Reviewerly/exHarmony huggingface dataset:
DEVELOPING THE MOTIVATION OF PUPILS TO LEARN WITH ICT MEANS

The article analyzes the latest research on the development of students' motivation to learn through ICT; defined the concept of "information and communication technologies" as the basis of quality educational process in the development of the information society; an attempt is made to characterize the pedagogical tasks of information and communication learning based on the study of the components of the motivational sphere of the modern generation and to describe the methodology and technology of the educational process using the latest electronic learning tools primarily computers. For the purpose of integration of modern information and communication technologies in training preparation of pupils for full life in the conditions of information society, and also formation of motives of training as process of creation in school of conditions for emergence of internal motivations (motives, the purposes, emotions) and their further self-development of their own motivational sphere. It is investigated that modern information technologies in the learning process allow: to intensify the cognitive activity of students; provide positive motivation for learning; to conduct lessons at a high aesthetic and emotional level; to ensure a high degree of differentiation of learning (almost individualization) increase the amount of work performed in the lesson by 1.5-2 times; improve knowledge control; rationally organize the educational process; to increase the efficiency of the lesson, to form skills of search and research activity; provide access to various reference systems, electronic libraries and other information resources. The author concludes on the choice of optimal methods for the development of motivation to learn, taking into account the psychological and pedagogical aspects of this process; the conditions necessary for formation of motivation of pupils to training and maintenance of humane relations between the teacher and the pupil are characterized; Prospects for further research are identified, which consist in studying the specifics of the transformation of educational and cognitive motives into "meaning-making", ie their transition to a new – personal level.
Data Flow Mapping onto DNN Accelerator Considering Hardware Cost

Deep Neural Networks (DNNs) has created outstanding results in computer vision and image processing recently. Computational Algorithms complexity has led to widespread research in this field. Besides, using a DNN accelerator is an effective method in order to hasten the computation in those algorithms. The majority of DNN accelerator use parallelism Processing Elements (PEs) in order to lessen hardware costs. Regarding high volume of input data in image processing, lessening computing runtime is considered as an open challenge in DNNs accelerator. All above-mentioned issues have created different data flow mapping method in DNNs accelerator. This paper presents the new data flow mapping on Resnet34, regarding stationaries concepts; moreover, evaluates computing runtime in each step of mapping. We also propose a new data flow mapping method based on weight stationary in Resnet34, which reduces computing runtime, by 50.6%.
Application of Deep Reinforcement Learning in the Board Game

Deep reinforcement learning is one of the core technologies, which leads to artificial intelligence. It has been used widely not only in robot, autonomous driving, power station control and human-computer interaction in mobile e-commerce platforms, but also in gambling games. The game software AlphaGo of artificial intelligence defeated Ke Jie in 2017. Since then, the study of artificial intelligence and deep reinforcement is widely concerned. We build a Neural Network model using TensorFlow and Monte Carlo tree search algorithm, so that agents can reach the level of playing chess with human beings in board games. The gobang interface is realized by Python, and the PhoenixGo recurrence is completed. The computation is reduced and the training effect of the agent is improved by optimizing the establishment of gobang strategic value network. Black hand first can achieve a 73% winning rate, and white hand first is 64%, when playing against human players.
A Likelihood Ratio Test of a Homoscedastic Multivariate Normal Mixture Against a Heteroscedastic Multivariate Normal Mixture

The multivariate finite normal mixture model is one of the most commonly used tools to analyze a heterogeneous data. When using the multivariate finite normal mixture model, one is usually interested in knowing whether a homoscedastic mixture model can be used to simplify the model. The likelihood ratio test (LRT) is the most popular statistic tool to choose between two nested models. Under the null model of a homoscedastic multivariate normal mixture, the asymptotic χ2 distribution is commonly used to approximate the null distribution of the LRT statistic. However, it is demonstrated using numerical studies that the χ2 distribution approximation is not satisfactory and fails to control the nominal type I error unless the sample size is larger than 2000, the mixture components are well-separated, and the singular solutions are avoided. A parametric bootstrap method is further proposed to approximate the distribution of the LRT statistic and its effectiveness is evaluated through extensive numerical studies.
Tuning Hyper-Parameters of Machine Learning Methods for Improving the Detection of Hate Speech

