Reviewerly/exHarmony huggingface dataset:
Simple Online Real-Time Tracking Algorithm with Improved YOLOV4 as Extractor

Multiple target tracking algorithm is an important research field of computer vision. We improve the YOLOV4 algorithm by using the MatrixNets feature extraction module to obtain a better target detection and feature extractor and then uses the improved YOLOV4 algorithm to improve the deep sort algorithm to improve the performance of the algorithm to propose the Deep SORT-Y algorithm. The Deep SORT-Y algorithm can track the lost target object through a longer occlusion period, which greatly reduces the target loss rate. Compared with sort and deep sort algorithms, the score is reduced by 61 and 18%. Experimental evaluation shows that this algorithm still maintains the overall competitive advantage compared with other online real-time tracking algorithms at a high frame rate.
ON QUASI-STATIC MODES OF MOTION OF UNDERWATER MOBILE ROBOTS WITH ANCHOR-ROPE-TRACK DRIVES

A method for calculating mobile robots with anchor-cable-tracked drives is considered. The influence of the mass-geometric characteristics of a mobile robot on the stability of its movement in quasi-static modes of movement along the bottom of a reservoir is considered.
The Implementation Of Discrete Cosine Transform (DCT) And Blowfish Methods In Digital Video Security

Collecting data partially or completely illegally without the permission of the owner causes losses to the copyright owner. This has a big impact in proving ownership of a digital media. To be able to prove ownership of the copyright, the Discrete Cosine Transform watermarking technique can be used by inserting the Blowfish cryptographic algorithm. Discrete Cosine Transform (DCT) is a method that converts digital data into the form of a frequency domain. The method used in the DCT transformation technique is to break the digital image into small blocks with a fixed size and then convert it from the spatial domain to the frequency domain. Blowfish works by dividing the message into 64-bit blocks of equal length with varying aty lengths that encrypt the data in 8 byte blocks. Messages that are not multiples of 8 bytes will be added extra bits (padding), so that the size for each block is the same. After the final result is obtained, the value is inserted into the digital video without destroying the original digital video. From the research that has been done in the implementation of digital video watermarking, it can be concluded that the Blowfish cryptography method and the Discrete Cosine Transform (DCT) watermarking cryptography method can be applied properly. In the watermarking process, digital image insertion does not damage the watermaratd video and at the security level it is provided with blowfish cryptographic encryption, so that it can increase security in copyright protection
Technology of critical thinking development as forming tools for teacher professional competencies in pandemic

The article was intended to determine the specifics of the problem of professionalization of the activities of a teacher of vocational training. The object is the professionalization of the teacher's activity. The subject is the problem of professionalizing the activities of a teacher of vocational training. A qualitative analysis of well-known literature sources in the field of technologies for the development of critical thinking in the formation of professional competencies of a higher school teacher in the context of a global pandemic has been carried out. The main methods used in the development of the article were: research methods were used to solve the set tasks: theoretical (study and analysis of scientific and pedagogical, psychological and pedagogical, reference, specialized literature, regulatory documentation on topic of research, additional professional advanced training programs; analysis, comparison, classification of the information received and generalization); empirical during an international pandemic. The following tasks are solved in the article: the concept and specificity of the phenomenon of professionalization of activity; the features of the professionalization of the activity of a teacher of vocational training are characterized; the possibilities of professional competence as a way to solve the problem of professionalization of the teacher's activity are determined.
A New Approach for Speech Keyword Spotting in Noisy Environment

Keyword Spotting works to detect wake-up keywords in a continuous voice stream, which is widely used in products such as mobile devices and smart home. Recently, DNNs dominate keyword spotting and dramatically improve performance. However, few researchers concerned about noise in speech keyword recognition. Thus, we propose an architecture for the detection under noisy scenario. Our framework combines attention mechanism and residual structure based on the CNN backbone. In addition, we use separable convolution to reduce the number of model’s parameters, which makes it applicable in the embedded devices. Noises from various scenes are utilized for data augmentation to boost performance. The proposed method achieves an accuracy of 94.93% on the noisy test set based on the Google Speech Commands dataset. We also compare performance between the proposed method and RNN-based algorithm, and prove our model achieve higher accuracy with fewer parameters.
A Gitbash-Generative Model for an Enhanced Query Processing In a Distributed Database System

