nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Physiological Responses to a Social Situation in an Immersive Virtual Environment

An experiment was conducted in a Cave-like environment to explore the relationship between physiological responses and breaks in presence and utterances by virtual characters towards the participants. Twenty people explored a virtual environment (VE) that depicted a virtual bar scenario. The experiment was divided into a training and an experimental phase. During the experimental phase breaks in presence (BIPs) in the form of whiteouts of the VE scenario were induced for 2 s at four equally spaced times during the approximately 5 min in the bar scenario. Additionally, five virtual characters addressed remarks to the subjects. Physiological measures including electrocardiagram (ECG) and galvanic skin response (GSR) were recorded throughout the whole experiment. The heart rate, the heart rate variability, and the event-related heart rate changes were calculated from the acquired ECG data. The frequency response of the GSR signal was calculated with a wavelet analysis. The study shows that the heart rate and heart rate variability parameters vary significantly between the training and experimental phase. GSR parameters and event-related heart rate changes show the occurrence of breaks in presence. Event-related heart rate changes also signified the virtual character utterances. There were also differences in response between participants who report more or less socially anxious.
Provable Low-Rank Tensor Recovery

In this paper, we rigorously study tractable models for provably recovering low-rank tensors. Unlike their matrix-based predecessors, current convex approaches for recovering low-rank tensors based on incomplete (tensor completion) and/or grossly corrupted (tensor robust principal analysis) observations still suffer from the lack of theoretical guarantees, although they have been used in various recent applications and have exhibited promising empirical performance. In this work, we attempt to fill this gap. Specifically, we propose a class of convex recovery models (including strongly convex programs) that can be proved to guarantee exact recovery under certain conditions. All parameters in our formulations can be determined beforehand based on the measurement data and thus there is no parameter tuning involved.
What Types of Defects Are Really Discovered in Code Reviews?

Research on code reviews has often focused on defect counts instead of defect types, which offers an imperfect view of code review benefits. In this paper, we classified the defects of nine industrial (C/C++) and 23 student (Java) code reviews, detecting 388 and 371 defects, respectively. First, we discovered that 75 percent of defects found during the review do not affect the visible functionality of the software. Instead, these defects improved software evolvability by making it easier to understand and modify. Second, we created a defect classification consisting of functional and evolvability defects. The evolvability defect classification is based on the defect types found in this study, but, for the functional defects, we studied and compared existing functional defect classifications. The classification can be useful for assigning code review roles, creating checklists, assessing software evolvability, and building software engineering tools. We conclude that, in addition to functional defects, code reviews find many evolvability defects and, thus, offer additional benefits over execution-based quality assurance methods that cannot detect evolvability defects. We suggest that code reviews may be most valuable for software products with long life cycles as the value of discovering evolvability defects in them is greater than for short life cycle systems.
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; ), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
Measuring technology self efficacy: reliability and construct validity of a modified computer self efficacy scale in a clinical rehabilitation setting.

To describe a modification of the computer self efficacy scale for use in clinical settings and to report on the modified scale's reliability and construct validity.



The computer self efficacy scale was modified to make it applicable for clinical settings (for use with older people or people with disabilities using everyday technologies). The modified scale was piloted, then tested with patients in an Australian inpatient rehabilitation setting (n = 88) to determine the internal consistency using Cronbach's alpha coefficient. Construct validity was assessed by correlation of the scale with age and technology use. Factor analysis using principal components analysis was undertaken to identify important constructs within the scale.



The modified computer self efficacy scale demonstrated high internal consistency with a standardised alpha coefficient of 0.94. Two constructs within the scale were apparent; using the technology alone, and using the technology with the support of others. Scores on the scale were correlated with age and frequency of use of some technologies thereby supporting construct validity.



The modified computer self efficacy scale has demonstrated reliability and construct validity for measuring the self efficacy of older people or people with disabilities when using everyday technologies. This tool has the potential to assist clinicians in identifying older patients who may be more open to using new technologies to maintain independence.
Compositional engineering of perovskite materials for high-performance solar cells

Of the many materials and methodologies aimed at producing low-cost, efficient photovoltaic cells, inorganic–organic lead halide perovskite materials appear particularly promising for next-generation solar devices owing to their high power conversion efficiency. The highest efficiencies reported for perovskite solar cells so far have been obtained mainly with methylammonium lead halide materials. Here we combine the promising—owing to its comparatively narrow bandgap—but relatively unstable formamidinium lead iodide (FAPbI3) with methylammonium lead bromide (MAPbBr3) as the light-harvesting unit in a bilayer solar-cell architecture. We investigated phase stability, morphology of the perovskite layer, hysteresis in current–voltage characteristics, and overall performance as a function of chemical composition. Our results show that incorporation of MAPbBr3 into FAPbI3 stabilizes the perovskite phase of FAPbI3 and improves the power conversion efficiency of the solar cell to more than 18 per cent under a standard illumination of 100 milliwatts per square centimetre. These findings further emphasize the versatility and performance potential of inorganic–organic lead halide perovskite materials for photovoltaic applications.
Leveraging big data for grasp planning

