Reviewerly/exHarmony huggingface dataset:
Trajectory prediction combined with FairMOT for multi-object tracking

Aiming at the problems of ID switching and tracking performance degradation caused by frequent occlusion and similar appearance of the tracked objects in dense scenes, a multi-object tracking method named TPFairMOT based on trajectory prediction and FairMOT is proposed in this paper. In the trajectory prediction branch, the object position of the future frame is predicted by using the object bounding box of the past frame and the velocity information learning network parameters, which overcomes the prediction failure caused by the uncertain motion state after the object is occluded in the tracking process. Secondly, the joint learning framework is used to combine the trajectory prediction branch with the detection and re-identification branch, and the tracking error caused by the high similarity between multiple objects in the tracking process is solved by integrating the appearance features and motion features of the tracked objects. Finally, MOTChallenge benchmarks (IDF1, IDs, MOTA, MT, and ML) are introduced to evaluate TPFairMOT, and different trajectory prediction strategies (FairMOT_KF and TPFairMOT_RNN) are used on FairMOT and TPFairMOT for comparative analysis. It is proved that the accuracy and ID switching times of trajectory prediction in this paper are better than other strategies. In addition, TPFairMOT, TPFairMOT_RNN, and FairMOT were compared on the public data sets MOT16, MOT17, and MOT20. The results show that TPFairMOT reduces the number of ID switching when the object is occluded, maintains the long-term validity of the identity information, and demonstrate good anti-occlusion performance.
Optimization of docker container live migration solution

Nowadays, all cloud platforms support the live migration technology of Docker containers, which plays an important role in improving the stability and quality of services. In this article, we will use the JNLP algorithm to determine which migration method to use in pre-copy and post-copy based on the actual network bandwidth and real-time monitoring of memory changes. Moreover, this article analyzes the storage mechanism of docker containers, using the hierarchical structure of docker to reduce the data that needs to be transferred during migration, thereby reducing the total migration time. Experimental results show that the live migration technique proposed in this paper is more general and reduces container data migration time by 40% and total migration time by 30% compared with state-of-the-art techniques.
Kegiatan preservasi di Perpustakaan Umum Kota Cimahi

Public Library of Cimahi City is to provide information needs for the users from Cimahi City and users from other city. The Public Library of Cimahi City has collections and library equipment that must be maintained and cared for so that they are not damaged and consumed over time, as well as the building where the collections are stored. It is necessary to pay attention to the continuity of the library service. So in library activities, it is necessary to have preservation activities to maintain collections and equipment in the library. This study aims to have a comprehensive description of preservation activities at the Public Library of Cimahi City which are divided into three stages, namely preventive, curative, and restorative. The method used is descriptive qualitative, by collecting data through observation, interviews and documentation. This study uses triangulation technique to test the validity and analysis techniques of thematic data for data analysis. The informants were librarians at the Public Library of Cimahi City. The results showed that the preservation process at the Cimahi City Public Library had not been fully implemented. The Public Library of Cimahi City focuses more on preventive activities, such as installing air conditioners to maintain humidity and room temperature; installing curtains on glass windows to prevent sunlight from hitting the collection; and perform cleaning and maintenance on every element of the library. Meanwhile, curative and restorative preservation activities at the Public Library of Cimahi City are still lacking when compared to preservation standards carried out by other libraries, which have only been implemented through collection repair activities (binding, gluing and using adhesive), and room renovation. So it can be concluded that the Public Library of Cimahi City needs to pay more attention to preservation activities so that every element that supports the sustainability of the Public Library of Cimahi City can be preserved.
Pengembangan Modul Pembelajaran Biologi Berbasis Potensi Lokal Danau Laut Tawar Takengon Ikan Depik (Rasbora Tawarensis) pada Materi Vertebrata

This study aims to determine the validity, practicality and effectiveness of biology learning modules based on local potential of Lake Laut Tawar Takengon Ikan Depik (Rasbora Tawarensis) on Vertebrate Material. This study used the Research and Development (R&amp;D) research method based on the Thiagarajan methodology with four stages including define, design, devolepment and disseminate stages. This research was carried out from September to January 2022 with a sample of 26 students from SMA N 8 Takengon Unggul. The results showed that the biology learning module based on the local potential of Lake Laut Tawar Takengon Ikan Depik (Rasbora Tawarensis) is very suitable for use in learning. The validity test was tested by material experts and media experts. The material expert test was obtained by 81.37% and the media expert test was obtained by 81.25% and was included in the very feasible category to be used in learning. The practicality test was tested based on the responses of the biology teacher at SMA N 8 Takengeon Unggul and the responses of 26 students who were seen based on the response questionnaire sheet. The teacher's response test was obtained at 98.23% and the student's response test was 93.6% in the practical category used in the learning process. Testing the effectiveness of obtaining a score of 100% improves student learning outcomes during learning, this is evidenced by students' answers when tested based on the post-test questions given. So it can be concluded that the biology learning module based on local potential of Lake Laut Tawar Takengon Depik Fish (Rasbora Tawarensis) on Vertebrate Material is effective and appropriate for use in learning.&#x0D; Keywords: Module, Local Potential, Depik Fish
A Blockchain-Trusted Scheme Based on Multimedia Content Protection

