nils-herrmann/clean_scirepeval huggingface dataset:
Absence management and presenteeism : the pressures on employees to attend work and the impact of attendance on performance

Absenteeism is an issue that has grown in importance over the past few years; however, little has been done to explore the impact of presenteeism on individual and organisational performance and well-being. This article is based on interviews collected in nine case study organisations in the UK. Two sector organisations (one private and one public) were studied to examine absence management and a conceptual model of presenteeism, with further illustration provided using data from the other seven case studies. This enabled a pattern of presenteeism to emerge, along with the contextual and individual factors which impact on it. In addition to previous research, we found that presenteeism is a complex ‘problem’ and that it is not a single one-dimensional construct, but is continually being shaped by individual and organisational factors. In addition, we found that performance and well-being are more closely related to the organisational reaction to presenteeism and absenteeism, rather than the act itself.  Dr Denise Baker-McClearn, Health Sciences Research Institute, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.
Graph Convolutional Matrix Completion for Bipartite Edge Prediction

Leveraging intrinsic graph structures in data to improve bipartite edge prediction has become an increasingly important topic in the recent machine learning area. Existing methods, however, are facing open challenges in how to enrich model expressiveness and reduce computational complexity for scalability. This paper addresses both challenges with a novel approach that uses a multi-layer/hop neural network to model a hidden space, and the first-order Chebyshev approximation to reduce training time complexity. Our experiments on benchmark datasets for collaborative filtering, citation network analysis, course prerequisite prediction and drug-target interaction prediction show the advantageous performance of the proposed approach over several state-of-theart methods.
Large-Scale Automatic Classification of Phishing Pages

Phishing websites, fraudulent sites that impersonate a trusted third party to gain access to private data, continue to cost Internet users over a billion dollars each year. In this paper, we describe the design and performance characteristics of a scalable machine learning classifier we developed to detect phishing websites. We use this classifier to maintain Google’s phishing blacklist automatically. Our classifier analyzes millions of pages a day, examining the URL and the contents of a page to determine whether or not a page is phishing. Unlike previous work in this field, we train the classifier on a noisy dataset consisting of millions of samples from previously collected live classification data. Despite the noise in the training data, our classifier learns a robust model for identifying phishing pages which correctly classifies more than 90% of phishing pages several weeks after training concludes.
Robust Tracking Using Region Proposal Networks

Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers. However, due to the drastic difference between image classification and tracking, extra treatments such as model ensemble and feature engineering must be carried out to bridge the two domains. Such procedures are either time consuming or hard to generalize well across datasets. In this paper we discovered that the internal structure of Region Proposal Network (RPN)’s top layer feature can be utilized for robust visual tracking. We showed that such property has to be unleashed by a novel loss function which simultaneously considers classification accuracy and bounding box quality. Without ensemble and any extra treatment on feature maps, our proposed method achieved state-of-the-art results on several large scale benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly available.
EduRank: A Collaborative Filtering Approach to Personalization in E-learning

The growing prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities, backgrounds and styles. There is thus a growing need to accomodate for individual differences in such e-learning systems. This paper presents a new algorithm for personliazing educational content to students that combines collaborative filtering algorithms with social choice theory. The algorithm constructs a “difficulty” ranking over questions for a target student by aggregating the ranking of similar students, as measured by different aspects of their performance on common past questions, such as grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for a target student, rather than ordering them according to predicted performance, which is prone to error. The algorithm was tested on two large real world data sets containing tens of thousands of students and a million records. Its performance was compared to a variety of personalization methods as well as a non-personalized method that relied on a domain expert. It was able to significantly outperform all of these approaches according to standard information retrieval metrics. Our approach can potentially be used to support teachers in tailoring problem sets and exams to individual students and students in informing them about areas they may need to strengthen.
Oral and respiratory control for preterm feeding.

Feeding competency is a frequent and serious challenge to the neonatal intensive care unit survivors and to the physician-provider-parent teams. The urgency of effective assessment and intervention techniques is obviated to promote safe swallow, as attainment of oral feeding for the preterm infant/newborn is one of the prerequisites for hospital discharge. If left unresolved, feeding problems may persist into early childhood and may require management by pediatric gastroenterologists and feeding therapists. This review highlights studies aimed at understanding the motor control and development of nonnutritive and nutritive suck, swallow, and coordination with respiration in preterm populations.



