Reviewerly/exHarmony huggingface dataset:
Optimized parallel causal product for linear transformer

The transformer has revolutionized Natural Language Processing and its exceptional performance has extended its application to computer vision. However, as more complex tasks arise, the demand for models to process longer sequences has increased. To address this, efficient transformers with reduced time and memory complexities have been developed. Linear transformer is an outstanding one of them with linear time complexity with input length. Causal product is the core computation of causal linear attention in linear transformer, which are commonly used to perform autoregressive tasks. However, in order to preserve the linearity, the causal products of queries are computed sequentially in Linear Transformer. We propose an innovative CUDA kernel function which allows for parallel computation of the query attention instead of sequential. Furthermore, we optimized the GPU computations of linear attention in Linear Transformer by double pipeline that decreases the time of data transmission. Our experiments demonstrate that our method achieves a 9x speedup compared to the original causal product function when implemented on a CPU.
Do painters need physics?

Abstract Article presents the possibilities of using the painting in physics education in the context of acquisition by students the ‘Learning to learn’ competence. Analysis of the works of 13 painters gave us the opportunity to formulate interesting research problems in physics. Examples of 18 problems and answers to them have been suggested. In the process of solving problems, students learn to work independently with printed and electronic sources of information, and also learn to independently conduct experiments and observations. The independent research activity of students involves searching of relevant information sources, collection and analysis of information, putting forward and testing a hypothesis, and formulation of conclusions. These valuable critical skills are components of ‘Learning to learn’ competence.
Helmet wearing detection based on YOLOv5

Complex environments, such as dense personnel and background interference, affect the detection accuracy of whether personnel wear helmets. To solve this problem, a new detection algorithm for helmets based on YOLOv5 is studied in this paper. Firstly, RepVGGblock is used to replace the common convolution in the network to effectively utilize the computing power of GPU. Secondly, the efficient channel attention mechanism is incorporated into the C3 module of the backbone network to enhance the feature identification ability of the backbone network for the hard hat. Then, the boundary box regression function is changed to SIoU to redefine the distance loss and improve the regression accuracy. Finally, the detection performance of multi-scale targets is improved by adding detection layers. The test results on the self-made safety helmet data set show that the average accuracy of the improved YOLOv5 model reaches 94.6%, which is 4.1% higher than that of the original model, and can meet the requirements of target detection.
Image captioning based on an improved attention mechanism

Attention mechanism in image captioning model can help model focus on relative regions while generating caption. However, existing attention mechanisms are unable to identify important regions and important visual features in images. This problem makes models sometimes pay excessive attention to non-important regions and non-important features in the process of generating image captions, which makes model generate coarse-grained and even wrong image captions. To address this problem, we propose an “Importance Discrimination Attention” (IDA) module, which could discriminate important feature and non-important features and reduce the possibility of misleading by non-important features in the process of generating image captions. We also propose a IDA-based image captioning model IDANet, which is completely based on transformer framework. The encoder of IDANet consists of two parts, one is pretrained Vision Transformer (VIT), which is used to extract visual features in a fast way. The other is refining module which is added into encoder to model position and semantic relationships of different grids. For the decoder, we propose IDA-Decoder which has similar framework with transformer decoder. IDA-Decoder is guided by IDA to focus on crucial regions and features instead of all regions and features while generating image caption. Compared with others attention mechanism, IDA could capture semantic relevance of important regions with other regions in a fine-grained and high-efficient way. The caption generated by IDANet could accurately capture the relevance of different objects and discriminate objects that have similar size and shape. The performance on MSCOCO “Karpathy” offline test split achieves 132.0 CIDEr-D score and 40.3 BLEU-4 score.
Application of SVD combined with PCA in delineation and evaluation of ore-prospecting targets in the Gejiu tin polymetallic cluster region, SW China

The Gejiu tin polymetallic cluster region, situated at the northeastern side of the Ailaoshan-Red River Fault and the junction area between the Cathaysia terrane and the Yangtze terrane, is associated with the late Cretaceous magmatic-hydrothermal metallogenetic event. In this study, a singular value decomposition (SVD) is effectively used to extract the anomaly components from the metallogenetic element groups established by the principal component analysis (PCA) for the identification of the ore-prospecting targets. Three steps are shown: (1) the metallogenetic element groups are obtained using PCA; (2) the element anomaly components formed by magma-hydrothermal metallogenetic events are extracted using SVD from the metallogenetic element groups; and (3) the local and regional element concentration anomaly components are identified by applying the element anomaly components corresponding to different eigenspaces. Finally, ten ore-prospecting targets for the Bi-Pb-Sn-Th-W-Cu mineralization, two ore-prospecting targets for the Ag-Cd-Hg-Zn-Mn mineralization, and two ore-prospecting targets for the La-Nb-Th-Zr-U-Y mineralization are delineated, respectively. The results certify that SVD can discriminate not only the regional element concentration anomaly components formed by the magmatism but also the local element concentration anomaly components triggered by the metallogenetic processes from the metallogenetic element groups established by the PCA.
Optimal KAZE and AKAZE Features for Facial Similarity Matching

