Reviewerly/exHarmony huggingface dataset:
Efektivitas pembelajaran daring di masa pandemi Covid-19 terhadap hasil belajar siswa SD

The development of education today is influenced by the rapid progress of information and communication technology. One of the information technologies that play a role in the world of education is online learning. Online learning functions as a liaison between educators and students with an internet network that can be accessed anytime and anywhere. The online media used by the research subjects are various (WhatsApp, Google Classroom, Website). The purpose of this study was to analyze the effectiveness of online learning applied to the elementary school level. The method collected in data collection was a question and answer session with the sources, namely elementary school students via WhatsApp and the results of observations made by researchers during online learning. Looking at the results of student learning in elementary school, it can be concluded that online learning can be said to be effective and the material delivered by the teacher is maximal if it is accompanied by parents.
Consensus protocol based on DPOS and aggregate signature

Consensus mechanism is the underlying technology of blockchain and the rule to maintain the normal operation of blockchain. Commonly used consensus mechanisms of blockchain public chain include POW, POS, DPOS, BFT and those mixed with many other mechanismsBy comparing the characteristics of several consensus mechanisms, the DPOS mechanism can generate more blocks with less computing power, and use BLS (Boneh-Lynn-Shacham) signatures for verification in the process of transaction verification to create a new consensus mechanism..Combined with DPOS mechanism, this paper deeply explored how to improve the efficiency of BLS signature in the process of blockchain transaction verification.
Background Suppression Method of Star Image Based on Improved CBDNet

The images collected by CCD devices have the characteristics of low SNR (Signal-to-Noise Ratio) and complex background. In order to reduce the difficulty of target extraction, this paper uses real star images to train a background suppression network based on CBDNet [1], a denoising network structure. The experimental result shows that the network can effectively suppress the background and improve the SNR of the star points while retaining the detailed information of the star points.
Research on Detecting and Tracking Algorithm of UAV Intrusion Based on YOLOv5+DeepSort

In order to accurately detect and track intruding UAVs for the phenomenon of ‘black flight’ and ‘indiscriminate flight’, this paper proposes an improved YOLOv5 algorithm combined with the DeepSort tracking algorithm, which adds the convolutional block attention module (CBAM) to the Neck module of the YOLOv5s network to enhance the extraction of network features. DIOU-NMS is introduced to improve the problem of missed detection due to the obscured UAV targets. The tracking part uses the DeepSort algorithm, in which the Kalman filter is used for predicting and updating the next frame position of the target, and DIOU is used to solve the problem of secondary matching failure. Experiments show that the proposed method can effectively identify and track UAVs, and furthermore reduce the problems of missed detection and target ID misdetection that occur after obstacle occlusion.
Investigation of Block chain Technology Based on Digital Management System with Data Mining Technology for Green Marketing

Digital archive management solutions have become increasingly popular in archive management as information technology and wireless technology have advanced. Unlike traditional paper archives, which have inherent distinctiveness and strong tamper-proof modification, electronic files and data are mainly stated based on the available data base. Today's Internet enables marketers to reach deeper into their present customers, establish new online marketplaces, and drive new demand. This dynamic market involvement employs modern technology to more effectively target customers. In this research article, we analyze how block chain technology may influence a company's marketing efforts. This research blends block chain technology alongside distributed ledgers, consensus processes, encryption techniques, and so forth. There are several incentives available to businesses who want to participate in green products. To beginning with, a company's perceived dedication to environmental concerns is a growing element affecting many consumers' buying patterns?As a result, electronic information management is required for its capacity to properly store and retrieve huge amounts of data while ensuring computer system performance integrity.
Machine Learning as a Predictive Technology And Its Impact on Digital Pricing and Cryptocurrency Markets

