Reviewerly/exHarmony huggingface dataset:
REAL-TIME DETECTION OF ANOMALOUS BEHAVIOR IN CCTV VIDEOS USING ADVANCED MACHINE LEARNING TECHNIQUES

The detection of suspicious activities in public areas is crucial for maintaining security and preventing criminal incidents such as terrorism, theft, and other unlawful acts.This study presents an original approach for realtime identification of suspicious human activity in live CCTV footage using neural networks.Our method focuses on predicting the placement of body parts or joints from images or videos, enabling the tracking and differentiation of normal and abnormal movements.Emphasizing public spaces like bus stations, railway stations, airports, banks, shopping malls, schools, colleges, parking lots, and roads, where intelligent video surveillance is essential, our approach incorporates state-of-the-art deep learning techniques for object detection and tracking.Through comprehensive evaluation on large-scale video datasets, we demonstrate that our proposed method surpasses existing approaches in terms of accuracy and efficiency, providing valuable insights for enhancing public safety and security.By adopting our novel machine learning-based approach, stakeholders and security personnel can effectively identify and respond to suspicious activities, mitigating potential risks and strengthening security measures in public environments..
Compressing the Embedding Matrix by a Dictionary Screening Approach in Text Classification

In this paper, we propose a dictionary screening method for embedding compression in text classification. The key point is to evaluate the importance of each keyword in the dictionary. To this end, we first train a pre-specified recurrent neural network-based model using a full dictionary. This leads to a benchmark model, which we use to obtain the predicted class probabilities for each sample in a dataset. Next, to evaluate the impact of each keyword in affecting the predicted class probabilities, we develop a novel method for assessing the importance of each keyword in a dictionary. Consequently, each keyword can be screened, and only the most important keywords are reserved. With these screened keywords, a new dictionary with a considerably reduced size can be constructed. Accordingly, the original text sequence can be substantially compressed. The proposed method leads to significant reductions in terms of parameters, average text sequence, and dictionary size. Meanwhile, the prediction power remains very competitive compared to the benchmark model. Extensive numerical studies are presented to demonstrate the empirical performance of the proposed method.
A Influência da Pandemia Covid-19 no Comportamento de Compra Online dos Jovens Adultos.

This study aims to characterize the changes in online purchasing behavior, resulting from the COVID-19 pandemic, and to assess the intention to maintain or break with behaviors acquired by young adults. A quantitative methodology was used, supported by an online questionnaire, with a sample of 355 answers. The results reveal an intensification of online shopping, changes in the catalog of products purchased, and an increase in spending. Trust has no particular influence on the buying behavior analyzed, but it is a determining factor in the decision to maintain or break with the behaviors acquired. From a theoretical perspective, this study presents an integrated model for analyzing buying behavior in the pandemic context. For management, the results of this study provide information about the behavior during and post-pandemic of the young adult segment. This segment is highlighted for its large adherence to e-commerce and for being the generation of buyers of the future.
An Effective WGAN-Based Anomaly Detection Model for IoT Multivariate Time Series

This paper studies an effective unsupervised deep learning model for multivariate time series anomaly detection. Since multivariate time series usually have problems of insufficient labeling and highly-complex temporal correlation, effectively detecting anomalies in multivariate time series data is particularly challenging. To solve this problem, we propose a model named Wasserstein-GAN with gradient Penalty and effective Scoring (WPS). In this model, Wasserstein Distance with Gradient Penalty helps to capture the data regularities between generator output and real data, thus improving the training stability. Meanwhile, an effective scoring function that consists of reconstruction error, discrimination error, and prediction error is designed to evaluate the accuracy of the abnormal prediction and recall. The experimental results show that compared with the suboptimal baseline model, our proposed WPS obtains 17.68% and 10.41% improvement in prediction precision and F1 score, respectively.
Enhancing an image’s compression while keeping quality standards utilizing new mathematical technology

