nils-herrmann/clean_scirepeval huggingface dataset:
Wireless charger networking for mobile devices: fundamentals, standards, and applications

Wireless charging is a technique of transmitting power through an air gap to an electrical device for the purpose of energy replenishment. Recently, wireless charging technology has significantly advanced in terms of efficiency and functionality. This article first presents an overview and fundamentals of wireless charging. We then provide the review of standards, that is, Qi and the Alliance for Wireless Power, and highlight their communication protocols. Next, we propose a novel concept of wireless charger networking that allows chargers to be connected to facilitate information collection and control. We demonstrate the application of the wireless charger network in user-charger assignment, which clearly shows the benefit in terms of reduced costs for users to identify the best chargers to replenish energy for their mobile devices.
Lighting Design: A Goal Based Approach using Optimisation

There is a need for reliable lighting design applications because available tools are limited and inappropriate for interactive or creative use. Architects and lighting designers need those applications to define, predict, test and validate lighting solutions for their problems. We present a new approach to the lighting design problem based on a methodology that includes the geometry of the scene, the properties of materials and the design goals. It is possible to obtain luminaire characteristics or other kind of results that maximise the attainment of the design goals, which may include different types of constraints or objectives (lighting, geometrical or others). The main goal, in our approach, is to improve the lighting design cycle. In this work we discuss the use of optimisation in lighting design, describe the implementation of the methodology, present real-world based examples and analyse in detail some of the complex technical problems associated and speculate on how to overcome them.
Dynamic scheduling on video transcoding for MPEG DASH in the cloud environment

The Dynamic Adaptive Streaming over HTTP (referred as MPEG DASH) standard is designed to provide high quality of media content over the Internet delivered from conventional HTTP web servers. The visual content, divided into a sequence of segments, is made available at a number of different bitrates so that an MPEG DASH client can automatically select the next segment to download and play back based on current network conditions. The task of transcoding media content to different qualities and bitrates is computationally expensive, especially in the context of large-scale video hosting systems. Therefore, it is preferably executed in a powerful cloud environment, rather than on the source computer (which may be a mobile device with limited memory, CPU speed and battery life). In order to support the live distribution of media events and to provide a satisfactory user experience, the overall processing delay of videos should be kept to a minimum. In this paper, we propose a novel dynamic scheduling methodology on video transcoding for MPEG DASH in a cloud environment, which can be adapted to different applications. The designed scheduler monitors the workload on each processor in the cloud environment and selects the fastest processors to run high-priority jobs. It also adjusts the video transcoding mode (VTM) according to the system load. Experimental results show that the proposed scheduler performs well in terms of the video completion time, system load balance, and video playback smoothness.
Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems

Spin-transfer torque magnetic memory (STT-MRAM) is currently under intense academic and industrial development, since it features non-volatility, high write and read speed and high endurance. In this work, we show that when used in a non-conventional regime, it can additionally act as a stochastic memristive device, appropriate to implement a “synaptic” function. We introduce basic concepts relating to spin-transfer torque magnetic tunnel junction (STT-MTJ, the STT-MRAM cell) behavior and its possible use to implement learning-capable synapses. Three programming regimes (low, intermediate and high current) are identified and compared. System-level simulations on a task of vehicle counting highlight the potential of the technology for learning systems. Monte Carlo simulations show its robustness to device variations. The simulations also allow comparing system operation when the different programming regimes of STT-MTJs are used. In comparison to the high and low current regimes, the intermediate current regime allows minimization of energy consumption, while retaining a high robustness to device variations. These results open the way for unexplored applications of STT-MTJs in robust, low power, cognitive-type systems.
An Exemplar-Based CRF for Multi-instance Object Segmentation

We address the problem of joint detection and segmentation of multiple object instances in an image, a key step towards scene understanding. Inspired by data-driven methods, we propose an exemplar-based approach to the task of instance segmentation, in which a set of reference image/shape masks is used to find multiple objects. We design a novel CRF framework that jointly models object appearance, shape deformation, and object occlusion. To tackle the challenging MAP inference problem, we derive an alternating procedure that interleaves object segmentation and shape/appearance adaptation. We evaluate our method on two datasets with instance labels and show promising results.
Analysis and Testing of Programs with Exception Handling Constructs

