Reviewerly/exHarmony huggingface dataset:
Biometric information security based on double chaotic rotating diffusion

Biometric technology is increasingly being used in various areas, including government law enforcement, exit and entry management, visitor access control, user identification, and daily attendance management for companies. This technology simplifies the procedure of user identity verification and reduces the time required for verification. However, the security of biological data stored in the system can be overlooked. To address this, we propose a secure biometric feature matching framework that encrypts all information stored in the biometric system. The system decrypts and extracts feature values only when recognition and matching are required, enabling secure matching judgment. This framework protects private data in the biometric system from illegal theft, tampering, and destruction, and promotes research and practical application of biometric security technology. To fully ensure system data security, we propose an adaptive rotation diffusion image encryption algorithm based on double chaos. The algorithm uses double chaos to control image scrambling and leverages the random characteristics of chaos. The algorithm adopts six different rotation diffusion methods, and different plaintext images lead to completely different diffusion methods, making the algorithm adaptive and effective against selected plaintext attacks. Experimental simulations and performance analysis confirm the feasibility and superiority of the proposed algorithm.
Analysis of Instructor and Learner Experience on the Utilization of Metaverse Classes: Focusing on ZEP

The purpose of this study is to examine the experiences of instructors and learners in university classes using the ZEP Metaverse platform. To achieve this, D University conducted a focus group interview with four Metaverse experts to develop an educational Metaverse platform proposal. Subsequently, the plan was reviewed twice by eight Metaverse experts, and the platform was developed in cooperation with the development team. Five classes, including an orientation session, were conducted in the graduate program of D University in the second semester of 2022, and data was collected online by dividing them into quantitative and qualitative evaluation items. As a result of the study, it was found that learners and instructors improved interaction between users through the use of the Metaverse, and the interesting environment had a positive effect on the class, but there were inconveniences due to problems caused by technical problems.
Research on Relation Classification Tasks Based on Cybersecurity Text

Relation classification is a significant task within the field of natural language processing. Its objective is to extract and identify relations between two entities in a given text. Within the scope of this paper, we construct an artificial dataset (CS13K) for relation classification in the realm of cybersecurity and propose two models for processing such tasks. For any sentence containing two target entities, we first locate the entities and fine-tune the pre-trained BERT model. Next, we utilize graph attention networks to iteratively update word nodes and relation nodes. A new relation classification model is constructed by concatenating the updated vectors of word nodes and relation nodes. Our proposed model achieved exceptional performance on the SemEval-2010 task 8 dataset, surpassing previous approaches with a remarkable F1 value of 92.3%. Additionally, we propose the integration of a ranking-based voting mechanism into the existing model. Our best results are an F1 value of 92.5% on the SemEval-2010 task 8 dataset and a value 94.6% on the CS13K dataset. These findings highlight the effectiveness of our proposed models in tackling relation classification tasks.
A Study on University Professors’ Perception on Educational Applicability of ChatGPT in English Classes

The purpose of this paper is to examine professors’ perceptions on chatGPT and provide some suggestions about them. To that end, a total of fifteen professors belonging to universities in Chungcheong-namdo participated in the online survey. The findings of this study are as follows. First, the meaning or concept of chatGPT was already known, and most of the participants had experience using it. Also, professors thought chatGPT is a useful educational tool and showed strong willingness to use in classes. However, the professors mentioned conducting teaching-learning activities by using chatGPT could be difficult. Second, It was believed that the expected effect of using chatGPT could serve as motivation, learning aid, and interaction. However, as expected difficulties, concerns such as limitations in English proficiency, risk of absolute trust, and ethical risks were raised. The study provided some suggestions to educators and administrators.
Key Role Guided Transformer for Group Activity Recognition

Group Activity Recognition (GAR) is a challenging task, where modeling spatio-temporal relationships among participants plays a fundamental role. To address this issue, we propose a novel end-to-end trainable network, termed Key Role Guided Transformer (KRGFormer). Different from current methods that concurrently take all individuals into account for global reasoning, it captures crucial contextual information by emphasizing a set of key individuals in a coarse-to-fine manner considering that group activities are usually dominated by them. A Key Individual-aware Block (KIaBlock) is designed to select relevant individuals and enhance their relationships with the reservation of global dependencies of the entire group. The representations are then iteratively refined by deploying multiple stacked KIaBlocks, leading to a stronger discriminative power to distinguish group activities. Moreover, along with general data augmentation schemes, several “actor-centric” ones are presented to relieve the over-fitting risk, which further boost the performance. We extensively evaluate the proposed approach on the Volleyball, VolleyTactic and NBA datasets, and the experimental results demonstrate its superiority.
Pseudo 3D Pose Recognition Network