Social media platforms have a main role in hate crimes worldwide. Detecting hate speech from social media is a big challenge. Many studies utilized machine learning methods for classifying the text as hate speech. However, the performance of machine learning method differs when using different parameters settings. Selecting the best values of parameters for machine learning method yields directly in the performance of the method. It is very time consuming for methods to find the best values manually. In this paper, grid search and random search hyper-parameters (HP) tuning methods were used with several machine learning methods in order to enhance the performance of detecting hate speech. The experimental results showed great improvements when HP methods were applied.
Gestalt Prototyping Framework - Evaluation Tool

The Gestalt Prototyping Framework linked the Nielsen Usability Heuristics to the principles of human perception and presents some parameters that can be used in developing high and low-fidelity prototypes for mobile application interfaces. The link between the fundamentals of usability and Gestalt principles focuses on the graphical components of the interfaces and the functions that they fulfill in the development of different functional actions. Previous articles have presented promising experimental results in reducing trial-error regressions in the interface design process and improving interface redesign processes; There have been positive results in parameters such as learnability, ease of use, perception of simplicity, and user preference. Within this same line of research, the present work describes the development process of an application that summarizes the fundamental aspects of the Gestalt prototyping framework in an evaluative model, which helps application designers, software engineers, and usability experts, to assess the prototypes of the interfaces and the incidence of the different graphic components in usability interactions. The tool is based on the most widely used usability parameters and is structured based on the responses that the development team fills in an automated LIKERT assessment. The results are processed based on an algorithm that simulates and predicts the results that could be obtained in high-fidelity prototype tests. This application issues specific recommendations on the visual components of the interfaces, to obtain better results in the production of high and low-fidelity prototypes.
Designed for Designer: An Online Co-design Workshop

One fundamental challenge for designing the human-machine interface of the future is to understand users' needs. This online form provides the possibility to expand the scope of recruitment. The setting of workshops can maximize the enthusiasm of designers, deeply explore their computer usage behavior and habits, and verify previous user research results. This paper reports on a study iterating the use of an online co-design workshop that recruited designers (n = 30) for computer and laptop design. The online workshop has three phases: exploring, brainstorming, and designing with useful toolkits. Finally, the research team collected 88 interaction design solutions to improve the designer's user experience when using the computer. Future work will look in-depth, reorganize, and redesign these solutions and test them in a real working context.
A restaurant recommendation approach with the contextual information

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones.
Fast Search and Estimation of Bayesian Nonparametric Mixture Models Using a Classification Annealing EM Algorithm

Bayesian nonparametric (BNP) infinite-mixture models provide flexible and accurate density estimation, cluster analysis, and regression. However, for the posterior inference of such a model, MCMC algorithms are complex, often need to be tailor-made for different BNP priors, and are intractable for large data sets. We introduce a BNP classification annealing EM algorithm which employs importance sampling estimation. This new fast-search algorithm, for virtually any given BNP mixture model, can quickly and accurately calculate the posterior predictive density estimate (by posterior averaging) and the maximum a-posteriori clustering estimate (by simulated annealing), even for data sets containing millions of observations. The algorithm can handle a wide range of BNP priors because it primarily relies on the ability to generate prior samples. The algorithm can be fast because in each iteration, it performs a sampling step for the (missing) clustering of the data points, instead of a costly E-step; and then performs direct posterior calculations in the M-step, given the sampled (imputed) clustering. The new algorithm is illustrated and evaluated through BNP Gaussian mixture model analyses of benchmark simulated data and real data sets. MATLAB code for the new algorithm is provided in Supplementary Materials.
Information Segmentation and Investing in Cybersecurity

This paper provides an analysis of how the benefits of information segmentation can assist an organization to derive the appropriate amount to invest in cybersecurity from a cost-benefit perspective.An analytical model based on the framework of the Gordon-Loeb Model ([1]) is presented that provides a set of sufficient conditions for information segmentation to lower the total investments in cybersecurity and the expected loss from cybersecurity breaches.A numerical example illustrating the insights gained from the model is also presented.
Learning Multi-level Interaction Relations and Feature Representations for Group Activity Recognition