Analysis techniques, such as control flow, data flow, and control dependence, are used for a variety of software-engineering tasks, including structural and regression testing, dynamic execution profiling, static and dynamic slicing, and program understanding. To be applicable to programs in languages, such as Java and C++, these analysis techniques must account for the effects of exception occurrences and exception-handling constructs; failure to do so can cause the analysis techniques to compute incorrect results and thus, limit the usefulness of the applications that use them. This paper discusses the effects of exceptionhandling constructs on several analysis techniques. The paper presents techniques to construct representations for programs with explicit exception occurrences—exceptions that are raised explicitly through throw statements—and exception-handling constructs. The paper presents algorithms that use these representations to perform the desired analyses. The paper also discusses several softwareengineering applications that use these analyses. Finally, the paper describes empirical results pertaining to the occurrence of exception-handling constructs in Java programs, and their effects on some analysis tasks.  Exception handling, control-flow analysis, control-dependence analysis, data-flow analysis, program slicing, structural testing.
The Business Case for Corporate Sustainability : Literature Review and Research Options

In the last ten years, the notion of a ‘business case’ for corporate sustainability has increasingly been used by the corporate sector, environmental organizations, consultancies and so on, to seek justification for sustainability strategies within organizations. In this paper, we aim to systemize and assess existing research and tools related to this increasingly popular concept. We present a review of (1) theoretical frameworks, (2) instrumental studies aiming to either prove or disprove a hypothesized causal sequence between corporate social or environmental performance and financial performance, (3) descriptive studies examining manager’s actual perceptions and practices, and finally (4) tools. We identify a clearly insufficient understanding of manager’s key arguments or business logic for adopting corporate sustainability strategies (how ‘business cases’ are built, how effective they are and what barriers they face). We attribute this primarily to lack of descriptive research in these areas.
Simultaneous Localization and Mapping : Literature Survey

One of the initial solutions to the SLAM problem was proposed by Smith and Cheeseman who used the Extended Kalman Filter (EKF) to jointly represent the landmark position with the pose [38]. Guivant and Nebot [18] developed compressed EKF which performed mapping in local space using a reduced number of landmarks and only performed global update when the robot moved from one local map to another local map. The worst case complexity in this case is O(kn) required for full global update. Paskin [36] proposed thin junction tree filter (TJTF) to maintain manageable complexity. It is an assumed density filtering algorithm where the belief state is represented using a junction tree and proposed a thinning operation to maintain an ever-increasing tree width caused by filtering operations. Estimation in this case is performed in O(kn) time by passing marginalized distribution along the edges of the junction tree. According to Frese et al.  “From the perspective of “linear equation solving” this approach is complementary to the approach proposed in this paper. In the Gaussian case the junction tree algorithm is a direct, i.e., exact equation solver based on Schur-complements (corresponding to the marginalized distributions) and approximation is performed on the equation level. In contrast Multilevel Relaxation is an iterative, i.e., approximate equation solver but does not approximate the equations themselves.”
Trusting Tweets : The Fukushima Disaster and Information Source Credibility on Twitter

This paper focuses on the micro-blogging service Twitter, looking at source credibility for information shared in relation to the Fukushima Daiichi nuclear power plant disaster in Japan. We look at the sources, credibility, and between-language differences in information shared in the month following the disaster. Messages were categorized by user, location, language, type, and credibility of information source. Tweets with reference to third-party information made up the bulk of messages sent, and it was also found that a majority of those sources were highly credible, including established institutions, traditional media outlets, and highly credible individuals. In general, profile anonymity proved to be correlated with a higher propensity to share information from low credibility sources. However, Japanese-language tweeters, while more likely to have anonymous profiles, referenced lowcredibility sources less often than non-Japanese tweeters, suggesting proximity to the disaster mediating the degree of credibility of shared content.
A Transformerless Intelligent Power Substation: A three-phase SST enabled by a 15-kV SiC IGBT

The solid-state transformer (SST) is a promising power electronics solution that provides voltage regulation, reactive power compensation, dc-sourced renewable integration, and communication capabilities, in addition to the traditional step-up/step-down functionality of a transformer. It is gaining widespread attention for medium-voltage (MV) grid interfacing to enable increases in renewable energy penetration, and, commercially, the SST is of interest for traction applications due to its light weight as a result of medium-frequency isolation. The recent advancements in silicon carbide (SiC) power semiconductor device technology are creating a new paradigm with the development of discrete power semiconductor devices in the range of 10-15 kV and even beyond-up to 22 kV, as recently reported. In contrast to silicon (Si) IGBTs, which are limited to 6.5-kV blocking, these high-voltage (HV) SiC devices are enabling much simpler converter topologies and increased efficiency and reliability, with dramatic reductions of the size and weight of the MV power-conversion systems. This article presents the first-ever demonstration results of a three-phase MV grid-connected 100-kVA SST enabled by 15-kV SiC n-IGBTs, with an emphasis on the system design and control considerations. The 15-kV SiC n-IGBTs were developed by Cree and packaged by Powerex. The low-voltage (LV) side of the SST is built with 1,200-V, 100-A SiC MOSFET modules. The galvanic isolation is provided by three single-phase 22-kV/800-V, 10-kHz, 35-kVA-rated high-frequency (HF) transformers. The three-phase all-SiC SST that interfaces with 13.8-kV and 480-V distribution grids is referred to as a transformerless intelligent power substation (TIPS). The characterization of the 15-kV SiC n-IGBTs, the development of the MV isolated gate driver, and the design, control, and system demonstration of the TIPS were undertaken by North Carolina State University's (NCSU's) Future Renewable Electrical Energy Delivery and Management (FREEDM) Systems Center, sponsored by an Advanced Research Projects Agency-Energy (ARPA-E) project.
TEXT: Automatic Template Extraction from Heterogeneous Web Pages

