nils-herrmann/clean_scirepeval huggingface dataset:
Robust Image Mosaicing of Soccer Videos using Self-Calibration and Line Tracking

In this paper we propose an accurate and robust image mosaicing method of soccer video taken from a rotating and zooming camera using line tracking and self-calibration. The mosaicing of soccer videos is not easy, because their playing fields are low textured and moving players are included in the fields. Our approach is to track line features on the playing fields. The line features are detected and tracked using a self-calibration technique for a rotating and zooming camera. To track line features efficiently, we propose a new line tracking algorithm, called camera parameter guided line tracking, which works even when the camera motion undergoes sudden changes. Since we do not need to know any model for scenes beforehand, the proposed algorithm can be easily extended to other video sources, as well as other sports videos. Experimental results show the accuracy and robustness of the algorithm. An application of mosaicing is also presented.
Nonharmonic multitone of singing bowls and digital signal generators

This paper presents the issue of a nonharmonic multitone generation with the use of singing bowls and the digital signal processors. The authors show the possibility of therapeutic applications of such multitone signals. Some known methods of the digital generation of the tone signal with the additional modulation are evaluated. Two projects of the very precise multitone generators are presented. In described generators, the digital signal processors synthesize the signal, while the additional microcontrollers realize the operator's interface. As a final result, the sound of the original singing bowls is confronted with the sound synthesized by one of the generators.
Women's underrepresentation in science: sociocultural and biological considerations.

The underrepresentation of women at the top of math-intensive fields is controversial, with competing claims of biological and sociocultural causation. The authors develop a framework to delineate possible causal pathways and evaluate evidence for each. Biological evidence is contradictory and inconclusive. Although cross-cultural and cross-cohort differences suggest a powerful effect of sociocultural context, evidence for specific factors is inconsistent and contradictory. Factors unique to underrepresentation in math-intensive fields include the following: (a) Math-proficient women disproportionately prefer careers in non-math-intensive fields and are more likely to leave math-intensive careers as they advance; (b) more men than women score in the extreme math-proficient range on gatekeeper tests, such as the SAT Mathematics and the Graduate Record Examinations Quantitative Reasoning sections; (c) women with high math competence are disproportionately more likely to have high verbal competence, allowing greater choice of professions; and (d) in some math-intensive fields, women with children are penalized in promotion rates. The evidence indicates that women's preferences, potentially representing both free and constrained choices, constitute the most powerful explanatory factor; a secondary factor is performance on gatekeeper tests, most likely resulting from sociocultural rather than biological causes.
CNN based spatio-temporal feature extraction for face anti-spoofing

Authentication of biometric system is vulnerable to impostor attacks. Recent research considers face anti-spoofing as a binary classification problem. To differentiate between genuine access and fake attacks, many systems are trained and the number of counter measures is gradually increasing. In this paper, we propose a novel technique for face anti-spoofing. This method is based on Spatio-temporal information to distinguish between legitimate access and impostor videos or video sequences of picture attacks. The idea is to utilize convolutional neural network (CNN) with handcrafted technique such as LBP-TOP for feature extraction and training of the classifier. Proposed approach requires no preprocessing steps such as face detection and refining face regions or enlarging the original images with particular re-scaling ratios. CNN itself cannot learn temporal features but for face anti-spoofing spatio-temporal features are important. We cascade LBP-TOP with CNN to extract spatio-temporal features from video sequences and capture the most discriminative clues between genuine access and impostor attacks. Extensive experiments are conducted on two very challenging datasets: CASIA and REPLAY-ATTACK which are publically available and achieved high competitive score compared with state-of-art techniques results.
A Taxonomy of Automatic Differential Tools