There are two types of content on the blockchain: centralized and decentralized. On centralized video platforms, the platform owner controls most of the content uploaded, rather than the creator. However, some content creators post low-quality content in exchange for free cryptocurrencies, creating a cryptocurrency algorithm that demotivates other content creators. In contrast, decentralized blockchain-based video platforms aim to lessen ad pressure and eliminate intermediaries. On video platforms, copyright violations and the unauthorized dissemination of protected information are also significant issues. Copyright protection, illegitimate access restriction, and legitimate dissemination of video files are necessary to guarantee that authors' original output is appropriately compensated.
Comparison of Kernel Functions in the Classification of Irradiance Zones from Multispectral Satellite Images

Due to the growing energy demand and the eminent global warming, there is special interest in the prediction of irradiance based on the reflectance obtained from satellites such as NASA Landsat, since it allows to know where it is more efficient to place photovoltaic receivers. Although there are studies for obtaining regression models with alternative Kernel functions, their performance for classification models is unknown and it is here where this research focuses. The study couples alternative Kernel functions to the support vector machines (SVM) algorithm for classification problems, where the best configuration for these algorithms is explored to finally obtain a set of irradiance maps zoned by class.
EVALUATION OF MICROSOFT TEAMS AS A TOOL FOR SYNCHRONOUS INTERACTION DURING DISTANCE AND HYBRID LEARNING

After the outbreak of the COVID-19 pandemic in 2019 and the beginning of the war in the country, educational institutions switched to online learning using tools for organizing and holding video conferences, ensuring synchronous and asynchronous interaction of participants in the educational process. This article offers definitions of advantages (organization of various types of educational activities online; organization of effective collaboration; provision of access to various types of educational content; organization of reflection and estimation) and disadvantages (unstable work and dependence on Internet access; inconvenience of use on mobile devices; the need for skills in using the tool) using Microsoft Teams for synchronous interaction during distance and hybrid learning. The authors developed criteria and indicators for evaluating Microsoft Teams as a tool for synchronous interaction in modern conditions, such as functionality, reliability, effectiveness, interaction, accessibility, security and privacy, technical support and assistance. An example of a scheme of synchronous interaction when organizing an educational activity using Microsoft Teams is given, namely: creating a meeting room, inviting students to an online class, creating a meeting class (lecture, laboratory work, discussion, class recording), uploading various types of educational content, placing an announcement or a post, assignment assessment, or test. 17 scientific and pedagogical workers who have practical experience of using Microsoft Teams as a tool of synchronous interaction during distance and hybrid studies were involved for expert evaluation according to the specified criteria and indicators. The provided assessment of the capabilities of Microsoft Teams for synchronous interaction shows that the most significant indicators are the presence of a function for organizing and supporting synchronous interaction between participants in the educational process (0.09); the ability to interact with video and audio content to improve understanding and assimilation of educational material in real time (0.08); availability of tools for setting up interaction and collaboration in synchronous mode (interactive whiteboard, screen sharing, various types of documents) (0.08).
Malpractice Detection Using Machine Learning

Cheating during exam is unfair behaviour of the students. This becomes disadvantage for other students who do not cheat and work hard to study. This project is meant to detect the suspicious activities of the students who intend to cheat during exams. This project is useful in educational institutions where in invigilators are not able to detect the cheating done by students in the exam hall. This project has two phases namely, training phase and the testing phase. The dataset is a pre-recorded video of students which is captured in real time. This system works well with live web camera input also. The algorithm used is DenseNet121.This system predicts three kinds of activities such as turning around, passing of paper and a normal activity differentiated by RGB colours.
Development of Creative Thinking Skills of Bachelor Engineers Based on STEM Technology

