Reviewerly/exHarmony huggingface dataset:
Pemanfaatan LKPD Interaktif Berbasis Liveworksheet untuk Meningkatkan Hasil Belajar Kognitif Muatan IPA Peserta Didik Kelas V di SD Negeri Jajar Kota Surakarta

The purpose of this service activity is to describe the use of interactive Student Worksheets (LKPD) with live worksheets as an effort to improve the cognitive aspects of learning outcomes for fifth-grade elementary school students on science content. This service activity is carried out in SD Negeri Jajar class V with a total of 22 students. The method used in this service program is the Asset-based Community Development (ABCD) approach which is carried out in three stages. Data collection techniques used test, observation, interview, and document studies. The results of this activity indicate that the first stage, second stage, and third stage increased initially. The average students who completed the KKM in stage I was 63 to 73 in stage II and 95 in stage II. Thus, it can be concluded that the cognitive learning outcomes of fifth-grade students through the use of interactive LKPD with live worksheets on the science content at SD Negeri Jajar, Surakarta City have increased.
A Survey of Museum Applied Research Based on Mobile Augmented Reality

Museums are the important places of education for preservation and dissemination of human material and intangible heritage. Nowadays, there are many problems in museums such as single display mode and fixed interaction style of collections, which makes cognitive load higher and visiting experience poorer for tourists. Mobile Augmented Reality is a technology that seamlessly integrates virtual information with real environment based on mobile devices. Museum applications based on Mobile Augmented Reality can improve the display richness and meet new visiting needs of the public in the digital era. Firstly, this article introduces the key technologies of Mobile Augmented Reality and its use in museums. Secondly, according to different application functions, classifiable explanations for Mobile Augmented Reality application examples are given. Thirdly, based on the progress in the application of Mobile Augmented Reality in museums at home and abroad, from three dimensions of content, guide, and interaction, designing thought regarding the application of Mobile Augmented Reality for museums is summarized.
English Learning Strategies by the Second Grade Students at a State Junior High School in Kupang, Indonesia (A Case Study)

Learning and mastering English will not be possible if from the beginning we do not have a strong determination. After having a strong determination, all you need to do is find the right English learning strategy. The students used different learning strategies in learning English as a foreign language. This study is designed to find out types of English language learning strategies are used by the students and the factors influencing the choice of learning strategy. On the basis of the nature of the data, a qualitative approach is appropriate to this study. Data were obtained by questionnaire and interview. The result of the analysis revealed that the students used three types of strategies in learning English. The types of strategies are cognitive, metacognitive and social/affective strategies. The more dominant strategy that is used by the students is social/ affective strategy. While the factor influencing their choices of strategies are classified into internal and external factors.
Towards quantum-based Search for industrial Data-driven Services

Linking and searching the right data for operational decision support in Cyber-Physical Systems remains a challenge. Links between logically related data are difficult to establish at the time of collection. In addition, for the execution of search queries issued by data-driven services, a full collection scan must often be performed. In this paper, we design a hybrid system that allows decision support services to use a quantum search algorithm for finding the right data in sub-linear time in order to link them.
Automatic Anomaly Monitoring in Public Surveillance Areas

With the dramatic increase in video surveillance applications and public safety measures, the need for an accurate and effective system for abnormal/suspicious activity classification also increases. Although it has multiple applications, the problem is very challenging. In this paper, a novel approach for detecting normal/abnormal activity has been proposed. We used the Gaussian Mixture Model (GMM) and Kalman filter to detect and track the objects, respectively. After that, we performed shadow removal to segment an object and its shadow. After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded humans. Fuzzy c-mean is utilized to verify human silhouettes and motion based features including velocity and optical flow are extracted for each identified silhouettes. Gray Wolf Optimizer (GWO) is used to optimize feature set followed by abnormal event classification that is performed using the XG-Boost classifier. This system is applicable in any surveillance application used for event detection or anomaly detection. Performance of proposed system is evaluated using University of Minnesota (UMN) dataset and UBI (University of Beira Interior)-Fight dataset, each having different type of anomaly. The mean accuracy for the UMN and UBI-Fight datasets is 90.14% and 76.9% respectively. These results are more accurate as compared to other existing methods.
Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce Network

