nils-herrmann/clean_scirepeval huggingface dataset:
Killing the Gatekeeper: Introducing a Continuous Integration System

This is the story of how the Launchpad (https://launchpad.net) development team switched to a continuous integration system to increase several flows in their development process: flow of changes on trunk; flow of changes requiring database schema upgrade; flow of deployed changes to end users. The switch to a buildbot-based system meant violating a very old company taboo: a trunk that doesn’t pass its test suite. The risk of a broken trunk was offset by allowing each developer to run the full test suite in the Amazon EC2 cloud.
Probabilistic models for answer-ranking in multilingual question-answering

This article presents two probabilistic models for answering ranking in the multilingual question-answering (QA) task, which finds exact answers to a natural language question written in different languages. Although some probabilistic methods have been utilized in traditional monolingual answer-ranking, limited prior research has been conducted for answer-ranking in multilingual question-answering with formal methods. This article first describes a probabilistic model that predicts the probabilities of correctness for individual answers in an independent way. It then proposes a novel probabilistic method to jointly predict the correctness of answers by considering both the correctness of individual answers as well as their correlations. As far as we know, this is the first probabilistic framework that proposes to model the correctness and correlation of answer candidates in multilingual question-answering and provide a novel approach to design a flexible and extensible system architecture for answer selection in multilingual QA. An extensive set of experiments were conducted to show the effectiveness of the proposed probabilistic methods in English-to-Chinese and English-to-Japanese cross-lingual QA, as well as English, Chinese, and Japanese monolingual QA using TREC and NTCIR questions.
Self-Capacitance of High-Voltage Transformers

The calculation of a transformer's parasitics, such as its self capacitance, is fundamental for predicting the frequency behavior of the device, reducing this capacitance value and moreover for more advanced aims of capacitance integration and cancellation. This paper presents a comprehensive procedure for calculating all contributions to the self-capacitance of high-voltage transformers and provides a detailed analysis of the problem, based on a physical approach. The advantages of the analytical formulation of the problem rather than a finite element method analysis are discussed. The approach and formulas presented in this paper can also be used for other wound components rather than just step-up transformers. Finally, analytical and experimental results are presented for three different high-voltage transformer architectures.
Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and environmental impact. Therefore, cloud providers must optimize the use of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This article surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, and pointing out areas that need further research.
Accelerated aging: prediction of chemical stability of pharmaceuticals.

Methods of rapidly and accurately assessing the chemical stability of pharmaceutical dosage forms are reviewed with respect to the major degradation mechanisms generally observed in pharmaceutical development. Methods are discussed, with the appropriate caveats, for accelerated aging of liquid and solid dosage forms, including small and large molecule active pharmaceutical ingredients. In particular, this review covers general thermal methods, as well as accelerated aging methods appropriate to oxidation, hydrolysis, reaction with reactive excipient impurities, photolysis and protein denaturation.
Media Multitasking Is Associated with Symptoms of Depression and Social Anxiety

We investigated whether multitasking with media was a unique predictor of depression and social anxiety symptoms. Participants (N=318) completed measures of their media use, personality characteristics, depression, and social anxiety. Regression analyses revealed that increased media multitasking was associated with higher depression and social anxiety symptoms, even after controlling for overall media use and the personality traits of neuroticism and extraversion. The unique association between media multitasking and these measures of psychosocial dysfunction suggests that the growing trend of multitasking with media may represent a unique risk factor for mental health problems related to mood and anxiety. Further, the results strongly suggest that future research investigating the impact of media use on mental health needs to consider the role that multitasking with media plays in the relationship.
Impact of facebook addiction on narcissistic behavior and self-esteem among students.

To investigate the relationship between Facebook addiction, narcissism and self-esteem and to see if gender played any role in this equation.



