Reviewerly/exHarmony huggingface dataset:
Detection of abnormal human behavior using deep learning

The complete human body or the various limb postures are involved in human action. These days,Abnormal Human Activity Recognition (Abnormal HAR) is highly well noticed and surveyed in manystudies. However, because of complicated difficulties such as sensor movement, positioning, and so on,as well as how individuals carry out their activities, it continues to be a difficult process. Identifyingparticular activities benefits human-centric applications such as postoperative trauma recovery, gesturedetection, exercise, fitness, and home care help. The HAR system has the ability to automate orsimplify most of the people’s everyday chores. HAR systems often use supervised or unsupervisedlearning as their foundation. Unsupervised systems operate according to a set of rules, whereassupervised systems need to be trained beforehand using specific datasets. This study conducts detailedliterature reviews on the development of various activity identification techniques currently being used.The three methods—wearable device-based, pose-based, and smartphone sensor—are examined in thisinquiry for identifying abnormal acts (AAD). The sensors in wearable devices collect data, whereas thegyroscopes and accelerometers in smartphones provide input to the sensors in wearable devices. Tocategorize activities, pose estimation uses a neural network. The Anomalous Action Detection Dataset(Ano-AAD) is created and improved using several methods. The study examines fresh datasets andinnovative models, including UCF-Crime. A new pattern in anomalous HAR systems has emerged,linking anomalous HAR tasks to computer vision applications including security, video surveillance,and home monitoring. In terms of issues and potential solutions, the survey looks at vision-based HAR
Prediction of Car Purchase based on User Demands using Supervised Machine Learning

One of the key sectors of the national economy is the auto industry. Cars are becoming more and more common as a form of private transportation. When a buyer wants to purchase the ideal vehicle, particularly a car, an evaluation is necessary. Because it is an expensive vehicle, there are a lot of conditions and elements to consider before buying a new one, including price, headlamp, cylinder volume, and spare parts. Therefore, it is crucial for the consumer to choose a purchase that can meet all of the criteria before making any other decisions. In our research, we therefore suggest various well-known methods to improve accuracy for a car purchase. These algorithms were used on our dataset, which consists of 50 data. With a prediction accuracy of 86.7%, Support Vector Machine (SVM) produces the best result of the bunch. In this study, we also present comparison findings for all data samples using various methods for precision, recall, and F1 score.
SPDGAN: a generative adversarial network based on SPD manifold learning for automatic image colorization

This paper addresses the automatic colorization problem, which converts a grayscale image to a colorized one. Recent deep learning approaches can colorize automatically grayscale images. However, when it comes to different scenes which contain distinct color styles, it is difficult to accurately capture the color characteristics. In this work, we propose a fully automatic colorization approach based on Symmetric Positive Definite (SPD) Manifold Learning with a generative adversarial network (SPDGAN) that improves the quality of the colorization results. Our SPDGAN model establishes an adversarial game between two discriminators and a generator. The latter is based on ResNet architecture with few alterations. Its goal is to generate fake colorized images without losing color information across layers through residual connections. Then, we employ two discriminators from different domains. The first one is devoted to the image pixel domain, while the second one is to the Riemann manifold domain which helps to avoid color misalignment. Extensive experiments are conducted on the Places365 and COCO-stuff databases to test the effect of each component of our SPDGAN. In addition, quantitative and qualitative comparisons with state-of-the-art methods demonstrate the effectiveness of our model by achieving more realistic colorized images with less artifacts visually, and good results of PSNR, SSIM, and FID values.
An Analysis of Early Childhood Teachers’ Perception of Professionalism Using IPA