Functional linkages between suck-swallow and swallow-respiration manifest transitional forms during late gestation and can be delayed or modified by sensory experience and/or disease processes. Moreover, brainstem central pattern generator (CPG) networks and their neuromuscular targets attain functional status at different rates, which ultimately influences cross-system interactions among individual CPGs. Entrainment of trigeminal primary afferents to activate the suck CPG is one example of a clinical intervention to prime cross-system interactions among ororhythmic pattern generating networks in the preterm and term infants.



The genesis of within-system CPG control for rate and amplitude scaling matures differentially for suck, mastication, swallow, and respiration. Cross-system interactions among these CPGs represent targets of opportunity for new interventions that optimize experience-dependent mechanisms to promote robust ororhythmic patterning and safe swallows among preterm infants.
Development of A GSM-Based Fire Detector System

Fire outage is one of the phenomena that still pose a serious challenge to the security of lives and properties. Fire, being an important process that affects ecological systems across the globe has both positive and negative effects. It has been used by humans for cooking, generating heat, signalling and propulsion purposes. However soil erosion, atmospheric pollution and hazard to life and property are majorly the negative effects. Although the use of wildland fire, controlled burns, and provision of fire fighting services is put in place to prevent the outbreak, it is only evident in most developed countries. Fire accident creates serious health and safety hazard in developing countries, which also resulted into catastrophic situation. Associated with it is unnecessary injury or complete loss of lives in one hand, partial or complete damage to expensive and valuable properties on the other hand. This huge loss is inestimably enormous; hence this paper proposes the development of a GSM -based fire detector system. A cost effective system that detects fire or smoke and sends alert information to a mobile phone for quick and immediate action thereby, avoiding unnecessary and costly industrial and domestic breakdown.
Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex.

Following damage to specific sectors of the prefrontal cortex, humans develop a defect in real-life decision making, in spite of otherwise normal intellectual performance. The patients so affected may even realize the consequences of their actions but fail to act accordingly, thus appearing oblivious to the future. The neural basis of this defect has resisted explanation. Here we identify a physiological correlate for the defect and discuss its possible significance. We measured the skin conductance responses (SCRs) of 7 patients with prefrontal damage, and 12 normal controls, during the performance of a novel task, a card game that simulates real-life decision making in the way it factors uncertainty, rewards, and penalties. Both patients and controls generated SCRs after selecting cards that were followed by penalties or by reward. However, after a number of trials, controls also began to generate SCRs prior to their selection of a card, while they pondered from which deck to choose, but no patients showed such anticipatory SCRs. The absence of anticipatory SCRs in patients with prefrontal damage is a correlate of their insensitivity to future outcomes. It is compatible with the idea that these patients fail to activate biasing signals that would serve as value markers in the distinction between choices with good or bad future outcomes; that these signals also participate in the enhancement of attention and working memory relative to representations pertinent to the decision process; and that the signals hail from the bioregulatory machinery that sustains somatic homeostasis and can be expressed in emotion and feeling.
Data-driven risk-averse stochastic optimization with Wasserstein metric

The traditional two-stage stochastic programming approach is to minimize the total expected cost with the assumption that the distribution of the random parameters is known. However, in most practices, the actual distribution of the random parameters is not known, and instead, only a series of historical data are available. Thus, the solution obtained from the traditional twostage stochastic program can be biased and suboptimal for the true problem, if the estimated distribution of the random parameters is not accurate, which is usually true when only a limited amount of historical data are available. In this paper, we propose a data-driven risk-averse stochastic optimization approach. Based on the observed historical data, we construct the confidence set of the ambiguous distribution of the random parameters, and develop a riskaverse stochastic optimization framework to minimize the total expected cost under the worstcase distribution within the constructed confidence set. We introduce the Wasserstein metric to construct the confidence set and by using this metric, we can successfully reformulate the risk-averse two-stage stochastic program to its tractable counterpart. In addition, we derive the worst-case distribution and develop efficient algorithms to solve the reformulated problem. Moreover, we perform convergence analysis to show that the risk averseness of the proposed formulation vanishes as the amount of historical data grows to infinity, and accordingly, the corresponding optimal objective value converges to that of the traditional risk-neutral twostage stochastic program. We further precisely derive the convergence rate, which indicates the value of data. Finally, the numerical experiments on risk-averse stochastic facility location and stochastic unit commitment problems verify the effectiveness of our proposed framework.
Biogenic amines in dry fermented sausages: a review.