The correlational study was conducted from February to March 2013 at the Department of Psychology, University of Sargodha, Punjab, Pakistan. Using convenient sampling, two equal groups of male and female students were enrolled from different departments of the university. Bergen Facebook Addiction Scale, Hypersensitive Narcissism Scale and Rosenberg's Self-esteem Scale were used for evaluation. SPSS 17 was used for statistical analysis.



Of the 200 subjects in the study, 100(50%) each were males and females. Facebook addiction was positively correlated with narcissism(r=0.20; p<0.05) and negatively with self-esteem(r=-0.18; p<0.05). Relationship between narcissism and self-esteem was non-significant(r=0.05; p>0.05). Facebook addiction was a significant predictor of narcissistic behaviour (b=0.202; p<0.001) and low self-esteem (b=-0.18; p<0.001). There were no significant gender differences in the three variables (p>0.05 each).



Facebook addiction was a significant predictor of narcissistic behaviour and low levels of self-esteem among students.
Why Heuristics Work.

The adaptive toolbox is a Darwinian-inspired theory that conceives of the mind as a modular system that is composed of heuristics, their building blocks, and evolved capacities. The study of the adaptive toolbox is descriptive and analyzes the selection and structure of heuristics in social and physical environments. The study of ecological rationality is prescriptive and identifies the structure of environments in which specific heuristics either succeed or fail. Results have been used for designing heuristics and environments to improve professional decision making in the real world.
Prediction of election result by enhanced sentiment analysis on Twitter data using Word Sense Disambiguation

Sentiment analysis is the computational study of opinions, sentiments, evaluations, attitudes, views and emotions expressed in text. It refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Sentiment analysis over Twitter offers people a fast and effective way to measure the public's feelings towards their party and politicians. The primary issues in previous sentiment analysis techniques are classification accuracy, as they incorrectly classify most of the tweets with the biasing towards the training data. In opinion texts, lexical content alone also can be misleading. Therefore, here we adopt a lexicon based sentiment analysis method, which will exploit the sense definitions, as semantic indicators of sentiment. Here we propose a novel approach for accurate sentiment classification of twitter messages using lexical resources SentiWordNet and WordNet along with Word Sense Disambiguation. Thus we applied the SentiWordNet lexical resource and Word Sense Disambiguation for finding political sentiment from real time tweets. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy.
Competition for consciousness among visual events: the psychophysics of reentrant visual processes.

Advances in neuroscience implicate reentrant signaling as the predominant form of communication between brain areas. This principle was used in a series of masking experiments that defy explanation by feed-forward theories. The masking occurs when a brief display of target plus mask is continued with the mask alone. Two masking processes were found: an early process affected by physical factors such as adapting luminance and a later process affected by attentional factors such as set size. This later process is called masking by object substitution, because it occurs whenever there is a mismatch between the reentrant visual representation and the ongoing lower level activity. Iterative reentrant processing was formalized in a computational model that provides an excellent fit to the data. The model provides a more comprehensive account of all forms of visual masking than do the long-held feed-forward views based on inhibitory contour interactions.
An Introduction to Model Selection.

The intention is to explain the ideas that generate frequentist methodology for model selection, for example the Akaike information criterion, bootstrap criteria, and cross-validation criteria. Bayesian methods, including the Bayesian information criterion, are also mentioned in the context of the framework outlined in the paper. The ideas are illustrated using an example in which observations are available for the entire population of interest. This enables us to examine and to measure effects that are usually invisible, because in practical applications only a sample from the population is observed. The problem of selection bias, a hazard of which one needs to be aware in the context of model selection, is also discussed.
ArguMed - A Template-Based Argument Mediation System for Lawyers

