nils-herrmann/clean_scirepeval huggingface dataset:
Cross-Dataset Recognition: A Survey

This paper summarise and analyse the cross-dataset recognition techniques with the emphasize on what kinds of methods can be used when the available source and target data are presented in different forms for boosting the target task. This paper for the first time summarises several transferring criteria in details from the concept level, which are the key bases to guide what kind of knowledge to transfer between datasets. In addition, a taxonomy of cross-dataset scenarios and problems is proposed according the properties of data that define how different datasets are diverged, thereby review the recent advances on each specific problem under different scenarios. Moreover, some real world applications and corresponding commonly used benchmarks of cross-dataset recognition are reviewed. Lastly, several future directions are identified.
Lack of exercise is a major cause of chronic diseases.

Chronic diseases are major killers in the modern era. Physical inactivity is a primary cause of most chronic diseases. The initial third of the article considers: activity and prevention definitions; historical evidence showing physical inactivity is detrimental to health and normal organ functional capacities; cause versus treatment; physical activity and inactivity mechanisms differ; gene-environment interaction (including aerobic training adaptations, personalized medicine, and co-twin physical activity); and specificity of adaptations to type of training. Next, physical activity/exercise is examined as primary prevention against 35 chronic conditions [accelerated biological aging/premature death, low cardiorespiratory fitness (VO2max), sarcopenia, metabolic syndrome, obesity, insulin resistance, prediabetes, type 2 diabetes, nonalcoholic fatty liver disease, coronary heart disease, peripheral artery disease, hypertension, stroke, congestive heart failure, endothelial dysfunction, arterial dyslipidemia, hemostasis, deep vein thrombosis, cognitive dysfunction, depression and anxiety, osteoporosis, osteoarthritis, balance, bone fracture/falls, rheumatoid arthritis, colon cancer, breast cancer, endometrial cancer, gestational diabetes, pre-eclampsia, polycystic ovary syndrome, erectile dysfunction, pain, diverticulitis, constipation, and gallbladder diseases]. The article ends with consideration of deterioration of risk factors in longer-term sedentary groups; clinical consequences of inactive childhood/adolescence; and public policy. In summary, the body rapidly maladapts to insufficient physical activity, and if continued, results in substantial decreases in both total and quality years of life. Taken together, conclusive evidence exists that physical inactivity is one important cause of most chronic diseases. In addition, physical activity primarily prevents, or delays, chronic diseases, implying that chronic disease need not be an inevitable outcome during life.
Agent-based modelling of customer behaviour in the telecoms and media markets

Agent-based modelling is a bottom-up approach to understanding systems which provides a powerful tool for analysing complex, non-linear markets. The method involves creating artificial agents designed to mimic the attributes and behaviours of their real-world counterparts. The system’s macro-observable properties emerge as a consequence of these attributes and behaviours and the interactions between them. The simulation output may be potentially used for explanatory, exploratory and predictive purposes. The aim of this paper is to introduce the reader to some of the basic concepts and methods behind agent-based modelling and to present some recent business applications of these tools, including work in the telecoms and media markets.
Clustered Multi-Task Learning: A Convex Formulation

In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others. In the context of learning linear functions for supervised classification or regression, this can be achieved by including a priori information about the weight vectors associated with the tasks, and how they are expected to be related to each other. In this paper, we assume that tasks are clustered into groups, which are unknown beforehand, and that tasks within a group have similar weight vectors. We design a new spectral norm that encodes this a priori assumption, without the prior knowledge of the partition of tasks into groups, resulting in a new convex optimization formulation for multi-task learning. We show in simulations on synthetic examples and on the iedb MHC-I binding dataset, that our approach outperforms well-known convex methods for multi-task learning, as well as related non convex methods dedicated to the same problem. ∗To whom correspondance should be addressed: 35, rue Saint Honoré, F-77300 Fontainebleau, France.
Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models.
Single Phase Application of Space Vector Pulse Width Modulation for Shunt Active Power Filters

