nils-herrmann/clean_scirepeval huggingface dataset:
Wavelet-Based Neural Network for Power Disturbance Recognition and Classification

In this paper, a prototype wavelet-based neuralnetwork classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval’s theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.
Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.
Fusion of Stereo Vision for Pedestrian Recognition using Convolutional Neural Networks

Pedestrian detection is a highly debated issue in the scientific community due to its outstanding importance for a large number of applications, especially in the fields of automotive safety, robotics and surveillance. In spite of the widely varying methods developed in recent years, pedestrian detection is still an open challenge whose accuracy and robustness has to be improved. Therefore, in this paper, we focus on improving the classification component in the pedestrian detection task on the Daimler stereo vision data set by adopting two approaches: 1) by combining three image modalities (intensity, depth and flow) to feed a unique convolutional neural network (CNN) and 2) by fusing the results of three independent CNNs.
Achieving ICS Resilience and Security through Granular Data Flow Management

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.
Progressive Ladder Networks for Semi-Supervised Transfer Learning

Semi-supervised learning has achieved remarkable success in the past few years at harnessing the power of unlabeled data and tackling domains where few labeled data examples exist. We test the hypothesis that deep semisupervised architectures learn general representations. We combine two well-known techniques for semi-supervised and transfer learning, ladder networks and progressive neural networks, to create the progressive ladder network, a framework for transferring from semi-supervised representations to supervised representations. We use this framework for domain adaptation in the digit recognition domain, from the MNIST handwritten digits dataset to the Street View House Numbers (SVHN) dataset. Our experiments show that semi-supervised representations are more transferable than supervised learning representations. We find that using our progressive ladder network architecture on simple fully connected neural networks trained on SVHN can yield an increase in test accuracy of about 40%, from around 35% to 77.7%.
Social computing for image matching

One of the main technological trends in the last five years is mass data analysis. This trend is due in part to the emergence of concepts such as social networks, which generate a large volume of data that can provide added value through their analysis. This article is focused on a business and employment-oriented social network. More specifically, it focuses on the analysis of information provided by different users in image form. The images are analyzed to detect whether other existing users have posted or talked about the same image, even if the image has undergone some type of modification such as watermarks or color filters. This makes it possible to establish new connections among unknown users by detecting what they are posting or whether they are talking about the same images. The proposed solution consists of an image matching algorithm, which is based on the rapid calculation and comparison of hashes. However, there is a computationally expensive aspect in charge of revoking possible image transformations. As a result, the image matching process is supported by a distributed forecasting system that enables or disables nodes to serve all the possible requests. The proposed system has shown promising results for matching modified images, especially when compared with other existing systems.
Applications of cloud computing in education

In this paper, we provide an overview of the cloud computing model and discuss its applications for collaboration between academic institutions. Cloud computing is considered a typical paradigm that provides suitable, efficient network login to an appropriate pool of computing resources which can be provided and released with just nominal assiduity and service providers reciprocity. However, many organizations understand cloud computing in different ways. We briefly analyse the cloud computing applications in this paper, and describe some current and accomplished educational and research products. Then, we go on to evaluate the successful applications of cloud computing models at educational institutions, and the different ways to implement cloud computing. Finally, we present different education applications for education infrastructures which are implemented for academic use.
Scalability and sparsity issues in recommender datasets: a survey

Recommender systems have been widely used in various domains including movies, news, music with an aim to provide the most relevant proposals to users from a variety of available options. Recommender systems are designed using techniques from many fields, some of which are: machine learning, information retrieval, data mining, linear algebra and artificial intelligence. Though in-memory nearest-neighbor computation is a typical approach for collaborative filtering due to its high recommendation accuracy; its performance on scalability is still poor given a huge user and item base and availability of only few ratings (i.e., data sparsity) in archetypal merchandising applications. In order to alleviate scalability and sparsity issues in recommender systems, several model-based approaches were proposed in the past. However, if research in recommender system is to achieve its potential, there is a need to understand the prominent techniques used directly to build recommender systems or for preprocessing recommender datasets, along with its strengths and weaknesses. In this work, we present an overview of some of the prominent traditional as well as advanced techniques that can effectively handle data dimensionality and data sparsity. The focus of this survey is to present an overview of the applicability of some advanced techniques, particularly clustering, biclustering, matrix factorization, graph-theoretic, and fuzzy techniques in recommender systems. In addition, it highlights the applicability and recent research works done using each technique.
A risk management ontology for Quality-by-Design based on a new development approach according GAMP 5.0