This paper introduces the ArguMed-system. It is an example of a system for computer-mediated defeasible argumentation, a new trend in the field of defeasible argumentation. In this research, computer systems are developed that mediate the process of argumentation by one or more users. Argument-mediation systems should be contrasted with systems for automated reasoning: the latter perform reasoning tasks for users, while the former play the more passive role of a mediator. E.g., mediation systems keep track of the arguments raised and of the justification status of statements. The argumentation theory of the ArguMed-system is an adaptation of Verheij's CumulA-model, a procedural model of argumentation with arguments and counterarguments. In the CumulA-model, the defeat of arguments is determined by the structure of arguments and the attack relation between arguments. It is completely independent of the underlying language. The process-model is free, in the sense that it allows not only inference (i.e., 'forward' argumentation, drawing conclusions form premises), but also justification (i.e., 'backward' argumentation, adducing reasons for issues). The ArguMed-system has been designed in an attempt to enhance the familiarity of the interface and the transparency of the underlying argumentation theory of its precursor, the Argue!-system. The ArguMed-system's user interface is template-based, as is currently common in window-style user interfaces. The user gradually constructs arguments, by fill ing in templates that correspond to common argument patterns. An innovation of the ArguMed-system is that it uses dedicated templates for different types of argument moves. Whereas existing mediation systems are issue-based (in the style of Rittel's well-known Issue-Based Information System), the ArguMed-system allows free argumentation, as in the CumulA-model. In contrast with the CumulA-model, which has a very general notion of defeat, defeat in the ArguMed-system is only of Pollock's undercutter-type. The system allows three types of argument moves, viz. making a statement, adding a reason and its conclusion, and providing an (undercutter-type) exception blocking the connection between a reason and a conclusion. To put the ArguMed-system in context, it is compared with selected existing systems for argument mediation. The differences between the underlying argumentation theories and user interfaces are striking, which is suggested to be a symptom of the early stages of development of argument mediation systems. Given the lack of system evaluation by users in the field, the paper concludes with a discussion of the relevance of current research on computer-mediated defeasible argumentation. It is claimed that the shift of argument mediation systems from theoretical to practical tools is feasible, but can as yet not be made by system developers alone: a strong input from the research community is required.
A Real Little Game: The Pinocchio Effect in Pervasive Play

Mobile digital technologies and networks have fueled a recent proliferation of opportunities for pervasive play in everyday spaces. In this paper, I examine how players negotiate the boundary between these pervasive games and real life. I trace the emergence of what I call “the Pinocchio effect” – the desire for a game to be transformed into real life, or conversely, for everyday life to be transformed into a "real little game.” Focusing on two examples of pervasive play – the 2001 immersive game known as the Beast, and the Go Game, an ongoing urban superhero game — I argue that gamers maximize their play experience by performing belief, rather than actually believing, in the permeability of the game-reality boundary.
Generating Natural Language Inference Chains

The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one. However, in most NLP applications only single source sentences instead of sentence pairs are available. Hence, we propose a new task that measures how well a model can generate an entailed sentence from a source sentence. We take entailment-pairs of the Stanford Natural Language Inference corpus and train an LSTM with attention. On a manually annotated test set we found that 82% of generated sentences are correct, an improvement of 10.3% over an LSTM baseline. A qualitative analysis shows that this model is not only capable of shortening input sentences, but also inferring new statements via paraphrasing and phrase entailment. We then apply this model recursively to input-output pairs, thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences. Finally, by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence.
Learning from Unscripted Deictic Gesture and Language for Human-Robot Interactions

As robots become more ubiquitous, it is increasingly important for untrained users to be able to interact with them intuitively. In this work, we investigate how people refer to objects in the world during relatively unstructured communication with robots. We collect a corpus of deictic interactions from users describing objects, which we use to train language and gesture models that allow our robot to determine what objects are being indicated. We introduce a temporal extension to stateof-the-art hierarchical matching pursuit features to support gesture understanding, and demonstrate that combining multiple communication modalities more effectively capture user intent than relying on a single type of input. Finally, we present initial interactions with a robot that uses the learned models to follow commands.
Affective Neuroscience : Past , Present , and Future