Face Recognition is one of the premier disciplines in the vast field of computer vision and image analysis. A popular method is the Gaussian scale space analysis which limits the performance by smoothing both the noise and natural boundaries in the same proportion. In order to prevent the loss of natural boundaries to smoothing we tap into nonlinear scale space techniques such as KAZE and Accelerated KAZE. KAZE is a multistage 2-D feature detection and description algorithm. It makes use of AOS schemes to develop the nonlinear scale space for analysis. Though the results are satisfactory, it is computationally intense as they solve a humongous module of linear equations. To ascertain the mentioned limitation of KAZE we make use of Accelerated-KAZE, which uses pyramidal structure with Fast Explicit Diffusion incorporated in it, thus minimizing the computation in the step of feature detection of nonlinear scale space. Also, with the use of M-LDB (Modified-Local Difference Binary) descriptor the problem of rotation is solved. Usage of RANSAC after processing in two methods had some disadvantages. It gave bad results when the inliers ratio in the dataset is low. Thus, Optimal RANSAC is employed which works well even when the inliers ratio is as low as 5%. The proposed methods are tested on many standard datasets and various performance parameters.
Students’ involvement as a factor of improving the efficiency of the educational process

The article deals with the most important pedagogical problems of increasing the involvement of students in the educational process. The authors aim to study the international theoretical and practical experience of increasing students’ involvement and analyze the complexity of the dynamic interaction between the students and the educational environment in class from the point of view of various types of involvement. To study the theoretical and practical materials, the authors use a combination of general scientific analytical, synthetic, and inductive-deductive approaches. The results of the study showed that the behavioral, academic, emotional, and cognitive involvement of students in learning a foreign language depends on various factors of the educational environment, including the choice of conceptual approaches to teaching, the level of students’ linguistic and communicative competences. The authors come to the conclusion that students’ involvement in the educational process in higher education reflects the effectiveness of the academic, social, and professional orientation of education, which contributes to academic success and professional self-realization of students.
PREVENTION OF PHLEBITIS

Phlebitis is an inflammation of a vein, usually caused by an infection, trauma, or chemical irritation. Symptoms include pain, swelling, and redness at the site of the affected vein, as well as fever and chills in more severe cases.
On the use of modern digital technologies in teaching a foreign language at a university

This paper examines the problem of using modern digital technologies in the process of teaching a foreign language in the academic learning environment. The study aims to identify the educational potential of digital tools such as training video, digital class, electronic simulators and digital applications in the foreign language learning environment at a non-linguistic university and develop methodological recommendations for using them in the learning process. The paper substantiates the relevance of using digital technologies in the education system, provides a review of digital tools for the foreign language classroom, tested in the learning process, as well as the description of their didactic and methodological potential for the increased efficiency of teaching a foreign language. The findings of the study contain methodological recommendations for integrating digital technologies into the foreign language learning environment for organizing classroom work and students’ selfstudy at a university.
Dual coding as one of the main cognitive educational categories in second language learning in a non-linguistic university

The given article continues the series of articles devoted to the peculiarities of foreign language education and those educational strategies that are used during the educational process. These strategies are aimed at improving the quality of the educational process and shaping of the necessary skills, which will later form the basis of communicative and socio-cultural competencies. It is known that foreign language education is multidimensional and one of the main aspects is the cognitive aspect, within which there is active communication between students and teachers, which makes the process of learning more interesting, attractive and productive for students. The cognitive process of language learning also involves the use of certain cognitive educational strategies (for example, dual coding strategies, strategies for distributing knowledge over time, etc.). In this article, our attention will be focused on the dual coding strategy which is considered one of the most widely used strategies, as well as one of the most productive.
Proposing a Method for Anomaly Detection in Trajectory of Patients With Alzheimer’s Disease Using Mobile GPS Data and Combination of Deep Neural Network and Adaptive Neuro-fuzzy Inference System