In this current era, Machine Learning (ML) Approach is widely used as a predictive technology in transportation, finance, advertising, travel, healthcare, and various manufacturing industries across the globe. The modern-day financial market around the globe has experienced its deep impact on various aspects of digital pricing. Enormous organizations use numerous digital pricing techniques in order to generate maximum profit percentages for a sustainable future while conducting global business. On the other hand, the effective applications of the machine learning approach also offer relevant advantages in the Cryptocurrency Markets worldwide. It has been identified that with the use of ML approaches, organizations can conduct faster as well as cheaper transfers and exchange of money through virtual mediums easier than before. Moreover, Machine learning can easily improve all the trading strategies in the cryptocurrency and digital pricing markets for gaining more profit as well as adaptable business experience for future experiments. Researchers are going to investigate the particular research topic with an effective quantitative method by conducting secondary method as well as asking topic-related questions. In the modern share markets, undertaking numerous innovative ML technologies for enhancing efficiency in digital pricing and cryptocurrency has become easier. Therefore, the research paper sheds some important light on the impacts of ML approaches and their contributions in cryptocurrency trading and digital pricing for obtaining further research scopes.
Motion Anomaly Detection in Surveillance Videos Using Spatial and Temporal Features

The problem of anomaly detection is defined as the task of detecting a deviation from usual conformity in a video sequence due to an event, and if such an event is found, determining its start and endpoints. Since anomalies seldom occur in the physical world, most datasets available for anomaly detection consist of only normal activities. In this paper, novel computer vision algorithms using spatial and temporal features to improve detection accuracy is proposed. Further optimizations would be performed by using principal component analysis to reduce the feature dimension. Finally, we would compare our results with existing literature on the Chinese University of Hong kong Avenue (CUHK Avenue) dataset.
Security Challenges and Cyber Forensics For IoT Driven BYOD Systems

Devices are dictating the market today. Devices are being used by employees whether their employer facilitated Bring Your Own Device (BYOD) utility or not. There are some differences observed in employee behaviour in different markets like mature markets, developing, high growth markets. BYOD is being driven by employee attitudes. Studies resulted that use of devices being useful in developing and high growth markets where employees are of thoughts. In this case they use to blur the line between personal and professional life. Work is being important for the people who work in these segments of market. They are not conscious about the work timing as well. Internet of Things (IoT) adoption and rising demand for BYOD have become one of the most essential requirements for increasing employee productivity and business agility in a company. The high danger of a cyber-attack, which is a major cause of business interruption, has grown significantly as a result of BYOD usage in large numbers in both the smart city environment and the company, raising the issue of how to get a cyber protected citizenship in both environments. It is necessary for the company to use a secure authentication mechanism that is equipped with advanced security features. Blockchain is the solution to this problem since blockchain makes use of cryptographic technology that is difficult to crack for hackers. There are two trust models proposed in this research: an agentless device trust model that can scan for malware and identify the location of the device, and a multifactor authentication model that incorporates public and private keys created by blockchain technology. In order to protect the record and sensitive data, a record keeping model based on blockchain technology is being developed, in which every action associated with the record is recorded in a digital ledger as part of the record keeping process. In the event of a further inquiry, this ledger may be utilised as evidence collecting. It is envisaged that the suggested BYOD trust model solution would assist enterprises in reducing the number of instances of data leakage while still enabling the BYOD idea to be implemented.
Comparison of Hidden Markov Model with other Machine Learning Techniques in Software Defect Prediction

In the field of Software Engineering, defect prediction is the most comprehensive and active area of study. It determines which modules are prone to errors and requires rigorous testing. This allows the testing resources to be used effectively while still adhering to the limits. Our project aims to find the model which gives the best results in identifying the defects. We have used the most commonly used machine learning (ML) models K Nearest Neighbours (KNN), Support Vector Machine (SVM), Radial Bias Function (RBF), Decision Tree (DT), Naive Bayes (NB), and the least commonly used model Hidden Markov Model (HMM). The datasets are taken from the NASA and SOFTLAB repository. We have chosen six datasets from the NASA CM1, KC1, JM1, MC1, MW1, and PC1 and four from SOFTLAB AR1, AR3, AR4, and AR5. Principal Component Analysis (PCA) is used to reduce the dimensions of the dataset as there were upto 39 metrics. The best performing model is then determined based on various performance measures including accuracy, precision, F1 score, and recall. Six out of ten datasets showed that SVM performed better than other techniques. HMM performed the best on the AR4 dataset, which had the least data imbalance, and the worst on the AR5 dataset, which had the fewest instances.
Comparative Advantages of Offline Digital Technology for Remote Indigenous Classrooms in Guatemala (2019-2020)