A new approach to the development of a risk management ontology is presented. This method meets the requirements of a pharmaceutical Quality by Design approach, good manufacturing practice and good automated manufacturing practice. The need for a risk management ontology for a pharmaceutical environment is demonstrated, and the term ‘‘ontology’’ is generally defined and described with regard to the knowledge domain of quality risk management. To fulfill software development requirements defined by good manufacturing practice regulations and good automated manufacturing practice 5.0 for the novel development approach, we used a V-model as a process model, which is discussed in detail. The development steps for the new risk management ontology, such as requirement specification, conceptualization, formalization, implementation and validation approach, are elaborated.
Reconsidering the Dimensions of Business-IT Alignment

While the literature on business-IT alignment has become increasingly mature in the past 20 years, different definitions and conceptualizations have emerged. Several dimensions like strategic, intellectual, structural, social, and cultural alignment have been developed. However, no integrated and broadly accepted categorization exists and these dimensions are non-selective and do overlap. In this paper, we critically compare and reflect the existing dimensions of alignment and synthesize what has been researched about alignment and guide future research by revealing gaps in alignment research suggesting an agenda for future research. In regard to analyzing alignment, we propose to clearly separate between the organizational level and the content of alignment, for which we suggest three distinct dimensions: (1) human dimension, (2) social dimension, (3) intellectual dimension. The result is a consolidated grid of alignment studies allowing to identify both areas with a mature and rich strand of literature and those lacking research.
The Time Course of Syntactic Activation During Language Processing: A Model Based on Neuropsychological and Neurophysiological Data

This paper presents a model describing the temporal and neurotopological structure of syntactic processes during comprehension. It postulates three distinct phases of language comprehension, two of which are primarily syntactic in nature. During the first phase the parser assigns the initial syntactic structure on the basis of word category information. These early structural processes are assumed to be subserved by the anterior parts of the left hemisphere, as event-related brain potentials show this area to be maximally activated when phrase structure violations are processed and as circumscribed lesions in this area lead to an impairment of the on-line structural assignment. During the second phase lexical-semantic and verb-argument structure information is processed. This phase is neurophysiologically manifest in a negative component in the event-related brain potential around 400 ms after stimulus onset which is distributed over the left and right temporo-parietal areas when lexical-semantic information is processed and over left anterior areas when verb-argument structure information is processed. During the third phase the parser tries to map the initial syntactic structure onto the available lexical-semantic and verb-argument structure information. In case of an unsuccessful match between the two types of information reanalyses may become necessary. These processes of structural reanalysis are correlated with a centroparietally distributed late positive component in the event-related brain potential.(ABSTRACT TRUNCATED AT 250 WORDS)
Problematic Social Media Use: Results from a Large-Scale Nationally Representative Adolescent Sample

Despite social media use being one of the most popular activities among adolescents, prevalence estimates among teenage samples of social media (problematic) use are lacking in the field. The present study surveyed a nationally representative Hungarian sample comprising 5,961 adolescents as part of the European School Survey Project on Alcohol and Other Drugs (ESPAD). Using the Bergen Social Media Addiction Scale (BSMAS) and based on latent profile analysis, 4.5% of the adolescents belonged to the at-risk group, and reported low self-esteem, high level of depression symptoms, and elevated social media use. Results also demonstrated that BSMAS has appropriate psychometric properties. It is concluded that adolescents at-risk of problematic social media use should be targeted by school-based prevention and intervention programs.
Microstrip antenna phased array with electromagnetic bandgap substrate

Uniplanar compact electromagnetic bandgap (UC-EBG) substrate has been proven to be an effective measure to reduce surface wave excitation in printed antenna geometries. This paper investigates the performance of a microstrip antenna phased array embedded in an UC-EBG substrate. The results show a reduction in mutual coupling between elements and provide a possible solution to the "blind spots" problem in phased array applications with printed elements. A novel and efficient UC-EBG array configuration is proposed. A probe fed patch antenna phased array of 7/spl times/5 elements on a high dielectric constant substrate was designed, built and tested. Simulation and measurement results show improvement in the active return loss and active pattern of the array center element. The tradeoffs used to obtain optimum performance are discussed.
Grid Cells and Neural Coding in High-End Cortices

An ultimate goal of neuroscience is to understand the mechanisms of mammalian intellectual functions, many of which are thought to depend extensively on the cerebral cortex. While this may have been considered a remote objective when Neuron was launched in 1988, neuroscience has now evolved to a stage where it is possible to decipher neural-circuit mechanisms in the deepest parts of the cortex, far away from sensory receptors and motoneurons. In this review, we show how studies of place cells in the hippocampus and grid cells in the entorhinal cortex may provide some of the first glimpses into these mechanisms. We shall review the events that led up to the discovery of grid cells and a functional circuit in the entorhinal cortex and highlight what we currently see as the big questions in this field--questions that, if resolved, will add to our understanding of cortical computation in a general sense.
GRAPHITE : Polyhedral Analyses and Optimizations for GCC