This research study is directly focused on an enhanced query processing system using GitBash- Deep Generative Model, it is use to improve the performance of big data. Due to Poor query execution plan and absence of an improved aggregate query method which impose threats to qualities of good query processing in distributed databases which also cause memory pressure in a database, inflated central processing unit (CPU) and an overall reduction in concurrency. Also the volume of data use in any organization is progressively increasing in it seize, thereby resulting or demand a large storage location or space system. The availability of intended software that will confront the problem of storage contents and execution plan for query processing is a vital challenge facing big data in a processing system, which can prompt to information loss and missing record of vital document in an enterprises. The study will boost solution and addresses the problem of latencies, inability to transform and store queries, and poor query processing plans for future improvement of distributed databases, using an unsupervised learning approach such as deep generative model. The adopted methodology is dynamic system development method (DSDM). php programming language and mongoDB database is adopted for implementation. The datasets used for the proposed system was generated from e-library server/data.json (query) and is inserted into MongoDB database json format. The datasets are all trained and recognized by the Deep Generative algorithm through the Git Bash server. The proposed system result shows highly increase in the efficiency of management of big data and improved query processing. General Term Query Processing System Keywords Big Data, Deep Generative Model, Query Processing System, Information Management, Gitbash.
Development of digital competence in teacher training: A systematic review

This article presents the results of a systematic review of different studies published in high-impact journals on the development of teaching digital competence (TDC) in initial teacher training. The methodology of the study is based on the documentary research approach, with the application of a systematic literature review covering the period between 2009 and 2019 in different databases. The sample is made up of 102 articles that are registered in a multidimensional analysis and classification matrix. One of the main findings is that in the last ten years there has been a significant increase in the literature related to teaching digital competence, with the prevailing use of the quantitative methodological approach. Likewise, more than half of the articles are the result of the application of instruments to assess TDC. It can be concluded that TDC is a line of research with multiple dimensions of analysis and in constant evolution. Keywords: communication technology; digital competence; educational technology; initial teacher training; information
Sentiment analysis of micro-blogging sites using supervised learning: a narrative review of recent studies

Sentiment analysis is a task of predicting sentiments from the opinionated data and classifies them as positive, negative, ratings (stars or numerical), thumbs up - thumbs down and so forth. In the present survey, we have covered numerous datasets, methodologies, developments together with indications for advances in the near future. Inspired by the achievements of deep learning, plenty of researchers are utilising the deep learning models for conducting sentiment analysis. Therefore, we have highlighted some studies regarding the use of machine learning and deep learning models on different micro-blogging sites with the evolution in sentiment analysis. The survey presents a number of suitable illustrations-most prominently, a table that summarises previous papers along different dimensions such as types of objectives, classification techniques and dataset used.
The impact of strategic intelligence on crisis management in light of the spread of the Corona pandemic by applying it to the Saudi banking sector in the Asir region: أثر الذكاء الاستراتيجي على إدارة الأزمات في ظل انتشار جائحة كورونا بالتطبيق على القطاع المصرفي السعودي بمنطقة عسير

There is no doubt that the Corona pandemic that struck the world had direct and indirect effects on all areas of our life. Finance and economy, represented in the banking sector, was no exception to this. To know the effects, this study aimed specifically at studying the impact of strategic intelligence on the management of crises in the banking sector in the Kingdom of Saudi Arabia, specifically in the Asir region (the cities of Abha and Khamis Mushait), and knowing the extent to which the employees of those banks are aware of the concept of strategic intelligence in achieving crisis management. The descriptive correlative approach was applied after designing a questionnaire that includes three sections, including the personal data section, the strategic intelligence section, and the crisis management section, and it was distributed to 154 samples, They were randomly selected from over 2000 employees in the area. As a result of the field survey, it showed that some indicators and dimensions were applied at high rates, such as foresight, vision, thinking, motivation, partnership, and the crisis management perspicacity, in addition to the availability of a high application of strategic intelligence. This study recommended the need to develop future forward-looking plans to reach a better distinction in the face of future crises, with the relentless pursuit by the management of banks to reach a better mental image of customers and to follow partnership in achieving a higher and more comprehensive development by collecting the elements of that image and then placing it under the brainstorming mechanism to be Analysis and detection of internal factors within a high capacity.
Gating Mechanism in Deep Neural Networks for Resource-Efficient Continual Learning

