nils-herrmann/clean_scirepeval huggingface dataset:
Privacy Concerns Versus Desire for Interpersonal Awareness in Driving the Use of Self-Disclosure Technologies: The Case of Instant Messaging in Two Cultures

This version of the referenced work is the post-print version of the article—it is NOT the final published version nor the corrected proofs. If you would like to receive the final published version please send a request to Paul.Lowry.PhD@gmail.com, and I will be happy to send you the latest version. Moreover, you can contact the publisher's website and order the final version there, as well. (2011). " Privacy concerns versus desire for interpersonal awareness in driving the use of self-disclosure technologies: The case of instant messaging in two cultures, " If you have any questions and/or would like copies of other articles I've published, please email me at Paul.Lowry. Alternatively, I have an online system that you can use to request any of my published or forthcoming articles. To go to this system, click on the following link: interests include human-computer interaction and cross-cultural issues in information systems.  We appreciate the support our data collections have received from the Kong University in China. We also acknowledge the participants who gave us useful feedback at the HCI International 2007 workshop and those who gave us feedback on the paper at a presentation at the University College Dublin, Spring 2008. Finally, we appreciate the insightful feedback and reviews and contributions to the literature from ABSTRACT Social computing technologies typically have multiple features that allow users to reveal their personal information to other users. Such self-disclosure (SD) behavior is generally considered positive and beneficial in interpersonal communication and relationships. Using a newly proposed model based on social exchange theory, this paper investigates and empirically validates the relationships between SD technology use and culture. In particular, we explore the effects of culture on information privacy concerns and the desire for online interpersonal awareness, which influence attitudes toward, intention to use, and actual use of SD technologies. Our model was tested using arguably the strongest social computing technology for online self-disclosure—instant messaging (IM)—with users from China and the US. Our findings reveal that cross-cultural dimensions are significant predictors of information privacy concerns and desire for online awareness, which were, in turn, found to be predictors of attitude toward, intention to use, and actual use of IM. Overall, our proposed model is applicable to both cultures. Our findings help enhance the theoretical understanding of the effects of culture and privacy concerns on SD technologies, and provide practical suggestions for developers of SD technologies, such as …
Blood Flow Restriction Training: Implementation into Clinical Practice

To improve muscular strength and hypertrophy the American College of Sports Medicine recommends moderate to high load resistance training. However, use of moderate to high loads are often not feasible in clinical populations. Therefore, the emergence of low load (LL) blood flow restriction (BFR) training as a rehabilitation tool for clinical populations is becoming popular. Although the majority of research on LL-BFR training has examined healthy populations, clinical applications are emerging. Overall, it appears BFR training is a safe and effective tool for rehabilitation. However, additional research is needed prior to widespread application.
Design of integrated planar Marchand balun using physical transformer model

A simple and effective technique for designing integrated planar Marchand balun is presented in this paper. The approach uses the physical transformer model to replace the lossy coupled transmission lines in a conventional Marchand balun design. As a demonstration and validation of the design approach, a Marchand balun using silicon-based integrated passive device (IPD) technology is carried out at a center frequency of 2.45 GHz. The measured results show low insertion loss and high balance property over a wide bandwidth for the implemented Marchand balun. Comparison among modeled, EM simulated and measured results shows good agreement.
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier

The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantages, the most important of all being monetization. Understanding of the sentiments of human masses towards different entities and products enables better services for contextual advertisements, recommendation systems and analysis of market trends. The focus of our project is sentiment focussed web crawling framework to facilitate the quick discovery of sentimental contents of movie reviews and hotel reviews and analysis of the same. We use statistical methods to capture elements of subjective style and the sentence polarity. The paper elaborately discusses two supervised machine learning algorithms: K-Nearest Neighbour(KNN) and Naïve Bayes‘ and compares their overall accuracy, precisions as well as recall values. It was seen that in case of movie reviews Naïve Bayes‘ gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.
Formative Research : A Methodology for Creating and Improving Design Theories

In Chapter 1, Reigeluth described design theory as being different from descriptive theory in that it offers means to achieve goals. For an applied field like education, design theory is more useful and more easily applied than its descriptive counterpart, learning theory. But none of the 22 theories described in this book has yet been developed to a state of perfection; at very least they can all benefit from more detailed guidance for applying their methods to diverse situations. And more theories are sorely needed to provide guidance for additional kinds of learning and human development and for different kinds of situations, including the use of new information technologies as tools. This leads us to the important question, " What research methods are most helpful for creating and improving instructional design theories? " In this chapter, we offer a detailed description of one research methodology that holds much promise for generating the kind of knowledge that we believe is most useful to educators—a methodology that several theorists in this book have intuitively used to develop their theories. We refer to this methodology as "formative research"—a kind of developmental research or action research that is intended to improve design theory for designing instructional practices or processes. Reigeluth (1989) and Romiszowski (1988) have recommended this approach to expand the knowledge base in instructional-design theory. Newman (1990) has suggested something similar for research on the organizational impact of computers in schools. And Greeno, Collins and Resnick (1996) have identified several groups of researchers who are conducting something similar that they call " design experiments, " in which " researchers and practitioners, particularly teachers, collaborate in the design, implementation, and analysis of changes in practice. " (p. 15) Formative research has also been used for generating knowledge in as broad an area as systemic change in education We intend for this chapter to help guide educational researchers who are developing and refining instructional-design theories. Most researchers have not had the opportunity to learn formal research methodologies for developing design theories. Doctoral programs in universities tend to emphasize quantitative and qualitative research methodologies for creating descriptive knowledge of education. However, design theories are guidelines for practice, which tell us "how to do" education, not "what is." We have found that traditional quantitative research methods (e.g., experiments, surveys, correlational analyses) are not particularly useful for improving instructional-design theory— especially in the early stages of development. Instead, …
Lightning performance of transmission lines partially protected by surge arresters considering typical Brazilian conditions