The advent of modern neuroscience methodologies has led to an explosion over the past 15 years in our understanding of brain regions involved in the recognition, generation, experience, and regulation of emotion. In this brief review, we suggest some early historical milestones in the development of what has become known as affective neuroscience, and then consider the extent to which recent findings have advanced understanding in the field (see Table 1). We begin with the pioneering work of James and Lange, go on discuss the early functional neuroanatomical models of Cannon and Bard, then outline the hypothalamic stimulation studies conducted by Hess, and finally, consider Papez and MacLean’s development of increasingly sophisticated neuroanatomical frameworks. We evaluate how well these key contributions fit with the current state of knowledge about which brain regions are seemingly central to the representation and processing of affect, including the amygdala, prefrontal cortex, thalamus, insular, anterior cingulate cortex, and midbrain. We next very briefly overview contemporary theoretical frameworks of how these regions might interact. Finally, we offer some thoughts about possible future directions for affective
Word Segmentation for Urdu OCR System

This paper presents a technique for word segmentation for the Urdu OCR system. Word segmentation or word tokenization is a preliminary task for Urdu language processing. Several techniques are available for word segmentation in other languages. A methodology is proposed for word segmentation in this paper which determines the boundaries of words given a sequence of ligatures, based on collocation of ligatures and words in the corpus. Using this technique, word identification rate of 96.10% is achieved, using trigram probabilities normalized over the number of ligatures and words in the sequence.
Diversity, stability and resilience of the human gut microbiota

Trillions of microbes inhabit the human intestine, forming a complex ecological community that influences normal physiology and susceptibility to disease through its collective metabolic activities and host interactions. Understanding the factors that underlie changes in the composition and function of the gut microbiota will aid in the design of therapies that target it. This goal is formidable. The gut microbiota is immensely diverse, varies between individuals and can fluctuate over time — especially during disease and early development. Viewing the microbiota from an ecological perspective could provide insight into how to promote health by targeting this microbial community in clinical treatments.
SiC MOSFET based single phase active boost rectifier with power factor correction for wireless power transfer applications

Wireless Power Transfer (WPT) technology is a novel research area in the charging technology that bridges the utility and the automotive industries. There are various solutions that are currently being evaluated by several research teams to find the most efficient way to manage the power flow from the grid to the vehicle energy storage system. There are different control parameters that can be utilized to compensate for the change in the impedance due to variable parameters such as battery state-of-charge, coupling factor, and coil misalignment. This paper presents the implementation of an active front-end rectifier on the grid side for power factor control and voltage boost capability for load power regulation. The proposed SiC MOSFET based single phase active front end rectifier with PFC resulted in >97% efficiency at 137mm air-gap and >95% efficiency at 160mm air-gap.
Differentially Private Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models. These tools provide a promising direction in the studies where data availability is limited. One common issue in GANs is that the density of the learned generative distribution could concentrate on the training data points, meaning that they can easily remember training samples due to the high model complexity of deep networks. This becomes a major concern when GANs are applied to private or sensitive data such as patient medical records, and the concentration of distribution may divulge critical patient information. To address this issue, in this paper we propose a differentially private GAN (DPGAN) model, in which we achieve differential privacy in GANs by adding carefully designed noise to gradients during the learning procedure. We provide rigorous proof for the privacy guarantee, as well as comprehensive empirical evidence to support our analysis, where we demonstrate that our method can generate high quality data points at a reasonable privacy level.
Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel

Personalized tag recommendation systems recommend a list of tags to a user when he is about to annotate an item. It exploits the individual preference and the characteristic of the items. Tensor factorization techniques have been applied to many applications, such as tag recommendation. Models based on Tucker Decomposition can achieve good performance but require a lot of computation power. On the other hand, models based on Canonical Decomposition can run in linear time and are more feasible for online recommendation. In this paper, we propose a novel method for personalized tag recommendation, which can be considered as a nonlinear extension of Canonical Decomposition. Different from linear tensor factorization, we exploit Gaussian radial basis function to increase the model’s capacity. The experimental results show that our proposed method outperforms the state-of-the-art methods for tag recommendation on real datasets and perform well even with a small number of features, which verifies that our models can make better use of features.