본 연구의 목적은 IPA를 활용하여 유아교사의 전문성 중요도와 실행도에 대한 인식을 분석하는 것이다. 본 연구를 위해 경기지역에 소재하고 있는 어린이집과 유치원에 근무 중인 교사 336명을 대상으로 설문 조사를 실시하였으며, 연구문제에 따라 빈도분석, 신뢰도 분석, 평균, 표준편차, t검정, IPA 기법으로 분석하였다. 연구결과를 요약하면 다음과 같다. 첫째, 전문성에 대한 유아교사의 중요도와 실행도 인식은 통계적으로 유의한 차이를 보였으며, 중요도가 실행도에 비해 높게 나타났다. 둘째, 유아교사의 전문성을 구성하는 5개 요소 가운데, 행동지도 및 치료적 능력, 환경조성능력, 교육프로그램 운영 관련 지식과 기술 및 수행능력은 중요도 인식과 실행도가 모두 높게 나타났다. 이는 앞으로도 지속적인 유지가 필요함을 의미한다. 셋째, 중요도는 높으나 실행도가 낮아 개선 노력이 필요한 영역에는 5개 요소(보호적 능력, 행동지도 및 치료적 능력, 환경조성능력, 교육프로그램 운영 관련 지식과 기술 및 수행능력, 평가능력) 모두 해당되지 않았다. 넷째, 보호적 능력과 평가능력에 대한 역량은 중요도와 실행도 모두 낮게 나타나 전문성에 대한 인식제고와 실행도를 높이려는 노력이 필요한 것으로 나타났다. 이러한 결과를 중심으로 유아교사의 전문성 인식에 어떠한 지원방안이 필요한지에 대한 시사점을 제시하였다. The purpose of this study is to analyze early childhood teachers’ perception of the importance and performance of professionalism using importance-performance analysis IPA. A questionnaire survey was conducted with 336 teachers who are serving childcare centers and kindergartens located in Gyeonggi region, and the data were analyzed using frequency analysis, reliability analysis, mean, standard deviation, t-test, and IPA, according to the research questions. The findings may be summarized by the following four points. First, the teachers’ perception of the importance and performance of professionalism showed a statistically significant difference, and the importance was higher than the performance. Second, as for behavior guidance and therapeutic ability, environment creation ability, and knowledge, skills, and performance ability related to the operation of educational programs, it was found that both the perception of the importance and the performance were high. This means that it is necessary to keep them up in the future as well. Third, as for the area that needs improvement effort because of high importance and low performance, all of the five factors were not shown. Fourth, as for the capabilities of protective ability and evaluation ability, both the importance and the performance were low, which means that the effort to enhance the performance as well as the perception of professionalism are required. Based on the above findings, the study suggests that some supportive actions in the teachers’ perception of professionalism should be provided.
Quantum Digital Signatures (QDS): A novel approach to safeguarding electronic health records

Electronic Health Records (EHRs) form the backbone of modern healthcare, ensuring seamless access to a patient’s medical history and fostering effective patient-doctor relationships. Their digital nature, however, exposes them to myriad security threats, including unauthorized access, tampering, and fraudulent alterations. Traditional cryptographic techniques, while effective in the current digital landscape, might not stand robust against future threats, especially those posed by quantum computers. This paper introduces Quantum Digital Signatures (QDS) as a cutting-edge approach to address this burgeoning concern. QDS leverages the principles of quantum mechanics to provide an additional layer of security, making it computationally infeasible for adversaries to forge or alter signed messages, including sensitive EHR data. Unlike conventional digital signatures that depend on the presumed difficulty of certain mathematical problems, the security attributes of QDS stem from the fundamental laws of quantum physics, ensuring a higher level of integrity and authenticity. We present a comprehensive overview of the QDS protocol and its integration into current EHR systems. Our comparative analysis with existing cryptographic techniques illuminates the advantages of QDS, especially in preventing undetected alterations and ensuring non-repudiation. Practical deployment challenges, including quantum device imperfections and potential integration bottlenecks, are also addressed. The adoption of Quantum Digital Signatures in safeguarding EHRs ushers in a new era of medical data security. While the large-scale implementation of such quantum technologies is still in its infancy, proactive research and early adoption are pivotal in preparing healthcare systems for future challenges. Through this research, we advocate for the broader integration of quantum cryptographic techniques, such as QDS, to uphold the sanctity and trustworthiness of electronic medical data in the face of evolving digital threats.
Improving Bert Model Accuracy for Uni-modal Aspect-Based Sentiment Analysis Task