The rapid development of technology led to the need to keep pace with it, especially in the field of image processing, because of its importance in the need to get better results. In this paper, new discrete Chebyshev wavelet transforms (DChWT) derived from Chebyshev polynomials (ChP) were proposed and characterized. In terms of orthogonality and approximation (convergence) in the field of mathematics, (ChP) were qualified to transform into discrete wavelets called (DChWT), depending on the mother function with operators (s, r), and were used in image processing to analyze them in the low filter and the high filter so that the image is analyzed into coefficients. Proximity and detail coefficients, which lead to dividing the image into four parts, low left (LL), in which the proximity coefficients are concentrated while the rest of the parts are centered on the detail coefficients, which are high left (HL), low right (LR), and high right (HR), and image compression through the new filter, which has proven its efficiency at level (8) in our results. The results of the proposed wavelets in this work were reached in calculating quality standards in the image obtained after the compression process after comparing them with the results obtained using the standard wavelet used in HaarSymlet 2, Conflict 2, and Daubecheis 2. The most important of these standards is a mean square error (MSE), peak signal-to-noise ratio (PSNR), bit per pixel (BPP), compression ratio (CR), and table one. In this paper, the efficiency of the proposed new wavelets is explained after adding it to MATLAB and according to a specific program to drop in with the basic wavelets to take on their role in important tasks in the image processing area, like artificial intelligence
Watermark Embedding Scheme with Variance of Chromatic Components

This paper contains the idea of inserting a watermark with the variance of color components of the image. The color image is converted into CIE color space. Chromatic components are transformed into a sequency domain by applying the complex Hadamard transform. The variance of the spatio-chromatic coefficients is calculated and the watermark is selected from the transformed image based on the variance by setting the threshold value. The watermark is only inserted in image blocks that have a smaller value of variance than the threshold value. Simulation results are presented and discussed using the two variants of complex Hadamard transform and discrete cosine transform.
Psychological effects of nursing professionals in front of the COVID-19 pandemic

On March 11, 2020, COVID-19 was defined by the World Health Organization as a pandemic, which refers to the geographic distribution of a pathology and not its severity. This term recognizes that there are currently outbreaks of COVID-19 in different countries and regions of the world. This article consists of an integrative review, in which it aims to discuss the psychological effects of nursing professionals in the face of the covid-19 pandemic, in order to explain the psychological damage brought by the pandemic to health professionals, more specifically, to the nurses who are on the front lines of COVID-19. In the continuous search for quality and good service provision, there is great psychological pressure and overload in the work environment, contributing to an unbalanced mental state, thus causing stress, anxiety and depression in professionals. In short, it is possible to conclude that understanding the responses of health professionals to a pandemic is quite complex, since the disease changes the individual's daily life and leads to a feeling of vulnerability due to several factors, such as: fear of getting sick and to die; loss of close people; loss of livelihood; and social exclusion for being associated with the disease.
Extending quantum key distribution through proxy re-encryption

Modern quantum key distribution (QKD) network designs are based on sending photons from one node to another and require free-space or dedicated fiber optic cables between nodes. The purpose of this is to co-generate secret key material on both sides of the quantum channel. In addition to this quantum link, there are several insecure classical channels that allow QKD algorithms to exchange book-keeping information and send symmetrically encrypted data. The attenuation of photons transmitted through fiber becomes too high to practically generate key material over fiber at distances of more than 100 km. Free-space transmission through the atmosphere or the vacuum of space can reduce attenuation, but at the cost of system complexity and sensitivity to other impairments, such as weather. To extend the effective range of QKD networks, we present a method that combines QKD algorithms with post-quantum, homomorphic key-switching to allow multiple parties to effectively share secret key material over longer distances through semi-trusted relay nodes. We define how such a system should work for arbitrary network topologies and provide proofs that our scheme is both correct and secure. We assess the feasibility of this solution by building and evaluating two implementations based on lattice-based cryptography: learning with errors.
WEB 3.0 SHOPPING WEBSITE

This research paper aims to investigate the application of Web 3.0 technologies in the development of a shopping website.By leveraging the capabilities of ethers.js,hardhat, Solidity, and React.js,we explore how these technologies can enhance the user experience, improve security, and provide new opportunities for decentralized e-commerce.The paper presents a case study of implementing a shopping website prototype using these tools and evaluates its performance and usability.The findings indicate that integrating Web 3.0 technologies into a shopping website can bring several advantages, including transparent and secure transactions, decentralized identity management, and improved user engagement.However, challenges related to scalability, user adoption, and regulatory frameworks should be addressed to fully realize the potential of Web 3.0 in the e-commerce domain.The research contributes to the understanding of how Web 3.0 technologies can revolutionize traditional e-commerce platforms and provides insights for developers, businesses, and researchers interested in exploring the possibilities of decentralized shopping experiences.
Prosedur Pengelolaan Arsip Dalam Peningkatan Efektivitas dan Efisiensi Kerja Studi Kasus Pada PT. Griya Asri Mandiri