This paper assess the efficiency of installing surge arresters only in one or two phases in terms of the lightning performance of TL. Taking as reference a typical 138-kV Brazilian transmission line, simulations are developed in ATP, considering a rigorous representation of the frequency-dependent behavior of tower-footing grounding. Preliminary results shown that considering moderate values of soil resistivity, a good design of grounding system may be sufficient to achieve the desired lightning performance of TL. In case of high-resistivity soils, the use of surge arresters may be necessary, and using surge arresters only in one or two phases does not guarantee the protection of the phase without SA, but decrease the probability of insulation breakdown occurrence in the tower. The methodology presented in this paper can be used to determine the optimal distribution of surge arresters along the line in order to achieve the desired lightning performance.
Effective injury forecasting in soccer with GPS training data and machine learning

Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer

In this paper, we present YAMAMA, a multi-dialect Arabic morphological analyzer and disambiguator. Our system is almost five times faster than the state-of-the-art MADAMIRA system with a slightly lower quality. In addition to speed, YAMAMA outputs a rich representation which allows for a wider spectrum of use. In this regard, YAMAMA transcends other systems, such as FARASA, which is faster but provides specific outputs catering to specific applications.
The Recovery of Repeated-Sprint Exercise Is Associated with PCr Resynthesis, while Muscle pH and EMG Amplitude Remain Depressed

The physiological equivalents of power output maintenance and recovery during repeated-sprint exercise (RSE) remain to be fully elucidated. In an attempt to improve our understanding of the determinants of RSE performance we therefore aimed to determine its recovery following exhaustive exercise (which affected intramuscular and neural factors) concomitantly with those of intramuscular concentrations of adenosine triphosphate [ATP], phosphocreatine [PCr] and pH values and electromyography (EMG) activity (a proxy for net motor unit activity) changes. Eight young men performed 10, 6-s all-out sprints on a cycle ergometer, interspersed with 30 s of recovery, followed, after 6 min of passive recovery, by five 6-s sprints, again interspersed by 30 s of passive recovery. Biopsies of the vastus lateralis were obtained at rest, immediately after the first 10 sprints and after 6 min of recovery. EMG activity of the vastus lateralis was obtained from surface electrodes throughout exercise. Total work (TW), [ATP], [PCr], pH and EMG amplitude decreased significantly throughout the first ten sprints (P<0.05). After 6 min of recovery, TW during sprint 11 recovered to 86.3±7.7% of sprint 1. ATP and PCr were resynthesized to 92.6±6.0% and 85.3±10.3% of the resting value, respectively, but muscle pH and EMG amplitude remained depressed. PCr resynthesis was correlated with TW done in sprint 11 (r = 0.79, P<0.05) and TW done during sprints 11 to 15 (r = 0.67, P<0.05). There was a ∼2-fold greater decrease in the TW/EMG ratio in the last five sprints (sprint 11 to 15) than in the first five sprints (sprint 1 to 5) resulting in a disproportionate decrease in mechanical power (i.e., TW) in relation to EMG. Thus, we conclude that the inability to produce power output during repeated sprints is mostly mediated by intramuscular fatigue signals probably related with the control of PCr metabolism.
Models of Metaphor in NLP

Automatic processing of metaphor can be clearly divided into two subtasks: metaphor recognition (distinguishing between literal and metaphorical language in a text) and metaphor interpretation (identifying the intended literal meaning of a metaphorical expression). Both of them have been repeatedly addressed in NLP. This paper is the first comprehensive and systematic review of the existing computational models of metaphor, the issues of metaphor annotation in corpora and the available resources.
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension

We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique questionanswer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie one from Wikipedia and the other from IMDb written by two different authors. We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize answers from the other version. This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version. Further, since the two versions have different levels of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating external background knowledge. Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences. Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset (Rajpurkar et al., 2016b), even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset). This opens up several interesting research avenues wherein DuoRC could complement other RC datasets to explore novel neural approaches for studying language understanding.
Detecting credit card fraud by ANN and logistic regression