Multi-view human pose recognition has been extensively studied in computer vision due to its significant practical implications. Nonetheless, it remains a challenging task to effectively integrate distinctive view-based features and perform thorough qualitative analysis and quantitative evaluations. In this paper, based on an innovative multi-view fusion module and a novel Mutable Scaling Shortcut Connection, a pseudo 3D pose recognition neural network was meticulously crafted. The proposed network framework comprises four modules: Front Residual Module, 3D Convolution Cross View Fusion Module, Rear Residual Module, and Detection Module. The Front Residual Module serves as the head module with incipient pose heatmaps extraction functionality, taking preprocessed images of various views as separate inputs. The 3D Convolution Cross View Fusion Module performs 3D convolution fusion for the heatmaps output from Front Residual Module of each view, enabling the heatmaps to benefit from each other consequently. The Rear Residual Module extracts deeper-level features, and ultimately the Detection Module performs pose classification and recognition. The proposed network can be trained end-to-end and was evaluated with a Self-Built Multi-View pose recognition dataset. Analytical and evaluation approaches were used to explain the contributory effects of the 3D Convolution Cross View Fusion Module, which significantly improve recognition accuracy from approximately 70% to 91%-94% through Feature Aggregation, Strong Interaction Property among views, Sparsity Reduction, and Increasing Euclidean Distance.
Research on hardware synchronization control system in 3D panorama construction

As the application of panorama technology is increasing widely, improving the speed of 3D panorama construction and the quality of image stitching has become a popular research content nowadays. By improving the synchronization of multi-camera acquisition in panorama construction technology, the reliability of the stitched panorama images can be increased. At present, there are mainly two methods to ensure the synchronization of multi-camera acquisition. One is to ensure the synchronization of acquisition by using multi-camera acquisition cards through hardware, but the drawback of this method is the high requirement for the interface between cameras and the host computer; the other is to achieve synchronized acquisition through multi-thread control of the host computer through software, but the drawback of this method is the high synchronization delay. This paper introduces the polling system and multitasking system in hardware synchronization control, and experimentally explores the difference between the two systems for the delay in image acquisition control process and uses an MCU equipped with FreeRTOS real-time operating system to control multiple industrial cameras for multiple GPIO acquisition. The conclusion is that by using the FreeRTOS multitasking system for image acquisition control, the acquisition latency between multiple image acquisition devices can be greatly reduced.
3D Multimedia E-Book Based on Mobile Learning Basic Competency of Artistic Hair Coloring

The use of appropriate learning media will greatly assist in the learning process. The problem of research is the low understanding of students in the subject of hair trimming and coloring which has an impact on student learning outcomes. The purpose of this study was to develop an e-book-based learning media on the subject matter of double-application hair coloring. This type of research is research and development using a 4-D model. The population of this research is the students totaling 32 people. The data collection technique used a validity and practicality questionnaire. The data analysis technique is by calculating the score of the e-book media assessment from the questionnaire given and categorizing it according to the interpretation obtained. The results of the media validation study were 0.89 (very valid category) and 0.82 material validation (very valid category). The results of the practicality of student responses were 86.07% (very practical category) and from the teacher's response 87.2 (very practical category). E-book-based learning can be said to be valid and practical to be used as teaching materials in the classroom on hair trimming and coloring subjects to improve student learning outcomes.
Choir Management in Ghana: Overcoming Challenges to Sustain Musical Culture and Community Engagement

