nils-herrmann/clean_scirepeval huggingface dataset:
Boost.Compute: A parallel computing library for C++ based on OpenCL

Boost.Compute is a powerful C++ header-only template library for parallel computing based on OpenCL. It has a layered architecture and acts both as a thin C++ wrapper over the OpenCL API and as a feature-rich interface to high-level constructs that resemble the functionality of the STL and is just as easy to use.
 Through its template-based design, Boost.Compute seeks to further provide flexible OpenCL support in C++ projects via meta-programming. Meta-programming is not available in standard C-based OpenCL, and OpenCL C++ kernel language is currently only provisional. SYCL is intended as a prospective C++ abstraction layer over OpenCL, but it is still under development and requires a special compiler. Bolt and Thrust are C++-based parallel-computing libraries, but they are tied to specific hardware vendors. Boost.Compute on the other hand is a vendor-neutral solution for GPUs and multi-core CPUs that is available now, and it works with any standard C++ compiler and on all OpenCL platforms.
 Boost.Compute has been accepted for integration with the official Boost C++ libraries. With this step, and considering the large number of Boost users, usage of Boost.Compute and visibility of OpenCL among C++ developers is likely to increase. This technical presentation is therefore intended as a comprehensive overview of Boost.Compute for current and prospective users of the library and covers the library's overall architecture, its low-level and high-level functionality and advanced topics such as custom functions, closures and lambda expressions. The presentation also describes how a custom template-based OpenCL library can be designed on top of Boost.Compute. Examples are included throughout the presentation to aid in a better understanding. Among others, I will demonstrate how advanced features of the library can lead to a simple and efficient C++-only solution for BLAS calculations. The architectural presentation of the library will be followed by a presentation of current performance results of the library and a comparison with competing solutions. I will conclude the presentation with insights that I gained during Google Summer of Code '15 and my overall experience in contributing to Boost.Compute, which I hope to be of interest to the wider developer community.
End-to-end pedestrian collision warning system based on a convolutional neural network with semantic segmentation

Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms can make it difficult for drivers to concentrate on their driving. In this paper, we propose a novel framework for an end-to-end pedestrian collision warning system based on a convolutional neural network. Semantic segmentation information is used to train the convolutional neural network and two loss functions, such as cross entropy and Euclidean losses, are minimized. Finally, we demonstrate the effectiveness of our method in reducing false alarms and increasing warning accuracy compared to a traditional histogram of oriented gradients (HoG)-based system.
Constrained Deep Metric Learning for Person Re-identification

Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently applied in person re-identification. However, in person re-identification, the deep networks often suffer from the over-fitting problem. In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification. Firstly, a novel deep architecture is built where the Mahalanobis metric is learned with a weight constraint. This weight constraint is used to regularize the learning, so that the learned metric has a better generalization ability. Secondly, we find that the selection of intraclass sample pairs is crucial for learning but has received little attention. To cope with the large intra-class variations in pedestrian images, we propose a novel training strategy named moderate positive mining to prevent the training process from over-fitting to the extreme samples in intraclass pairs. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.
PREDICTORS OF KNOWLEDGE SHARING BEHAVIOUR : A PROPOSED MODEL FOR THE MALAYSIAN PUBLIC SECTOR MANAGERS

Limited studies have focused on the factors that influence knowledge sharing behaviour of managers in the Malaysian public service. Moreover, little is known about the predictors of knowledge sharing behaviour, incorporating its two dimensions i.e. knowledge donating and knowledge collecting. Therefore, the aim of this paper is to build a knowledge sharing model among managers in the Malaysian public service. Based on the reviews of relevant knowledge sharing literature and from the lens of Theory of Planned Behaviour and Self-Determination Theory, several factors were found to be potential predictors of manager’s knowledge sharing behaviour. Individual factor (attitude towards knowledge sharing), organisational factor (organisational climate) and resource factors (time availability and ICT support) are important predictors of intention to share knowledge. Intention to share knowledge and intrinsic motivational factors (knowledge self-efficacy and enjoyment in helping others) are important predictors of knowledge sharing behaviour (knowledge donating and knowledge collecting).
Nutritional support for wound healing.

