Reviewerly/exHarmony huggingface dataset:
Face Identification using Histogram

Various experiments or methods can be used for face recognition and detection however two of the main contain an experiment that evaluates the impact of facial landmark localization in the face recognition performance and the second experiment evaluates the impact of extracting the HOG from a regular grid and at multiple scales. We observe the question of feature sets for robust visual object recognition. The Histogram of Oriented Gradients outperform other existing methods like edge and gradient based descriptors. We observe the influence of each stage of the computation on performance, concluding that fine-scale gradients, relatively coarse spatial binning, fine orientation binning and high- quality local contrast normalization in overlapping descriptor patches are all important for good results. Comparative experiments show that though HOG is simple feature descriptor, the proposed HOG feature achieves good results with much lower computational time.
The Exploration of Chinese Excellent Traditional Culture Education in the Future under the Background of 5G

The current excellent traditional culture education in China can not meet the basic requirements of improving the quality of learners, so it is urgent to explore excellent traditional culture education in the future. 5G is a mature network technology in recent years, which has the characteristics of high speed and low delay. The popularization of 5G brings hope for the reform of Chinese excellent traditional culture education. The education of Chinese excellent traditional culture should rely on 5G to realize the reform of educational content, method and evaluation, so as to improve the educational quality of traditional culture and further expand the influence of Chinese excellent traditional culture.
Verification of lexic ontologies by the method of using semantic proximity of words calculated by large corpus of texts

Abstract The paper analyzes the concepts and relations between them in the RuWordNet digital thesaurus of the Russian language. Semantically related words automatically extracted from the corpus of texts are compared with the words of the thesaurus. The characteristic cases of information gap (omission of word meanings and semantic relationships) in the thesaurus are revealed, their causes of a computer and cognitive nature are described. Recommendations on improving the RuWordNet thesaurus are given.
The Use of Picture Media in Teaching Writing of Descriptive Text

In learning English, writing is one of the important skills that must be learned for students in high school. One good strategy that can be used by teachers in English classrooms is the image media strategy. Image media strategy is one good strategy to express their ideas and be applied in written form. The objectives of this study are: (1) to describe the implementation of the image media strategy in writing. (2) to find out the benefits for students when the teacher applies the image media strategy in teaching writing descriptive texts. This study uses a qualitative method that uses descriptive qualitative to analyze data. This research was conducted At tenth-grade student and tenth grade English teacher MA Al Amiriyyah Blokagung. The findings in this study using image media in teaching descriptive text have many advantages for students and teachers in teaching descriptive text. First, this strategy can improve writing skills. Secondly, students can easily write descriptive texts in their own words. Third, increase students' feelings and motivation.
Seismic Soundoff

SEG President-elect Anna Shaughnessy joins the podcast to highlight her 2020 Honorary Lecture tour, “Developing a successful career in geophysics today.” In this timely discussion, Anna shares her insights on choosing a career field, preparing for a successful career, navigating a career in a transitional industry, and staying relevant no matter the discipline. Hear the full episode at https://seg.org/podcast/post/10202 .
Camera-aware Proxies for Unsupervised Person Re-Identification

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxy-balanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain $14.3\%$ Rank-1 and $10.2\%$ mAP improvements when compared to the second place. Code is available at: \texttt{https://github.com/Terminator8758/CAP-master}.
Science in the Age of Post-Globalism: Current Trends and Contradictions

The article aims to describe the situation in which modern science is developing and to determine the way science responds to pivotal historic transformations. Scientific novelty of the research lies in the fact that it gives a comprehensive assessment to development of technology, natural science and the humanities amid current social changes. As a result, the authors show how the main characteristics of the post-global age find their reflection in trends of modern science, identify current contradictions in development of different areas of scientific knowledge, uncover reasons for multidisciplinary approaches having a special role in modern research, formulate priority issues and fundamental premises characterising the state and prospects of modern science.
Evolutionary transformation of the journal. Part 7

