nils-herrmann/clean_scirepeval huggingface dataset:
Swiss rectifier — A novel three-phase buck-type PFC topology for Electric Vehicle battery charging

This paper introduces a novel three-phase buck-type unity power factor rectifier appropriate for high power Electric Vehicle battery charging mains interfaces. The characteristics of the converter, named the Swiss Rectifier, including the principle of operation, modulation strategy, suitable control structure, and dimensioning equations are described in detail. Additionally, the proposed rectifier is compared to a conventional 6-switch buck-type ac-dc power conversion. According to the results, the Swiss Rectifier is the topology of choice for a buck-type PFC. Finally, the feasibility of the Swiss Rectifier concept for buck-type rectifier applications is demonstrated by means of a hardware prototype.
The Bag of Communities: Identifying Abusive Behavior Online with Preexisting Internet Data

Since its earliest days, harassment and abuse have plagued the Internet. Recent research has focused on in-domain methods to detect abusive content and faces several challenges, most notably the need to obtain large training corpora. In this paper, we introduce a novel computational approach to address this problem called Bag of Communities (BoC)---a technique that leverages large-scale, preexisting data from other Internet communities. We then apply BoC toward identifying abusive behavior within a major Internet community. Specifically, we compute a post's similarity to 9 other communities from 4chan, Reddit, Voat and MetaFilter. We show that a BoC model can be used on communities "off the shelf" with roughly 75% accuracy---no training examples are needed from the target community. A dynamic BoC model achieves 91.18% accuracy after seeing 100,000 human-moderated posts, and uniformly outperforms in-domain methods. Using this conceptual and empirical work, we argue that the BoC approach may allow communities to deal with a range of common problems, like abusive behavior, faster and with fewer engineering resources.
Development of 6kV SiC hybrid power switch based on 1200V SiC JFET and MOSFET

Series-connected power switch provides a viable solution to implement high voltage and high frequency converters. By using the commercially available 1200V Silicon Carbide (SiC) Junction Field Effect Transistor (JFET) and Metal Oxide semiconductor Filed-effect Transistor (MOSFET), a 6 kV SiC hybrid power switch concept and its application are demonstrated. To solve the parameter deviation issue in the series device structure, an optimized voltage control method is introduced, which can guarantee the equal voltage sharing under both static and dynamic state. Without Zener diode arrays, this strategy can significantly reduce the turn-off switching loss. Moreover, this hybrid MOSFET-JFETs concept is also presented to suppress the silicon MOSFET parasitic capacitance effect. In addition, the positive gate drive voltage greatly accelerates turn-on speed and decreases the switching loss. Compared with the conventional super-JFETs, the proposed scheme is suitable for series-connected device, and can achieve better performance. The effectiveness of this method is validated by simulations and experiments, and promising results are obtained.
Waste collection multi objective model with real time traceability data.

Waste collection is a highly visible municipal service that involves large expenditures and difficult operational problems, plus it is expensive to operate in terms of investment costs (i.e. vehicles fleet), operational costs (i.e. fuel, maintenances) and environmental costs (i.e. emissions, noise and traffic congestions). Modern traceability devices, like volumetric sensors, identification RFID (Radio Frequency Identification) systems, GPRS (General Packet Radio Service) and GPS (Global Positioning System) technology, permit to obtain data in real time, which is fundamental to implement an efficient and innovative waste collection routing model. The basic idea is that knowing the real time data of each vehicle and the real time replenishment level at each bin makes it possible to decide, in function of the waste generation pattern, what bin should be emptied and what should not, optimizing different aspects like the total covered distance, the necessary number of vehicles and the environmental impact. This paper describes a framework about the traceability technology available in the optimization of solid waste collection, and introduces an innovative vehicle routing model integrated with the real time traceability data, starting the application in an Italian city of about 100,000 inhabitants. The model is tested and validated using simulation and an economical feasibility study is reported at the end of the paper.
A Measurement Based Rogue AP Detection Scheme

This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing based technique that allows the user to avoid connecting to rogue APs. Our method employs the round trip time between the user and the DNS server to independently determine whether an AP is legitimate or not without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance.
Virtual Network Embedding: A Survey

