Reviewerly/exHarmony huggingface dataset:
Real-Time Hand Gesture Recognition for Device Control: An OpenCV-Based Approach to Shape-Based Element Identification and Interaction

A modern, inventive, and natural method of nonverbal communication is offered by a hand gesture recognition system. It can be used in many different contexts, including sign language and human-computer interaction. This implementation's objective is to discuss a novel method of hands gestures recognition based on the identification of certain shape-based elements. A single camera is used to record the user's gesture and feed it into the system. The development of a system that can recognise certain human gestures and use them to transfer data for device control is one of the main goals of gesture recognition. Real-time gesture recognition enables users to control computers by making certain gestures in front of a video monitor. The development of a system that can recognise certain human gestures and use them to communicate data for device control is one of the main objectives of gesture recognition A user can operate a computer by making a certain gesture in front of a video camera that is attached to it by implementing real-time gesture recognition. In this project, we'll build a hand gesture volume control system with the help of the OpenCV module. Here you can use hand gestures to operate the system without a keyboard or mouse.
A Comparative Study of Machine Learning and Deep Learning Algorithm for Handwritten Digit Recognition

Handwritten digit recognition is a complex task in various real-world applications such as bank check processing, postal automation recognition etc. In recent time, different kind of learning algorithms are used to analyze and resolve this issue. This paper presents a comparative study on machine learning and deep learning algorithms such as Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, LeNet-5, and YOLOv7. Publically available MNIST, DIDA, and MNIST MIX handwritten digit dataset were used in experimental work. The objective of this study is to find the best algorithm which can give an acceptable accuracy. For measuring the accuracy various parameters such as precision, recall, specificity, and F-measure were used. On the basic of experimental results, YOLOv7 has achieved detection accuracies above 98.3% and LeNet 5 has detection accuracy of 99.15%. It has been observed that deep learning algorithms have achieved higher accuracy than machine learning algorithms.
Building Trust in the IoT: A Comprehensive Review of Device Authentication Approaches

IoT (Internet of Things) is a technology which is a part of our daily lives. So, it is necessary to secure and privatise the data of all IoT devices and apps. The authentication procedure is done to solve this crucial issue from a security perspective. In this paper, we have discussed multiple authentication techniques which protect IoT -based device data from intruders. It needs to be protected against numerous attacks like Man-in-the-Middle (MITM), DoS, replay, impersonation, real-time, and coordinated attacks. We have also discussed all authentication schemes i.e., Multi-Factor, Cryptographic, Biometric, Token-Based and Password-Based Authentication each having unique approaches and being used in a variety of applications.
Blockchain-Enabled Consensus Mechanisms for Data Integrity and Security in Edge and Cloud Computing Environments

The integrity and security of data have been growing more crucial as the quantity of data generated and maintained expands. The open-source characteristic of edge systems raises the risk of improper access to the data preserved on the system and a slew of security issues like the release of consumers' data security and a breakdown of integrity. The present security mechanism primarily confirms the individuality of the user or encrypted the data integrity of the information posted by the port, yet the function of confirmation is primarily believed by the system, and it is not possible to prevent the collaboration of the edge system with fraudulent activity. The blockchain serves a crucial part in protecting identities by addressing present technical difficulties such as scaling, non-tampering, reliability, data management, and openness. This work designs and implements a consensus mechanism, builds an edge cloud data integrity surveillance system, and proposes blockchain-based proof norms that accomplish depended on proof without depending on an impartial third party. In addition, a blockchain-based model framework for edge cloud computing data integrity security is being built to avoid data loss. The outcomes of the security proof and testing proof demonstrate that the blockchain-based consensus mechanism is capable of safeguarding in opposition to 3 cloud assistance supplier assaults, has better calculation, and minimizes the expense of storage while safeguarding the user's data integrity.
iPIC-XAI: Improving PIC-XAI for Enhanced Image Captioning Explanation

Image captioning task with its complexity has taken advantage of the recent developments in Deep learning (DL). However, DL-models are fundamentally abstruse and explaining their behaviour is a challenge. In this paper we present an algorithm to explain an image captioning model behavior. We enhanced PIC-XAI performance by introducing various components; (1) we utilize CLIP multimodal similarity for more efficiency, (2) we consider the query dependency tag as a clue for elements with bigger size, (3) we provide an algorithm to automatically set the preprocessing blurring kernel size, and (4) we use a similarity comparison technique to get more relevant answers. Additionally, we provide an improved version of XIC metric, aiming for more consistent objective evaluation for XAI methods in image captioning field <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at https://github.com/modafarshouha/PIC-XAI..
Evaluation of ICT Security Implementation in Banks: Bangladesh Perspective