In recent years, Internet of Things has not only promoted the continuous development of e-commerce transaction but also brought loop-hole to the fraud gangs who always utilize mobile devices to commit fraud crimes. For example, fraud gangs are usually organized to purchase commodities at low prices in e-commerce promotions. They benefit from the price spread by reselling commodities at high prices. In the past few years, the transaction fraud caused serious financial losses to merchants in e-commerce platform. To detect the fraudulent user and behavior effectively, a multiview graph clustering-based abnormal detection model is developed in this paper. In the proposed model, two fraudulent behavior patterns are proposed by abstracting the e-commerce network as a heterogeneous information graph. On this basis, two user-similarity graphs are reorganized from the heterogeneous graph with the help of different metapaths. Subsequently, in order to capture the corresponding fraudulent behavior patterns, the above two graphs are encoded into user embeddings and assigned to specific clusters in respective views. Finally, the consensus detection result is produced by fusing the complementary information of different views in a joint multiview learning framework. As we know, our work is the first one that uses multiview graph clustering in e-commerce fraud detection, which will provide a new research perspective for fraud detection in e-commerce platform. Extensive experiments are conducted on real and semisynthetic datasets, and the results demonstrate the effectiveness and superiority of the proposed model.
Deep-Learning-Guided Student Classroom Action Understanding for Preschool Education

A deep architecture for enhancing students' action recognition is proposed to improve preschool education. This paper seamlessly combines the teaching objectives, teaching scope, teaching implementation, and breeding evaluation status of preschool breeding practice theory. We attempt to solve the problem of effective preschool teaching, based on which we propose the simple adaptation strategies. We further evaluate the practice of preschool breeding and its effectiveness. In this way, civilized and high-quality preschool talents will be cultivated, and preschool educational experiences will be promoted. In the method of promoting the preschool culture of weak-aged children, owing to the problem that the traditional action recognition algorithm can indicate the specific students' actions, an action recognition method based on the combination of deep integration and human skeleton representation is proposed. First, the connected spatial locations and constraints are fed into a long-short-specified recall (LSTM) mode with a spatially and temporally aware algorithm which is designed to obtain spatiotemporal feature and highly separable deep joint features. Afterward, a new mechanism is introduced to resolve keyframes as well as the joints. Finally, based on the two-stream deep architecture, the effective discrimination of similar actions is achieved by integrating the color and shape features into the skeleton features by designing the deep model. Extensive experiments have demonstrated that, compared with the mainstream algorithms, this method can effectively distinguish students' action types in the classroom of homogeneous preschool children. Thus, we can substantially improve the efficiency of preschool teaching.
IFFMStyle: High-Quality Image Style Transfer Using Invalid Feature Filter Modules

Image style transfer is a challenging problem in computer vision which aims at rendering an image into different styles. A lot of progress has been made to transfer the style of one painting of a representative artist in real time, whereas less attention has been focused on transferring an artist’s style from a collection of his paintings. This task requests capturing the artist’s precise style from his painting collection. Existing methods did not pay more attention on the possible disruption of original content details and image structures by texture elements and noises, which leads to the structure deformation or edge blurring of the generated images. To address this problem, we propose IFFMStyle, a high-quality image style transfer framework. Specifically, we introduce invalid feature filtering modules (IFFM) to the encoder–decoder architecture to filter the content-independent features in the original image and the generated image. Then, the content-consistency constraint is used to enhance the model’s content-preserving capability. We also introduce style perception consistency loss to jointly train a network with content loss and adversarial loss to maintain the distinction of different semantic content in the generated image. Additionally, we have no requirement for paired content image and style image. The experimental results show that the stylized image generated by the proposed method significantly improves the quality of the generated images, and can realize the style transfer based on the semantic information of the content image. Compared with the advanced method, our method is more favored by users.
Video Analysis in Sports by Lightweight Object Detection Network under the Background of Sports Industry Development

This study uses the video image information in sports video image analysis to realize scientific sports training. In recent years, game video image analysis has referenced athletes' sports training. The sports video analysis is a widely used and effective method. First, the you only look once (YOLO) method is explored in lightweight object detection. Second, a sports motion analysis system based on the YOLO-OSA (you only look once-one-shot aggregation) target detection network is built based on the dense convolutional network (DenseNet) target detection network established by the one-shot aggregation (OSA) connection. Finally, object detection evaluation principles are used to analyze network performance and object detection in sports video. The results show that the more obvious the target feature, the larger the size, and the more motion information contained in the sports category feature, the more obvious the effect of the detected target. The higher the resolution of the sports video image, the higher the model detection accuracy of the YOLO-OSA target detection network, and the richer the visual video information. In sports video analysis, video images of the appropriate resolution are fed into the system. The YOLO-OSA network achieved 21.70% precision and 54.90% recall. In general, the YOLO-OSA network has certain pertinence for sports video image analysis, and it improves the detection speed of video analysis. The research and analysis of video in sports under the lightweight target detection network have certain reference significance.
An Analytical approach for optimal secured data storage on cloud server for online education platform