The most important part of the active power filters is generating of gate signal for inverters. This paper presents Single Phase Application of Space Vector Pulse Width Modulation for shunt active power filters. In conventional SVPWM, all of the phase's currents are controlled together, but in this method each of phase currents is controlled independently from the measured currents of other phases. In another word, this method prevents from influence of other phase's errors in the control of considered phase. In this method, the implementation of control logic will be simpler than the conventional SVPWM. For showing the performance of proposed method a typical system has been simulated by MATLAB/SIMULINK. At last, the results of proposed method are compared with the conventional SVPWM. The results show that proposed method have better performance in generating of the compensation current in active power filter.
Pediatric resistance training: benefits, concerns, and program design considerations.

A growing number of children and adolescents are involved in resistance training in schools, fitness centers, and sports training facilities. In addition to increasing muscular strength and power, regular participation in a pediatric resistance training program may have a favorable influence on body composition, bone health, and reduction of sports-related injuries. Resistance training targeted to improve low fitness levels, poor trunk strength, and deficits in movement mechanics can offer observable health and fitness benefits to young athletes. However, pediatric resistance training programs need to be well-designed and supervised by qualified professionals who understand the physical and psychosocial uniqueness of children and adolescents. The sensible integration of different training methods along with the periodic manipulation of programs design variables over time will keep the training stimulus effective, challenging, and enjoyable for the participants.
Popular Books Similar With Social Network Data Analytics Social Network Data Analytics Are Listed

Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading social network data analytics social network data analytics, you can take more advantages with limited budget.
Pingmesh: A Large-Scale System for Data Center Network Latency Measurement and Analysis

Can we get network latency between any two servers at any time in large-scale data center networks? The collected latency data can then be used to address a series of challenges: telling if an application perceived latency issue is caused by the network or not, defining and tracking network service level agreement (SLA), and automatic network troubleshooting. We have developed the Pingmesh system for largescale data center network latency measurement and analysis to answer the above question affirmatively. Pingmesh has been running in Microsoft data centers for more than four years, and it collects tens of terabytes of latency data per day. Pingmesh is widely used by not only network software developers and engineers, but also application and service developers and operators. CCS Concepts •Networks→Network measurement; Cloud computing; Network monitoring; •Computer systems organization → Cloud computing;
Successfully completing case study research: combining rigour, relevance and pragmatism

The organizational and social issues associated with the development, implementation and use of computer-based information systems have increasingly attracted the attention of information systems researchers. Interest in qualitative research methods such as action research, case study research and ethnography, which focus on understanding social phenomena in their natural setting, has consequently grown. Case study research is the most widely used qualitative research method in information systems research, and is well suited to understanding the interactions between information technology-related innovations and organizational contexts. Although case study research is useful as ameans of studying information systems development and use in the field, there can be practical difficulties associated with attempting to undertake case studies as a rigorous and effective method of research. This paper addresses a number of these difficulties and offers some practical guidelines for successfully completing case study research. The paper focuses on the pragmatics of conducting case study research, and draws from the discussion at a panel session conducted by the authors at the 8th Australasian Conference on Information Systems, September 1997 (ACIS 97), from the authors' practical experiences, and from the case study research literature.
Accuracy Analysis of Machine Learning Algorithms for Intrusion Detection System using NSL-KDD Dataset

Intrusion Detection System (IDS) that turns to be a vital component to secure the network. The lack of regular updation, less capability to detect unknown attacks, high non adaptable false alarm rate, more consumption of network resources etc., makes IDS to compromise. This paper aims to classify the NSL-KDD dataset with respect to their metric data by using the best six data mining classification algorithms like J48, ID3, CART, Bayes Net, Naïve Bayes and SVM to find which algorithm will be able to offer more testing accuracy. NSL-KDD dataset has solved some of the inherent limitations of the available KDD’99 dataset. KeywordsIDS, KDD, Classification Algorithms, PCA etc.
Seeing Through Misinformation: A Framework for Identifying Fake Online News