Group activity recognition is an challenging task with a major issue that reasons about complex interaction relations in the context of multi-person scenes. Most existing approaches concentrate on capturing interaction relations and learning features of the group activity at individual or group levels. These approaches lose sight of multi-level structures and interaction relations of the group activity. To overcome this challenge, we propose a Multi-level Interaction Relation model (MIR) to flexibly and efficiently learn multi-level structures of the group activity and capture multi-level interaction relations in the group activity. MIR employs graph pooling and unpooling networks to build multi-grained group relation graphs, and thus divide the group activity into multiple levels. Specifically, the Key Actor based Group Pooling layer (KeyPool) selects key persons in the activity to build the coarser-grained graph while the Key Actor based Group Unpooling layer (KeyUnPool) reconstructs the finer-grained graph according the corresponding KeyPool. Multiple KeyPool and KeyUnPool progressively build multi-grained graphs and learn multi-level structures of the group activity. Thanks to graph convolutions performed on multi-grained relation graphs, multi-level interactions are finally captured. In addition, graph readout (GR) layers are added to obtain multi-level spatio-temporal features of The group activity. Experimental results on two publicly available datasets demonstrate the effectiveness of KeyPool and KeyUnPool, and show our model can further improve the performance of group activity recognition.
Information Extraction Method based on Dilated Convolution and Character-Enhanced Word Embedding

With the establishment and application of various kinds of knowledge graphs, information extraction has become one of the most important tasks in natural language processing. Due to the complexity of the Chinese language, traditional pipeline and joint extraction methods cannot solve entity confusion in most cases when dealing with one-to-many entity relationships. In order to solve this problem, this paper presents an idea based on seq2seq decoding, which directly predicts two object entities of a triple through the subject entity, uses the method of dilated convolution and character-enhanced word embedding, and adds self-attention to optimize the encoding process of Chinese words. Not only simplifies the extraction process, but also solves the entity conflict when facing the one-to-many entity relationship extraction problem. Experiments show that our proposed method performs better on datasets than traditional relational extraction models and has better scalability.
Stable recovery of entangled weights: Towards robust identification of deep neural networks from minimal samples

In this paper we approach the problem of unique and stable identifiability from a finite number of input-output samples of generic feedforward deep artificial neural networks of prescribed architecture with pyramidal shape up to the penultimate layer and smooth activation functions. More specifically we introduce the so-called entangled weights, which compose weights of successive layers intertwined with suitable diagonal and invertible matrices depending on the activation functions and their shifts. We prove that instances of entangled weights are completely and stably approximated by an efficient and robust algorithm as soon as O(D2×m) nonadaptive input-output samples of the network are collected, where D is the input dimension and m is the number of neurons of the network. Moreover, we empirically observe that the approach applies to networks with up to O(D×mL) neurons, where mL is the number of output neurons at layer L. Provided knowledge of layer assignments of entangled weights and of remaining scaling and shift parameters, which may be further heuristically obtained by least squares fitting, the entangled weights identify the network completely and uniquely. To highlight the relevance of the theoretical result of stable recovery of entangled weights, we present numerical experiments, which demonstrate that multilayered networks with generic weights can be robustly identified and therefore uniformly approximated by the presented algorithmic pipeline.
The Researchers Profile with Topic Modeling

Modeling the interests of researchers in academic social networks is a crucial step in a process of recommending scientific articles, linked to their areas of competence and expertise. In this context, a researcher profile constructed from non-observable variables on the basis of articles which interests him by the LDA (Latent Dirichlet Allocation) topic modeling technique allows the system to capture knowledge about his area of competence and skills, in order to predict these needs in terms of relevant research articles. In this article we are interested in the results produced by two different implementations of LDA Gensim and Mallet on the basis of information provided by the researchers (explicit information), in order to compare their interpretability and checked if they are reliable sources for model the areas of competence and expertise of scientists.
Research on Real-time Automatic Acquisition Technology of Topology Data in Distribution Network

Abstract The current distribution network equipment renovation plan arrangement is mainly based on the equipment health level, operating years and other indicators. Coordination and optimization between renovation projects are still lacking. Scattered and disorderly transformation will cause potential safety hazards in the distribution network operation. The paper proposes to establish a unified real-time data collection platform for distribution network based on real-time/historical database, and gives the construction idea and implementation plan of the platform. The designed platform has data interfaces with mainstream production control systems, user meters and electrical related systems, and integrates a wealth of common software tools, which can manage and analyze the real-time/quasi-real-time data of the distribution network in a unified manner and can facilitate Coordinated with the implementation of advanced applications of smart distribution networks, it has laid the foundation for the analysis and control of the optimized operation of the entire grid/local area grid and the construction of an integrated model of smart grid operation and distribution. At the same time, the paper proposes a method for checking the topology of low-voltage distribution networks based on outlier detection, calculates the correlation coefficient between the voltage sequence data to measure the similarity of the voltage curves of different users, and finally verifies the users and stations in the grid GIS system The correctness of the zone transformer topology connection relationship.
Exploration advancement of the Stewardson Lake uranium project (Athabasca Basin, Canada) using an integrated geological and geochemical methodology and academic–industry collaboration