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change. Application of this technology relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as "Virtual Network Embedding (VNE)" algorithms. This paper presents a survey of current research in the VNE area. Based upon a novel classification scheme for VNE algorithms a taxonomy of current approaches to the VNE problem is provided and opportunities for further research are discussed.
Query-Oriented Multi-Document Summarization via Unsupervised Deep Learning

Extractive style query oriented multi document summariza tion generates the summary by extracting a proper set of sentences from multiple documents based on the pre given query. This paper proposes a novel multi document summa rization framework via deep learning model. This uniform framework consists of three parts: concepts extraction, summary generation, and reconstruction validation, which work together to achieve the largest coverage of the docu ments content. A new query oriented extraction technique is proposed to concentrate distributed information to hidden units layer by layer. Then, the whole deep architecture is fi ne tuned by minimizing the information loss of reconstruc tion validation. According to the concentrated information, dynamic programming is used to seek most informative set of sentences as the summary. Experiments on three bench mark datasets demonstrate the effectiveness of the proposed framework and algorithms.
Digital jewelry: wearable technology for everyday life

Pervasive technology devices that intend to be worn must not only meet our functional requirements but also our social, emotional, and aesthetic needs. Current pervasive devices such as the PDA or cell phone are more portable than wearable, yet still they elicit strong consumer demand for intuitive interfaces and well-designed forms. Looking to the future of wearable pervasive devices, we can imagine an even greater demand for meaningful forms for objects nestled so close to our bodies. They will need to reflect our tastes and moods, and allow us to express our personalities, cultural beliefs, and values. Digital Jewelry explores a new wearable technology form that is based in jewelry design, not in technology. Through prototypes and meaningful scenarios, digital jewelry offers new ideas to consider in the design of wearable devices.
CONSUMPTION AND PERCEPTIONS OF SOCIAL REALITY Effects and underlying processes

I opened this chapter in the second edition of Bryant and Zillmann’s Media Effects series with the same quote. I retained it for this updated volume because it still rings true, despite some significant changes in the media landscape. Although in the movie it is unclear whether his words were those of a madman or a sage, few would be likely to question Howard Beale’s claim that television presents a distorted view of reality. Certainly, one can argue that aspects of media content, format, and presentation have changed significantly in just the last few years, with a rise in so-called “reality programming,” made popular by the initial success of programs such as Survivor, and more recently by programs such as American Idol. Yet charges such as scripting of outcomes of competitions, selection of contestants based on audience appeal, and product placements have undermined the claim that these programs present the world as it really is. But even if most people do not question the premise that typical television fare distorts reality, what they do question is if the distortion has any effect, and if so, why and how. These interrelated questions about the why and how of media effects lie at the heart of scholarly debates and critiques of media effects research. Over the past few decades, there have been two persistent criticisms. One is that the evidence accumulated to date has provided little indication of sizable media effects on viewers’ thoughts, feelings, or actions, in spite of a generally held “myth of massive media impact” by many researchers (McGuire, 1986). The second criticism is that it has for the most part lacked any focus on explanatory mechanisms. That is, media effects research has been primarily concerned with relations between input variables (e.g., media information and its characteristics) and output variables (e.g., attitudes, beliefs, behavior), with little consideration of the cognitive processes that might mediate these relations (Hawkins & Pingree, 1990; Reeves, Chaffee, & Tims, 1982; see also Wyer, 1980).
Empowering individuals with do-it-yourself assistive technology

Technologies empower individuals to accomplish tasks they might not be able to do otherwise. Unfortunately, a large percentage of Assistive Technology devices that are purchased (35% or more) end up unused or abandoned [7,10], leaving many people with Assistive Technology that is inappropriate for their needs. Low acceptance rates of Assistive Technology occur for many reasons, but common factors include 1) lack of considering user opinion in selection, 2) ease in obtaining devices, 3) poor device performance, and 4) changes in user needs and priorities [7]. We are working to help more people gain access to the Assistive Technology they need by empowering non-engineers to "Do-It-Yourself" (DIY) and create, modify, or build. This paper illustrates that it is possible to custom-build Assistive Technology, and argues why empowering users to make their own Assistive Technology can improve the adoption process (and subsequently adoption rates). We discuss DIY experiences and impressions from individuals who have either built Assistive Technology before, or rely on it. We found that increased control over design elements, passion, and cost motivated individuals to make their own Assistive Technology instead of buying it. We discuss how a new generation of rapid prototyping tools and online communities can empower more individuals. We synthesize our findings into design recommendations to help promote future DIY-AT success.
Recommender Systems for the Semantic Web