Nowadays, banks are the foundation of a strong economy in any country, whereas information and communication technology (ICT) has become the heart of any bank. As a result, one of the major challenges has become ensuring the security of the banks. Whenever ICT security comes up, the foremost thing that needs to be considered is” PAM, PCI DSS, SIEM, and ICT risk management,” which measures are improving immensely day by day. Under the ICT security guidelines of Bangladesh Bank, all banks are implementing many security measures to ensure ICT security standards. This paper mainly emphasis on the implementation of ICT security measures such as PAM, PCI DSS, SIEM, and ICT risk management through surveys from different banks. It also focuses on the challenges and future prospects of ICT security measures
An Effective FL-CNN Based Data Securing Model for Heart Disease Prediction

Cardiovascular disease is the leading cause of death worldwide, according to the WHO. Coronary heart disease is most dangerous. 2015 saw 360,000 US heart attack deaths. Effective heart disease treatment prevents global deaths. An updated FL-CNN model improved cardiac disease diagnosis and prognosis for doctors and patients. Hospitals cannot disclose patient data for security and privacy reasons. Thus, centralizing data is hard. Federated Learning can train machine learning and deep learning models using massive volumes of distributed data. On the UCI Cleveland dataset, CNN with Federated Learning achieves 94.99% accuracy, while CNN with centralized learning achieves 97% accuracy.
A Systematic Survey on Breast Cancer Detection Using Machine Learning

Approximately 270,000 new cases of Breast cancer, the most common cancer in women, are reported each year, with instances identified in 2022. Software for detection is required to find it before it becomes dead because of this. The software can be created to identify this dangerous cancer and treat it before it results in the patient's death by using Machine learning algorithms. Additionally, Breast canceris the most common cancer among Indian women. A lady with breast cancerhas a 50% chance of survival. Many machine-learning techniques can be used to detect BC. This survey paper provides a summary of different ML techniques used for detecting breast cancer. And also provide a comparative analysis of different machine-learning techniques in the field of breast cancer detection.
Fostering reading competence in English as a second language students: exploring motivation, strategies, and contextual factors

This contribution is associated with the research project entitled "Improvement of pedagogical practices in educational institutions in the southern area of Manabí". Reading persists as one of the least popular practices among students of all educational levels in Ecuador, despite its contribution to strengthening communication skills in using a foreign language. This bibliographic review study presents the perspectives of educational practices applied worldwide and analyzes the elements such as motivation and success strategies that contribute to improving reading competence in their students. This work aims to recognize the factors that can influence students' reading comprehension abilities of English as second language (ESL) or as a foreign language (EFL) in various contexts. In pursuit of this objective, the authors reviewed 70 scientific articles available on the Scielo, Web of Science, and Scopus platforms to explain the characteristics of students of English as a second language, the understanding of reading practices, and finally, the review of the influential factors of the process. They concluded that motivation factors, reading strategies, and elements of the student's context affect ESL students' reading comprehension ability.
Penggunaan Metode Delone dan Mclean Untuk Menganalisis Kesuksesan Mulawarman Online Learning System (MOLS)

In 2019, the Mulawarman Online Learning System (MOLS) was created. All Mulawarman University students can use MOLS for support their study. The purpose of this reasearch is to determine the level of success in using the Mulawarman Online Learning System (MOLS) using the Delone and Mclean method. In this study using the Delone and Mclean method. Delone and Mclean are methods for measuring the success rate of users of an information system. Delone and Mclean have 6 (six) measurement variables including system quality, information&#x0D; User Satisfaction&#x0D; Delone and Mclean&#x0D; E-Learning quality, user satisfaction, use, service quality and net benefit. This research is quantitative data with a sample of 100 Mulawarman University student respondents. The results of this study indicate that the independent variables, namely system quality, information quality and service quality can affect the dependent variable, namely user satisfaction, use and net benefits.
Image recognition of bearing fault based on parameterized AlexNet with interpretable decision fusion