Biogenic amines are compounds commonly present in living organisms in which they are responsible for many essential functions. They can be naturally present in many foods such as fruits and vegetables, meat, fish, chocolate and milk, but they can also be produced in high amounts by microorganisms through the activity of amino acid decarboxylases. Excessive consumption of these amines can be of health concern because their not equilibrate assumption in human organism can generate different degrees of diseases determined by their action on nervous, gastric and intestinal systems and blood pressure. High microbial counts, which characterise fermented foods, often unavoidably lead to considerable accumulation of biogenic amines, especially tyramine, 2-phenylethylamine, tryptamine, cadaverine, putrescine and histamine. However, great fluctuations of amine content are reported in the same type of product. These differences depend on many variables: the quali-quantitative composition of microbial microflora, the chemico-physical variables, the hygienic procedure adopted during production, and the availability of precursors. Dry fermented sausages are worldwide diffused fermented meat products that can be a source of biogenic amines. Even in the absence of specific rules and regulations regarding the presence of these compounds in sausages and other fermented products, an increasing attention is given to biogenic amines, especially in relation to the higher number of consumers with enhanced sensitivity to biogenic amines determined by the inhibition of the action of amino oxidases, the enzymes involved in the detoxification of these substances. The aim of this paper is to give an overview on the presence of these compounds in dry fermented sausages and to discuss the most important factors influencing their accumulation. These include process and implicit factors as well as the role of starter and nonstarter microflora growing in the different steps of sausage production. Moreover, the role of microorganisms with amino oxidase activity as starter cultures to control or reduce the accumulation of biogenic amines during ripening and storage of sausages is discussed.
Smart Home Based on WiFi Sensing: A Survey

Conventional sensing methodologies for smart home are known to be labor-intensive and complicated for practical deployment. Thus, researchers are resorting to alternative sensing mechanisms. Wi-Fi is one of the key technologies that enable connectivity for smart home services. Apart from its primary use for communication, Wi-Fi signal has now been widely leveraged for various sensing tasks, such as gesture recognition and fall detection, due to its sensitivity to environmental dynamics. Building smart home based on Wi-Fi sensing is cost-effective, non-invasive, and enjoys convenient deployment. In this paper, we survey the recent advances in the smart home systems based on the Wi-Fi sensing, mainly in such areas as health monitoring, gesture recognition, contextual information acquisition, and authentication.
3D Shape Induction from 2D Views of Multiple Objects

In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called "projective generative adversarial networks" (PrGANs) trains a deep generative model of 3D shapes whose projections match the distributions of the input 2D views. The addition of a projection module allows us to infer the underlying 3D shape distribution without using any 3D, viewpoint information, or annotation during the learning phase. We show that our approach produces 3D shapes of comparable quality to GANs trained on 3D data for a number of shape categories including chairs, airplanes, and cars. Experiments also show that the disentangled representation of 2D shapes into geometry and viewpoint leads to a good generative model of 2D shapes. The key advantage is that our model allows us to predict 3D, viewpoint, and generate novel views from an input image in a completely unsupervised manner.
Taxing unhealthy food and drinks to improve health.

Oliver T Mytton, academic clinical fellow, Dushy Clarke, researcher, Mike Rayner, director Author Affiliations 1British Heart Foundation Health Promotion Research Group, Department of Public Health, University of Oxford, Oxford OX3 7LF, UK Correspondence to: O T Mytton oliver.mytton@dph.ox.ac.uk Accepted 2 April 2012 An increasing number of countries are introducing taxes on unhealthy food and drinks, but will they improve health? Oliver Mytton, Dushy Clarke, and Mike Rayner examine the evidence In the past year Denmark has introduced a “fat tax,” Hungary a “junk food tax,” and France a tax on sweetened drinks.1 2 Peru has announced plans to tax junk food, and other countries, notably Ireland, are also considering such taxes. Last year’s UN high level summit on non-communicable disease recognised a role for food taxes,3 and the UK prime minster, David Cameron has said the UK should consider  Despite this recent interest among policy makers there has been relatively little critical analysis. Discussion of the evidence of health effects and the important question of what to tax has often been lacking. Government intervention in the food market, in the form of agricultural subsidies and taxation that is unrelated to health, is often overlooked. Taxing unhealthy food and drinks to improve health | BMJ http://www.bmj.com.proxy.library.vcu.edu/content/344/bmj.e2931
DDefender: Android application threat detection using static and dynamic analysis