Techniques and methods for examining users’ feelings, emotions, and views in text or other media are known as ”sentiment analysis,” this phrase is used frequently. In many areas, including marketing and online social media, analysis of user and consumer opinions has always been essential to decision-making processes. The development of new methodologies that concentrate on analysing the sentiment associated with specific product characteristics, such as aspect-based sentiment analysis (ABSA), was prompted by the need for a deeper understanding of these opinions. Despite the growing interest in this field, some misunderstanding exists about ABSA’s core ideas. Even though sentiment, affect, emotion, and opinion refer to various ideas, they are frequently used synonymously. This ambiguity commonly causes user opinions to be analysed incorrectly. This work provides an overview of ABSA and the issue of overfitting. Following this analysis, we improved the model by enhancing the accuracy and F1 score of the existing model by fine-tuning the technique. Our model outperformed the others, achieving the best results for the restaurant dataset with an 85.02 accuracy and a 79.19 F1 score, respectively.
A Study of Algorithm-Based Detection of Fake News in Brazilian Election: Is BERT the Best

The recent Brazilian election was plagued by the proliferation of false news on the internet. Many people turned to social media to fact-check information and verify its authenticity. In today's digital and data-driven world, fake news can spread rapidly, causing detrimental effects, such as potentially influencing the outcome of an election. In light of this, verifying information has become increasingly reliant on software. While intelligent software can be used to detect and mitigate the spread of fake news, there is a lack of research on the use of such technology in the Portuguese language, particularly when it comes to the implementation of newer strategies such as the Representation of a Bidirectional Transformer Encoder (BERT). Our study evaluated BERT's ability to detect fake news compared to traditional machine learning algorithms, using text classification to identify false news. The results demonstrate BERT's superiority over other algorithms, with a statistically significant difference in all cases. BERT can considered a viable option for detecting fake news.
Radar Signal Recognition Based on MSST and Dual Channel Feature Extraction

Aiming at the problems of the traditional low probability of intercept (LPI) radar signal recognition algorithm, which is difficult to extract effective features and low recognition accuracy under low signal-to-noise ratio (SNR), a radar signal recognition method based on multi-synchrosqueezing transform (MSST) and two channel feature extraction was proposed. First, MSST is used to obtain signal time frequency images, and perform a series of pre-processing operations. Then, the image features are extracted through the gray co-incidence matrix (GLCM) and the local binary variance mode (LBPV), and the feature dimension is reduced by the principal component analysis (PCA). Finally, the dimensionality reduction features are sent to the support vector machine (SVM) to complete the classification and recognition of radar signals. According to the simulation analysis results, the recognition accuracy of the two-channel feature extraction algorithm is 15.67% higher than that of the two single channels on average under a low SNR and significantly higher than that of the symmetric Holder coefficient method. Moreover, when the SNR is as low as -2dB, the overall recognition rate of the algorithm is still 96.44%. This algorithm not only has low computational complexity but also has high recognition accuracy, especially compared with single-channel feature extraction and symmetric Holder coefficient methods at low SNR, the recognition accuracy has significant advantages.
Self-Supervised 3D Skeleton Representation Learning with Active Sampling and Adaptive Relabeling for Action Recognition

Self-supervised 3D skeleton representation learning has recently shown great potential for action recognition via contrastive learning. However, existing methods suffer from limited learning efficiency and the unreliability of representations, which is not conducive to action recognition. To this end, we propose an Active Sampling and Adaptive Relabeling (ASAR) contrastive learning method to achieve efficient and reliable learning of 3D skeleton representations. Specifically, the active sampling strategy is used to build a dictionary with informative samples for efficient representation learning. Additionally, the adaptive relabeling strategy is proposed to automatically modify the confidence scores of the extra positive samples and alleviate the unreliability of representations. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets demonstrate the superiority of our approach.
TAQ: Top-K Attention-Aware Quantization for Vision Transformers