Technology has been viewed as a means to improve the quality of education for children globally, particularly in remote and marginal communities. This study examines the comparative advantages of the use of appropriate technology (off-line servers with digital libraries connected to a classroom set of laptops) in ten intervention schools in Indigenous communities in Guatemala for one school year. The study was too short (due to pandemic restrictions) to demonstrate statistically significant differences for learning outcomes. However, using an instructional core model as a framework, qualitative findings supported four previously identified comparative advantages, and identified four additional ones relevant to remote Indigenous communities. The intervention validated the ability of technology to improve standardized instruction, differentiated instruction, opportunities for practice, and learner engagement. Newly identified advantages are: access to high-quality educational resources (substitution for print materials), teacher capacity-building, student technical skills and digital literacy, and sharing cultural knowledge.
Integration of Infomedia Literacy in the Process of Teaching Ukrainian Literature in Grades 5-6 of the New Ukrainian School

The article reveals the basic principles and ways of forming information and media literacy of students during the study of Ukrainian literature in the adaptive cycle of basic secondary education. The analysis of methodical materials and scientific literature on the experience of introducing info media literacy in the educational process is carried out. The methods of integration of literary texts and other types of media texts in the process of studying Ukrainian literature are generalized and substantiated. It was found that the use of modern technologies for the development of critical thinking, the use of different types of media texts contribute to the formation of students in grades 5-6 reading competence, develop skills in working with information obtained from works of art. Areas of integration of information literacy in the process of teaching Ukrainian literature are the development of exercises to develop critical and analytical thinking of younger teenagers and attracting a broad artistic context, the availability of which allows the use of different types of media resources. The most important role in the process of teaching Ukrainian literature, taking into account modern educational trends and the requirements of an informative society, is played by a teacher of Ukrainian language and literature prepared for the current challenges. As a result of such a system of work – students of 5-6 grades consciously perceive and analyse literary works, understand their relevance, relate to the general cultural context, can identify facts of reality reflected in the works and the writer's judgments about them.
Angle error control model of laser profilometer contact measurement

Abstract In controlling the angle error of laser profilometer contact measurement, the control model used in the past is subject to more external interference, which affects the measurement of other parameters, resulting in poor error control effect. Therefore, the angle error control model of laser profilometer contact measurement is designed by determining the initial zero position of the profilometer measuring sensor, compensating the non-linear error, designing the interference signal processing circuit, and avoiding the error measurement influenced by external factors. From the main error sources such as the perpendicularity, standard error, sensor error and other errors of the coordinate axis of the profilometer, the accuracy is distributed. According to the equal action principle, the measurement uncertainty of the profilometer is calculated, and the measurement is obtained. According to the interference signal processing circuit and measurement index, the serial communication control circuit is designed, and the angle error control model of laser profilometer contact measurement is constructed. The experimental results show that the designed laser profilometer contact measurement angle error control model has high precision and does not affect other parameters in the control process, which shows that the control model has good effect in practical application.
Linear complementary dual codes and double circulant codes over a semi-local ring

&lt;p style='text-indent:20px;'&gt;Let &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ q $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; be an odd prime power and &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \mathbb{F}_q $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; be the finite field with &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ q $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; elements. In this paper, suppose ring &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ R = \mathbb{F}_{q}+ \mu \mathbb{F}_{q}+ \nu \mathbb{F}_{q}+ \mu \nu \mathbb{F}_{q} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ \mu \nu = \nu \mu, \mu^{2} = \mu, \nu^{2} = \nu. $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; We first give a Gray map from &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; onto &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ \mathbb{F}_q^{4} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and consider a decomposition of the ring &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Additionally, we investigate linear complementary dual (LCD) codes over the ring &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Some conditions for such linear codes over &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; to be linear complementary dual are given. Furthermore, based on the Artin conjecture, we get a class of good codes by calculating the total number of LCD double circulant codes over &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Rebate Auction Mechanisms for Bidirectional Grid Balancing Using Cloud Workload Migrations