The article outlines the seventh phase of the development of the journal Studia Historiae Scientiarum (previous name Prace Komisji Historii Nauki PAU / Proceedings of the PAU Commission on the History of Science). The information is provided on the following matters: the realization of the ministerial program “Support for scientific journals 2019–2020”, the evaluation of the journal in “ICI Master Journal List 2018” (published at the end of 2019), in Scimago Journal Ranks 2019 (published on 11 June 2020), in CWTS Journal Indicators (published on the beginning of June 2020) and in Scopus (published on 6 June 2020), a systemic obstacle in the further developing of the journal related to the journal’s underrated rating in the “List of journals of the Ministry of Science and Higher Education of the Polish Republic 2019” (published on 31 July 2019 and 18 December 2020), the indexation of the journal in the Scopus database (from September 2019), the works on updating the journal’s website in OJS (3.1.2.), and the number of foreign authors and the number of reviewers of the current volume of the journal.
Image Translation via Fine-grained Knowledge Transfer

Prevailing image-translation frameworks mostly seek to process images via the end-to-end style, which has achieved convincing results. Nonetheless, these methods lack interpretability and are not scalable on different image-translation tasks (e.g., style transfer, HDR, etc.). In this paper, we propose an interpretable knowledge-based image-translation framework, which realizes the image-translation through knowledge retrieval and transfer. In details, the framework constructs a plug-and-play and model-agnostic general purpose knowledge library, remembering task-specific styles, tones, texture patterns, etc. Furthermore, we present a fast ANN searching approach, Bandpass Hierarchical K-Means (BHKM), to cope with the difficulty of searching in the enormous knowledge library. Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks. In particular, backtracking experiments verify the interpretability of our method. Our code soon will be available at https://github.com/AceSix/Knowledge_Transfer.
Guiding Neural Network Initialization via Marginal Likelihood Maximization

We propose a simple, data-driven approach to help guide hyperparameter selection for neural network initialization. We leverage the relationship between neural network and Gaussian process models having corresponding activation and covariance functions to infer the hyperparameter values desirable for model initialization. Our experiment shows that marginal likelihood maximization provides recommendations that yield near-optimal prediction performance on MNIST classification task under experiment constraints. Furthermore, our empirical results indicate consistency in the proposed technique, suggesting that computation cost for the procedure could be significantly reduced with smaller training sets.
CLASSIFICATION OF FACTORS THAT INFLUENCE THE SEVERITY OF WORKER’S INJURIES USING THE CART METHOD (CLASSIFICATION AND REGRESSION TREE)

ABSTRACT PT Varia Usaha Beton is a company engaged in the manufacture of ready-made concrete, pre-cast concrete, concrete mansory, and broken stone. This company is a subsidiary of PT Semen Indonesia. In the production process, many cases of work accidents occur to workers. The high level of accidents that occur can disrupt the production process and cause the company's production to be less than optimal. Based on this urgency, this study was conducted to make predictions on the severity of injuries in the event of workplace accidents on workers using work accident data from 2016-2019, as many as 129 cases. The data used was obtained from the K3 unit at PT Varia Usaha Beton. Classification are made using a data mining approach with the decision tree method. The CART (Classification and Regression Tree) method is a type of decision tree used to make predictions. The results of this study are in the form of influential factors related to the severity of injury in the event of workplace accidents on workers which results in an accuracy rate of 81.1%. There are 3 factors that affect the severity of workers' injuries in the event of a work accident, where of the 3 factors there is 1 factor that is most influential on the occurrence of work accidents, namely work shifts. Two other influential factors are the age and location of the accident. CART method can be used in predicting the severity of injuries if work accidents occur in the concrete manufacturing industry. Thus the severity of injuries due to workplace accidents can be reduced by using a classification model for prevention and training in the company. Key Words : Classification of Influential Factors, Severity of Injury in Workers, CART (Classification and Regression Tree) Method, Decision Tree.
Playing Minecraft with Behavioural Cloning

MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment. We approached this task with behavioural cloning by predicting what actions human players would take, and reached fifth place in the final ranking. Despite being a simple algorithm, we observed the performance of such an approach can vary significantly, based on when the training is stopped. In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.
Pricing Security in Proof-of-Work Systems