This paper presents a semantics-based approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. To achieve such dynamic behaviour the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items.
Modeling Bitcoin Contracts by Timed Automata

Bitcoin is a peer-to-peer cryptographic currency system. Since its introduction in 2008, Bitcoin has gained noticeable popularity, mostly due to its following properties: (1) the transaction fees are very low, and (2) it is not controlled by any central authority, which in particular means that nobody can “print” the money to generate inflation. Moreover, the transaction syntax allows to create the so-called contracts, where a number of mutually-distrusting parties engage in a protocol to jointly perform some financial task, and the fairness of this process is guaranteed by the properties of Bitcoin. Although the Bitcoin contracts have several potential applications in the digital economy, so far they have not been widely used in real life. This is partly due to the fact that they are cumbersome to create and analyze, and hence risky to use. In this paper we propose to remedy this problem by using the methods originally developed for the computer-aided analysis for hardware and software systems, in particular those based on the timed automata. More concretely, we propose a framework for modeling the Bitcoin contracts using the timed automata in the UPPAAL model checker. Our method is general and can be used to model several contracts. As a proof-of-concept we use this framework to model some of the Bitcoin contracts from our recent previous work. We then automatically verify their security in UPPAAL, finding (and correcting) some subtle errors that were difficult to spot by the manual analysis. We hope that our work can draw the attention of the researchers working on formal modeling to the problem of the Bitcoin contract verification, and spark off more research on this topic.
Appraisals of Emotion-Eliciting Events : Testing a Theory of Discrete Emotions

A recent theory (Roseman, 1979,1984) attempts to specify the particular appraisals of events that elicit 16 discrete emotions. This study tested hypotheses from the latest version of the theory and compared them with hypotheses derived from appraisal theories proposed by Arnold (1960) and by Scherer (1988), using procedures designed to address some prior methodological problems. Results provided empirical support for numerous hypotheses linking particular appraisals of situational state (motive-inconsistent/motive-consistent), motivational state (punishment/reward), probability (uncertain/certain), power (weak/strong), legitimacy (negative outcome deserved/positive outcome deserved), and agency (circumstances/other person/self) to particular emotions. Where hypotheses were not supported, new appraisal-emotion relationships that revise the theory were proposed.
Fast Bayesian hyperparameter optimization on large datasets ∗

Bayesian optimization has become a successful tool for optimizing the hyperparameters of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single configuration often takes hours, days, or even weeks, which limits the achievable performance. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed Fabolas, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that Fabolas often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods or the recently proposed bandit strategy Hyperband.
Big Data: Astronomical or Genomical?

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a "four-headed beast"--it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the "genomical" challenges of the next decade.
Cognitively Engaging Chronic Physical Activity, But Not Aerobic Exercise, Affects Executive Functions in Primary School Children: A Group-Randomized Controlled Trial.

Although the positive effects of different kinds of physical activity (PA) on cognitive functioning have already been demonstrated in a variety of studies, the role of cognitive engagement in promoting children's executive functions is still unclear. The aim of the current study was therefore to investigate the effects of two qualitatively different chronic PA interventions on executive functions in primary school children. Children (N = 181) aged between 10 and 12 years were assigned to either a 6-week physical education program with a high level of physical exertion and high cognitive engagement (team games), a physical education program with high physical exertion but low cognitive engagement (aerobic exercise), or to a physical education program with both low physical exertion and low cognitive engagement (control condition). Executive functions (updating, inhibition, shifting) and aerobic fitness (multistage 20-m shuttle run test) were measured before and after the respective condition. Results revealed that both interventions (team games and aerobic exercise) have a positive impact on children's aerobic fitness (4-5% increase in estimated VO2max). Importantly, an improvement in shifting performance was found only in the team games and not in the aerobic exercise or control condition. Thus, the inclusion of cognitive engagement in PA seems to be the most promising type of chronic intervention to enhance executive functions in children, providing further evidence for the importance of the qualitative aspects of PA.
A Method for Hand Kinematics Designers 7 Billion Perfect Hands

