to be the least accurate of the four with and overall accuracy of 82%. The sensitivity analysis revealed that previous test experience, whether a student has a scholarship, student’s number of siblings, previous years’ grade point average are among the most important predictors of the placement test scores.
Non-sexually related acute genital ulcers in 13 pubertal girls: a clinical and microbiological study.

To describe the clinical and microbiological features of acute genital ulcers (AGU), which have been reported in virgin adolescents, predominantly in girls.



Descriptive study. We collected data on the clinical features, sexual history, blood cell count, biochemistry, microbiological workup, and 1-year follow-up.



Departments of dermatology of 3 university hospitals in Paris.  Thirteen immunocompetent female patients with a first flare of non-sexually transmitted AGU.



Clinical and microbiological data, using a standardized form.



Mean age was 16.6 years (range, 11-19 years). Eleven patients denied previous sexual contact. A fever or flulike symptoms preceded AGU in 10 of the 13 patients (77%), with a mean delay of 3.8 days before the AGU onset (range, 0-10 days). The genital ulcers were bilateral in 10 patients. The final diagnosis was Epstein-Barr virus primary infection in 4 patients (31%) and Behçet disease in 1 patient (8%). No other infectious agents were detected in this series.



We recommend serologic testing for Epstein-Barr virus with IgM antibodies to viral capsid antigens in non-sexually related AGU in immunocompetent patients. Further microbiological studies are required to identify other causative agents.
Bluetooth Remote Home Automation System Using Android Application

This paper presents the overall design of Home Automation System (HAS) with low cost and wireless remote control. This system is designed to assist and provide support in order to fulfill the needs of elderly and disabled in home. Also, the smart home concept in the system improves the standard living at home. The main control system implements wireless Bluetooth technology to provide remote access from PC/laptop or smart phone. The design remains the existing electrical switches and provides more safety control on the switches with low voltage activating method. The switches status is synchronized in all the control system whereby every user interface indicates the real time existing switches status. The system intended to control electrical appliances and devices in house with relatively low cost design, user-friendly interface and ease of installation.