We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard υ-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the υ-metric and therefore lead to a better classification performance.
An Elastic Multi-tenant Database Schema for Software as a Service

Software as a Service (SaaS) is an online software delivery model which permits a third party provider offering software services to be used on-demand by tenants over the internet, instead of installing and maintaining them in their premises. Nowadays, more and more companies are offering their web-base business application by adopting this model. Multi-tenancy is the primary characteristic of SaaS, it allows SaaS vendors to run a single instance application which supports multiple tenants on the same hardware and software infrastructure. This application should be highly customizable to meet tenants' expectations and business requirements. In this paper, we propose a novel customizable database design for multi-tenant applications. Our design introduces an Elastic Extension Tables (EET) which consists of Common Tenant Tables (CTT) and Virtual Extension Tables (VET). This design enables tenants to create their own elastic database schema during multi-tenant application run-time execution to satisfy their business needs.
The revised Conners' Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity.

The Conners' Parent Rating Scale (CPRS) is a popular research and clinical tool for obtaining parental reports of childhood behavior problems. The present study introduces a revised CPRS (CPRS-R) which has norms derived from a large, representative sample of North American children, uses confirmatory factor analysis to develop a definitive factor structure, and has an updated item content to reflect recent knowledge and developments concerning childhood behavior problems. Exploratory and confirmatory factor-analytic results revealed a seven-factor model including the following factors: Cognitive Problems, Oppositional, Hyperactivity-Impulsivity, Anxious-Shy, Perfectionism, Social Problems, and Psychosomatic. The psychometric properties of the revised scale appear adequate as demonstrated by good internal reliability coefficients, high test-retest reliability, and effective discriminatory power. Advantages of the CPRS-R include a corresponding factor structure with the Conners' Teacher Rating Scale-Revised and comprehensive symptom coverage for attention deficit hyperactivity disorder (ADHD) and related disorders. Factor congruence with the original CPRS as well as similarities with other parent rating scales are discussed.
A Bit of Progress in Language Modeling

In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n-grams, skipping, interpolated Kneser–Ney smoothing, and clustering. We present explorations of variations on, or of the limits of, each of these techniques, including showing that sentence mixture models may have more potential. While all of these techniques have been studied separately, they have rarely been studied in combination. We compare a combination of all techniques together to a Katz smoothed trigram model with no count cutoffs. We achieve perplexity reductions between 38 and 50% (1 bit of entropy), depending on training data size, as well as a word error rate reduction of 8 .9%. Our perplexity reductions are perhaps the highest reported compared to a fair baseline.
Managing information extraction: state of the art and research directions

This tutorial makes the case for developing a unified framework that manages information extraction from unstructured data (focusing in particular on text). We first survey research on information extraction in the database, AI, NLP, IR, and Web communities in recent years. Then we discuss why this is the right time for the database community to actively participate and address the problem of managing information extraction (including in particular the challenges of maintaining and querying the extracted information, and accounting for the imprecision and uncertainty inherent in the extraction process). Finally, we show how interested researchers can take the next step, by pointing to open problems, available datasets, applicable standards, and software tools. We do not assume prior knowledge of text management, NLP, extraction techniques, or machine learning.
Self-heating on bulk FinFET from 14nm down to 7nm node

Self-heating effects in scaled bulk FinFETs from 14nm to 7nm node are discussed based on 3D FEM simulations and experimental measurements. Following a typical 0.7x scaling, heat confinement is expected to increase by 20% in Si-channel FinFETs and by another 57% for strained Ge-channel. Reducing the drive current needed to reach target performance by reducing capacitances, and fin depopulation help mitigate self-heating effects. These thermal behaviors propagates to AC circuit benchmark, resulting in ~5% performance variation for high performance devices due to device scaling and increased number of fins.
Beyond Locality-Sensitive Hashing

We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in R, our algorithm achieves Oc(n + d log n) query time and Oc(n + d log n) space, where ρ ≤ 0.73/c + O(1/c) + oc(1). This is the first improvement over the result by Andoni and Indyk (FOCS 2006) and the first data structure that bypasses a locality-sensitive hashing lower bound proved by O’Donnell, Wu and Zhou (ICS 2011). By known reductions we obtain a data structure for the Hamming space and l1 norm with ρ ≤ 0.73/c+O(1/c) + oc(1), which is the first improvement over the result of Indyk and Motwani .   Professor of Electrical Engineering and Computer Science
Domain Controlled Architecture - A New Approach for Large Scale Software Integrated Automotive Systems