Many of the current automatic di erentiation (AD) tools have similar characteristics. Unfortunately, it is often the case that the similarities between these various AD tools can not be easily ascertained by reading the corresponding documentation. To clarify this situation, a taxonomy of AD tools is presented. The taxonomy places AD tools into the Elemental, Extensional, Integral, Operational and Symbolic classes. This taxonomy is used to classify twenty-nine AD tools. Each tool is examined individually with respect to the mode of di erentiation used and the degree of derivatives computed. A list detailing the availability of the surveyed AD tools is provided in
Structured Learning of Tree Potentials in CRF for Image Segmentation

We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some predefined parametric models, and then, methods, such as structured support vector machines, are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests—ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn classwise decision trees for each object that appears in the image. Experimental results on several public segmentation data sets demonstrate the power of the learned nonlinear nonparametric potentials.
PKM2 released by neutrophils at wound site facilitates early wound healing by promoting angiogenesis.

Neutrophils infiltration/activation following wound induction marks the early inflammatory response in wound repair. However, the role of the infiltrated/activated neutrophils in tissue regeneration/proliferation during wound repair is not well understood. Here, we report that infiltrated/activated neutrophils at wound site release pyruvate kinase M2 (PKM2) by its secretive mechanisms during early stages of wound repair. The released extracellular PKM2 facilitates early wound healing by promoting angiogenesis at wound site. Our studies reveal a new and important molecular linker between the early inflammatory response and proliferation phase in tissue repair process.
LBP maps for improving fractal based texture classification

This paper presents an innovative manner of obtaining discriminative texture signatures by using the LBP approach to extract additional sources of information from an input image and by using fractal dimension to calculate features from these sources. Four strategies, called Min, Max, Diff Min and Diff Max , were tested, and the best success rates were obtained when all of them were employed together, resulting in an accuracy of 99.25%, 72.50% and 86.52% for the Brodatz, UIUC and USPTex databases, respectively, using Linear Discriminant Analysis. These results surpassed all the compared methods in almost all the tests and, therefore, confirm that the proposed approach is an effective tool for texture analysis.
Understanding Diverse Usage Patterns from Large-Scale Appstore-Service Profiles

The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering research. This article presents an empirical study of behavioral service profiles collected from millions of users whose devices are deployed with Wandoujia, a leading Android app-store service in China. The dataset of Wandoujia service profiles consists of two kinds of user behavioral data from using 0.28 million free Android apps, including (1) app management activities (i.e., downloading, updating, and uninstalling apps) from over 17 million unique users and (2) app network usage from over 6 million unique users. We explore multiple aspects of such behavioral data and present patterns of app usage. Based on the findings as well as derived knowledge, we also suggest some new open opportunities and challenges that can be explored by the research community, including app development, deployment, delivery, revenue, etc.
A robotic finger mechanism for robust industrial applications

The purpose of this paper is to present the design and analysis of a robotic finger mechanism for robust industrial applications.  The resultant design is a compact rigid link finger, which is adaptive to different shapes and sizes providing necessary grasping features. A number of such fingers can be assembled to function as a special purpose end effector.  The mechanism removes a number of significant problems usually experienced with tendon-based designs. The finger actuation mechanism forms a compact and positive drive unit within the end effector’s body using solid mechanical linkages and integrated actuators.  The paper discusses the design issues associated with a limited number of actuators to operate in a constrained environment and presents various considerations necessary to ensure safe and reliable operations.  The design is original in existence and developed for special purpose handling applications that offers a strong and reliable system where space and safety is of prime concern.
Social and monetary reward learning engage overlapping neural substrates.

Learning to make choices that yield rewarding outcomes requires the computation of three distinct signals: stimulus values that are used to guide choices at the time of decision making, experienced utility signals that are used to evaluate the outcomes of those decisions and prediction errors that are used to update the values assigned to stimuli during reward learning. Here we investigated whether monetary and social rewards involve overlapping neural substrates during these computations. Subjects engaged in two probabilistic reward learning tasks that were identical except that rewards were either social (pictures of smiling or angry people) or monetary (gaining or losing money). We found substantial overlap between the two types of rewards for all components of the learning process: a common area of ventromedial prefrontal cortex (vmPFC) correlated with stimulus value at the time of choice and another common area of vmPFC correlated with reward magnitude and common areas in the striatum correlated with prediction errors. Taken together, the findings support the hypothesis that shared anatomical substrates are involved in the computation of both monetary and social rewards.
Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication

