nils-herrmann/clean_scirepeval huggingface dataset:
Asymmetrical interleaving strategy for multi-channel PFC

The continuously increasing efficiency and power density requirement of the AC-DC front-end converter posed a big challenge for today's power factor correction (PFC) circuit design. The multi-channel interleaved PFC is a promising candidate to achieve the goals. In this paper, the multi-channel interleaving impact on the EMI filter design and the output capacitor life time is investigated. By properly choosing the interleaving channel number and the switching frequency, the EMI filter size and cost can be effectively reduced. Further more; multi-channel PFC with asymmetrical interleaving strategy is introduced, and the additional benefit on the EMI filter is identified. At the output side, different interleaving schemes impact on the output capacitor ripple cancellation effect is also investigated and compared.
#Unconfirmed: Classifying Rumor Stance in Crisis-Related Social Media Messages

It is well-established that within crisis-related communications, rumors are likely to emerge. False rumors, i.e. misinformation, can be detrimental to crisis communication and response; it is therefore important not only to be able to identify messages that propagate rumors, but also corrections or denials of rumor content. In this work, we explore the task of automatically classifying rumor stances expressed in crisisrelated content posted on social media. Utilizing a dataset of over 4,300 manually coded tweets, we build a supervised machine learning model for this task, achieving an accuracy over 88% across a diverse set of rumors of different types.
Unsupervised Detection of Music Boundaries by Time Series Structure Features

Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.
Real time car driver's condition monitoring system

Driver's drowsiness and fatigue have been major causes of the serious traffic accidents, which make this an area of great socioeconomic concern. This paper describes the design of ECG (Electrocardiogram) sensor with conductive fabric electrodes and PPG (Photoplethysmogram) sensor to obtain physiological signals for car driver's health condition monitoring. ECG and PPG signals are transmitted to base station connected to the server PC via personal area network for practical test. Intelligent health condition monitoring system is designed at the server to analyze the PPG and ECG signals. Our purpose for intelligent health condition monitoring system is managed to process HRV signals analysis derived from the physiological signals in time and frequency domain and to evaluate the driver's drowsiness status.
Part-Stacked CNN for Fine-Grained Visual Categorization

In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that encodes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from multiple perspectives of classification accuracy, model interpretability, and efficiency. Being able to provide interpretable recognition results in realtime, the proposed method is believed to be effective in practical applications.
A Review of Machine Learning Algorithms for Text-Documents Classification

With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and knowledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing techniques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing literature.
Pancultural nostalgia: prototypical conceptions across cultures.

Nostalgia is a frequently experienced complex emotion, understood by laypersons in the United Kingdom and United States of America to (a) refer prototypically to fond, self-relevant, social memories and (b) be more pleasant (e.g., happy, warm) than unpleasant (e.g., sad, regretful). This research examined whether people across cultures conceive of nostalgia in the same way. Students in 18 countries across 5 continents (N = 1,704) rated the prototypicality of 35 features of nostalgia. The samples showed high levels of agreement on the rank-order of features. In all countries, participants rated previously identified central (vs. peripheral) features as more prototypical of nostalgia, and showed greater interindividual agreement regarding central (vs. peripheral) features. Cluster analyses revealed subtle variation among groups of countries with respect to the strength of these pancultural patterns. All except African countries manifested the same factor structure of nostalgia features. Additional exemplars generated by participants in an open-ended format did not entail elaboration of the existing set of 35 features. Findings identified key points of cross-cultural agreement regarding conceptions of nostalgia, supporting the notion that nostalgia is a pancultural emotion.
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose predictions are combined using a random forest classifier. Our approach was evaluated on the datasets of the SemEval-2016 competition (Task 4) outperforming all other approaches for the Message Polarity Classification task.
Knowledge discovery, analysis and prediction in healthcare using data mining and analytics

