nils-herrmann/clean_scirepeval huggingface dataset:
Big Data: Survey, Technologies, Opportunities, and Challenges

Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.
Reviewer Recommender of Pull-Requests in GitHub

Pull-Request (PR) is the primary method for code contributions from thousands of developers in GitHub. To maintain the quality of software projects, PR review is an essential part of distributed software development. Assigning new PRs to appropriate reviewers will make the review process more effective which can reduce the time between the submission of a PR and the actual review of it. However, reviewer assignment is now organized manually in GitHub. To reduce this cost, we propose a reviewer recommender to predict highly relevant reviewers of incoming PRs. Combining information retrieval with social network analyzing, our approach takes full advantage of the textual semantic of PRs and the social relations of developers. We implement an online system to show how the reviewer recommender helps project managers to find potential reviewers from crowds. Our approach can reach a precision of 74% for top-1 recommendation, and a recall of 71% for top-10 recommendation.
MULTIMEDIA MINING

Advances in multimedia acquisition and storage technology have led to tremendous growth in very large and detailed multimedia databases. If these multimedia files are analyzed, useful information to users can be revealed. Multimedia mining deals with the extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia files. Multimedia mining is more than just an extension of data mining, as it is an interdisciplinary endeavor that draws upon expertise in computer vision, multimedia processing, multimedia retrieval, data mining, machine learning, database and artificial intelligence. This paper briefly describes the multimedia mining, while references cited cover the major theoretical issues.  text mining; image mining; audio mining; video mining
SphereFace: Deep Hypersphere Embedding for Face Recognition

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.
Rotor Integrity Design for a High-Speed Modular Air-Cored Axial-Flux Permanent-Magnet Generator

The rotor integrity design for a high-speed modular air-cored axial-flux permanent-magnet (AFPM) generator is presented. The main focus is on the mechanical parametric optimization of the rotor, which becomes a more dominating design issue over electromagnetic optimization at high operational speeds. Approximate analytical formulas are employed for preliminary sizing of the mechanical parameters of the rotor, which consists of the permanent magnets, retainment ring, and back iron. Two-dimensional (2-D) finite-element analysis (FEA) models are used to optimize the values of the parameters. Then, 3-D FEA models are developed to verify the final design. Finally, based on the final design, an AFPM prototype is built for experimental validation, and mechanical integrity tests for the rotor are undertaken. The results confirm the validity of the analytical and FEA models, as well as the overall design approach.
SLAC: A Sparsely Labeled Dataset for Action Classification and Localization

This paper describes a procedure for the creation of large-scale video datasets for action classification and localization from unconstrained, realistic web data. The scalability of the proposed procedure is demonstrated by building a novel video benchmark, named SLAC (Sparsely Labeled ACtions), consisting of over 520K untrimmed videos and 1.75M clip annotations spanning 200 action categories. Using our proposed framework, annotating a clip takes merely 8.8 seconds on average. This represents a saving in labeling time of over 95% compared to the traditional procedure of manual trimming and localization of actions. Our approach dramatically reduces the amount of human labeling by automatically identifying hard clips, i.e., clips that contain coherent actions but lead to prediction disagreement between action classifiers. A human annotator can disambiguate whether such a clip truly contains the hypothesized action in a handful of seconds, thus generating labels for highly informative samples at little cost. We show that our large-scale dataset can be used to effectively pretrain action recognition models, significantly improving final metrics on smaller-scale benchmarks after fine-tuning. On Kinetics , UCF-101 [30] and HMDB-51 , models pre-trained on SLAC outperform baselines trained from scratch, by 2.0%, 20.1% and 35.4% in top-1 accuracy, respectively when RGB input is used. Furthermore, we introduce a simple procedure that leverages the sparse labels in SLAC to pre-train action localization models. On THUMOS14 [12] and ActivityNet-v1.3[2], our localization model improves the mAP of baseline model by 8.6% and 2.5%, respectively.
From the bird's eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets

This paper presents stylized facts concerning the spot intra-daily foreign exchange markets. It first describes intra-daily data and proposes a set of definitions for the variables of interest. Empirical regularities of the foreign exchange intra-daily data are then grouped under three major topics: the distribution of price changes, the process of price formation and the heterogeneous structure of the market. The stylized facts surveyed in this paper shed new light on the market structure that appears composed of heterogeneous agents. It also poses several challenges such as the definition of price and of the time-scale, the concepts of risk and efficiency, the modeling of the markets and the learning process.
A survey of inter-vehicle communication protocols and their applications

Inter-vehicle communication (IVC) protocols have the potential to increase the safety, efficiency, and convenience of transportation systems involving planes, trains, automobiles, and robots. The applications targeted include peer-to-peer networks for web surfing, coordinated braking, runway incursion prevention, adaptive traffic control, vehicle formations, and many others. The diversity of the applications and their potential communication protocols has challenged a systematic literature survey. We apply a classification technique to IVC applications to provide a taxonomy for detailed study of their communication requirements. The applications are divided into type classes which share common communication organization and performance requirements. IVC protocols are surveyed separately and their fundamental characteristics are revealed. The protocol characteristics are then used to determine the relevance of specific protocols to specific types of IVC applications.
Chronic stress and obesity: a new view of "comfort food".