In today's dynamic, rapidly changing world, a bachelor's engineer needs to have creative thinking skills in order to be successful in their professional activities. A future graduate must learn to see the problem as a whole, and not in the context of one area of science or technology. This causes the education system to develop and implement innovative forms of engineering personnel training aimed at solving practice-oriented, research problems. The authors considered the issue of applying an educational model that combines natural sciences and engineering subjects into a single system in the process of teaching engineering bachelors. The issues of effective formation of critical thinking skills based on an integrative approach are analyzed. The results of experimental work convincingly prove that the majority of students studying on the basis of the author's methodology have achieved an average and high level of creative thinking skills.
Determine Upgrades for MSMEs: A Model Implemented at the Center for Integrated Service of SMEsCO Banten Province using AHP

The various problems that have occurred in MSMEs affected by the pandemic have been felt by business actors. The Government recognizes the importance of developing the competitive advantage of MSMEs by empowering MSMEs to become more skilled and competitive businesses. Every MSME has the opportunity to Upgrade Classes. However, this implementation experienced difficulties in obtaining data on the results of appropriate, fast, and accurate assistance related to the results of the MSMEs criteria for upgrading at the Center for Integrated Service of SMEsCO (PLUT) Banten Province. The concept of a decision support system can be used as a tool for determining upgrade classes for MSMEs. One of the methods involved in determining the precedence level of criteria and performing consistency score calculations is the Analysis Hierarchy process method. The result of this research is a system that can provide SMEs with alternate recommendations for upgrade classes where the hierarchy formed is acceptable. The results of the sampling taken from 56 SMEs in this study can be obtained grade up by process max 0.46, min 0.25 and by output is max 0.75, min 0.36 So these values can be used as a decision support for MSMEs to go up a good grade in process and output.
Real Time Shot Boundary Detection in Live 360 Degree VR Streaming Using Deep Learning

We suggest a real-time approach for detecting shot boundaries in live 360-degree VR streaming, utilizing deep learning techniques. Seamless transitions between shots are vital to providing users with an immersive experience in VR streaming. However, the current methods of shot boundary detection face issues in handling the real-time nature of live streaming and the distortions caused by 360-degree projection. To tackle these issues, we recommend a convolutional neural network (CNN) based method for identifying shot boundaries in 360-degree VR videos in real-time. This approach preprocesses the 360-degree VR video stream and feeds it to the CNN for detecting shot boundaries in real-time. We evaluated the suggested approach on a dataset of live 360-degree VR streams and compared its performance with existing shot boundary detection methods, including traditional techniques and deep learning-based approaches. The results of our experiment demonstrate that the proposed method delivers high accuracy and performs well in real-time shot boundary detection, surpassing the current methods. To demonstrate its robustness and versatility, we assessed the suggested method under different scenarios, such as different types of VR streams and network configurations. The suggested method can significantly enhance the user experience in live 360-degree VR streaming and can be beneficial for several applications, such as live events, sports, and concerts. Moreover, the proposed method can serve as a base for future research on shot boundary detection in VR streaming.
Energy Efficient and Differentially Private Federated Learning via a Piggyback Approach

This paper aims to develop a differential private federated learning (FL) scheme with the least artificial noises added while minimizing the energy consumption of participating mobile devices. By observing that some communication efficient FL approaches and even the nature of wireless communications contribute to the differential privacy (DP) preservation of training data on mobile devices, in this paper, we propose to jointly leverage gradient compression techniques (i.e., gradient quantization and sparsification) and additive white Gaussian noises (AWGN) in wireless channels to develop a piggyback DP approach for FL over mobile devices. Even with the piggyback DP approach, information distortion caused by gradient compression and noise perturbation may slow down FL convergence, which in turn consumes more energy of mobile devices for local computing and model update communications. Thus, we theoretically analyze FL convergence and formulate an energy efficient FL optimization under piggyback DP, transmission power, and FL convergence constraints. Furthermore, we propose an efficient iterative algorithm where closed-form solutions for artificial DP noise and power control are derived. Extensive simulation and experimental results demonstrate the effectiveness of the proposed scheme in terms of energy efficiency and privacy preservation.
Education for seniors: eating behavior and perception of students of the Open College for Third Age in the face of the COVID-19 pandemic

The Open College for the Third Age (Fati) emerges as a possibility for education for elderly people, offering education in various forms, as well as a range of information, knowledge, and social contacts capable of improving their quality of life and eating behaviors. The aim of this study is to highlight the eating behavior and perception of elderly participants in Fati in the face of the Covid-19 pandemic. This is an exploratory descriptive study using a qualitative-quantitative approach, aiming to gather information on the social, emotional, and health conditions during the Covid-19 pandemic, as well as the eating behavior of the elderly. It was found that there were indeed emotional and social impacts, fears, and impossibility of carrying out day-to-day activities in the lives of these elderly people, but there were no major impacts on their eating behavior. The Covid-19 pandemic affected and impacted elderly individuals and modified the eating habits in Brazil. Thus, new habits can be acquired.
Co-movement between RMB and Bitcoin with Effects of DCEP Using Wavelet Coherence Analysis