The fake news epidemic makes it imperative to develop a diagnostic framework that is both parsimonious and valid to guide present and future efforts in fake news detection. This paper represents one of the very first attempts to fill a void in the research on this topic. The LeSiE (Lexical Structure, Simplicity, Emotion) framework we created and validated allows lay people to identify potential fake news without the use of calculators or complex statistics by looking out for three simple cues.  A panel of experts convened by the BBC in 2016 named the breakdown of trusted sources of information as one of the most pressing societal challenges in the 21 century. In the same year, the Oxford Dictionaries named “post-truth” the “word of the year”. The outcomes of two of the most momentous events in 2016 — the US presidential election and Brexit — were thought to have been significantly influenced by the prevalence of fake news surrounding both events. The speed with which misinformation make its way online and find an audience is unprecedented in the history of communication. This phenomenon is fuelled by a combination of the ubiquity of social networks, credulous online media, and the peculiarities of human information processing (Silverman, 2015). As fake news articles are unconstrained by reality or facts, they can be crafted with considerable latitude to appeal to hopes, fears, wishes and curiosity, which in turn drives online virality and engagement (Silverman, 2015). This is complicated by the popularity of rumors as conversational currency in interpersonal interactions, especially under conditions of uncertainty (e.g. Southwell, 2013). Fake news viewing has been shown to foster feelings of inefficacy, alienation, and cynicism (Balmas, 2014). Moreover, fakes news (if plausible) can create a complex and demanding rhetorical situation for organizations (Veil et al., 2012), such that organizational legitimacy could be threatened and undermined (Sellnow, Littlefield, Vidolof, & Webb, 2009). On the political/international stage, the consequences of fake news can take on epic proportions: for example, over half of those who recalled seeing fake news about Donald Trump and Hilary Clinton during the US presidential election 1 Director of Operations and Technology, SSON Analytics. 2 Associate Professor, Corporate Communication (Practice), Singapore Management University.
Employee Motivation : “ Just Ask Your Employees ”

Employee motivation is an intricate and sophisticated subject; however, contemporary managers must face and deal with this topic to obtain organizational success. To enhance understanding of employee motivation, mangers must recognize the imperativeness of employee motivation, its concepts, and differences in individual needs. Subsequently, managers need to be aware of a variety of employee motivational factors and the changes in priorities of these factors over time. Moreover, managers have to learn previous and current motivational programs, examples, and theories behind them because understanding of these fundamentals can enhance their ability to identify rewards systems that could be matched with employee needs. This understanding of the employee motivation process requires a systematic approach, and managers must realize that employee motivation and its process are there to motivate their employees; therefore, employee input must be valued and included throughout this process.
DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars

Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployment of autonomous vehicles on their roads.
 However, despite their spectacular progress, DNNs, just like traditional software, often demonstrate incorrect or unexpected corner-case behaviors that can lead to potentially fatal collisions. Several such real-world accidents involving autonomous cars have already happened including one which resulted in a fatality. Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases.
 In this paper, we design, implement, and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explore different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.
Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

This paper introduces adaptor grammars, a class of probabil istic models of language that generalize probabilistic context-free grammar s (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with “ada ptors” that can induce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian mo dels f language using Dirichlet processes and hierarchical Dirichlet proc esses can be written as simple grammars. We present a general-purpose inference al gorithm for adaptor grammars, making it easy to define and use such models, and ill ustrate how several existing nonparametric Bayesian models can be expressed wi thin this framework.
Cognitive Strategy Instruction