Electric and electronic functionalities increase exponentially in every mobility domain. The automotive industry is confronted with a rising system complexity and several restricting requirements and standards (like AUTOSAR), in particular to design embedded software for electronic control units. To stand against rampant functionalities software units could be restructured according to their affiliation and should not be attached to a certain place. This can be effected by integration on single controllers. On the one hand the system wide amount of hardware controllers could such be limited. On the other hand the workload for integration CPUs will rise. To support this paradigm, multi-core systems can provide enough processing power in an efficient way. This paper shows a first approach to combine automotive functionality on such a single controller.
Fast Multi-Swarm Optimization for Dynamic Optimization Problems

In the real world, many applications are non-stationary optimization problems. This requires that the optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a multi-swarm algorithm based on fast particle swarm optimization for dynamic optimization problems. The algorithm employs a mechanism to track multiple peaks by preventing overcrowding at a peak and a fast particle swarm optimization algorithm as a local search method to find the near optimal solutions in a local promising region in the search space. The moving peaks benchmark function is used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for dynamic optimization problems.
Supporting personal photo storytelling for social albums

Information overload is one of today's major concerns. As high-resolution digital cameras become increasingly pervasive, unprecedented amounts of social media are being uploaded to online social networks on a daily basis. In order to support users on selecting the best photos to create an online photo album, attention has been devoted to the development of automatic approaches for photo storytelling. In this paper, we present a novel photo collection summarization system that learns some of the users' social context by analyzing their online photo albums, and includes storytelling principles and face and image aesthetic ranking in order to assist users in creating new photo albums to be shared online. In an in-depth user study conducted with 12 subjects, the proposed system was validated as a first step in the photo album creation process, helping users reduce workload to accomplish such a task. Our findings suggest that a human audio/video professional with cinematographic skills does not perform better than our proposed system.
A Combined Smartphone and Smartwatch Fall Detection System

Falls are behind many elderly hospitalizations and can lead to injuries that greatly debilitate old patients. Much of the deployed fall detection systems rely on the user wearing a personal emergency response device, being conscious and at home. The limitations of the existing systems regarding usability and efficiency have yield an overarching research question on whether systems based on new and advanced consumer mobile devices can be used as ubiquitous automatic fall detectors for seniors. This paper specifically looks into the accuracy of a fall detection system based on an off-the-shelf smartwatch and smartphone. We have implemented a system which combines threshold based and pattern recognition techniques in both devices, with the intent of having the watch to contribute to the specificity of the fall detection strategy. We tested the accuracy of the system through a series of simulated falls and activities of daily living, resulting on the correct identification of 63% of the falls and 78% of the activities and outperforming two other baseline fall detection applications (iFall and Fade). The sensors and algorithm on the watch were able to provide a marginal contribution to the system's accuracy. Indications from the tests suggest that it should be possible to improve the system accuracy by adjusting the used thresholds and fuzzyfying them. Moreover, it is expected that the open source nature of this work and it's results boost such threshold tuning and serve as a better basis for researchers to benchmark their work.
Mobile M2M communication architectures, upcoming challenges, applications, and future directions

Machine-to-machine (M2M) communication is becoming an increasingly important part of mobile traffic and thus also a topic of major interest for mobile communication research and telecommunication standardization bodies. M2M communication offers various ubiquitous services and is one of the main enablers of the vision inspired by the Internet of Things (IoT). The concept of mobile M2M communication has emerged due to the wide range, coverage provisioning, high reliability, and decreasing costs of future mobile networks. Nevertheless, M2M communications pose significant challenges to mobile networks, e.g., due to the expected large number of devices with simultaneous access for sending small-sized data, and a diverse application range. This paper provides a detailed survey of M2M communications in the context of mobile networks, and thus focuses on the latest Long-Term Evolution-Advanced (LTE-A) networks. Moreover, the end-to-end network architectures and reference models for M2M communication are presented. Furthermore, a comprehensive survey is given to M2M service requirements, major current standardization efforts, and upcoming M2M-related challenges. In addition, an overview of upcoming M2M services expected in 5G networks is presented. In the end, various mobile M2M applications are discussed followed by open research questions and directions.
Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users

If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives. Based on this motivation, the current study administered the Suicide Probability Scale(SPS) to 1041 weibo users at Sina Weibo, which is a leading microblog service provider in China. Two NLP (Natural Language Processing) methods, the Chinese edition of Linguistic Inquiry and Word Count (LIWC) lexicon and Latent Dirichlet Allocation (LDA), are used to extract linguistic features from the Sina Weibo data. We trained predicting models by machine learning algorithm based on these two types of features, to estimate suicide probability based on linguistic features. The experiment results indicate that LDA can find topics that relate to suicide probability, and improve the performance of prediction. Our study adds value in prediction of suicidal probability of social network users with their behaviors.