A bearing fault diagnosis technique based on interpretable decision fusion and parameterized AlexNet is developed in order to address the issues of the standard depth model of rolling bearing fault detection, specifically regarding the network’s weak anti-interference capabilities and the challenging selection of training parameters. Firstly, to improve the anti-noise performance of the network, Gaussian white noise is implanted into the original data. The original data undergoes continuous wavelet transform processing to create a two-dimensional time-frequency map, which is then fed into a convolutional neural network to diagnose bearing faults. The Seagull optimization algorithm is utilized to optimize the training parameters, resulting in obtaining network training parameters with robust feature extraction ability. Then we train multiple networks to make decision fusion, and finally give the interpretability of the networks based on Grad-CAM. Verified by the case of Western Reserve University’s open-bearing fault data set, the method proposed in this paper has a good effect on fault image recognition accuracy and interpretability and can be popularized in engineering applications.
Temporal Feature Mining in Dynamic Graph of Brain Connectivity Data

In recent years, the graph feature mining method of brain connection data based on graph theory has been regarded as a popular and universal technology in the field of neuroscience. How to mine valuable information from brain connection data has become a research hotspot. Current research shows that the pathogenic factors of attention deficit and hyperactivity disorder (ADHD) may be caused by the abnormal connection between brain network structures. In order to find out the pathogenic factors of ADHD patients, we also carried out frequent sub-graph mining on the connectivity graph data of brain functional network. By constantly adjusting the sup-port threshold, all the subgraphs of ADHD patients and healthy control group were mined, and the differences in brain region connectivity were successfully found out. By combining the recently introduced neural document embedding model with traditional pattern mining techniques, we regard the brain network connection structure graph as the document and frequent subgraph as the atomic unit of the embedding process. By learning the mapping, each graph can be mapped to a D-dimensional continuous vector. The mapping needs to capture the similarity between the graphs. Feature vectors can be used as the direct input of graph classification in many traditional machine learning methods. Finally, support vector machine in machine learning is used to verify the accuracy of classification, and the results show that the accuracy is high.
Evaluation on the generalization of coded features across neural networks of different tasks

Recent advances in deep neural networks (DNNs) for computer vision tasks have made intelligent analysis on edge devices more prevalent and practical. To better distribute computational load between edge devices and the cloud, a novel deep learning deployment strategy called Collaborative Intelligence (CI) has been proposed. In this strategy, features extracted from edge devices are first compressed and then transmitted to the cloud. However, it is unclear whether these compressed features have enough information to perform diverse downstream tasks. This paper focuses on the generalization of compressed features from one neural network among other object detection and instance segmentation task networks. We first propose a scheme to evaluate the generalization of features and further perform experiments on feature compression. Our experiments show that the extracted features contain enough information for other task networks and feature compression scheme for multi-task networks offers a 82.04% average bitrate saving compared to VVC.
The Potential of RISC-V Platform in Financial Computing on Option Pricing and Energy Efficiency

The fifth version of the Reduced Instruction Set Computer (RISC-V) is a popular instruction set architecture (ISA) featured for low energy consumption. Currently, a growing number of industrial applications are based on RISC-V platforms, especially Internet of Things (IoT) devices. Those applications pursue low power and just sufficient computing capacity. However, low power consumption shall not be directly regarded as weak in computation. Recent advancement in RISC- V shows the potential for building a computing platform capable of handling tasks requiring considerable computing power. Traditional financial computing platforms are generally based on Complex Instruction Set Computers (CISC), like x86 platforms. As green computing is sweeping, it is meaningful to handle financial computing tasks with less energy consumption. To explore the potential of RISC- V in financial computing, we set up a typical financial computing task - American option implied volatility calculation, and examine the performance and power consumption of x86 and RISC-V platforms. The result shows that the RISC-V CPU is sufficient for some financial computing scenarios considering actual requirements. A heterogeneous computing system composed of x86 and RISC-V platforms could significantly improve energy efficiency.
An Extended Sparse Bayesian Learning for Recovery of High-Dimensional Sparse Vector