Increasing the power-grid's flexibility is essential for expanding the integration of renewable energy sources in modern power grids.This work presents a new rebate auction framework that allows power grids to use cloud datacenters as managed loads to provide upward/downward grid flexibility.Since the energy consumption of datacenters is proportional to their computational workload, this paper presents a rebate auction framework that can induce cloud workload migrations between datacenters to correct energy imbalances.Unlike existing datacenter-based power grid balancing approaches that have only focused on providing downward flexibility and only considered owner-operated datacenters, the proposed framework provides bidirectional flexibility and accommodates both owner-operated and public cloud datacenters.Thus, providing a general framework for creating workload migrations between datacenters to balance the power grid.Because workloads on public cloud datacenters are managed by end-users, the proposed framework uses monetary incentives (auctioned rebates) to encourage large-scale end-users to migrate their cloud workloads between datacenters to correct energy imbalances.The use of monetary rewards as an incentive hides the complexity of grid-balancing from auction participants, who only participate in the auction to lower their cost, while grid-balancing happens as a result of workload migrations.This paper presents and compares two auction implementations under the proposed framework, a strategy-proof implementation that guarantees truthful bidding as a dominant strategy, but has NP-hard computational complexity, and an alternative implementation that does not guarantee truthful bidding, but has polynomial time complexity.Simulation results show that the proposed framework is effective in incentivizing cloud workload migrations to achieve the grid balancing goal and provides positive utility to all participants.
SiamBC: Context-Related Siamese Network for Visual Object Tracking

The existing Siamese trackers have achieved increasingly results in visual tracking. However, the contextual association between template and search region is not been fully studied in previous Siamese Network-based methods, meanwhile, the feature information of the cross-correlation layer is investigated insufficiently. In this paper, we propose a new context-related Siamese network called SiamBC to address these issues. By introducing a cooperate attention mechanism based on block deformable convolution sampling features, the tracker can pre-match and enhance similar features to improve accuracy and robustness when the context embedding between the template and search fully interacted. In addition, we design a cascade cross correlation module. The cross-correlation layer of the stacked structure can gradually refine the deep information of the mined features and further improve the accuracy. Extensive experiments demonstrate the effectiveness of our tracker on six tracking benchmarks including OTB100, VOT2019, GOT10k, LaSOT, TrackingNet and UAV123. The code will be available at https://github.com/Soarkey/SiamBC.
Corrigendum to “Social Recommendation System Based on Hypergraph Attention Network”

[This corrects the article DOI: 10.1155/2021/7716214.].
METHOD OF STRUCTURAL IDENTIFICATION OF NONLINEAR INTERVAL MODELS OF STATIC OBJECTS

The process of building mathematical models of static objects and systems, which includes the solution of two problems: structural and parametric identification, is considered. At the same time, the task of identifying the structure of the model is more difficult and primary. The problem of structural identification of interval models of the characteristic of a static object is the problem of multiple solving of problems of parametric identification of this model, and therefore, from a computational point of view, it is NP complex. The procedure for finding the optimal structure of the model is considered as a directed selection of a defined set of structures in such a way as to minimize the number of iterations of the formation of interval systems of nonlinear algebraic equations. The article formulates the problem of structural identification of interval models of static objects, as a problem of repeatedly searching for solutions of interval systems of nonlinear algebraic equations, in the form of optimization problems with a nonlinear objective function and nonlinear constraints. For the first time, a method of structural identification of interval models of the characteristics of static objects based on the analysis of interval data is proposed and substantiated, which, unlike the existing ones, is based on self-organization and self-adaptation procedures of computing procedures by analogy with artificial bee colony (АBC), which gives the ability to implement model structure identification procedures with lower computational complexity and obtain interval models with simpler structures compared to known methods. The proposed method was tested on the example of building an interval model of the characteristics of a small hydroelectric plant for the purpose of research and ensuring the maximum efficiency of the use of hydropower resources, which demonstrated the effectiveness of using computational procedures based on the artificial bee colony. Accordingly, the proposed method makes it possible to obtain simple, from the point of view of complexity, interval models of complex static objects with a given guaranteed accuracy and with a lower computational complexity of identifying these models. Such features of the method ensure the effective development of the mathematical apparatus, which is used both in decision-making processes and in the processes of preparing decisions in intellectualized data-oriented systems.