Archives are a collection of documents that are stored systematically so that anything needed can be found quickly and accurately (Gie, 1996:118). Every work and activity of government and private offices requires data and information. One source of information that must be owned by an office is archives. Therefore good archive management is needed. Organizing records properly and correctly will facilitate retrieval, so that when records are needed in making a decision, they can be found immediately. The problem in this research is how to manage archives at the office of PT. Griya Asri Mandiri and what are the obstacles experienced when managing records at the PT. Griya Asri Mandiri. The purpose of this research is to find out how the procedures for managing records at the office of PT. Griya Asri Mandiri and to find out what are the obstacles experienced when managing records at the office of PT. Griya Asri Mandiri. This research is a descriptive research with a qualitative approach. Data collection techniques used are observation, interviews and documentation. The results of this study indicate that in general archive management at the PT. Griya Asri Mandiri, among others: 1) Arrangement of archives 2) Storage of archives 3) Borrowing of archives 4) Retrieval of archives 5) Maintenance of archives 6) Security of archives 7) and shrinkage of archives. The obstacles faced when managing records include: 1) Human resources managing records. 2) Limited filing facilities.
CREDIT CARD FRAUD DETECTION USING MACHINE-LEARNING

The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using ML classifiers: Decision Tree (DT), Logistic Regression (LR), Artificial Neural Network (ANN). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.
MODEL OF MIMIC EXPRESSIONS OF HUMAN EMOTIONAL STATES FOR THE VIDEO SURVEILLANCE SYSTEMS

The presented paper proposes a novel computational model for generating facial expressions that mimic human emotional states. The authors aim to create a system that can generate realistic facial expressions to be used in human-robot interactions. The proposed model is based on the Facial Action Coding System, a widely used tool for describing facial expressions. FACS is used in this study to identify the muscles involved in each facial expression and the degree to which each muscle is activated. Several machine-learning techniques were utilized to learn the relationships between facial muscle activations and emotional states. In particular, a hyperplane classification was employed in the system for facial expressions representing major emotional states. The model’s primary advantage lies in its low computational complexity, which enables it to recognize changes in human emotional states through facial expressions without requiring specialized equipment, such as low-resolution or long-distance video cameras. The proposed approach is intended for use in control systems for various purposes, including security systems or monitoring drivers while operating vehicles. It was investigated that the proposed model could generate facial expressions similar to those produced by humans and that these expressions were recognized as conveying the intended emotional state by human observers. The authors also investigated the effect of different factors on the generation of facial expressions. Overall, the proposed model represents a promising approach for generating realistic facial expressions that mimic human emotional states and could have applications in improving security compliance in sensitive environments. However, carefully considering and managing potential ethical issues will be necessary to ensure the responsible use of this technology.
Research on Optimizing the Cultivation of Professional Talents in Financial Technology Applications in the Context of "1+X" System

With the rapid development of information technology in society and the increasing demand for high-level technical and skilled talents from enterprises, the talent training mode of financial technology application majors in vocational undergraduate institutions needs to be developed and updated under the "1+X" system.This paper explores a practical path for vocational undergraduates to cultivate high-level technical skills talents under the background of the "1+X" certificate system through an in-depth analysis of the six current situations of the cultivation of financial technology application professionals in vocational undergraduate institutions.In this paper, we explore a practical path for cultivating mid-to-high-level technical skills talents in the context of the "1+X" certificate system, namely, constructing a book-certificate integrated curriculum system for financial technology application majors, developing mid-to-high-level vocational skills certificate courses and supporting resources that are compatible with undergraduate vocational education, building a cross-border integrated dual-mentor teaching team that meets the requirements of the times, and building a talent training base with in-depth integration between schools and enterprises led by qualification skills standards. Research background 1.1 Definition of "1+X" certificateIn April 2019, the pilot of the "1+X" certificate system was officially launched nationwide. in which "1" refers to academic certificates, which reflect the quality of talent training in school education; "X" refers to several vocational skills level certificates, which reflect the comprehensive abilities required for vocational activities and personal career development [1] , and are credentials of the vocational skills level of graduates and members of society.
Thoughts on the Construction of the Development Mode of Vocational Education

With the continuous development of culture and economy, the reform of education is driven, the development of vocational teaching is more standardized and practical, the importance and characteristics of vocational education from different angles, and the necessity of thinking and analysis.In order to vigorously develop vocational education, relevant ideas and suggestions are put forward to ensure that they can cultivate more valuable talents and promote the progress of education.Based on this, this paper mainly from the following aspects to carry out the relevant exploration and thinking, hoping to bring the role and value of reference.
A Handheld Visitor Guidance Device