Android is the most widespread mobile operating system in the world. Due to its popularity, malware has been increasing every year steadily, which causes lots of problems to users, such as using the device's resources and transmitting private information without user's awareness. As malware has increased, anti-malware solutions have as well. Current anti-malware solutions often have very serious limitations and malware is becoming more apt to take advantage of them. In this paper, we present DDefender, a user-friendly application that detects Android malicious applications on device. DDefender is a comprehensive solution that utilizes static and dynamic analysis techniques to extract features from the user's device, then applies deep learning algorithm to detect malicious applications. At first, we use dynamic analysis to extract system calls, system information, network traffics, and requested permissions of an inspected application. Then we use static analysis to extract significant features from the inspected application such as application's components. By utilizing neural network and a large feature set of 1007 features, we evaluated our system with 4208 applications (2104 benign applications and 2104 malicious applications) and we achieved up to 95% accuracy.
Deep Imbalanced Attribute Classification Using Visual Attention Aggregation

For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods follow either a computer vision approach while failing to account for class imbalance, or explore machine learning solutions, which disregard the spatial and semantic relations that exist in the images. With that in mind, we propose an effective method that extracts and aggregates visual attention masks at different scales. We introduce a loss function to handle class imbalance both at class and at an instance level and further demonstrate that penalizing attention masks with high prediction variance accounts for the weak supervision of the attention mechanism. By identifying and addressing these challenges, we achieve state-of-the-art results with a simple attention mechanism in both PETA and WIDER-Attribute datasets without additional context or side information.
Deep Learning for the Segmentation of Vessels in Retinal Fundus images and its Interpretation

Vessel segmentation is a key step for various medical applications, it is widely used in monitoring the disease progression, and evaluation of various ophthalmologic diseases. However, manual vessel segmentation by trained specialists is a repetitive and time-consuming task. In the last two decades, many approaches have been introduced to segment the retinal vessels automatically. With the more recent advances in the field of neural networks and deep learning, multiple methods have been implemented with focus on the segmentation and delineation of the blood vessels. Deep Learning methods, such as the Convolutional Neural Networks (CNN), have recently become one of the new trends in the Computer Vision area. Their ability to find strong spatially local correlations in the data at different abstraction levels allows them to learn a set of filters that are useful to correctly segment the data, when given a labeled training set. In this dissertation, different approaches based on deep learning techniques for the segmentation of retinal blood vessels are studied. Furthermore, in this dissertation are also studied and evaluated the different techniques that have been used for vessel segmentation, based on machine learning (Random Forests and Support vector machine algorithms), and how these can be combined with the deep learning approaches.
A Non-Technical Survey on Deep Convolutional Neural Network Architectures

Artificial neural networks have recently shown great results in many disciplines and a variety of applications, including natural language understanding, speech processing, games and image data generation. One particular application in which the strong performance of artificial neural networks was demonstrated is the recognition of objects in images, where deep convolutional neural networks are commonly applied. In this survey, we give a comprehensive introduction to this topic (object recognition with deep convolutional neural networks), with a strong focus on the evolution of network architectures. Therefore, we aim to compress the most important concepts in this field in a simple and non-technical manner to allow for future researchers to have a quick general understanding. This work is structured as follows: 1) We will explain the basic ideas of (convolutional) neural networks and deep learning and examine their usage for three object recognition tasks: image classification, object localization and object detection. 2) We give a review on the evolution of deep convolutional neural networks by providing an extensive overview of the most important network architectures presented in chronological order of their appearances.
Applications of agent-based systems in intelligent manufacturing: An updated review

Agent technology has been recognized as a promising paradigm for next generation manufacturing systems. Researchers have attempted to apply agent technology to manufacturing enterprise integration, enterprise collaboration (including supply chain management and virtual enterprises), manufacturing process planning and scheduling, shop floor control, and to holonic manufacturing as an implementation methodology. This paper provides an update review on the recent achievements in these areas, and discusses some key issues in implementing agent-based manufacturing systems such as agent encapsulation, agent organization, agent coordination and negotiation, system dynamics, learning, optimization, security and privacy, tools and standards.