Abstract. Cloud computing is becoming increasingly popular in the IT business because of its higher performance, widespread access, cheap cost, and other benefits. It is also a pay-as-you-go approach; hence, anyone can access cloud data from anywhere, and it is employed in education platforms for online classes due to its ease of use. However, many educational institutions hesitate to use the cloud educational platform due to security and privacy issues. Hence in this study, the performance analysis of various cryptographic algorithms such as Elliptic Curve Cryptography (ECC), Advanced encryption standard (AES), Two Fish, Blowfish, Data Encryption Standard (DES), Triple Data Encryption Standard (TDES) and role-based access control (RBAC) was analyzed and compared with each other in a view to ensuring the protection of cloud data storage used for educational purpose in the NPTEL database. Encryption time, decryption time, and retrieval time with different data sizes were used as performance evaluation factors to figure out the best End-To-End Encryption security in a network system. Moreover, an ElGamal SBO with Delta Competitive NN Cryptography has been proposed in which ElGamal Stag beetle optimization performs ElGamal encryption with the generation of an optimized key with low execution time, thereby allowing only authorized users to access the educational data in the cloud and the data transmission has been secured using Delta Competitive NN that minimizes vulnerable attacks while controlling the decryption activity. Results showed that the proposed ElGamal SBO with Delta Competitive NN Cryptography performs better than all other techniques in terms of retrieval time, encryption time, communication cost, computational overhead and decryption time and hence when applied in a security scenario, it can improve the encryption effectiveness.
The effect of strategic leadership on intellectual capital: The mediating role of the knowledge sharing in Kuwait industrial companies

The study aimed to figure out the effect of the strategic leadership on the intellectual capital, in addition to the mediating role of the knowledge-sharing mediating variable in the Kuwaiti industry companies. To achieve the objectives of the study, the study adopted the descriptive, analytical approach by preparing a questionnaire. After verifying its validity and reliability, the researcher distributed it to a sample of (284) male and female employees in the Kuwaiti industrial companies. After conducting the statistical analysis through the (SPSS) and (Amos V23), the study concluded a statistically significant effect at sig. (α≤0.05) for the strategic leadership on the intellectual capital in the Kuwaiti industrial companies and a statistically significant effect at sig. (α≤0.05) for the strategic leadership of the intellectual capital through knowledge sharing as a mediating variable in the industrial companies in the State of Kuwait.
Sentiment Analysis on the Metaverse: Twitter Data

In recent days, the metaverse which is defined as virtual-reality space in which people can interact with each other in a computer-generated environment, has attracted people’s attention. People have posted their opinions about the metaverse on social media platforms. Twitter is one of those platforms in which people have tweeted about the metaverse. Tweets help researchers to understand public attitudes on a subject. This research focuses on two analyses: The first one used sentiment analysis on Turkish tweets about various alternative words of the metaverse, such as karma evren, misal evren, ote evren, sahte dunya, sanal evren and internet otesi. The second focus of our research was on an analysis of a questionnaire that aimed to understand whether people are aware of the metaverse and willing to experience it or not. The result of sentiment analysis showed that the most of the collected tweets were positive about the collected tweets. The questionnaire analysis showed that the majority of participants were aware of the metaverse and would like to experience that virtual space
Traffic Sign Recognition with Vision Transformers

Traffic sign recognition is an integral part of future autonomous driving systems. Deep learning has been applied in this task, while the performance of the recent vision Transformers is unexplored. In this study, eight different vision Transformers are validated in three real-world traffic sign datasets for the first time. The experimental results demonstrate that the best vision Transformer has a performance between the pre-trained DenseNet and the DenseNet trained from scratch. Besides, the best vision Transformers model has less training time compared to DenseNet.
Conventional Neural Network-Based Radio Frequency Fingerprint Identification Using Raw I/Q Data

