nils-herrmann/clean_scirepeval huggingface dataset:
A study on receiver circuit topology of a cordless battery charger for electric vehicles

A cordless battery charger will greatly improve the user friendliness of electric vehicles (EVs), accelerating the replacement of traditional internal combustion engine (ICE) vehicles with EVs and improving energy sustainability as a result. Resonant circuits are used for both the power transmitter and receiver of a cordless charger to compensate their coils and improve power transfer efficiency. However, conventional compensation circuit topology is not suitable for application to an EV, which involves very large power, a wide gap between the transmitter and receiver coils, and large horizontal misalignment. This paper proposes a novel compensation circuit topology that has a carefully designed series capacitor added to the parallel resonant circuit of the receiver. The proposed circuit has been implemented and tested on an EV. The simulation and experimental results are presented to show that the circuit can improve the power factor and power transfer efficiency, and as a result, allow a larger gap between the transmitter and receiver coils.
Markov Chain Monte Carlo Methods: Computation and Inference

This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods These methods, which are concerned with the simulation of high dimensional probability distributions, have gained enormous prominence and revolutionized Bayesian statistics The chapter provides background on the relevant Markov chain theory and provides detailed information on the theory and practice of Markov chain sampling based on the Metropolis-Hastings and Gibbs sampling algorithms Convergence diagnostics and strategies for implementation are also discussed A number of examples drawn from Bayesian statistics are used to illustrate the ideas The chapter also covers in detail the application of MCMC methods to the problems of prediction and model choice.
Video Games and Spatial Cognition

Video game enthusiasts spend many hours at play, and this intense activity has the potential to alter both brain and behavior. We review studies that investigate the ability of video games to modify processes in spatial cognition. We outline the initial stages of research into the underlying mechanisms of learning, and we also consider possible applications of this new knowledge. Several experiments have shown that playing action games induces changes in a number of sensory, perceptual, and attentional abilities that are important for many tasks in spatial cognition. These basic capacities include contrast sensitivity, spatial resolution, the attentional visual field, enumeration, multiple object tracking, and visuomotor coordination and speed. In addition to altering performance on basic tasks, playing action video games has a beneficial effect on more complex spatial tasks such as mental rotation, thus demonstrating that learning generalizes far beyond the training activities in the game. Far transfer of this sort is generally elusive in learning, and we discuss some early attempts to elucidate the brain functions that are responsible. Finally, we suggest that studying video games may contribute not only to an improved understanding of the mechanisms of learning but may also offer new approaches to teaching spatial skills.
Sgx-Lapd: Thwarting Controlled Side Channel Attacks via Enclave Verifiable Page Faults

To make outsourcing computing more practical, Intel recently introduced SGX, a hardware extension that creates secure enclaves for the execution of client applications. With SGX, instruction execution and data access inside an enclave are invisible to the underlying OS, thereby achieving both confidentiality and integrity for outsourced computing. However, since SGX excludes the OS from its trusted computing base, now a malicious OS can attack SGX applications, particularly through controlled side channel attacks, which can extract application secrets through page fault patterns. This paper presents SGX-LAPD, a novel defense that uses compiler instrumentation and enclave verifiable page fault to thwart malicious OS from launching page fault attacks. We have implemented SGX-LAPD atop Linux kernel 4.2.0 and LLVM 3.6.2. Our experimental results show that it introduces reasonable overhead for SGX-nbench, a set of SGX benchmark programs that we developed.
Evolutionary population dynamics and grey wolf optimizer

Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.
ANEMA: Autonomic network management architecture to support self-configuration and self-optimization in IP networks

The increasing scale, technology advances and services of modern networks have dramatically complicated their management such that in the near future it will be almost impossible for human administrators to monitor them. To control this complexity, IBM has introduced a promising approach aiming to create self-managed systems. This approach, called Autonomic Computing, aims to design computing equipment able to self-adapt its configuration and to self-optimize its performance depending on its situation in order to fulfill high-level objectives defined by the human operator. In this paper, we present our autonomic network management architecture (ANEMA) that implements several policy forms to achieve autonomic behaviors in the network equipments. In ANEMA, the high-level objectives of the human administrators and the users are captured and expressed in terms of ‘Utility Function’ policies. The ‘Goal’ policies describe the high-level management directives needed to guide the network to achieve the previous utility functions. Finally, the ‘behavioral’ policies describe the behaviors that should be followed by network equipments to react to changes in their context and to achieve the given ‘Goal’ policies. In order to highlight the benefits of ANEMA architecture and the continuum of policies to introduce autonomic management in a multiservice IP network, a testbed has been implemented and several scenarios have been executed.
Summarization beyond sentence extraction: A probabilistic approach to sentence compression