Purpose: The purpose of this article is to examine the unique challenges faced by choir directors in Ghana and provide recommendations for managing and thriving in this context. It aims to highlight the importance of resilience, resourcefulness, and cultural sensitivity in navigating the funding constraints, limited resources, intense competition, and cultural expectations that characterize the Ghanaian choir environment.&#x0D; Methodology: The methodology used in this article is not explicitly stated. However, the recommendations and insights provided are based on a combination of scholarly research, professional experiences, and observations related to choir management in Ghana. The authors gathered data through a literature review, interviews, and observations to understand the challenges faced by choir directors and identify effective strategies for managing them.&#x0D; Findings: The findings of this study suggest that choir directors in Ghana can overcome challenges and contribute to the country's musical heritage by implementing specific strategies. These include seeking diverse funding sources, engaging with local businesses, cultural organizations, and government agencies for financial support, promoting the unique qualities of their choirs, and emphasizing cultural authenticity, repertoire diversity, and innovative performances to attract audiences and sponsors.&#x0D; Recommendations: The theoretical ideas that informed this article's recommendations and the actual experiences that took place in the Ghanaian choir context validated them. The authors likely drew upon their expertise, scholarly research, and consultation with practitioners to develop and validate these recommendations. They should seek diverse funding sources, engage with local businesses and organizations, and promote the unique qualities of their choirs to attract audiences and sponsors. Policymakers are encouraged to support the development and sustainability of choirs in Ghana by recognizing their cultural significance and providing financial and logistical support. Policies that promote the arts, cultural preservation, and community engagement can contribute to the growth and success of choirs. Additionally, policies addressing funding constraints and resource limitations can alleviate some of the challenges faced by choir directors, enabling them to thrive and contribute to Ghana's musical landscape. The article aims to inspire and guide directors in managing choirs in Ghana, highlighting the potential for cultural enrichment and artistic excellence within this unique context.&#x0D;
Geometric Shape Characterisation Based on a Multi-Sweeping Paradigm

The characterisation of geometric shapes produces their concise description and is, therefore, important for subsequent analyses, for example in Computer Vision, Machine Learning, or shape matching. A new method for extracting characterisation vectors of 2D geometric shapes is proposed in this paper. The shape of interest, embedded into a raster space, is swept several times by sweep-lines having different slopes. The interior shape’s points, being in the middle of its boundary and laying on the actual sweep-line, are identified at each stage of the sweeping process. The midpoints are then connected iteratively into chains. The chains are filtered, vectorised, and normalised. The obtained polylines from the vectorisation step are used to design the shape’s characterisation vector for further application-specific analyses. The proposed method was verified on numerous shapes, where single- and multi-threaded implementations were compared. Finally, characterisation vectors, among which some were rotated and scaled, were determined for these shapes. The proposed method demonstrated a good rotation- and scaling-invariant identification of equal shapes.
Efficient Diversification for Recommending Aggregate Data Visualizations

Visual data exploration is ubiquitous in nearly every industry and organization to support discovering data-driven actionable insights. However, unlocking those insights requires analysts to manually construct a prohibitively large number of aggregate queries and visually explore their results, looking for those valuable and insightful visualizations. Such a challenge naturally motivated the development of novel solutions that automate the visual exploration process, and recommend to analysts those particular queries that best visualize their data and reveal interesting actionable insights. In such automated solutions, there is a clear need for providing analysts with a diversified and concise set of recommended visualizations, which cover and represent a large combinatorial high-dimensional space of possible visualizations. However, directly incorporating existing diversification methods leads to a “process-first-diversify-next” approach, in which all possible data visualizations are generated first through executing a large number of aggregate queries. To address this challenge and minimize the incurred query processing costs, in this work, we propose novel optimization techniques for the efficient diversification of recommended insightful visualizations. The key idea underlying our proposed techniques is to identify and eliminate the processing of a large number of low-utility insignificant visualizations. Meanwhile, for the potentially high-utility insightful visualizations, shared multi-query optimization techniques are proposed for further reduction in data processing cost. Our extensive experimental evaluation on real datasets demonstrates the performance gains provided by our proposed techniques, in terms of minimizing the query processing cost (i.e., efficiency), as well as maximizing the quality of recommendations (i.e., effectiveness).
A non-invasive method for the cataloguing and authentication of surveillance video using on-camera blockchain participation, machine learning and signal analysis

Presented here is a non-invasive system capable of cataloguing, identifying, and authenticating surveillance video throughout its lifecycle, with particular application to law enforcement. It delivers benefits similar to digital watermarking by utilising signal processing, machine learning object detection and a supporting blockchain infrastructure, and is proposed as a method for managing large volumes of data whilst overcoming issues associated with real time application of digital watermarking or hashing. A functional reference system is tested for effectiveness and its performance is analysed on test data, including evaluation of four search optimisation strategies, with a clear performance ranking shown. The system allows for the ability to identify and evaluate video integrity without requiring modification or access to the original video, making it of specific interest to law enforcement applications.
Integrating Usage Control Into Distributed Ledger Technology for Internet of Things Privacy