Healing of wounds, whether from accidental injury or surgical intervention, involves the activity of an intricate network of blood cells, tissue types, cytokines, and growth factors. This results in increased cellular activity, which causes an intensified metabolic demand for nutrients. Nutritional deficiencies can impede wound healing, and several nutritional factors required for wound repair may improve healing time and wound outcome. Vitamin A is required for epithelial and bone formation, cellular differentiation, and immune function. Vitamin C is necessary for collagen formation, proper immune function, and as a tissue antioxidant. Vitamin E is the major lipid-soluble antioxidant in the skin; however, the effect of vitamin E on surgical wounds is inconclusive. Bromelain reduces edema, bruising, pain, and healing time following trauma and surgical procedures. Glucosamine appears to be the rate-limiting substrate for hyaluronic acid production in the wound. Adequate dietary protein is absolutely essential for proper wound healing, and tissue levels of the amino acids arginine and glutamine may influence wound repair and immune function. The botanical medicines Centella asiatica and Aloe vera have been used for decades, both topically and internally, to enhance wound repair, and scientific studies are now beginning to validate efficacy and explore mechanisms of action for these botanicals. To promote wound healing in the shortest time possible, with minimal pain, discomfort, and scarring to the patient, it is important to explore nutritional and botanical influences on wound outcome.
Alignment-Based Neural Machine Translation

Neural machine translation (NMT) has emerged recently as a promising statistical machine translation approach. In NMT, neural networks (NN) are directly used to produce translations, without relying on a pre-existing translation framework. In this work, we take a step towards bridging the gap between conventional word alignment models and NMT. We follow the hidden Markov model (HMM) approach that separates the alignment and lexical models. We propose a neural alignment model and combine it with a lexical neural model in a loglinear framework. The models are used in a standalone word-based decoder that explicitly hypothesizes alignments during search. We demonstrate that our system outperforms attention-based NMT on two tasks: IWSLT 2013 German→English and BOLT Chinese→English. We also show promising results for re-aligning the training data using neural models.
PKLot - A robust dataset for parking lot classification

http://dx.doi.org/10.1016/j.eswa.2015.02.009 0957-4174/ 2015  E-mail addresses: prlalmeida@inf.ufpr.br (P.R.L. de Almeida), lesoliveira@inf. ufpr.br (L.S. Oliveira), alceu@ppgia.pucpr.br (A.S. Britto Jr.), eunelson@ppgia.pucpr. br (E.J. Silva Jr.), alessandro.koerich@etsmtl.ca (A.L. Koerich). Paulo R.L. de Almeida , Luiz S. Oliveira , Alceu S. Britto Jr. b,⇑, Eunelson J. Silva Jr. b Alessandro L. Koerich b,c
Mapping the game landscape: Locating genres using functional classification

Are typical computer game genres still valid descriptors and useful for describing game structure and game content? Games have changed from simple to complex and from single function to multi function. By identifying structural differences in game elements we develop a more nuanced model to categorized games and use cluster analysis as a descriptive tool in order to do so. The cluster analysis of 75 functionally different games shows that the two perspectives (omnipresent and vagrant), as well as challenges, mutability and savability are important functional categories to use in order to describe games.
A 5MHz, 24V-to-1.2V, AO2T current mode buck converter with one-cycle transient response and sensorless current detection for medical meters

With a stringent input-to-output conversion ratio (CR) of 20 (24V input and 1.2V output) for a DC-DC converter, two stage cascaded architectures are easy to implement, but suffer from poor efficiency and doubled number of power components. This paper presents a single-stage solution with a proposed Adaptive ON-OFF Time (AO2T) control. In steady state, the control works as an adaptive ON-time valley current mode control to accommodate the large CR. During load transient periods, both ON- and OFF-time are adaptively adjustable to instantaneous load change, in order to accomplish fast load transient response within one switching cycle. To facilitate the high speed current mode control, a sensorless current detection circuit is also proposed. Operating at 5MHz, the converter achieves a maximum efficiency of 89.8% at 700mA and an efficiency of 85% at 2A full load. During a load current slew rate of 1.8A/200ns, the undershoot/overshoot voltages at VO are 23mV and 37mV respectively.
Integrating Arduino-based educational mobile robots in ROS

This article presents the full integration of compact educational mobile robotic platforms built around an Arduino controller board in the Robotic Operating System (ROS). To that end, a driver interface in ROS was created to drastically decrease the development time, providing hardware abstraction and intuitive operation mode, allowing researchers to focus essentially in their main research motivation. Moreover, the full integration in ROS provided by the driver enables the use of several tools for data analysis, easiness of interaction between multiple robots, sensors and teleoperation devices, thereby targeting engineering education. To validate the approach, diverse experimental field tests were conducted using different Arduino-based robotic plat-forms.
CORPORATE SOCIAL RESPONSIBILITY IN EMERGING MARKETS

This paper analyses CSR initiatives in emerging markets from developed-countriesbased multinational companies (MNCs).  The analysis is based on eight case studies with data collected through in-depth interviews with senior managers of the companies representing 85% of the Spanish foreign investments in Latin America.  The results show that CSR initiatives from these companies seem to be guided by Instrumental Theories as they use these initiatives as a strategic tool to achieve economic objectives, seek a positive relation between them and their financial performance, and use them to strengthen their reputation.  The findings tend to indicate that Instrumental Theories of CSR seem to apply for Western MNCs operating in emerging markets.
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly relies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.
Deep Learning for Depression Detection of Twitter Users

Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people’s life. This close relationship between social media platforms and their users has made these platforms to reflect the users’ personal life on many levels. In such an environment, researchers are presented with a wealth of information regarding one’s life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.
Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories

The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed.
Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists

For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system “in the wild”, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of “exploring” a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.
Forensic mycology: current perspectives

(unported, v3.0) License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Limited. Information on how to request permission may be found at: http://www.dovepress.com/permissions.php Research and Reports in Forensic Medical Science 2015:5 75–83 Research and Reports in Forensic Medical Science Dovepress
Disgust is a factor in extreme prejudice.

Understanding intergroup prejudice is a dominant research focus for social psychology. Prejudice is usually conceptualized as a continuum of positive/negative affect, but this has limitations. It neither reflects people's ability to maintain simultaneous positive and negative stereotypes of others nor explains extreme prejudice (bigotry). Some researchers have proposed multidimensional models of prejudice in which different negative emotions are evoked depending on the situation. Extending this to bigotry raises the question of which emotions are most relevant. Therefore, this study looked at 'anti-group' texts--writings which promote extreme intergroup hostility--and analysed the frequency of emotive language. Findings suggest that bigotry may be distinguished by high levels of disgust.
Grounded Theory and the Constant Comparative Method : Valid Research Strategies for Educators

University of Wisconsin-Whitewater. USA ___________________________________________________________________________ Grounded theory was developed by Glaser and Strauss who believed that theory could emerge through qualitative data analysis. In grounded theory, the researcher uses multiple stages of collecting, refining, and categorizing the data. The researcher uses the strategies of the making constant comparisons and applying theoretical sampling to obtain a theory grounded in the data. The justification of this paper is to provide discussion on the validity of grounded theory and the constant comparative method as effective research strategies for educators. The qualitative design of grounded theory will be the focus of this paper, along with a discussion of the constant comparative method, issues related to trustworthiness, and limitations inherent in grounded theory methodology __________________________________________________________________________________________
Determination of differential leakage factors in electrical machines with non-symmetrical full and dead-coil windings

In this paper Görges polygons are used in conjunction with masses geometry to find an easy and affordable way to compute the differential leakage factor of non symmetrical full and dead coil winding. By following the traditional way, the use of the Ossanna's infinite series which has to be obviously truncated under the bound of a predetermined accuracy is mandatory. In the presented method no infinite series is instead required. An example is then shown and discussed to demonstrate practically the effectiveness of the proposed method.
Improved LSB based Steganography Techniques for Color Images in Spatial Domain

This research paper aims to propose a new improved approach for Information Security in RGB Color Images using a Hybrid Feature detection technique; Two Component based Least Significant Bit (LSB) Substitution Technique and Adaptive LSB substitution technique for data hiding. Advanced Encryption Standard (AES) is used to provide Two Tier Security; Random Pixel Embedding imparts resistant to attacks and Hybrid Filtering makes it immune to various disturbances like noise. An image is combination of edge and smooth areas which gives an ample opportunity to hide information in it. The proposed work is direct implementation of the principle that edge areas being high in contrast, color, density and frequency can tolerate more changes in their pixel values than smooth areas, so can be embedded with a large number of secret data while retaining the original characteristics of image. The proposed approach achieved Improved Imperceptibility, Capacity than the various existing techniques along with Better Resistance to various Steganalysis attacks like Histogram Analysis, Chi-Square and RS Analysis as proven experimentally.
Analysis and Design of Current-Fed Half-Bridge (C)(LC)–( LC) Resonant Topology for Inductive Wireless Power Transfer Application

This paper proposes and analyzes a new power electronics circuit topology for wireless inductive power transfer application using current-fed half-bridge converter with CCL-LC resonant network. The major focus is analysis and implementation of a new current-fed resonant topology with current-sharing and voltage doubling features. Generally, inductive power transfer circuits with current fed converter use parallel CL resonant tank to transfer power effectively through air gap. However, in medium power application, this topology suffers from a major limitation of high voltage stress across the inverter semiconductor devices owing to high reactive power consumed by loosely coupled coil. In proposed topology, this is mitigated by adding a capacitor in series with the coil developing series-parallel CCL tank. The power flow is controlled through variable frequency modulation. Soft-switching of the devices is obtained irrespective of the load current. For grid-to-vehicle or solar-to-vehicle, the converter is analyzed and detailed design procedure is illustrated. Experimental results are presented to verify the analysis and demonstrate the performance.