Model quantization can reduce the memory footprint of the neural network and improve the computing efficiency. However, the sparse attention in Transformer models is difficult to quantize, the main challenge is that changing the order of attention values and shifting attention regions might lead to incorrect prediction results. To address this problem, we propose quantization method, termed TAQ, which uses the proposed TOP-K attention-aware loss to search the quantization parameters. Further, we combine the sequential and parallel quantization methods to optimize the procedure. We evaluate the generalization ability of TAQ on various vision Transformer variants, and its performance on image classification and object detection tasks. TAQ makes the TOP-K attention ranking more consistent before and after quantization, and significantly reduces the attention shifting rate, compared with PTQ4ViT, TAQ improves the performance by 0.66 and 0.45, respectively on ImageNet and COCO, achieves the state-of-the-art performance.
AI-Based Energy-Saving for Fog Computing-Empowered Data Centers

With the rapid development of cloud computing services and big data applications, the number of data centers is proliferating, and with it, the problem of energy consumption in data centers is becoming more and more serious. Data center energy-saving has received more and more attention as a way to reduce carbon emissions and power costs. The main energy consumption of data centers lies in IT equipment energy consumption and end air conditioning energy consumption. In this paper, we propose a data center energy-saving application system based on fog computing architecture to reduce air conditioning energy consumption, and thus reduce data center energy consumption. Specifically, the intelligent module is placed in the fog node to take advantage of the low latency, proximal computing, and proximal storage of fog computing to shorten the network call link and improve the stability of acquiring energy-saving policies and the frequency of energy-saving regulation, thus solving the disadvantages of high latency and instability in the energy-saving approach of cloud computing architecture. The AI technology is used in the intelligent module to generate energy-saving strategies and remotely regulate the end air conditioners to achieve better energy-saving effects. This solves the shortcomings of the traditional manual regulation based on expert experience with low adjustment frequency and serious loss of cooling capacity of the terminal air conditioner. According to the experimental results, statistics show that compared with the traditional manual regulation based on expert experience, the data center energy-saving application based on fog computing can operate safely and efficiently, and reduce the PUE to 1.04. Compared with the AI energy-saving strategy based on cloud computing, the AI energy-saving strategy based on fog computing generates strategies faster and with lower latency, and the speed is increased by 29.84%.
3D Face Reconstruction Based on Weakly-Supervised Learning Morphable Face Model

In this paper, we propose a system for 3D face model reconstruction. Earlier studies on reconstruction methods included the software modeling methods or the instrument scanning modeling methods. But both of the above methods require a lot of development resources and time costs. Therefore, we develop a reconstruction system using a weakly supervised approach combining Convolutional Neural Networks (CNN) and 3D Morphable Face Models (3DMM). Given a sufficient number of 2D face images to train and learn the main features of the face, our system is capable of rapidly constructing 3D face models. The proposed method enhances the efficiency of preprocessing and improves the performance of loss function through image depth feature extraction and regression coefficients. Using two datasets for model evaluation and analysis, this study efficiently reconstructs faces without ground-truth labels.
Development of Trio Optimal Feature Extraction Model for Attention-Based Adaptive Weighted RNN-Based Lung and Colon Cancer Detection Framework Using Histopathological Images