ABSTRACT Kurt Kyser contributed significantly to understanding the role that fluids play in the formation of unconformity-related U deposits in Canada and Australia and also in the exploration for these deposits. Kurt's exploration research was collaborative with industry, and arguably the most productive of the many industry-collaborative relationships Kurt developed was with Uravan Minerals, a junior exploration company. Ten years of collaborative U-deposit and exploration geochemical research with Uravan, including development of innovative multi-media surface sampling and analytical methods, culminated with the Stewardson Lake uranium exploration project in the Proterozoic Athabasca Basin, Canada. Soil clay separates, pine and spruce tree cores and vegetation, and glacially transported sandstone boulders collected at Stewardson Lake record 207Pb/206Pb ratios as low as 0.18, indicative of radioactive decay of U in a Proterozoic-aged U deposit, and elevated pathfinder elements such as Ni and Co in two areas of the Stewardson Lake property named Areas A and B. Four diamond drill holes in Areas A and B tested targets consisting of anomalous surface-media geochemistry coincident with conductive host rocks determined using geophysical methods. The favorable targeting characteristics were not explained by two of the drill holes in Area A, where only narrow intervals of elevated U, radiogenic Pb, and pathfinder elements or structural disruption that can accompany Athabasca U deposits were intersected. Two drill holes in Area B intersected a broad zone of characteristic chlorite + hematite + illite alteration and U concentrations &amp;gt;1 ppm (aqua regia) in the basal host sandstone. One drill hole in Area B intersected a ∼30-meter-thick hydrothermal alteration zone consisting of silicification, chlorite + kaolinite clay alteration, smoky quartz, Ni-Fe sulfides, and disseminated uraninite accompanied by elevated gamma count rates. Fractures in the host sandstone in Area B record the highest pathfinder concentrations and the most radiogenic Pb isotope ratios and were likely a conduit for secondary dispersion of alteration-related components to the surface. The results validate the Uravan/QFIR exploration model developed over a decade of collaborative applied research and confirm that the Stewardson Lake area is highly prospective to host unconformity-related U mineralization. Lastly, clay alteration mineralogy in the Stewardson Lake area is contrasted with that in the eastern Athabasca Basin, and the value of the industry–academic relationship that benefited both Uravan Minerals and Kurt's Queen's Facility for Isotope Research laboratory is discussed.
Semantic Gap Reduction From Mouth Feature Threshold Value Using Viola Jones Algorithm

Image features are useful in defining image precisely and uniquely which are helpful in classification and recognition of images. Various feature values can be extracted by using different techniques used in image processing applications such as pattern recognition, feature matching, image segmentation, image fusion, video processing, visual surveillance, remote sensing, traffic safety monitoring, medical diagnosis, and human computer interaction etc. Number of factors of image affects on accuracy of feature extraction from different databases of images. The distance of the object (or person) from the camera and threshold value for feature detected plays an important role to capture accurate feature from the image database. The runtime required to capture the feature from image is also considerable factor when an algorithm for feature extraction is selected. The paper considers the distance and threshold value used for mouth feature detection and run-time required to capture the mouth feature from face database using Viola Jones algorithm. The experimental result shows increased threshold value at certain level for mouth feature detection gives better result and improved mouth detection rate for semantic gap reduction.
A Hazy Image Restoration Algorithm via JND Based Histogram Equalization and Weighted DCP Transmission Factor

Abstract This paper proposed a three-step hazy image restoration method. Firstly, in HSI color space, we proposed a novel histogram equalization algorithm based on just noticeable difference (JND) to enhance the hazy image. Secondly, we apply the dark channel prior (DCP) to remove the image haze and compute the weighted transmission factor map. Finally, we integrated the enhancement results and dehazing results by the weighted transmission factor maps adaptively, and then the restoration result is obtained. Since the proposed method takes account of visual perception, and well balance image enhancement and image dehazing. Therefore, it results in a more natural haze-free scene for the human visual system (HVS). Experiment results demonstrate that our method achieves better restorations compared with some other state-of-the-art approaches.