Utilizing wavelet coherence analysis, we investigate the correlation of fluctuations and phase differences between Bitcoin and RMB to identify capital flows between the two currencies. The effects of Digital Currency Electronic Payment (DCEP) on their co-movement are further analyzed. Our findings reveal that the RMB exchange rate leads the price of Bitcoin in all significant co-movement areas. Furthermore, it appears that from February 2017 to September 2018, the Sino-US trade frictions and US dollar interest rate hikes may have resulted in a long-term negative co-movement, which seems to have been driven by RMB and possibly indicated capital flows from RMB to Bitcoin. The short-term positive co-movement between November 2019 and July 2020 could be attributed to the COVID-19 pandemic. Finally, we also demonstrate that the DCEP trial event has the potential to strengthen the positive co-movement between these currencies.
Criteria for entanglement and separability of discrete quantum states

Entanglement is an important quantum resource, which can be used in quantum teleportation and quantum computation. How to judge and measure entanglement or separability has become a basic problem in quantum information theory. In this paper, by analyzing the properties of generalized ring $\mathbb{Z}[i]^{{2}^{n}}$, a new method is presented to judge the entanglement or separability of any quantum state in the discrete quantum computing model proposed by Gatti and Lacalle. Different from previous criteria based on matrices, it is relatively simple to operate in mathematical calculation. And if a quantum state is separable, it can calculate the separable mathematical expression. Taking $n=2,3$ as examples, the concrete forms of all separable states in the model are presented. It provides a new research perspective for the discrete quantum computing model.
Integral Education with Societal Extension: Factoring Social Environment to Empower Future Generations with Holistic Human Development and Social Leadership

This paper explores ways to integrate social responsibility approach to human development and analyses the feasibility of imparting social-leadership among the youth through incorporating social environment and integral teaching-learning pedagogy. The research method adopted is narrative-analytical exposition. The paper finds that co-operative rights-based approach and social responsibility approach in education can collectively create an appropriate ecosystem that can fulfill suitable developmental needs of the society and help in sustenance of development attainments.
Visual fileless malware classification via few-shot learning

The use of fileless technologies in malware continues to grow and fileless malware becomes more dangerous and difficult to detect. To address this challenge, we propose a novel visual method for classifying fileless malware based on few-shot learning. First, we built a fileless malware dataset, which is executed through a local virtual environment to collect malware memory dumps. Secondly, memory dumps are clipped and visualized. We developed a new memory dumps trimming method and a novel binary file visualization technique, which can remove redundant data from memory dumps, significantly compress the file size, and then represent the trimmed memory dumps as RGB images. Finally, we propose a few-shot learning framework, namely MMEL (MAML + Mean_subtraction + Euclidean_normalization + Label_Smothing), to improve the performance of the classification method. Experimental results show that our visualization technique and framework outperform other state-of-the-art few-shot learning methods.
Optimizing topic classification and sentiment analysis in Chinese car comments using CorEx algorithm based on BERT model

Users' comments on cars are the focus of automobile companies, which can be used to analyze people's concerns and satisfaction with cars. Such a task is of great commercial value. There are more studies on end-to-end models for topic classification, but the method of manually extracting obvious features and transferring them to the model does not take much time and can improve the accuracy and convergence speed of the model. This study is based on a labeled car comments dataset. First, distinctive feature words are selected as anchor words, and the inclusion of anchor words will anchor this comment to the corresponding topic, making the topic classification and sentiment analysis more accurate. In addition, it is feasible to use a single Bert model to connect the token [CLS] to two linear layers respectively for topic classification and sentiment analysis. It shows that the BERT model with CorEx can improve the classification accuracy and convergence speed significantly, and has a good value in car review classification.
Chinese long text news summary based on BERTSUM-BART

For time-sensitive and large-scale text information such as news, traditional abstract summarization methods may lead to deviation from the subject, distortion and incompleteness of information. To this end, we propose an improved scheme based on automatic text summarization technology, which combines the characteristics of BERTSUM(BERT-based Text Summarization) and BART (Bidirectional and Auto-Regressive Transformers) models. Specifically, we input the same text information into the BERTSUM and BART models respectively, and obtain the key sentence granules of relevant text information through the BERTSUM model and the summary text processed by the BART model, and combine the processing results of the two models into a mixed data Set, and generate a summary through the BART model again to get the final summary result. The effectiveness of the BERTSUM-BART method was verified through experiments based on news long text data. The average recall rate of ROUGE-1, ROUGE-2 and ROUGE-L reached 39.83, 18.05 and 36.46.