India is known for its cultural heritage. Historical monuments, palaces, forts, etc., add to the grandeur of the country. They attract tourists from all over the world. The Indian tourism industry has grown dramatically in recent years, making a considerable contribution to the country's growth, employment, foreign exchange profit, and Gross Domestic Product (GDP). As many tourists are interested to know about the historical culture of India, there is an increase in the number of tourists coming to India from different parts of the world. Most of them are unaware of the local language. Hence, there is a need for an audio guidance device in a language that they can understand. Thus, they will get information about the importance of the exhibit effortlessly. This work focuses on developing an audio guide service for visitors. It is an embedded system consisting of sensors, a controller, and an audio player. When a tourist stands in front of the exhibit, the device senses the particular exhibit and plays the appropriate message automatically.
Maverick Matters: Client Contribution and Selection in Federated Learning

Abstract Federated learning (FL) enables collaborative learning between parties, called clients, without sharing the original and potentially sensitive data. To ensure fast convergence in the presence of such heterogeneous clients, it is imperative to timely select clients who can effectively contribute to learning. A realistic but overlooked case of heterogeneous clients are Mavericks, who monopolize the possession of certain data types, e.g., children hospitals possess most of the data on pediatric cardiology. In this paper, we address the importance and tackle the challenges of Mavericks by exploring two types of client selection strategies. First, we show theoretically and through simulations that the common contribution-based approach, Shapley Value , underestimates the contribution of Mavericks and is hence not effective as a measure to select clients. Then, we propose FedEMD , an adaptive strategy with competitive overhead based on the Wasserstein distance, supported by a proven convergence bound. As FedEMD adapts the selection probability such that Mavericks are preferably selected when the model benefits from improvement on rare classes, it consistently ensures the fast convergence in the presence of different types of Mavericks. Compared to existing strategies, including Shapley Value -based ones, FedEMD improves the convergence speed of neural network classifiers with FedAvg aggregation by 26.9% and its performance is consistent across various levels of heterogeneity.
Pendampingan Kegiatan Literasi sebagai Upaya Meningkatkan Minat Baca Pada Siswa Sekolah Menengah Pertama di Desa Bogares Lor Kecamatan Pangkah-Tegal

The Indonesian government has implemented the School Literacy Movement as a solution to the problem of low student interest in reading, but not all schools are able to implement the program. The pesantren-based school, Attin Al Khair Junior High School is one of the private Junior High Schools in Pangkah Sub-districts, Tegal, which has not been able to implement the School Literacy Movement because of their limited facilities, such as the absence of a school library. In addition, the economic background of students' family also affects students' interest in reading. Based on these conditions, literacy activities are focused on basic literacy skills, including reading and writing. Literacy activities are carried out by reading non-academic books for 15 minutes and writing a daily literacy journal. These activities are accompanied by a mentor, a teacher who is appointed by the school. The mentoring activities carried out by English Literature Study Program Community Service team have helped Attin Al Khair Junior High School to start their literacy activities and this program gets positive responses from both teachers and students. Therefore, the literacy activities can be implemented continuously, and become a literacy culture in the school environment.
Preference-Aware Light Graph Convolution Network for Social Recommendation

Social recommendation systems leverage the abundant social information of users existing in the current Internet to mitigate the problem of data sparsity, ultimately enhancing recommendation performance. However, most existing recommendation systems that introduce social information ignore the negative messages passed by high-order neighbor nodes and aggregate messages without filtering, which results in a decline in the performance of the recommendation system. Considering this problem, we propose a novel social recommendation model based on graph neural networks (GNNs) called the preference-aware light graph convolutional network (PLGCN), which contains a subgraph construction module using unsupervised learning to classify users according to their embeddings and then assign users with similar preferences to a subgraph to filter useless or even negative messages from users with different preferences to attain even better recommendation performance. We also designed a feature aggregation module to better combine user embeddings with social and interaction information. In addition, we employ a lightweight GNN framework to aggregate messages from neighbors, removing nonlinear activation and feature transformation operations to alleviate the overfitting problem. Finally, we carried out comprehensive experiments using two publicly available datasets, and the results indicate that PLGCN outperforms the current state-of-the-art (SOTA) method, especially in dealing with the problem of cold start. The proposed model has the potential for practical applications in online recommendation systems, such as e-commerce, social media, and content recommendation.