Background and Aims Today, with the industrialization of societies and the reduction of the size of households and tendency to live alone, caring for the elderly and monitoring their performance in daily life has become doubly important. Carrying devices such a GPS is one of the proposed solutions, which may not be a suitable solution due to the unpleasant feeling of carrying such equipment and due to disorders such as Alzheimer’s disease. A proper solution for taking care of old people especially for their outdoor activities is to observe their behavior by using their mobile GPS sensor by which it is possible to detect possible abnormal events. An important challenge in this method is the high number of abnormal events. In this paper, this problem is solved by applying an adaptive neuro-fuzzy inference system (ANFIS). Other important challenge is how to carefully analyze the training data to achieve a powerful model. For tackling this problem, we used a deep neural network. Methods In this paper, by combination of ANFIS and convolutional neural networks (CNN), a method was proposed for anomaly detection in trajectory of patients with Alzheimer’s disease. The CNN was optimized by the Whale algorithm. The proposed method was applied on a set of movement path data with a specific origin and destination based on the mobile GPS sensor of subjects. Results The proposed method had an accuracy of 95.5% for classification of test data, which indicated the effectiveness of the proposed method. Conclusion It seems that the combination of ANFIS and a CNN is a good method for anomaly detection in trajectory of older people with Alzheimer’s disease.
PENERAPAN FUNGSI MANAJEMEN DALAM PENGELOLAAN PERPUSTAKAAN DI SEKOLAH SMP NEGERI 5 MEDAN TAHUN AJARAN 2020/2021

Application of Management Functions in Library Management at Medan 5 Public Middle School for the 2020/2021 Academic Year. The purpose of this research is to find out what the school currently needs to carry out management functions at SMP Negeri 5 Medan and how the school's future efforts will limit librarian human resources. The method used in this study is qualitative, the source of research data is primary data secondary data, data collection techniques are observation, interviews and documentation. The data collected through this technique is examined and data reduction, data presentation, verification and conclusion are carried out. Based on the results of the study, it was concluded that the first process of implementing management functions in the school library at SMP Negeri 5 Medan could very well be improved in the future. Because all groups in school institutions, stakeholders and layers of the surrounding community have tried to be active and synergize in increasing interest in children's learning services, especially at SMP Negeri 5 Medan, Medan Labuhan District, Martubung Village, Medan City. Second, the role of the government among schools, all processes supporting learning activities in schools such as the facilities and infrastructure at SMP Negeri 5 Medan and other school levels, as well as the central government's active role and motto which wants to educate the life of the nation, this role is also synergized by local government, especially at SMP N 5 Medan, Medan Labuhan District. Third, SMP Negeri 5 Medan, in the future this school will enlarge the library space in the school and increase the number of librarians in the school, by providing special training beforehand so that the function and role of service management in managing the school library can run according to the function of management itself in accordance with the reality of the facts on the ground.
Multilayer network representation learning model with structure entropy information

Network representation learning maps network nodes to a unified vector space, which has found widespread use in various complex network analysis tasks. The resulting embedding vectors can reflect the original network's features and improve the accuracy of downstream tasks. However, current multi-layer network representation learning models have limited node sequence semantics and cannot fully capture the node's local and global structural information. To address this issue, we propose a multi-layer network representation learning model that integrates structural entropy information. Our model comprehensively considers the local and global structure of a multi-layer network, using local structure entropy and network standard structure entropy to guide the intra-layer and inter-layer migration processes, and then trains the node sequence using the skip-gram model to obtain the node vector. Experimental results on five public datasets demonstrate that our proposed model's embedding vector quality is significantly improved, with AUC scores on different datasets improving by 5% to 10% in the link prediction task.
Semantics-aware query expansion using pseudo-relevance feedback

In this article, a pseudo-relevance feedback (PRF)–based framework is presented for effective query expansion (QE). As candidate expansion terms, the proposed PRF framework considers the terms that are different morphological variants of the original query terms and are semantically close to them. This strategy of selecting expansion terms is expected to preserve the query intent after expansion. While judging the suitability of an expansion term with respect to a base query, two aspects of relation of the term with the query are considered. The first aspect probes to what extent the candidate term is semantically linked to the original query and the second one checks the extent to which the candidate term can supplement the base query terms. The semantic relationship between a query and expansion terms is modelled using bidirectional encoder representations from transformers (BERT). The degree of similarity is used to estimate the relative importance of the expansion terms with respect to the query. The quantified relative importance is used to assign weights of the expansion terms in the final query. Finally, the expansion terms are grouped into semantic clusters to strengthen the original query intent. A set of experiments was performed on three different Text REtrieval Conference (TREC) collections to experimentally validate the effectiveness of the proposed QE algorithm. The results show that the proposed QE approach yields competitive retrieval effectiveness over the existing state-of-the-art PRF methods in terms of the mean average precision (MAP) and precision P at position 10 (P@10).
Testing in the educational process of the university: features, tasks and application formats