Taking care and maintenance of a healthy population is the Strategy of each country. Information and communication technologies in the health care system have led to many changes in order to improve the quality of health care services to patients, rational spending time and reduce costs. In the booming field of IT research, the reach of drug delivery, information on grouping of similar drugs has been lacking. The wealth distribution and drug affordability at a certain demographic has been interlinked and proposed in this paper. Looking at the demographic we analyze and group the drugs based on target action and link this to the wealth and the people to medicine ratio, which can be accomplished via data mining and web mining. The data thus mined will be analysed and made available to public and commercial purpose for their further knowledge and benefit.
Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction

Suppose a bank has a database of people’s details and their credit rating. These details would probably be the person’s financial characteristics such as how much they earn, whether they own or rent a house, and so on, and would be used to calculate the person’s credit rating. However, the process for calculating the credit rating from the person’s details is quite expensive, so the bank would like to find some way to reduce this cost. They realise that by the very nature of a credit rating, people who have similar financial details would be given similar credit ratings. Therefore, they would like to be able to use this existing database to predict a new customer’s credit rating, without having to perform all the calculations.
Evidence that gendered wording in job advertisements exists and sustains gender inequality.

Social dominance theory (Sidanius & Pratto, 1999) contends that institutional-level mechanisms exist that reinforce and perpetuate existing group-based inequalities, but very few such mechanisms have been empirically demonstrated. We propose that gendered wording (i.e., masculine- and feminine-themed words, such as those associated with gender stereotypes) may be a heretofore unacknowledged, institutional-level mechanism of inequality maintenance. Employing both archival and experimental analyses, the present research demonstrates that gendered wording commonly employed in job recruitment materials can maintain gender inequality in traditionally male-dominated occupations. Studies 1 and 2 demonstrated the existence of subtle but systematic wording differences within a randomly sampled set of job advertisements. Results indicated that job advertisements for male-dominated areas employed greater masculine wording (i.e., words associated with male stereotypes, such as leader, competitive, dominant) than advertisements within female-dominated areas. No difference in the presence of feminine wording (i.e., words associated with female stereotypes, such as support, understand, interpersonal) emerged across male- and female-dominated areas. Next, the consequences of highly masculine wording were tested across 3 experimental studies. When job advertisements were constructed to include more masculine than feminine wording, participants perceived more men within these occupations (Study 3), and importantly, women found these jobs less appealing (Studies 4 and 5). Results confirmed that perceptions of belongingness (but not perceived skills) mediated the effect of gendered wording on job appeal (Study 5). The function of gendered wording in maintaining traditional gender divisions, implications for gender parity, and theoretical models of inequality are discussed.
Fast Trajectory Planning and Robust Trajectory Tracking for Pedestrian Avoidance

This paper presents an integrated design method for pedestrian avoidance by considering the interaction between trajectory planning and trajectory tracking. This method aims to reduce the need for control calibration by properly considering plant uncertainties and tire force limits at the design stage. Two phases of pedestrian avoidance—trajectory planning and trajectory tracking—are designed in an integrated manner. The available tire force is distributed to the feedforward part, which is used to generate the nominal trajectory in trajectory planning phase, and to the feedback part, which is used for trajectory tracking. The trajectory planning problem is solved not by searching through a continuous spectrum of steering/braking actions, but by examining a limited set of “motion primitives,” or motion templates that can be adopted in sequence to avoid the pedestrian. An emergency rapid random tree (RRT) methodology is proposed to quickly identify a feasible solution. Subsequently, in order to guarantee accuracy and provide safety margin in trajectory tracking with presence of model uncertainties and exogenous disturbance, a simplified LQR-based funnel algorithm is proposed. Simulation results provide insight into how pedestrian collisions can be avoided under given initial vehicle and pedestrian states.
Enabling Client-Side Crash-Resistance to Overcome Diversification and Information Hiding