The Internet of Things brings new ways to collect privacy-sensitive data from billions of devices. Well-tailored distributed ledger technologies (DLTs) can provide high transaction processing capacities to IoT devices in a decentralized fashion. However, privacy aspects are often neglected or unsatisfying, with a focus mainly on performance and security. In this paper, we introduce decentralized usage control mechanisms to empower IoT devices to control the data they generate. Usage control defines obligations, i.e., actions to be fulfilled to be granted access, and conditions on the system in addition to data dissemination control. The originality of this paper is to consider the usage control system as a component of distributed ledger networks, instead of an external tool. With this integration, both technologies work in synergy, benefiting their privacy, security and performance. We evaluated the performance improvements of integration using the IOTA technology, particularly suitable due to the participation of small devices in the consensus. The results of the tests on a private network show an approximate 90% decrease of the time needed for the UCS to push a transaction and make its access decision in the integrated setting, regardless of the number of nodes in the network.
Query graph attention for video relation detection

As a bridge to connect vision and language, visual relations between objects, visual relation provide a more comprehensive visual content understanding beyond objects. Most previous works adopt the track-to-detect framework for video visual relation detection (VidVRD), which cannot capture long-term spatio- temporal contexts in different stages and also suffers from inefficiency. In this work, we propose a query-based method for video visual relation detection. Our model exploits graph structure to autoregressively generate relation graphs with spatio-temporal contexts and uses an attentional graph convolutional network to fuse the contexts. Experiments on benchmark datasets ImageNet-VidVRD demonstrate the accuracy of our method.
Improved Extreme Learning Machine Power Load Forecasting Based on Firefly Optimization Algorithms

Power load forecasting is a crucial safeguard for the reliable, efficient, and safe operation of the power system, which is connected to the smooth operation of society at all levels. In practical applications, extreme learning machines have advantages like quick learning rates and minimal training error, but they have poor stability and generalization skills. The learning of the sample lacks relevance because the weight matrix between the hidden layer and the output layer in the limit learning is chosen at random. Because of its simple algorithm flow and strong global optimization capabilities, the firefly algorithm helps to simplify the calculation process. To address the drawbacks of the extreme learning machine and combine its benefits, this paper integrates the firefly algorithm into limit learning and makes use of its potent optimization capabilities to determine the connection weight between the extreme learning machine’s hidden layer and output layer when the training error is minimal.
Budidaya Tanaman Sayuran Menggunakan Sistem Pendukung Keputusan Dengan Metode Profile Matching

Cultivating vegetable plants is very important for farmers. In the cultivation of vegetable plants, there are problems that are often faced by farmers, one of which is the lack of information about natural conditions and limited knowledge of farmers. so it is necessary to have an auxiliary media in the form of a system that can provide solution information at any time. Selection of plants that are not suitable will get unsatisfactory results. Farmers must know that what needs to be considered in cultivating vegetable crops is compatibility with natural conditions. This research builds a web-based decision support system software regarding vegetable cultivation that meets the criteria using the profile matching method as a tool to facilitate the decision-making process with several comparison criteria, namely: temperature, sunlight, rainfall and humidity, altitude, harvest time, marketability, susceptibility to disease, and level of care. This application is made web-based using PHP as the programming language and MySQL as the data storage. The purpose of this application is to provide effective and efficient information to farmers in making decisions and alternatives that are in accordance with the natural conditions of the area. The result of making this system is that farmers make the right decisions when cultivating vegetables based on the criteria.
THE IMPACT OF USING E-BOOK AND P-BOOK IN STUDENTS’ READING ACHIEVEMENT

This research aimed at finding out the significant different impact between using e-book and p-book in students’ reading achievement at the third grade of MTSN 4 Jeneponto. The researcher used a quasi-experimental research with two group pretest-posttest which was carried out online during the covid-19 pandemic. The sample was class IX A and IX 18 students. The instrument of this research was reading test. The result of the research showed that the value of t-test in terms of accuracy was 3,509. It was higher than the t-table namely 2, 11991 at the level of significant (p) = 0, 05 with degree of freedom (df) = 16. The differences between t-test and t-table value was very significant. It means that the Null Hypothesis (H0) was rejected and Alternative Hypothesis (H1) was accepted. Therefore, it can be concluded that using P-book is giving more positive impact than using E-book in students’ reading achievement at MTSN 4 Jeneponto.