World Wide Web is the most useful source of information. In order to achieve high productivity of publishing, the webpages in many websites are automatically populated by using the common templates with contents. The templates provide readers easy access to the contents guided by consistent structures. However, for machines, the templates are considered harmful since they degrade the accuracy and performance of web applications due to irrelevant terms in templates. Thus, template detection techniques have received a lot of attention recently to improve the performance of search engines, clustering, and classification of web documents. In this paper, we present novel algorithms for extracting templates from a large number of web documents which are generated from heterogeneous templates. We cluster the web documents based on the similarity of underlying template structures in the documents so that the template for each cluster is extracted simultaneously. We develop a novel goodness measure with its fast approximation for clustering and provide comprehensive analysis of our algorithm. Our experimental results with real-life data sets confirm the effectiveness and robustness of our algorithm compared to the state of the art for template detection algorithms.
A Dual Linearly Polarized Transmitarray Element With 1-Bit Phase Resolution in X-Band

In this letter, we propose a dual linearly polarized unit cell with 1-bit phase resolution for transmitarray application in X-band. It consists of two-layer metallic patterns connected by a metallized via-hole. One layer of the metallic pattern is a rectangular patch with two p-i-n diodes loaded in O-slot along electric field polarization direction, which is utilized as a receiver-antenna to achieve 1-bit phase tuning. The other metallic pattern is a dual linearly polarized transmitter-antenna that adopts a square ring patch with two p-i-n diodes distributed at the cross-polarization directions. The simulation results show that the designed antenna can achieve 1-bit phase tuning and linearly polarization reconfiguration at 10.5 GHz with insertion loss of about 1.1 dB. The characteristic of the designed transmitarray element is then experimentally validated by an ad-hoc waveguide simulator. The measured results agree with the simulated ones.
A Privacy Model for Smart Metering

Electricity suppliers have started replacing traditional electricity meters with so-called smart meters, which can transmit current power consumption levels to the supplier within short intervals. Though this is advantageous for the electricity suppliers' planning purposes, and also allows the customers a more detailed look at their usage behavior, it means a considerable risk for privacy. The detailed information can be used to judge whether persons are in the household, when they come home, which electric devices they use (e.g. when they watch TV), and so forth. In this work, we introduce the "smart metering privacy model" for measuring the degree of privacy that a smart metering application can provide. Moreover, we present two design solutions both with and without involvement of trusted third parties. We show that the solution with trusted party can provide "perfect privacy" under certain conditions.
Analysis-aware modeling : Understanding quality considerations in modeling for isogeometric analysis

Isogeometric analysis has been proposed as a methodology for bridging the gap between computer aided design (CAD) and finite element analysis (FEA). Although both the traditional and isogeometric pipelines rely upon the same conceptualization to solid model steps, they drastically differ in how they bring the solid model both to and through the analysis process. The isogeometric analysis process circumvents many of the meshing pitfalls experienced by the traditional pipeline by working directly within the approximation spaces used by the model representation. In this paper, we demonstrate that in a similar way as how mesh quality is used in traditional FEA to help characterize the impact of the mesh on analysis, an analogous concept of model quality exists within isogeometric analysis. The consequence of these observations is the need for a new area within modeling – analysis-aware modeling – in which model properties and parameters are selected to facilitate isogeometric analysis.
Relationships between bullying and violence among US youth.

To determine the extent to which bullying and being bullied, both in and away from school, is associated with involvement in violent behavior.



A US representative cross-sectional sample of 15 686 students in grades 6 through 10 in public and private schools completed the World Health Organization's Health Behaviour in School-aged Children survey in 1998.



Self-report of weapon carrying, weapon carrying in school, physical fighting, and being injured in a physical fight.