In the past decade the design of kinematic models for hands has been addressed using several different approaches such as numerical optimization or direct measurement of human hands. However, once the mechanical and structural design constraints are included, no existing kinematic model of the hand fits the needs of the DLR Hand Arm System. The diversity of human hands reveals that the functional aspects are more important than the kinematics itself. In order to develop a suitable model for the hand arm system a more empirical method was developed. Based on an basic hand skeleton, that incorporates the structural constraints, incremental variations are performed to improve the parameters to fit the functional needs of the hand. The tests are derived from a set of common grasps as well as some tests performed by surgeons. To be able to rapidly appreciate the results simple cardboard prototypes are constructed. The resulting kinematics revealed to be a promising and suitable hand kinematics for the DLR Hand Arm System. The design is realized in hardware.
Single-channel multi-talker speech recognition with permutation invariant training

Although great progresses have been made in automatic speech recognition (ASR), significant performance degradation is still observed when recognizing multi-talker mixed speech. In this paper, we propose and evaluate several architectures to address this problem under the assumption that only a single channel of mixed signal is available. Our technique extends permutation invariant training (PIT) by introducing the frontend feature separation module with the minimum mean square error (MSE) criterion and the back-end recognition module with the minimum cross entropy (CE) criterion. More specifically, during training we compute the average MSE or CE over the whole utterance for each possible utterance-level output-target assignment, pick the one with the minimum MSE or CE, and optimize for that assignment. This strategy elegantly solves the label permutation problem observed in the deep learning based multi-talker mixed speech separation and recognition systems. The proposed architectures are evaluated and compared on an artificially mixed AMI dataset with both twoand threetalker mixed speech. The experimental results indicate that our proposed architectures can cut the word error rate (WER) by 45.0% and 25.0% relatively against the state-of-the-art singletalker speech recognition system across all speakers when their energies are comparable, for twoand three-talker mixed speech, respectively. To our knowledge, this is the first work on the multi-talker mixed speech recognition on the challenging speakerindependent spontaneous large vocabulary continuous speech task. permutation invariant training, multi-talker mixed speech recognition, feature separation, joint-optimization
KP-Miner: A Simple System for Effective Keyphrase Extraction

Automatic key phrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents a simple, yet effective algorithm (KP-Miner) for achieving this task. The result of an experiment carried out to investigate the effectiveness of this algorithm is also presented. In this experiment the devised algorithm is applied to six different datasets consisting of 481 documents. The results are then compared to two existing sophisticated machine learning based automatic keyphrase extraction systems. The results of this experiment show that the devised algorithm is comparable to both systems
Privacy-preserving outsourced classification in cloud computing

Classifier has been widely applied in machine learning, such as pattern recognition, medical diagnosis, credit scoring, banking and weather prediction. Because of the limited local storage at user side, data and classifier has to be outsourced to cloud for storing and computing. However, due to privacy concerns, it is important to preserve the confidentiality of data and classifier in cloud computing because the cloud servers are usually untrusted. In this work, we propose a framework for privacy-preserving outsourced classification in cloud computing (POCC). Using POCC, an evaluator can securely train a classification model over the data encrypted with different public keys, which are outsourced from the multiple data providers. We prove that our scheme is secure in the semi-honest model
Black-Scholes versus artificial neural networks in pricing FTSE 100 options

This paper compares the performance of Black-Scholes with an artificial neural network (ANN) in pricing European style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed-form model. For out-of themoney options, the ANN is clearly superior to Black-Scholes. For in-the-money options, if the sample space is restricted by excluding deep in-the-money and long maturity options (3.4% of total volume), the performance of the ANN is comparable with that of Black-Scholes. The superiority of the ANN is a surprising result, given that European style equity options are the home ground of Black-Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed-form model, or the closed-form model is less successful than Black-Scholes for equity options.