Due to the combination of genetic diseases as well as a variety of biomedical abnormalities, the fatal disease named cancer is caused. Colon and lung cancer are regarded as the two leading diseases for disability and death. The most significant component for demonstrating the best course of action is the histopathological identification of such malignancies. So, in order to minimize the mortality rate caused by cancer, there is a need for early detection of the aliment on both fronts accordingly. In this case, both the deep and machine learning techniques have been utilized to speed up the detection process of cancer which may also help the researchers to study a huge amount of patients over a short period and less loss. Hence, it is highly essential to design a new lung and colon detection model based on deep learning approaches. Initially, a different set of histopathological images is collected from benchmark resources to perform effective analysis. Then, to attain the first set of features, the collected image is offered to the dilated net for attaining deep image features with the help of the Visual Geometry Group (VGG16) and Residual Neural Network (ResNet). Further, the second set of features is attained by the below process. Here, the collected image is given to pre-processing phase and the image is pre–pre-processed with the help of Contrast-limited Adaptive Histogram Equalization (CLAHE) and filter technique. Then, the pre-processed image is offered to the segmentation phase with the help of adaptive binary thresholding and offered to a dilated network that holds VGG16 and ResNet and attained the second set of features. The parameters of adaptive binary thresholding are tuned with the help of a developed hybrid approach called Sand Cat swarm JAya Optimization (SC-JAO) via Sand Cat swarm Optimization (SCO) and JAYA (SC-JAO). Finally, the third set of features is attained by offering the image to pre-processing phase. Then, the pre-processed image is offered to the segmentation phase and the image is a segmented phase and features are tuned by developed SC-JAO. Further, the segmented features are offered to attain the textural features like Gray-Level Co-Occurrence Matrix (GLCM) and Local Weber Pattern (LWP) and attained the third set of features. Then, the attained three different sets of features are given to the optimal weighted feature phase, where the parameters are optimized by the SC-JAO algorithm and then given to the disease prediction phase. Here, disease prediction is made with the help of Attention-based Adaptive Weighted Recurrent Neural Networks (AAW-RNN), and their parameters are tuned by developed SC-JAO. Thus, the developed model achieved an effective lung and colon detection rate over conventional approaches over multiple experimental analyses.
A Case Study of Process Drama-Based Korean Speaking Classes Utilizing Short-Form Videos

The purpose of this study is to examine a case of process drama-based Korean speaking classes utilizing short-form videos, and approach to explore the effectiveness and the significance of Korean speaking education. To this end, the study develops an educational framework based on process drama, in advantage of the curriculum, assessing the satisfaction of the learners through surveys, focus group interviews, and teacher meetings. The results demonstrate that the process drama-based Korean speaking classes, using short-form videos, offer practical speaking tasks and foster interactive engagement among learners, thereby proving positive aspects on delivering instructions and educational outcomes. Moreover, the use of short-form videos as an instructional tool not only captivates learners’ attention but also facilitates feedback based on visual contents and enhances the outcomes with shared tasks. Nevertheless, it is acknowledged that the classroom environment and course structure impose certain limitations in terms of the contraints in a physical and a timely manner. By proposing process drama as a viable means of Korean speaking education and applying it in real classroom settings, this research contributes to the discourse on effective language instruction.(Gyeongsang National UniversityㆍBusan University of Foreing StudiesㆍChangwon National University)
Roles of computer agents in digital games: effects on learning performance, perceptions, and behaviors

When encountering difficulties in conventional educational games, learners seldom self-regulate to discover and organize the learning content in the game environment. With the development of the human–computer interaction technology, computer agents are gradually being applied to educational games to provide personalized guidance or support to learners in specific situations. Different roles of computer agents may affect students' learning achievements and feelings in the learning environment. Therefore, to compare the effects of different roles of computer agents on students' learning outcomes in digital games, this study developed a Tutor Agent-based Digital Game (TA-DG), a Peer Agent-based Digital Game (PA-DG) and a Student Agent-based Digital Game (SA-DG). The experimental results showed that the TA-DG and PA-DG learners had better learning achievements than the SA-DG learners. The PA-DG and SA-DG learners had higher motivation than the TA-DG learners. The PA-DG learners had a higher critical thinking tendency than the other two groups. In addition, the SA-DG learners had the lowest cognitive load, followed by the TA-DG and PA-DG learners. This implies that the individual roles of the agents had different impacts during the digital game-based learning process. The findings provide a good reference for developing future digital game-based learning research with personalized support.