It is a well-known issue that attack primitives which exploit memory corruption vulnerabilities can abuse the ability of processes to automatically restart upon termination. For example, network services like FTP and HTTP servers are typically restarted in case a crash happens and this can be used to defeat Address Space Layout Randomization (ASLR). Furthermore, recently several techniques evolved that enable complete process memory scanning or code-reuse attacks against diversified and unknown binaries based on automated restarts of server applications. Until now, it is believed that client applications are immune against exploit primitives utilizing crashes. Due to their hard crash policy, such applications do not restart after memory corruption faults, making it impossible to touch memory more than once with wrong permissions. In this paper, we show that certain client application can actually survive crashes and are able to tolerate faults, which are normally critical and force program termination. To this end, we introduce a crash-resistance primitive and develop a novel memory scanning method with memory oracles without the need for control-flow hijacking. We show the practicability of our methods for 32-bit Internet Explorer 11 on Windows 8.1, and Mozilla Firefox 64-bit (Windows 8.1 and Linux 3.17.1). Furthermore, we demonstrate the advantages an attacker gains to overcome recent code-reuse defenses. Latest advances propose fine-grained re-randomization of the address space and code layout, or hide sensitive information such as code pointers to thwart tampering or misuse. We show that these defenses need improvements since crash-resistance weakens their security assumptions. To this end, we introduce the concept of CrashResistant Oriented Programming (CROP). We believe that our results and the implications of memory oracles will contribute to future research on defensive schemes against code-reuse attacks.
Drivers, challenges and consequences of E-recruiting: a literature review

Using a literature review of 80 journals and proceedings we identified 23 research papers discussing driver, challenges and consequences of e-recruiting. In total 14 drivers, 15 challenges and 9 consequences of implementing and using e-recruiting has been identified. Based on these results the paper introduces a model of drivers, challenges and consequences of e-recruiting and discusses implications for research and practice. The analysis reveals that e-recruiting will reduce costs for recruitment and selection, increase the number of suitable applicants, enable time savings for both organizations and applicants and improve the corporate image. These four consequences also have been identified as major drivers of e-recruiting projects. The identified challenges include the exclusion of potential applicants, the deception of applicants in e-assessment procedures, the security of applicants' data and low qualification of applicants.
Efficient covariance matrix update for variable metric evolution strategies

Randomized direct search algorithms for continuous domains, such as evolution strategies, are basic tools in machine learning. They are especially needed when the gradient of an objective function (e.g., loss, energy, or reward function) cannot be computed or estimated efficiently. Application areas include supervised and reinforcement learning as well as model selection. These randomized search strategies often rely on normally distributed additive variations of candidate solutions. In order to efficiently search in non-separable and ill-conditioned landscapes the covariance matrix of the normal distribution must be adapted, amounting to a variable metric method. Consequently, covariance matrix adaptation (CMA) is considered state-of-the-art in evolution strategies. In order to sample the normal distribution, the adapted covariance matrix needs to be decomposed, requiring in general Θ(n 3) operations, where n is the search space dimension. We propose a new update mechanism which can replace a rank-one covariance matrix update and the computationally expensive decomposition of the covariance matrix. The newly developed update rule reduces the computational complexity of the rank-one covariance matrix adaptation to Θ(n 2) without resorting to outdated distributions. We derive new versions of the elitist covariance matrix adaptation evolution strategy (CMA-ES) and the multi-objective CMA-ES. These algorithms are equivalent to the original procedures except that the update step for the variable metric distribution scales better in the problem dimension. We also introduce a simplified variant of the non-elitist CMA-ES with the incremental covariance matrix update and investigate its performance. Apart from the reduced time-complexity of the distribution update, the algebraic computations involved in all new algorithms are simpler compared to the original versions. The new update rule improves the performance of the CMA-ES for large scale machine learning problems in which the objective function can be evaluated fast.
Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of the opinions of different experts. The experts in this work are two existing techniques for feature location: a scenario-based probabilistic ranking of events and an information-retrieval-based technique that uses latent semantic indexing. The combination of these two experts is empirically evaluated through several case studies, which use the source code of the Mozilla Web browser and the Eclipse integrated development environment. The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently
Screaming fast Galois field arithmetic using intel SIMD instructions