The effects of adrenal corticosteroids on subsequent adrenocorticotropin secretion are complex. Acutely (within hours), glucocorticoids (GCs) directly inhibit further activity in the hypothalamo-pituitary-adrenal axis, but the chronic actions (across days) of these steroids on brain are directly excitatory. Chronically high concentrations of GCs act in three ways that are functionally congruent. (i) GCs increase the expression of corticotropin-releasing factor (CRF) mRNA in the central nucleus of the amygdala, a critical node in the emotional brain. CRF enables recruitment of a chronic stress-response network. (ii) GCs increase the salience of pleasurable or compulsive activities (ingesting sucrose, fat, and drugs, or wheel-running). This motivates ingestion of "comfort food." (iii) GCs act systemically to increase abdominal fat depots. This allows an increased signal of abdominal energy stores to inhibit catecholamines in the brainstem and CRF expression in hypothalamic neurons regulating adrenocorticotropin. Chronic stress, together with high GC concentrations, usually decreases body weight gain in rats; by contrast, in stressed or depressed humans chronic stress induces either increased comfort food intake and body weight gain or decreased intake and body weight loss. Comfort food ingestion that produces abdominal obesity, decreases CRF mRNA in the hypothalamus of rats. Depressed people who overeat have decreased cerebrospinal CRF, catecholamine concentrations, and hypothalamo-pituitary-adrenal activity. We propose that people eat comfort food in an attempt to reduce the activity in the chronic stress-response network with its attendant anxiety. These mechanisms, determined in rats, may explain some of the epidemic of obesity occurring in our society.
A Strategy for Comparing Alternative Software Development Life Cycle Models

The classic waterfall model of software engineering is used throughout the production software development community. The escalating costs associated with software development and the unsatisfactory reliability, performance, and functionality of the resulting software have motivated software engineers to develop new alternate models of software development including prototyping, software synthesis, and reusable software. It is difficult to compare and contrast these new models of software development because their disciples often use different terminology, and the models often have little in common except their beginnings (marked by a recognition that a problem exists) and ends (marked by the existence of a software solution). This paper provides a framework which can serve 1) as a hasis for analyzing the similarities and differences among alternate life cycle models; 2) as a tool for software engineering researchers to help describe the probable impacts of a new life cycle model; and 3) as a means to help software practitioners decide on an appropriate life cycle model to utilize on a particular project or in a particular application area. ’
New image descriptors based on color, texture, shape, and wavelets for object and scene image classification

This paper presents new image descriptors based on color, texture, shape, and wavelets for object and scene image classification. First, a new three Dimensional Local Binary Patterns (3D-LBP) descriptor, which produces three new color images, is proposed for encoding both color and texture information of an image. The 3D-LBP images together with the original color image then undergo the Haar wavelet and local features. Second, a novel H-descriptor, which integrates the 3D-LBP and the HOG of its wavelet transform, is presented to encode color, texture, shape, as well as local information. Feature extraction for the H-descriptor is implemented by means of Principal Component Analysis (PCA) and Enhanced Fisher Model (EFM) and classification by the nearest neighbor rule for object and scene image classification. And finally, an innovative H-fusion descriptor is proposed by fusing the PCA features of the H-descriptors in seven color spaces in order to further incorporate color information. Experimental results using three datasets, the Caltech 256 object categories dataset, the UIUC Sports Event dataset, and the MIT Scene dataset, show that the proposed new image descriptors achieve better image classification performance than other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), Spatial Envelope, Color SIFT four Concentric Circles (C4CC), Object Bank, the Hierarchical Matching Pursuit, as well as LBP. & 2013 Elsevier B.V. All rights reserved.
End-to-End Neural Relation Extraction with Global Optimization

Neural networks have shown promising results for relation extraction. State-ofthe-art models cast the task as an end-toend problem, solved incrementally using a local classifier. Yet previous work using statistical models have demonstrated that global optimization can achieve better performances compared to local classification. We build a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations. In addition, we present a novel method to integrate syntactic information to facilitate global learning, yet requiring little background on syntactic grammars thus being easy to extend. Experimental results show that our proposed model is highly effective, achieving the best performances on two standard benchmarks.
Towards Bayesian Deep Learning: A Survey

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a general introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this survey, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.
Smart city architecture for community level services through the internet of things

Today, more than half of the world's population spend their lives in cities, and this number will jump to 70 percent by 2050. Increasing population density in urban environments demands adequate provision of services and infrastructure. This explosion in city population will present major challenges including air pollution, traffic congestion, health concerns, energy and waste management. Solution to these challenges might require the integration of various Information and Communication Technologies into the artifact of the city. This paper presents an architecture for smart cities, where city management, community service providers and citizens have access to real time data which has been gathered using various sensory mechanisms in order to analyze and make decisions for future planning.
5G Radio Access Network Design with the Fog Paradigm: Confluence of Communications and Computing