Involvement in each of the violence-related behaviors ranged from 13% to 23% of boys and 4% to 11% of girls. Bullying others and being bullied were consistently related to each violence-related behavior for both boys and girls. Greater odds of involvement occurred with bullying others than being bullied, and greater odds of involvement occurred with bullying that took place away from school than that occurring in school. For example, the adjusted odds ratio for weapon carrying associated with being bullied in school weekly was 1.5, for bullying others in school 2.6, for being bullied away from school 4.1, and for bullying others away from school 5.9.



Bullying should not be considered a normative aspect of youth development, but rather a marker for more serious violent behaviors, including weapon carrying, frequent fighting, and fighting-related injury.
Animal-Assisted Therapy: A Meta-Analysis

Animal-assisted therapy (AAT) has been practiced for many years and there is now increasing interest in demonstrating its efficacy through research. To date, no known quantitative review of AAT studies has been published; our study sought to fill this gap. We conducted a comprehensive search of articles reporting on AAT in which we reviewed 250 studies, 49 of which met our inclusion criteria and were submitted to meta-analytic procedures. Overall, AAT was associated with moderate effect sizes in improving outcomes in four areas: Autism-spectrum symptoms, medical difficulties, behavioral problems, and emotional well-being. Contrary to expectations, characteristics of participants and studies did not produce differential outcomes. AAT shows promise as an additive to established interventions and future research should investigate the conditions under which AAT can be most helpful.
Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video

We present a computer vision tool that analyses video from a CCTV system installed on fishing trawlers to monitor discarded fish catch. The system aims to support expert observers who review the footage and verify numbers, species and sizes of discarded fish. The operational environment presents a significant challenge for these tasks. Fish are processed below deck under fluorescent lights, they are randomly oriented and there are multiple occlusions. The scene is unstructured and complicated by the presence of fishermen processing the catch. We describe an approach to segmenting the scene and counting fish that exploits the N4-Fields algorithm. We performed extensive tests of the algorithm on a data set comprising 443 frames from 6 belts. Results indicate the relative count error (for individual fish) ranges from 2% to 16%. We believe this is the first system that is able to handle footage from operational trawlers.
Retrieval based interactive cartoon synthesis via unsupervised bi-distance metric learning

Cartoons play important roles in many areas, but it requires a lot of labor to produce new cartoon clips. In this paper, we propose a gesture recognition method for cartoon character images with two applications, namely content-based cartoon image retrieval and cartoon clip synthesis. We first define Edge Features (EF) and Motion Direction Features (MDF) for cartoon character images. The features are classified into two different groups, namely intra-features and inter-features. An Unsupervised Bi-Distance Metric Learning (UBDML) algorithm is proposed to recognize the gestures of cartoon character images. Different from the previous research efforts on distance metric learning, UBDML learns the optimal distance metric from the heterogeneous distance metrics derived from intra-features and inter-features. Content-based cartoon character image retrieval and cartoon clip synthesis can be carried out based on the distance metric learned by UBDML. Experiments show that the cartoon character image retrieval has a high precision and that the cartoon clip synthesis can be carried out efficiently.
An empirical analysis of the optimization of deep network loss surfaces

The success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions. In this paper, we empirically investigate the loss functions of state-of-the-art networks, and how commonlyused stochastic gradient descent variants optimize these loss functions. To do this, we visualize the loss function by projecting them down to low-dimensional spaces chosen based on the convergence points of different optimization algorithms. Our observations suggest that optimization algorithms encounter and choose different descent directions at many saddle points to find different final weights. Based on consistency we observe across re-runs of the same stochastic optimization algorithm, we hypothesize that each optimization algorithm makes characteristic choices at these saddle points.
Automatic and Robust Detection of Facial Features in Frontal Face Images

Automatic detection of facial features in an image is important stage for various facial image interpretation work, such as face recognition, facial expression recognition, 3Dface modeling and facial features tracking. Detection of facial features like eye, pupil, mouth, nose, nostrils, lip corners, eye corners etc., with different facial expression and illumination is a challenging task. In this paper, we presented different methods for fully automatic detection of facial features. Viola-Jones' object detector along with haar-like cascaded features are used to detect face, eyes and nose. Novel techniques using the basic concepts of facial geometry, are proposed to locate the mouth position, nose position and eyes position. The estimation of detection region for features like eye, nose and mouth enhanced the detection accuracy significantly. An algorithm, using the H-plane of the HSV color space is proposed for detecting eye pupil from the eye detected region. FEI database of frontal face images is mainly used to test the algorithm. Proposed algorithm is tested over 100 frontal face images with two different facial expression (neutral face and smiling face). The results obtained are found to be 100% accurate for lip, lip corners, nose and nostrils detection. The eye corners, and eye pupil detection is giving approximately 95% accurate results.