Radio frequency (RF) fingerprint identification is a nonpassword authentication method based on the physical layer of communication devices. Deep learning methods have thrown new light on RF fingerprint identification. In this paper, a conventional neural network- (CNN-) based RF identification model is proposed. The CNN models are designed to be lightweight. Raw data that reflects the characteristics of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>I</mi></math> channel, the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>Q</mi></math> channel, and the 2-dimensional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><mi>I</mi><mo>+</mo><mi>Q</mi></math> data is successively fed into a CNN model. Therefore, three submodels are generated. The final predictive labels are determined by the results of the three submodels through a voting scheme. Experimental results have demonstrated that in the SNR setting at 5 dB, the final recognition accuracy of four transmit devices could achieve as high as 97.25%, while the identification accuracies based on the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"><mi>I</mi></math> channel data, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"><mi>Q</mi></math> channel data, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"><mi>I</mi><mo>+</mo><mi>Q</mi></math> channel data are 94.5%, 95%, and 94.5%, respectively. The training time for the 4 devices is around 30 seconds.
Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing

The study explores the risks and benefits of investors in e-commerce financing under the background of "double carbon" to maximize investors' interests and reduce investment losses. The Back Propagation Neural Network (BPNN) algorithm model of e-commerce enterprise financing based on the Capital Asset Pricing Model (CAPM) is mainly studied. First, according to the worldwide literature, the theoretical concept and principle of the CAPM are deeply studied and analyzed. Then, from the perspective of "double carbon," with the financing risk characteristics of listed companies responding to the "double carbon" policy as samples, the CAPM model of e-commerce financing under the BPNN algorithm is established. Next, the BPNN is used to input the financing samples of e-commerce enterprises and train the model. The verification experiment of the capital asset financing model of e-commerce enterprises is further conducted. The experimental results show that the model error is the smallest when the number of neurons in the hidden layer reaches about 20. Therefore, the number of neurons in the hidden layer of the model is set to 20. When the number of iterations in training reaches 3000, the financing risk model begins to show a convergence trend. Finally, it can be determined that the number of adaptive iterations of the model is 3000. When the learning rate is 0.03, the oscillation of the model is smaller and stabler, so the model learning rate is 0.03, and the final model error is only 9.96 × 10-8. Based on this, e-commerce enterprises can achieve the purpose using this model to adjust the coefficient in financing in the future. The results have certain reference significance for e-commerce financing risk assessment under a "double carbon" background.
Decision Support Model for Determining the Best Employee using Fuzzy Logic and Simple Additive Weighting

Specifically, for an information technology company, identifying the best employees (the best programmers) is valuable.The election is intended to improve the performance of the company's programmers.The company's performance will improve as programmers' performance improves.This study attempts to develop a Decision Support Model (DSM) to identify the best employees (i.e., programmers) in the firm.The model considered nine parameters (technical skills, problem-solving, communication skills, teamwork, discipline, work progress, time management, formal education, and informal education) by integrating the Fuzzy Logic (FL) with the Simple Additive Weighting method (SAW).This model is finally able to be benefitted by information and technology firms.This can eventually be used to assess and view employee evaluations, making it simpler for businesses to make key decisions (e.g., granting incentives, salary raises, or promotions).The model is based on data collected from ten company programmers (where six of them are real data).The model concludes that programmer 2 is the best employee in the firm, with a total score of 97.94, based on the suggestions from the constructed DSM.
The Relationship Between Learning Spaces and the Development of Computational Thinking Skill

Modern society pressures the educational systems.Technologies transformed industries requiring competencies involving new methods of solving problems.The need to solve today's complex problems gives rise to new types of workspaces, new approaches and new forms of thinking, such as computational thinking.However, in the scientific literature, the relationship between learning spaces and the process of computational thinking is still unclear.Our case study draws on the grounded theory method answering the question: How do learning spaces contribute to the process of computational thinking?We aim to explicit the process of computational thinking in a context of creating a computational thinking challenge and to identify the characteristics of learning spaces that could potentially contribute to this process.Using a qualitative methodology, results show that students applied the computational thinking process model drawing upon concepts and approaches in a multifunctional, comfortable, diversified and engaging environment that constitute the foundations of the 21st century learning.
Bootstrapped Representation Learning for Skeleton-Based Action Recognition

In this work, we study self-supervised representation learning for 3D skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL) for representation learning on skeleton sequence data and propose a new data augmentation strategy including two asymmetric transformation pipelines. We also introduce a multi-viewpoint sampling method that leverages multiple viewing angles of the same action captured by different cameras. In the semi-supervised setting, we show that the performance can be further improved by knowledge distillation from wider networks, leveraging once more the unlabeled samples. We conduct extensive experiments on the NTU-60, NTU-120 and PKU-MMD datasets to demonstrate the performance of our proposed method. Our method consistently outperforms the current state of the art on linear evaluation, semi-supervised and transfer learning benchmarks.