The article deals with tests application and correction in various formats. It is noted that the use of tests at the university will allow solving issues of students’ knowledge control, and testing formats allow solving the problems of both classical, classroom work, and distance learning. The necessity of timely adjustment of test tasks corresponding to the requirements and conditions of the educational process is analyzed. Particular attention is paid to modern testing and diagnostic systems implemented though the artificial intelligence algorithms. Examples of modern platforms are given and the necessary requirements for them are distinguished, namely: creating various kinds of tasks, openness of the platform, automatic presentation of test results according to various criteria. The results of the study conducted in four groups of first-year students of the Financial University specializing in Advertising and Public Relations are presented.
Effectiveness of virtual reality possibilities during COVID-19: A Study

Life with unheard challenges of the 21st century flooded over the world with the spread of COVID-19 pandemic. This pandemic has not only impacted the physical health but also the living style of every individual. Technology played a vital role in helping us to get over it. As the Corona virus spreads, so do innovations to fight it in other way. When physical distance and quarantine was mandatory, only technological remedies saved from the psychological distress of the people. The business model was also revived only because of technology. And the new ways and trends of digitality led everyone to solace in that crucial time. We can say only digital technology made life a little easier and life in quarantine more livable. As these technological innovations like virtual reality practices accelerate, the lifestyle and the routines of people also change according to it. This study is about the same and based on primary data.
Model-based clustering using a new multivariate skew distribution

Abstract Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, and present a latent group structure. In this paper, we first propose the multivariate skew shifted exponential normal distribution that can account for these non-normal characteristics. Then, we use this distribution in a finite mixture modeling framework. An EM algorithm is illustrated for maximum-likelihood parameter estimation. We provide a simulation study that compares the fitting performance of our model with those of several alternative models. The comparison is also conducted on a real dataset concerning the log returns of four cryptocurrencies.
Changing the role of a lecturer in educating of contemporary manager

The article examines and actualizes the linguodidactic roles of the teacher: designer, source of knowledge, manager, leader of the educational process, facilitator, diagnostician, expert, creator of the developmental environment, — which appear in the course of foreign language training of managers. The conclusion is made about the influence of the competent use of these roles on the level of preparation of future professionals. The author draws attention to the fact that other participants of the educational process influence the quality of training of future management specialists: administrative and managerial staff, internal university service of psychological support of students, educational work department and students themselves, and speaks about the connection of foreign language education with delayed investment both in students and in the future economy of the country.
Exploring the Development of School-based Curriculum in Junior High Schools based on Chengdu's Indigenous Culture

The development and implementation of school-based curriculum helps excellent local culture find a stable and systematic carrier of inheritance and innovation in school education. Aiming at the singularity and one-sidedness of local culture education at the junior high school stage at present, this study attempts to integrate Chengdu local culture into the junior high school school-based curriculum, and on the basis of the practice that meets the needs of the times and the school's philosophy of school running, establishes the practical forms of reciting ancient poems with inheritance and innovation and talking about hometowns in the form of understanding and communication, and explores the path of implementation of the Chengdu school-based curriculum based on the Chengdu school-based curriculum, i.e., refining the theme classification and carrying out the fusion of disciplines; focusing on the local culture of Focus on Chengdu's local culture to cultivate a sense of home and country; create an exchange platform to increase international interaction, etc. A junior high school school-based curriculum based on Chengdu's local culture is of positive significance to the development of students' cultural awareness, the improvement of teachers' professionalism, and the creation of schools with special characteristics.
Case Study of Junior High School English Reading Teaching Pointing to the Development of Thinking Quality

In this paper, we analyze the current situation of reading teaching in junior high schools in China in terms of students' thinking skills, and use the short text 'How to be a The text structure is explained in the middle of the reading, and logical thinking is cultivated by using a mind map to sort out the content of the text; after reading, role-playing and writing activities are carried out according to the content learned to cultivate creative thinking. Thinking.