When humans produce summaries of documents, they do not simply extract sentences and concatenate them. Rather, they create new sentences that are grammatical, that cohere with one another, and that capture the most salient pieces of information in the original document. Given that large collections of text/abstract pairs are available online, it is now possible to envision algorithms that are trained to mimic this process. In this paper, we focus on sentence compression, a simpler version of this larger challenge. We aim to achieve two goals simultaneously: our compressions should be grammatical, and they should retain the most important pieces of information. These two goals can conflict. We devise both a noisy-channel and a decision-tree approach to the problem, and we evaluate results against manual compressions and a simple baseline.
Infrastructure for Usable Machine Learning: The Stanford DAWN Project

Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What’s Next) project at Stanford.
Aerial Shepherds: Coordination among UAVs and Swarms of Robots

We address the problem of deploying groups of tens or hundreds of unmanned ground vehicles (UGVs) in urban environments where a group of aerial vehicles (UAVs) can be used to coordinate the ground vehicles. We envision a hierarchy in which UAVs with aerial cameras can be used to monitor and command a swarm of UGVs, controlling the splitting and merging of the swarm into groups and the shape (distribution) and motion of each group. We call these UAVs Aerial Shepherds. We show a probabilistic approach using the EM algorithm for the initial assignment of shepherds to groups and present behaviors that allow an efficient hierarchical decomposition. We illustrate the framework through simulation examples, with applications to deployment in an urban environment.
Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems

High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.
Chaos in random neural networks.

A continuous-time dynamic model of a network of N nonlinear elements interacting via random asymmetric couplings is studied. A self-consistent mean-field theory, exact in the N ~ limit, predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter. The autocorrelations of the chaotic flow as well as the maximal Lyapunov exponent are calculated.
Design for 5G Mobile Network Architecture

In this paper we propose a new design solution for network architecture of future 5G mobile networks. The proposed design is based on user-centric mobile environment with many wireless and mobile technologies on the ground. In heterogeneous wireless environment changes in all, either new or older wireless technologies, is not possible, so each solution towards the next generation mobile and wireless networks should be implemented in the service stratum, while the radio access technologies belong to the transport stratum regarding the Next Generation Networks approach. In the proposed design the user terminal has possibility to change the Radio Access Technology RAT based on certain criteria. For the purpose of transparent change of the RATs by the mobile terminal, we introduce so-called Policy-Router as node in the core network, which establishes IP tunnels to the mobile terminal via different available RATs to the terminal. The selection of the RAT is performed by the mobile terminal by using the proposed user agent for multi-criteria decision making based on the experience from the performance measurements performed by the mobile terminal. For the process of performance measurements we introduce the QoSPRO procedure for control information exchange between the mobile terminal and the Policy Router.
How brains make chaos in order to make sense of the world

Recent "connectionist" models provide a new explanatory alternative to the digital computer as a model for brain function. Evidence from our EEG research on the olfactory bulb suggests that the brain may indeed use computational mechanisms like those found in connectionist models. In the present paper we discuss our data and develop a model to describe the neural dynamics responsible for odor recognition and discrimination. The results indicate the existence of sensoryand motor-specific information in the spatial dimension of EEG activity and call for new physiological metaphors and techniques of analysis. Special emphasis is placed in our model on chaotic neural activity. We hypothesize that chaotic behavior serves as the essential ground state for the neural perceptual apparatus, and we propose a mechanism for acquiring new forms of patterned activity corresponding to new learned odors. Finally, some of the implications of our neural model for behavioral theories are briefly discussed. Our research, in concert with the connectionist work, encourages a reevaluation of explanatory models that are based only on the digital computer metaphor.
Randomizing Social Networks: a Spectrum Preserving Approach

Understanding the general properties of real social networks has gained much attention due to the proliferation of networked data. The nodes in the network are the individuals and the links among them denote their relationships. Many applications of networks such as anonymous Web browsing require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of the relationship. One general approach for this problem is to randomize the edges in true networks, and only disclose the randomized networks. In this paper, we investigate how various properties of networks may be affected due to randomization. Specifically, we focus on the spectrum since the eigenvalues of a network are intimately connected to many important topological features. We also conduct theoretical analysis on the extent to which edge anonymity can be achieved. A spectrum preserving graph randomization method, which can better preserve network properties while protecting edge anonymity, is then presented and empirically evaluated.
Beyond Skip Connections: Top-Down Modulation for Object Detection

In recent years, we have seen tremendous progress in the field of object detection. Most of the recent improvements have been achieved by targeting deeper feedforward networks. However, many hard object categories such as bottle, remote, etc. require representation of fine details and not just coarse, semantic representations. But most of these fine details are lost in the early convolutional layers. What we need is a way to incorporate finer details from lower layers into the detection architecture. Skip connections have been proposed to combine high-level and low-level features, but we argue that selecting the right features from low-level requires top-down contextual information. Inspired by the human visual pathway, in this paper we propose top-down modulations as a way to incorporate fine details into the detection framework. Our approach supplements the standard bottom-up, feedforward ConvNet with a top-down modulation (TDM) network, connected using lateral connections. These connections are responsible for the modulation of lower layer filters, and the top-down network handles the selection and integration of contextual information and lowlevel features. The proposed TDM architecture provides a significant boost on the COCO benchmark, achieving 28.6 AP for VGG16 and 35.2 AP for ResNet101 networks. Using InceptionResNetv2, our TDM model achieves 37.3 AP, which is the best single-model performance to-date on the COCO testdev benchmark, without any bells and whistles.
Effects of Excessive Internet Use on Undergraduate Students in Pakistan