A key component of security in decentralized blockchains is proof of opportunity cost among block producers. In the case of proof-of-work (PoW), currently used by the most prominent systems, the cost is due to spent computation. In this paper, we characterize the security investment of miners in terms of its cost in fiat money. This enables comparison of security allocations across PoW blockchains that generally use different PoW algorithms and reward miners in different cryptocurrency units. We prove that there exists a unique allocation equilibrium, depending on market prices only, that is achieved by both strategic miners (who contemplate the actions of others) and by miners seeking only short-term profit. In fact, the latter will unknowingly compensate for any attempt to deliberately shift security allocation away from equilibrium. Our conclusions are supported analytically through the development of a Markov decision process, game theoretical analysis, and derivation of no arbitrage conditions. We corroborate those results with empirical evidence from more than two years of blockchain and price data. Overall agreement is strong. We show that between January 1, 2018 and August 1, 2020, market prices predicted security allocation between Bitcoin and Bitcoin Cash with error less than 0.6%. And from the beginning of October 2019, until August 1, 2020, market prices predicted security allocation between Bitcoin and Litecoin with error of 0.45%. These results are further corroborated by our establishment of Granger-causality between change in market prices and change in security allocation. To demonstrate the practicality of our results, we describe a trustless oracle that leverages the equilibrium to estimate the price ratios of PoW cryptocurrencies from on-chain information only.
Secure Kernel Execution with Intel SGX

Intel SGX is not accessible from the most privileged execution level, known as ring zero, where the operating system kernel is placed. However, it is possible to split the execution responsibility between kernel and userspace by creating a dependency among these two levels that allow internal kernel data to be stored or processed within SGX private enclaves. In this paper we present SKEEN, an enhanced way to isolate internal operating system components and structures with Intel SGX technology, preventing information leak to different components of the same operating system. A proof-of-concept is provided to exemplify its usage.
DEVELOPING AND VALIDATING EFL READING TEST

EFL students need to learn about reading comprehension because, it is one of the essential abilities in learning English. To measure the students reading comprehension, a reading test is needed. Nevertheless, many students find difficulties in working the reading test. It happens because the reading tests which are given by teachers who do not analyze item difficulty, discrimination, distractor, and reliability. Those analyses are needed to prove that the reading test has met criteria of good tests. Criteria of good tests are needed in making the reading test because the reading test is used for measuring the achievement of EFL students. This study purposes at developing the reading test based on Indonesia National Curriculum basic competence, learning indicators, English lessons, and it examines to meet the criteria of good test. The researcher adapted Borg and Gall's (1983) theory in developing the reading test.
Adaptively Solving the Local-Minimum Problem for Deep Neural Networks

This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss criterion's convexification by transforming the cross-entropy loss into a risk averting error (RAE) criterion. To alleviate numerical difficulties, a normalized RAE (NRAE) is employed. The convexity region of the cross-entropy loss expands as its risk sensitivity index (RSI) increases. Making the best use of the convexity region, our method starts training with an extensive RSI, gradually reduces it, and switches to the RAE as soon as the RAE is numerically feasible. After training converges, the resultant deep learning machine is expected to be inside the attraction basin of a global minimum of the cross-entropy loss. Numerical results are provided to show the effectiveness of the proposed method.
REVIEWING MALAYSIAN UNIVERSITY ENGLISH TEST (MUET) REPORT WRITING

This paper explores a potential methodology to discover the discourse construction of Malaysian University English Test (MUET) Paper 4- Writing: Question 1- Report Writing (P4-Q1), one of the important components that make up the significant test. First, it reviews the details of this task as assigned by the MOE followed by the critical role that the test plays at various levels of educational institutions nationwide, justifying on the unequal allocation of emphasis and guidelines that govern the task which is translated through the test candidates’ achievements. Next, it reviews the evidence of how move analysis is likely impactful to shedlight for a reliable solution to this issue by accounting on the practicality of reviewing a widely used model in the field, the BCU Approach. In this way, an established framework can possibly be constructed. Finally, it calls for a future research to further exercise this framework through the analysis of reputable corpus where the highly potential of texts samples are contained.
Cache Aware Task Scheduling Algorithm for Heterogeneous Cloud Computing Environment

The following topics are dealt with: learning (artificial intelligence); Internet of Things; convolutional neural nets; Internet; feature extraction; image segmentation; cloud computing; resource allocation; image enhancement; diseases.
Activity recommendations based on heterogeneous social networks