How can we generate realistic graphs? In addition, how can we do so with a mathematically tractable model that makes it feasible to analyze their properties rigorously? Real graphs obey a long list of surprising properties: Heavy tails for the inand out-degree distribution; heavy tails for the eigenvalues and eigenvectors; small diameters; and the recently discovered “Densification Power Law” (DPL). All published graph generators either fail to match several of the above properties, are very complicated to analyze mathematically, or both. Here we propose a graph generator that is mathematically tractable and matches this collection of properties. The main idea is to use a non-standard matrix operation, the Kronecker product, to generate graphs that we refer to as “Kronecker graphs”. We show that Kronecker graphs naturally obey all the above properties; in fact, we can rigorously prove that they do so. We also provide empirical evidence showing that they can mimic very well several real graphs.
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on SPADES fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 F1 points.
Inclined planes optimization algorithm in optimal architecture of MLP neural networks

In this paper an Inclined Planes Optimization algorithm, is used to optimize the performance of the multilayer perceptron. Indeed, the performance of the neural network depends on its parameters such as the number of neurons in the hidden layer and the connection weights. So far, most research has been done in the field of training the neural network. In this paper, a new algorithm optimization is presented in optimal architecture for data classification. Neural network training is done by backpropagation (BP) algorithm and optimization the architecture of neural network is considered as independent variables in the algorithm. The results in three classification problems have shown that a neural network resulting from these methods have low complexity and high accuracy when compared with results of Particle Swarm Optimization and Gravitational Search Algorithm.
The JIRA Repository Dataset: Understanding Social Aspects of Software Development

Issue tracking systems store valuable data for testing hypotheses concerning maintenance, building statistical prediction models and recently investigating developers "affectiveness". In particular, the Jira Issue Tracking System is a proprietary tracking system that has gained a tremendous popularity in the last years and offers unique features like the project management system and the Jira agile kanban board. This paper presents a dataset extracted from the Jira ITS of four popular open source ecosystems (as well as the tools and infrastructure used for extraction) the Apache Software Foundation, Spring, JBoss and CodeHaus communities. Our dataset hosts more than 1K projects, containing more than 700K issue reports and more than 2 million issue comments. Using this data, we have been able to deeply study the communication process among developers, and how this aspect affects the development process. Furthermore, comments posted by developers contain not only technical information, but also valuable information about sentiments and emotions. Since sentiment analysis and human aspects in software engineering are gaining more and more importance in the last years, with this repository we would like to encourage further studies in this direction.
Child abuse and neglect.

he publication of “The Battered Child” by C. Henry Kempe and coworkers introduced child maltreatment (CM) to the pediatric literature, and in the 46 years following its publication, the knowledge in the field has expanded. CM pervades every area of pediatrics. Pediatricians are mandated reporters and play an important role in the identification of child abuse and neglect. This article seeks to inform the primary care clinician about the current state of knowledge of child abuse and neglect. The epidemiology and diagnosis of the different types of abuse, the effects of abuse on children, documentation, reporting, and prevention are covered. The intent of the article is to help primary care clinicians more accurately identify and report child abuse and neglect.
WHAT IS COMPUTER ETHICS?*