This study was undertaken to investigate the positive and negative effects of excessive Internet use on undergraduate students. The Internet Effect Scale (IES), especially constructed by the authors to determine these effects, consisted of seven dimensions namely: behavioral problems, interpersonal problems, educational problems, psychological problems, physical problems, Internet abuse, and positive effects. The sample consisted of 200 undergraduate students studying at the GC University Lahore, Pakistan. A set of Pearson Product Moment correlations showed positive associations between time spent on the Internet and various dimensions of the IES indicating that excessive Internet use can lead to a host of problems of educational, physical, psychological and interpersonal nature. However, a greater number of students reported positive than negative effects of Internet use. Without negating the advantages of Internet, the current findings suggest that Internet use should be within reasonable limits focusing more on activities enhancing one's productivity.
A Critical Evaluation and Framework of Business Process Improvement Methods

The redesign of business processes has a huge potential in terms of reducing costs and throughput times, as well as improving customer satisfaction. Despite rapid developments in the business process management discipline during the last decade, a comprehensive overview of the options to methodologically support a team to move from as-is process insights to to-be process alternatives is lacking. As such, no safeguard exists that a systematic exploration of the full range of redesign possibilities takes place by practitioners. Consequently, many attractive redesign possibilities remain unidentified and the improvement potential of redesign initiatives is not fulfilled. This systematic literature review establishes a comprehensive methodological framework, which serves as a catalog for process improvement use cases. The framework contains an overview of all the method options regarding the generation of process improvement ideas. This is established by identifying six key methodological decision areas, e.g. the human actors who can be invited to generate these ideas or the information that can be collected prior to this act. This framework enables practitioners to compose a well-considered method to generate process improvement ideas themselves. Based on a critical evaluation of the framework, the authors also offer recommendations that support academic researchers in grounding and improving methods for generating process Accepted after two revisions by the editors of the special issue. Electronic supplementary material The online version of this article (doi:10.1007/s12599-015-0417-x) contains supplementary material, which is available to authorized users. ir. R. J. B. Vanwersch (&) Dr. ir. I. Vanderfeesten Prof. Dr. ir. P. Grefen School of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, De Lismortel, room K.3, P.O. Box 513, 5600 MB Eindhoven, The Netherlands e-mail: r.j.b.vanwersch@tue.nl Dr. K. Shahzad College of Information Technology, University of the Punjab, Lahore, Pakistan Dr. K. Vanhaecht Department of Public Health and Primary Care, KU Leuven, University of Leuven, Leuven, Belgium Dr. K. Vanhaecht Department of Quality Management, University Hospitals KU Leuven, Leuven, Belgium Prof. Dr. ir. L. Pintelon Centre for Industrial Management/Traffic and Infrastructure, KU Leuven, University of Leuven, Leuven, Belgium Prof. Dr. J. Mendling Institute for Information Business, Vienna University of Economics and Business, Vienna, Austria Prof. Dr. G. G. van Merode Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands Prof. Dr. ir. H. A. Reijers Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands Prof. Dr. ir. H. A. Reijers Department of Computer Science, VU University Amsterdam, Amsterdam, The Netherlands 123 Bus Inf Syst Eng 58(1):43–53 (2016) DOI 10.1007/s12599-015-0417-x
Affective News : The Automated Coding of Sentiment in Political Texts LORI YOUNG and STUART SOROKA

An increasing number of studies in political communication focus on the “sentiment” or “tone” of news content, political speeches, or advertisements. This growing interest in measuring sentiment coincides with a dramatic increase in the volume of digitized information. Computer automation has a great deal of potential in this new media environment. The objective here is to outline and validate a new automated measurement instrument for sentiment analysis in political texts. Our instrument uses a dictionary-based approach consisting of a simple word count of the frequency of keywords in a text from a predefined dictionary. The design of the freely available Lexicoder Sentiment Dictionary (LSD) is discussed in detail here. The dictionary is tested against a body of human-coded news content, and the resulting codes are also compared to results from nine existing content-analytic dictionaries. Analyses suggest that the LSD produces results that are more systematically related to human coding than are results based on the other available dictionaries. The LSD is thus a useful starting point for a revived discussion about dictionary construction and validation in sentiment analysis for political communication.
Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning

Melanoma is amongst most aggressive types of cancer. However, it is highly curable if detected in its early stages. Prescreening of suspicious moles and lesions for malignancy is of great importance. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation of skin lesions is accurate detection of lesion’s region, i.e. segmentation of an image into two regions as lesion and normal skin. Accurate segmentation can be challenging due to burdens such as illumination variation and low contrast between lesion and healthy skin. In this paper, a method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN). Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is used for more accurate detection of a lesion’s border. The output segmentation mask is refined by some post processing operations. The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.