Abstract EBSN (event-based Social Network) is a Heterogeneous Network, namely, H-EBSN (Heterogeneous event-based Social Network), which contains both online interactive information and offline interactive information. How to effectively use the interaction between online and offline to recommend activities is one of the hot issues at present. The following work has been done for the activity recommendation of H-EBSN: (1) Construct H-EBSN, extract all link-roads and obtain user interest through link-roads’ characteristics. (2) Analyze H-EBSN meta path, take the path number of extracted link-roads as feature vector, establish user interest model, and use generalized regression neural network to solve the model. (3) The training set was constructed by using the movie-watching activity data set, and the generalized regression neural network was used to solve the model. Experiments show that the activity recommendation method based on H-EBSN proposed in this paper has a good recommendation effect.
Creative potential of physical education teacher as one of the key elements of professional competence

The article considers the creative potential of a teacher as one of the key indicators of professional competence, and in particular a teacher of physical education. Further, the concept of "creative potential" is given and detailed and the professional competencies of the teacher of physical culture are presented. In addition, possible ways to develop the creative potential of the teacher were identified. In conclusion, the possible inclusion of creative options for solving professional pedagogical problems is presented.
Neural Network Model for Face Recognition from Dynamic Vision Sensor

In this work, we consider the applicability of the face recognition algorithms to the data obtained from a dynamic vision sensor. A basic method using a neural network model comprised of reconstruction, detection, and recognition is proposed that solves this problem. Various modifications of this algorithm and their influence on the quality of the model are considered. A small test dataset recorded on a DVS sensor is collected. The relevance of using simulated data and different approaches for its creation for training a model was investigated. The portability of the algorithm trained on synthetic data to the data obtained from the sensor with the help of fine-tuning was considered. All mentioned variations are compared to one another and also compared with conventional face recognition from RGB images on different datasets. The results showed that it is possible to use DVS data to perform face recognition with quality similar to that of RGB data.
Management of Public Information Disclosure in Indonesian Autonomous University: Case Study in Universitas Brawijaya

This study aimed to understand, describe, and analyze the management of public information disclosure, support and inhibiting factors, and efforts to improve the management of public information disclosure in one of the autonomous universities, Universitas Brawijaya (UB). This study uses the case study method with a qualitative approach. This study adopted a participative observation, in-depth interviews with five key informants, collecting documents, and audio-visual material to collected data. The finding of this study shows that management of public information disclosure in UB refers to Public Information Disclosure Number 14 the Year of 2008 (UU KIP). It refers to Regulation from Minister of Education and Culture Number 75 the Year of 2016 about Public Information Services in Universities that are provided through integrated service information center (PINTER) UB. UB also provided service of complaints and community satisfaction surveys. Management of Public Information Disclosure in UB influenced by the existence of supporting factors from UB manager and e-complaint system and internet network to support services activity. In contrast, the inhibiting factors are arising from lack of management in faculty or work unit and the lack of socialization from Information and Documentation Management Officer (PPID / IDMO) and Information, Documentation, and Complaints Center (PIDK / IDCC) to the academic communities. It made service yet utilized optimally. Besides, the efforts to improve the management of public information disclosure always carried out to improve service quality such as 1) establish good relations between staff, enhancing coordination between faculties and work units, 2) Increasing coordination with Manager and Quality Assurance Unit (QUA), 3) Using new regulation the Minister of Administrative and Bureaucratic Reform Number 14 of 2017, and 4) Adding three-division managers, such as Manager of information services, the Manager of user complaints and survey, and Manager of verification and data integration mapping.
Technological and pluricultural teacher training for a quality educational inclusion

The present work aims to establish the links that may exist between technological training and multicultural training of teachers with educational inclusion. The research design is non-experimental, explanatory and correlational, with a quantitative method. A reliable and content-validated Likert-type scale has been used. The sample used is 594 subjects, formed by university students in their last year of Primary Education and graduate students (Master of Teacher Training) (University of Jaen, Spain). An exploratory factorial analysis was carried out which validated the scale's construct and which allows us to conclude that greater value is given to foreign training than to national training, and furthermore, little value is given to teacher training as a key to an educational system in accordance with the 21st century. Spearman's Rho was applied, where the significant correlation between the training provided by non-university institutions and pluricultural training can be observed. Structural equation modeling (SEM) concludes by demonstrating the strong relationship between inclusive and pluricultural teacher education, and between the latter and technology, with a weak relationship between technology and general teacher education.