Cloud-based wireless networking system applies centralized resource pooling to improve operation efficiency. Fog-based wireless networking system reduces latency by placing processing units in the network edge. Confluence of fog and cloud design paradigms in 5G radio access network will better support diverse applications. In this article, we describe the recent advances in fog radio access network (F-RAN) research, hybrid fog-cloud architecture, and system design issues. Furthermore, the GPP platform facilitates the confluence of computational and communications processing. Through observations from GPP platform testbed experiments and simulations, we discuss the opportunities of integrating the GPP platform with F-RAN architecture.
An 11 $\mu$ w, two-electrode transimpedance biosignal amplifier with active current feedback stabilization

A novel two-electrode biosignal amplifier circuit is demonstrated by using a composite transimpedance amplifier input stage with active current feedback. Micropower, low gain-bandwidth product operational amplifiers can be used, leading to the lowest reported overall power consumption in the literature for a design implemented with off-the-shelf commercial integrated circuits (11 μW). Active current feedback forces the common-mode input voltage to stay within the supply rails, reducing baseline drift and amplifier saturation problems that can be present in two-electrode systems. The bandwidth of the amplifier extends from 0.05-200 Hz and the midband voltage gain (assuming an electrode-to-skin resistance of 100 kΩ) is 48 dB. The measured output noise level is 1.2 mV pp, corresponding to a voltage signal-to-noise ratio approaching 50 dB for a typical electrocardiogram (ECG) level input of 1 mVpp. Recordings were taken from a subject by using the proposed two-electrode circuit and, simultaneously, a three-electrode standard ECG circuit. The residual of the normalized ensemble averages for both measurements was computed, and the power of this residual was 0.54% of the power of the standard ECG measurement output. While this paper primarily focuses on ECG applications, the circuit can also be used for amplifying other biosignals, such as the electroencephalogram.
Personality and coping.

Personality psychology addresses views of human nature and individual differences. Biological and goal-based views of human nature provide an especially useful basis for construing coping; the five-factor model of traits adds a useful set of individual differences. Coping-responses to adversity and to the distress that results-is categorized in many ways. Meta-analyses link optimism, extraversion, conscientiousness, and openness to more engagement coping; neuroticism to more disengagement coping; and optimism, conscientiousness, and agreeableness to less disengagement coping. Relations of traits to specific coping responses reveal a more nuanced picture. Several moderators of these associations also emerge: age, stressor severity, and temporal proximity between the coping activity and the coping report. Personality and coping play both independent and interactive roles in influencing physical and mental health. Recommendations are presented for ways future research can expand on the growing understanding of how personality and coping shape adjustment to stress.
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes

Although mobile, tablet, large display, and tabletop computers increasingly present opportunities for using pen, finger, and wand gestures in user interfaces, implementing gesture recognition largely has been the privilege of pattern matching experts, not user interface prototypers. Although some user interface libraries and toolkits offer gesture recognizers, such infrastructure is often unavailable in design-oriented environments like Flash, scripting environments like JavaScript, or brand new off-desktop prototyping environments. To enable novice programmers to incorporate gestures into their UI prototypes, we present a "$1 recognizer" that is easy, cheap, and usable almost anywhere in about 100 lines of code. In a study comparing our $1 recognizer, Dynamic Time Warping, and the Rubine classifier on user-supplied gestures, we found that $1 obtains over 97% accuracy with only 1 loaded template and 99% accuracy with 3+ loaded templates. These results were nearly identical to DTW and superior to Rubine. In addition, we found that medium-speed gestures, in which users balanced speed and accuracy, were recognized better than slow or fast gestures for all three recognizers. We also discuss the effect that the number of templates or training examples has on recognition, the score falloff along recognizers' N-best lists, and results for individual gestures. We include detailed pseudocode of the $1 recognizer to aid development, inspection, extension, and testing.
Applying game achievement systems to enhance user experience in a photo sharing service

Achievement systems are reward structures providing additional goals for players, and thus extending the play time of videogames. In this paper, we explore how applications other than games could benefit from achievement systems, and how users perceive this additional content in a service. For this purpose, we added an achievement system to a geo-tagged photo sharing service called Nokia Image Space. The results suggest that there is some potential in achievement systems outside the game domain. The achievements triggered some friendly competition and comparison between users. However, many users were not convinced, expressing concerns about the achievements motivating undesirable usage patterns. Therefore, an achievement system poses certain design considerations when applied in nongame software.
Table frame line detection in low quality document images based on Hough transform

Table detection is of importance in the field of document images analysis and processing, especially table frame line detection. Although a great success has been achieved for high quality images during the past decade, table detection in low quality images still remains a challenge. To address this problem, we proposed a neoteric method to detect table frame line automatically in low quality document images. Firstly, Radon transform is adopted to detect skew of document images and then correct it. Secondly, run length smoothing algorithm (RLSA) is used to extract the lines longer than a predefined threshold. Thirdly, we locate table regions according to table features and detect frame lines of the detected tables using Hough transform method. The experimental results show that this method could obtain a better performance even in the low quality document images compared to the conventional method.