Galois Field arithmetic forms the basis of Reed-Solomon and other erasure coding techniques to protect storage systems from failures. Most implementations of Galois Field arithmetic rely on multiplication tables or discrete logarithms to perform this operation. However, the advent of 128-bit instructions, such as Intel’s Streaming SIMD Extensions, allows us to perform Galois Field arithmetic much faster. This short paper details how to leverage these instructions for various field sizes, and demonstrates the significant performance improvements on commodity microprocessors. The techniques that we describe are available as open source software.
A GUI Crawling-Based Technique for Android Mobile Application Testing

As mobile applications become more complex, specific development tools and frameworks as well as cost effective testing techniques and tools will be essential to assure the development of secure, high-quality mobile applications. This paper addresses the problem of automatic testing of mobile applications developed for the Google Android platform, and presents a technique for rapid crash testing and regression testing of Android applications. The technique is based on a crawler that automatically builds a model of the application GUI and obtains test cases that can be automatically executed. The technique is supported by a tool for both crawling the application and generating the test cases. In the paper we present an example of using the technique and the tool for testing a real small size Android application that preliminary shows the effectiveness and usability of the proposed testing approach.
Activity Theory as a potential framework for human-computer interaction research

During the recent years the mainstream framework for HCI research — the informationprocessing cognitive psychology —has gained more and more criticism because of serious problems in applying it both in research and practical design. In a debate within HCI research the capability of information processing psychology has been questioned and new theoretical frameworks searched. This paper presents an overview of the situation and discusses potentials of Activity Theory as an alternative framework for HCI research and design.
Generating test data with enhanced context-free grammars

The use of context-free grammars to improve functional testing of very-large-scale integrated circuits is described. It is shown that enhanced context-free grammars are effective tools for generating test data. The discussion covers preliminary considerations, the first tests, generating systematic tests, and testing subroutines. The author's experience using context-free grammars to generate tests for VLSI circuit simulators indicates that they are remarkably effective tools that virtually anyone can use to debug virtually any program.<<ETX>>
Funneling and saltation effects for tactile interaction with virtual objects

Funneling and saltation are two major illusory feedback techniques for vibration-based tactile feedback. They are often put into practice e.g. to reduce the number of vibrators to be worn on the body and thereby build a less cumbersome feedback device. Recently, these techniques have been found to be applicable to eliciting "out of the body" experiences as well (e.g. through user-held external objects). This paper examines the possibility of applying this phenomenon to interacting with virtual objects. Two usability experiments were run to test the effects of funneling and saltation respectively for perceiving tactile sensation from a virtual object in an augmented reality setting. Experimental results have shown solid evidences for phantom sensations from virtual objects with funneling, but mixed results for saltation.
Improved Bully Election Algorithm for Distributed Systems

Electing a leader is a classical problem in distributed computing system. Synchronization between processes often requires one process acting as a coordinator. If an elected leader node fails, the other nodes of the system need to elect another leader without much wasting of time. The bully algorithm is a classical approach for electing a leader in a synchronous distributed computing system, which is used to determine the process with highest priority number as the coordinator. In this paper, we have discussed the limitations of Bully algorithm and proposed a simple and efficient method for the Bully algorithm which reduces the number of messages during the election. Our analytical simulation shows that, our proposed algorithm is more efficient than the Bully algorithm with fewer messages passing and fewer stages.
Real-time self-localization from panoramic images on mobile devices

Self-localization in large environments is a vital task for accurately registered information visualization in outdoor Augmented Reality (AR) applications. In this work, we present a system for self-localization on mobile phones using a GPS prior and an online-generated panoramic view of the user's environment. The approach is suitable for executing entirely on current generation mobile devices, such as smartphones. Parallel execution of online incremental panorama generation and accurate 6DOF pose estimation using 3D point reconstructions allows for real-time self-localization and registration in large-scale environments. The power of our approach is demonstrated in several experimental evaluations.