2 There was a time, and not that long ago, when few people knew what cognitive strategy instruction was. A relatively small group of educational psychologists and reading researchers had conducted two decades of research on cognitive strategies, but they were the only ones familiar with the work. Cognitive strategies and the instructional research behind them remained in rather esoteric research journals read by few other than the people who conducted the research. Today, though, toward the end of the first decade in the twenty-first century, all reading educators have heard the term strategy instruction, and many of them incorporate strategy instruction into their literacy programs. In the translation from research to practice, strategy instruction has made its way into mainstream education. Teacher resource books about teaching strategies abound Some educators think that teaching comprehension means simply teaching strategies. In the transition from research to practice, strategy instruction has morphed into so many things that it no longer has a shared meaning. The purpose of this chapter is to help researchers and educators understand cognitive strategy instruction from both research and practice perspectives. It could be argued that the literature is currently saturated with research, articles and books about 3 cognitive strategies. Nevertheless, there is still much to write about cognitive strategies— both in terms of more recent research and also in terms of how the construct has made its way into the reading instruction practitioner field. This chapter first focuses on defining cognitive strategy instruction in terms of its genesis and on presenting the landmark studies that defined the field. Then, the chapter reviews several more recent sets of studies in which cognitive strategy instruction was embedded in conceptual and programmatic frameworks for comprehension instruction. Third, the chapter reviews more recent research in the content areas in which cognitive strategy instruction has been adapted for instructional purposes in secondary content areas, especially history. After this research has been reviewed, we take a more applied examination of cognitive strategy instruction as it has been conceptualized in practice. Here we make conceptual distinctions between strategies and the plethora of related constructs in the field today. We conclude with a discussion of the important distinction between the curriculum of cognitive strategies and the instructional delivery system used to teach strategies. We begin the chapter with a definition of cognitive strategies and the foundational body of theory and research in cognitive strategy instruction …
SMaRtLight: A Practical Fault-Tolerant SDN Controller

The increase in the number of SDN-based deployments in production networks is triggering the need to consider fault-tolerant designs of controller architectures. Commercial SDN controller solutions incorporate fault tolerance, but there has been little discussion in the SDN literature on the design of such systems and the tradeoffs involved. To fill this gap, we present a by-construction design of a fault-tolerant controller, and materialize it by proposing and formalizing a practical architecture for small to medium-sized scale networks. A central component of our particular design is a replicated shared database that stores all network state. Contrary to the more common primary-backup approaches, the proposed design guarantees a smooth transition in case of failures and avoids the need of an additional coordination service. Our preliminary results show that the performance of our solution fulfills the demands of the target networks. We hope this paper to be a first step in what we consider a necessary discussion on how to build robust SDNs.
HGsuspector : Scalable Collective Fraud Detection in Heterogeneous Graphs

Graph can straightforwardly represent the relations between the objects, which inevitably draws a lot of attention of both academia and industry. Achievements mainly concentrate on homogeneous graph and bipartite graph. However, it is difficult to use existing algorithm in actual scenarios. Because in the real world, the type of the objects and the relations are diverse and the amount of the data can be very huge. Considering of the characteristics of "black market", we proposeHGsuspector, a novel and scalable algorithm for detecting collective fraud in directed heterogeneous graphs.We first decompose directed heterogeneous graphs into a set of bipartite graphs, then we define a metric on each connected bipartite graph and calculate scores of it, which fuse the structure information and event probability. The threshold for distinguishing between normal and abnormal can be obtained by statistic or other anomaly detection algorithms in scores space. We also provide a technical solution for fraud detection in e-commerce scenario, which has been successfully applied in Jingdong e-commerce platform to detect collective fraud in real time. The experiments on real-world datasets, which has billion nodes and edges, demonstrate that HGsuspector is more accurate and fast than the most practical and state-of-the-art approach by far.
End-to-End Dynamic Query Memory Network for Entity-Value Independent Task-Oriented Dialog

In this paper, we propose an end-to-end Dynamic Query Memory Network (DQMemNN) with a delexicalization mechanism for task-oriented dialog systems. The added dynamic component enables memory networks to capture the dialog's sequential dependencies by using a context-based query. Besides, the delexicalization mechanism reduces learning complexity and it alleviates the out-of-vocabulary entity problems. Experiments show that DQMemNN outperforms original end-to-end memory network models on bAbI full-dialog task by 3.1 % per-response and 39.3% per-dialog accuracy. In addition, the proposed framework achieves a promising average per-response accuracy of 99.7% and per-dialog accuracy of 97.8% without hand-crafted rules and features.