Catastrophic forgetting is a well-known tendency in continual learning of a deep neural network to forget previously learned knowledge when optimizing for sequentially incoming tasks.To address the issue, several methods have been proposed in research on continual learning.However, these methods cannot preserve the previously learned knowledge when training for a new task.Moreover, these methods are susceptible to negative interference between tasks, which may lead to catastrophic forgetting.It even becomes increasingly severe when there exists a notable gap between the domains of tasks.This paper proposes a novel method of controlling gates to select a subset of parameters learned for old tasks, which are then used to optimize a new task while avoiding negative interference efficiently.The proposed approach executes the subset of old parameters that provides positive responses by evaluating the effect when the old and new parameters are used together.The execution or skipping of old parameters through the gates is based on several responses across the network.We evaluate the proposed method in different continual learning scenarios involving image classification datasets.The proposed method outperforms other competitive methods and requires fewer parameters than the state-of-the-art methods during inference by applying the proposed gating mechanism that selectively involves a set of old parameters that provides positive prior knowledge to newer tasks.Additionally, we further prove the effectiveness of the proposed method through various analyses.
Research on Cross-cultural Text Reconstruction of Urban Publicity Translation Based on Computer Corpus

Urban publicity translation, as a cross-cultural communication activity, should aim for communication, employ various translation strategies, adapt to the target language’s expression habits, overcome cultural differences, and make the translation easy to accept for target readers. In order to achieve the goal of external promotion, publicity texts should respect and conform to the target culture’s language expression as well as the psychology of the audience during the initial stage of urban publicity translation. This paper analyzes the causes of cultural vacancies in the translation of urban publicity materials, starting with the classification and sorting of cultural vacancies in the translation of publicity materials. This paper focuses on using a computer corpus to reconstruct cross-cultural text for urban publicity translation. An automatic corpus expansion method combined with the EM (expectation-maximization) algorithm is proposed to solve this problem. The model is iteratively trained after the generated single corpus is combined with the original data set to create a parallel corpus. Finally, as another important feature of words, the word cooccurrence degree is incorporated into the interword relationship extraction model to create a new word translation evaluation index. Finally, the experiment demonstrates that the EIWR (extraction of interword relations) has higher accuracy than the VSM (vector space model).
Cryptographic Circuit Technology Consisting of Optical Logic Gates

AES encryption schemeAES is a block cipher with a 128-bit block length.Key length may be selected from 128, 192, or 256 bits [2].Here, 128 bits of intermediate values called a "state" are represented by a 4 × 4 matrix with each element consisting of 8 bits.Repeated application of a round function-the basic structure of encryptionto the state outputs the ciphertext.The round function
Analysis of Image Evolution of Ancient Large Figurines Based on Deep Neural Network

Unlike traditional image recognition technology, DL can automatically extract features and improve recognition accuracy by combining feature extraction and classification. The challenges and shortcomings of traditional image recognition methods are discussed in this article, as well as the development process and research status of DL. Related theories in image recognition based on deep learning (DL) are proposed, DL’s basic models and methods are analyzed, and related image data sets are demonstrated experimentally. Furthermore, because DL is typically used for large sample sets, this paper proposes an improved algorithm based on small samples, as well as a DNN-based analysis model for the evolution of ancient large figurine images. This model, when compared to the traditional neural network model, can speed up the network’s convergence speed and reduce training time to a certain extent. This model improves the rate of image recognition while lowering the error rate.
Face photo-sketch synthesis based on multi-scale feature fusion

The synthesis of face sketches from real face photos and its inverse process have a wide range of applications, such as digital entertainment and assisting in the investigation of criminal cases. However, due to the significant discrepancies in texture between photos and sketches, the transformation between them is still a challenging problem. Recently, the methods based on generative adversarial nets (GANs) have shown encouraging results in the transformation between images, especially the transformation from photos to sketches. However, most of them produce different deformations or blur in the key components of the face, which affects the realism of the synthetic image. To address this challenge, we propose a novel face photo-sketch synthesis algorithm based on multi-scale feature fusion to improve the structural integrity and texture fidelity of the synthetic image. Firstly, an encoder is utilized to extract the multi-scale coding features of the input image. Then the bottom coding features are passed by a dilated convolution module and transmitted to the decoder for decoding. In the decoding process, the decoding features of different scales and the coding features of corresponding scales are concatenated in the channel dimension to obtain the multi-scale coding and decoding fusion features. Finally, the coding and decoding fusion features of different scales are further fused at the output of the decoder and the final synthesis result is generated through a convolution layer. By combining the features of different scales in the encoding and decoding processes in the channel dimension at the same time, we can maintain good image structure and texture details and generate realistic face sketch/photo images. We verify the effectiveness of the proposed method in several challenging datasets. The quantitative and qualitative evaluation shows that the proposed method is superior to other state-of-the-art methods in generating face sketches (or photos) with high visual quality.
THE IMPACT OF INFORMATION SERVICES THROUGH WHATSAPP MOTIVATION OF CLASS X STUDENTS OF SMAN 3 SAMARINDA IN THE PANDEMIC PERIOD OF THE 2020/2021 ACADEMIC YEAR