We present a plan to add loop nest optimizations in GCC based on polyhedral representations of loop nests. We advocate a static analysis approach based on a hierarchy of interchangeable abstractions with solvers that range from the exact solvers such as OMEGA, to faster but less precise solvers based on more coarse abstractions. The intermediate representation GRAPHITE1 (GIMPLE Represented as Polyhedra with Interchangeable Envelopes), built on GIMPLE and the natural loops, hosts the high level loop transformations. We base this presentation on the WRaP-IT project developed in the Alchemy group at INRIA Futurs and Paris-Sud University, on the PIPS compiler developed at École des mines de Paris, and on a joint work with several members of the static analysis and polyhedral compilation community in France. The main goal of this project is to bring more high level loop optimizations to GCC: loop fusion, tiling, strip mining, etc. Thanks to the 1This work was partially supported by ACI/APRON. WRaP-IT experience, we know that the polyhedral analyzes and transformations are affordable in a production compiler. A second goal of this project is to experiment with compile time reduction versus attainable precision when replacing operations on polyhedra with faster operations on more abstract domains. However, the use of a too coarse representation for computing might also result in an over approximated solution that cannot be used in subsequent computations. There exists a trade off between speed of the computation and the attainable precision that has not yet been analyzed for real world programs.
A Novel Locally Linear KNN Method With Applications to Visual Recognition

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients’ truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.
An Approach for Schema Extraction of JSON and Extended JSON Document Collections

JSON documents are raising as a common format for data representation due to the increasing popularity of NoSQL document-oriented databases. One of the reasons for such a popularity is their ability to handle large volumes of data at the absence of an explicit data schema. However, schema information is sometimes essential for applications during data retrieval, integration and analysis tasks, for example. Given this context, this paper presents an approach that extracts a schema from a JSON or Extended JSON document collection stored in a NoSQL document-oriented database or other document repository. Aggregation operations are considered in order to obtain a schema for each distinct structure in the collection, and a hierarchical data structure is proposed to group these schemas in order to generate a global schema in JSON Schema format. Experiments conducted on actual datasets, like DBPedia and Foursquare, demonstrate that the accuracy of the generated schemas is equivalent or even superior than related work.
Extracting Social Structures from Conversations in Twitter: A Case Study on Health-Related Posts

Online Social Networks (e.g., Twitter, Facebook) have dramatically grown in usage and popularity in recent years. In addition to keeping track of friends and acquaintances, these networks provide powerful means of exchanging and sharing information on many different topics of interest (e.g., sports, religion, politics, health concerns, etc.). Moreover, the use of these networks has introduced a completely new way of collaboration among people, virtually creating spheres of friends who generally feel comfortable discussing a variety of subjects and even helping each other to grow in knowledge about certain subjects. In this work, we built and analyzed networks of social groups on Twitter related to the top leading causes of death in the United States. Due to space limitations, we present results for the state of Florida and only for the top four leading causes of death. We show that using a concept of time window in the creation of relations between users, we can reconstruct social networks for these conversations and these networks have characteristics that are similar to typical social networks. The argument is that social network information can be extracted even in cases where users are not directly talking to each other (as it is the case in most of Twitter).
Classification and evolution of type II CRISPR-Cas systems

The CRISPR-Cas systems of archaeal and bacterial adaptive immunity are classified into three types that differ by the repertoires of CRISPR-associated (cas) genes, the organization of cas operons and the structure of repeats in the CRISPR arrays. The simplest among the CRISPR-Cas systems is type II in which the endonuclease activities required for the interference with foreign deoxyribonucleic acid (DNA) are concentrated in a single multidomain protein, Cas9, and are guided by a co-processed dual-tracrRNA:crRNA molecule. This compact enzymatic machinery and readily programmable site-specific DNA targeting make type II systems top candidates for a new generation of powerful tools for genomic engineering. Here we report an updated census of CRISPR-Cas systems in bacterial and archaeal genomes. Type II systems are the rarest, missing in archaea, and represented in ∼ 5% of bacterial genomes, with an over-representation among pathogens and commensals. Phylogenomic analysis suggests that at least three cas genes, cas1, cas2 and cas4, and the CRISPR repeats of the type II-B system were acquired via recombination with a type I CRISPR-Cas locus. Distant homologs of Cas9 were identified among proteins encoded by diverse transposons, suggesting that type II CRISPR-Cas evolved via recombination of mobile nuclease genes with type I loci.