With the developments in information technology and improvements in communication channels, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS terminals through Internet or mail orders. As a result, fraud detection is the essential tool and probably the best way to stop such fraud types. In this study, classification models based on Artificial Neural Networks (ANN) and Logistic Regression (LR) are developed and applied on credit card fraud detection problem. This study is one of the firsts to compare the performance of ANN and LR methods in credit card fraud detection with a real data set.
Interacting Conceptual Spaces I : Grammatical Composition of Concepts

The categorical compositional approach to meaning has been successfully applied in natural language processing, outperforming other models in mainstream empirical language processing tasks. We show how this approach can be generalized to conceptual space models of cognition. In order to do this, first we introduce the category of convex relations as a new setting for categorical compositional semantics, emphasizing the convex structure important to conceptual space applications. We then show how to construct conceptual spaces for various types such as nouns, adjectives and verbs. Finally we show by means of examples how concepts can be systematically combined to establish the meanings of composite phrases from the meanings of their constituent parts. This provides the mathematical underpinnings of a new compositional approach to cognition.
Semantic Dependency Parsing via Book Embedding

We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm to combine pages into a book. Experiments demonstrate the effectiveness of the book embedding framework across a wide range of conditions. Our parser obtains comparable results with a state-of-the-art transition-based parser.
Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
How consumers perceive brands when social media influencers disclose paid partnerships

An over-commercialized society has made consumers prefer the opinions of influential people in online settings. Companies have a harder time reaching consumers and therefore turn to social media influencers (SMIs) for exposure. With the growth of influencer marketing, regulations and guidelines in Sweden have been put in place to ensure consumers integrity. SMIs and companies are required to disclose paid partnerships. However, many companies have the perception that truthful marketing can affect consumers brand perception negatively. Research based on consumer brand perception and disclosed paid partnerships between SMIs and companies have not previously been explored. Therefore, the purpose of this thesis is to explore how consumers perceive brands when SMIs disclose paid partnerships. In order to gather consumers ́ brand perception when SMIs and companies collaborate, a qualitative study using focus groups was conducted, where the participants thoughts and opinions have been studied. The findings of this thesis indicate that the communication and characteristics of SMIs influences consumers brand perception. The factors analyzed were: influence of SMIs, content, trust and truthfulness. The factors were important for how consumers perceive the SMI and brand of companies. The most important implication of this thesis is that the study can be of use for companies that seek to collaborate with SMIs when the aim is to reach target groups and enhance brand perception.
Risk factors for suicide in later life

Suicide rates are higher in later life than in any other age group. The design of effective suicide prevention strategies hinges on the identification of specific, quantifiable risk factors. Methodological challenges include the lack of systematically applied terminology in suicide and risk factor research, the low base rate of suicide, and its complex, multidetermined nature. Although variables in mental, physical, and social domains have been correlated with completed suicide in older adults, controlled studies are necessary to test hypothesized risk factors. Prospective cohort and retrospective case control studies indicate that affective disorder is a powerful independent risk factor for suicide in elders. Other mental illnesses play less of a role. Physical illness and functional impairment increase risk, but their influence appears to be mediated by depression. Social ties and their disruption are significantly and independently associated with risk for suicide in later life, relationships between which may be moderated by a rigid, anxious, and obsessional personality style. Affective illness is a highly potent risk factor for suicide in later life with clear implications for the design of prevention strategies. Additional research is needed to define more precisely the interactions between emotional, physical, and social factors that determine risk for suicide in the older adult.
Flipped Classroom with Problem Based Activities: Exploring Self-regulated Learning in a Programming Language Course

This study intended to explore the development of self-regulation in a flipped classroom setting. Problem based learning activities were carried out in flipped classrooms to promote self-regulation. A total of 30 undergraduate students from Mechatronic department participated in the study. Self-regulation skills were discussed through students’ and the instructor’s experiences including their opinions and behaviours. Qualitative data was collected with an observation form, discussion messages and interviews with selected participants. As a result, in terms of self-regulated learning, the goal setting and planning, task strategies and help seeking skills of the students were high in the face to face learning designed with problem based activities through flipped classroom model, their goal setting and planning, task strategies and help seeking skills were appeared moderately. In the home sessions, environment structuring, goal setting and planning skills were developed in high level while task strategies, help seeking, time management, monitoring, selfefficacy and self-evaluation skills were moderate and monitoring skills was lower. Consequently, it is hoped that the study may provide some suggestions for using problem based activities in flipped learning.
Tensor Train decomposition on TensorFlow (T3F)

Tensor Train decomposition is used across many branches of machine learning, but until now it lacked an implementation with GPU support, batch processing, automatic differentiation, and versatile functionality for Riemannian optimization framework, which takes in account the underlying manifold structure in order to construct efficient optimization methods. In this work, we propose a library that aims to fix it and makes machine learning papers that rely on Tensor Train decomposition easier to implement. The library includes 92% test coverage, examples, and API reference documentation.