The background of this research is due to a decrease in students' learning motivation during the pandemic so that BK teachers provide information services with the help of Whatsapp Group media to increase students' learning motivation during the pandemic. The formulation of the research problem is how the impact of information services through Whatsapp Group in increasing the learning motivation of class X SMA Negeri 3 Samarinda students with the aim of knowing the impact of information services through Whatsapp Group in increasing the learning motivation of class X students of SMA Negeri 3 Samarinda during the pandemic. The type of data used is quantitative research with ex post facto correlational design research methods. The population is all students of class X in SMA Negeri 3 Samarinda with a sample of 138 respondents, purposive sampling technique. Data collection techniques are questionnaires on information services and students' learning motivation. The instrument validity test used the Pearson Product Moment correlation formula, and the instrument reliability test used the Cronbach Alpha coefficient approach reliability index calculation using the SPSS 25 program. The data analysis technique used descriptive analysis and simple linear regression analysis. Before testing the hypothesis, the analysis requirements test was conducted, namely normality and linearity tests using the SPSS 25 program. The results of the research from information services had an average of 132, a frequency of 60% with a useful category and learning motivation in the medium category had an average of 195, a frequency of 71%. In the results of tcount 10.253 &gt; ttable 1.97756, a significance level of 5% means that information services through the Whatsapp Group media have a significant impact on increasing students' learning motivation during the pandemic with an R Square of 44% being in the strong category. This shows that information services through Whatsapp Group can be used as a strategy in increasing students' learning motivation, especially during this pandemic. This research is expected to be used as a reference and reference material to further innovate in providing information services to students' learning motivation.
System Requirements Of Authentic Assessment Information Systems Model Applications

Human needs to meet their needs cannot be separated from the role of information technology (IT) to provide data and information needs. In the education sector, the need for application of IT is carried out as an effort to support learning activities. One of application for education is a learning management system. Through this research, it will explain the system requirements for an authentic assessment information system based on web application. When conducting an authentic assessment, the teacher will assess the aspects of attitudes, knowledge and skills of student for vocational high school levels. The assessment tools for authentic assessments can be through by performance, writing, projects and portfolios assessments. The software development method used is the waterfall method, where for the needs analysis stage have used user requirement analysis and PIECES table. Meanwhile, for the design stage, we will use design tools by unified modeling language (UML). From the analysis and design of an authentic assessment information system based on web application, it is hoped that it can be one of the recommendations in building a learning management system media that is made in accordance with the assessment guidelines in the 2013 Curriculum.
PICTURE SERIES TO INCREASE WRITING SKILL OF EFL JUNIOR HIGH SCHOOL STUDENTS

The purpose of this study was to find out whether picture series media was effective on writing knowledge in EFL students of eighth grade at SMPN 31 Gresik in the 2020/2021 school year. The participants in this study were students in eighth grade at SMPN 31 Gresik. The number of samples in this study was 64 students. The classes are divided into two classes; experimental class and controlled class. The method used is a quantitative method that adopts a quasi-experimental design. The instrument used was a recount text writing test consisting of a pre-test and a post-test. After collecting student scores from the two tests, the author uses SPSS v.22 to analyze normality and homogeneity to determine whether the data are normally distributed and homogeneous or not. After analyzing the normality and homogeneity, the writer also tested the hypothesis by using a paired sample test to determine whether there was the effectiveness of using picture series in learning to write recount text. The study's findings found that the average post-test score in the experimental class was higher than the post-test mean in the control class (82.58 &gt; 53.20). Furthermore, the paired sample test analysis proved that the post-test p-score was 0.00 with a significance level of 5% (0.05). In other words, it can be concluded that the p-value (0.00) there is a significant difference between students' writing recount text using picture series and without picture series. That is, picture series is effective and applicable as a medium in writing recount text.