A Proposed Definition Computers are special technology and they raise some special ethical issues. In this essay I will discuss what makes computers different from other technology and how this difference makes a difference in ethical considerations. In particular, I want to characterize computer ethics and show why this emerging field is both intellectually interesting and enormously important. On my view, computer ethics is the analysis of the nature and social impact of computer technology and the corresponding formulation and justification of policies for the ethical use of such technology. I use the phrase “computer technology” because I take the subject matter of the field broadly to include computers and associated technology. For instance, I include concerns about software as well as hardware and concerns about networks connecting computers as well as computers themselves. A typical problem in computer ethics arises because there is a policy vacuum about how computer technology should be used. Computers provide us with new capabilities and these in turn give us new choices for action. Often, either no policies for conduct in these situations exist or existing policies seem inadequate. A central task of computer ethics is to determine what we should do in such cases, i.e., to formulate policies to guide our actions. Of course, some ethical situations confront us as individuals and some as a society. Computer ethics includes consideration of both personal and social policies for the ethical use of computer technology. Now it may seem that all that needs to be done is the mechanical application of an ethical theory to generate the appropriate policy. But this is usually not possible. A difficulty is that along with a policy vacuum there is often a conceptual vacuum. Although a problem in computer ethics may seem clear initially, a little reflection reveals a conceptual muddle. What is needed in such cases is an analysis which provides a coherent conceptual framework within which to formulate a policy for action. Indeed, much of the important work in computer ethics is devoted to proposing conceptual frameworks for understanding ethical problems involving computer technology. An example may help to clarify the kind of conceptual work that is required. Let’s suppose we are trying to formulate a policy for protecting computer programs. Initially, the idea may seem clear enough. We are looking for a policy for protecting a kind of intellectual property. But then a
At the interface of the auditory and vocal motor systems: NIf and its role in vocal processing, production and learning

Communication between auditory and vocal motor nuclei is essential for vocal learning. In songbirds, the nucleus interfacialis of the nidopallium (NIf) is part of a sensorimotor loop, along with auditory nucleus avalanche (Av) and song system nucleus HVC, that links the auditory and song systems. Most of the auditory information comes through this sensorimotor loop, with the projection from NIf to HVC representing the largest single source of auditory information to the song system. In addition to providing the majority of HVC's auditory input, NIf is also the primary driver of spontaneous activity and premotor-like bursting during sleep in HVC. Like HVC and RA, two nuclei critical for song learning and production, NIf exhibits behavioral-state dependent auditory responses and strong motor bursts that precede song output. NIf also exhibits extended periods of fast gamma oscillations following vocal production. Based on the converging evidence from studies of physiology and functional connectivity it would be reasonable to expect NIf to play an important role in the learning, maintenance, and production of song. Surprisingly, however, lesions of NIf in adult zebra finches have no effect on song production or maintenance. Only the plastic song produced by juvenile zebra finches during the sensorimotor phase of song learning is affected by NIf lesions. In this review, we carefully examine what is known about NIf at the anatomical, physiological, and behavioral levels. We reexamine conclusions drawn from previous studies in the light of our current understanding of the song system, and establish what can be said with certainty about NIf's involvement in song learning, maintenance, and production. Finally, we review recent theories of song learning integrating possible roles for NIf within these frameworks and suggest possible parallels between NIf and sensorimotor areas that form part of the neural circuitry for speech processing in humans.
Back to Basics: The Role of Agile Principles in Success with an Distributed Scrum Team

Agile processes rely on feedback and communication to work and they often work best with co-located teams for this reason. Sometimes agile makes sense because of project requirements and a distributed team makes sense because of resource constraints. A distributed team can be productive and fun to work on if the team takes an active role in overcoming the barriers that distribution causes. This is the story of how one product team used creativity, communications tools, and basic good engineering practices to build a successful product.
A 39 dB DR CMOS log-amp RF power detector with ±1.1 dB temperature drift from −40 to 85°C

This paper presents a temperature compensated logarithmic amplifier (log-amp) RF power detector implemented in CMOS 0.18μm technology. The input power can range from -50 to +10 dBm for RF signals ranging from 100MHz to 1.5 GHz. This design attains a typical DR of 39 dB for a ±1 dB log-conformance error (LCE). Up to 900MHz the temperature drift is never larger than ±1.1 dB for all 24 measured samples over a temperature range from -40 to +85°C. The current consumption is 6.3mA from a 1.8V power supply and the chip area is 0.76mm2.