The sparse Bayesian learning (SBL) is an essential technique in sparse recovery problems but is subject to the cubic complexity of matrix inversion. Existing fast algorithms evaded this problem at the cost of rapid performance degrades with non-sparse sampling matrix or high-dimensional sparse vector. In this letter, an extended sparse Bayesian learning (E-SBL) algorithm was developed. Specifically, it further takes the sparse vector and sampling matrix into various groups and separately considers their corresponding posterior distributions by mean-field variational inference (MFVI). It is found that the E-SBL will be inverse-free if treating each sparse element as a group. As a result, it outperforms other state-of-the-art fast sparse recovery methods for high-dimensional sparse vector.
Research On Privacy Protection Scheme For Educational Data Based On Blockchain

This study aims to explore the issue of educational data privacy, based on the “privacy computing + blockchain” technology system, and analyze its operating mechanism from the perspective of the entire life cycle. With the advancement of educational digitization, privacy protection is facing huge challenges. This paper analyzes the various educational data subjects and their relationships. In order to cope with this challenge, the article starts from the technical direction of privacy computing and introduces the technical process of “privacy computing + blockchain”. The constructed technical system integrates the security of blockchain and the protection of privacy computing, with special emphasis on data security and trusted computing, thus promoting the harmonious and longterm development of the educational data society.
RACCS: Real-Time Cloud-Edge Collaborative Anomaly Analysis System with Compressive Sensing Joint Optimization

In the context of the Internet of Things (IoT), ensuring public safety relies heavily on the real-time detection of anomalies in monitoring systems. Thus, video anomaly analysis has garnered significant attention. We propose a collaborative anomaly analysis system(RACCS) that leverages cloud-edge-end computing. The system receives real-time video packets generated by cameras through edge gateways and utilizes load balancing algorithms to determine the destination node, reducing system latency. Using the MQTT protocol of the IoT, the video packets are transmitted to edge nodes or the cloud for inference. To prevent highly confident video packets from being transmitted to the cloud for secondary inference, we deploy a lightweight model on the edge nodes for initial inference, thereby reducing bandwidth consumption and latency. To achieve this goal, we adopt specific quantization methods for both the input video packets and complex cloud model parameters. This converts the complex cloud model into a lightweight model, reducing model storage and improving inference speed. We also propose a joint optimization framework for compressive sensing and anomaly classification that meets both high compression rates for video packets and high accuracy for anomaly classification. We further conduct a series of experiments to determine the optimal configuration and compare our approach with other mainstream methods, demonstrating the superiority of our system.
Blockchain-Based Data Verification and Quality Assessment Framework in Data Trading Systems

In recent years, data trading has become an effective means for obtaining data resources and mitigating the data island phenomenon in fields like big data and artificial intelligence. However, traditional centralized data trading models are hampered by issues such as single-point failures and data leaks. Existing solutions have limitations, like the inability to perform trustworthy data verification and quality assessment before trading. Moreover, trading based on raw data can lead to reselling issues. To tackle these challenges, we introduce a novel framework and protocol that uses Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARKs) for secure and private data verification. Our approach also employs accumulators and other technologies to set new standards for objective data quality assessment. The experimental results indicate that our solution excels in computational efficiency and storage cost, effectively addressing data verification and quality assessment issues in data trading.
Enhancing Cross-Border Data Sharing in Blockchain Networks: A Compliance-Centric Approach Ensuring Anonymity and Traceability

The rise of digital globalization necessitates robust cross-border data sharing mechanisms, presenting challenges in security, privacy, and regulatory compliance. This paper introduces a novel blockchain-based framework to address these challenges, facilitating secure and efficient data exchange while meeting diverse international compliance requirements. Our approach combines advanced cryptographic techniques with smart contracts to create a dual-layered blockchain architecture. The first layer provides participant anonymity through zero-knowledge proofs, and the second ensures traceable, compliant data transactions. This solution streamlines compliance with varied data protection laws, offering a transparent and privacy-preserving platform for global data sharing initiatives. It promises significant benefits for entities engaged in multinational operations, enhancing security, simplifying regulatory adherence, and protecting user privacy.
Improved YOLOv7 Based on Transformer for Object Detection in UAV-Captured Images

As the drone captures image targets at different flying altitudes, their scales may vary significantly, which can pose challenges for the object detection model to accurately detect them. Additionally, tiny objects in the image contain minimal information, making them difficult to distinguish from the background. To overcome these two challenges, we proposed a network architecture that aims to improve the accuracy of tiny object detection in drone images. Specially, we designed a tiny object detector(TOD) that can effectively extract features of tiny objects and distinguish between tiny object features and image background. Furthermore, this TOD module contains a Convolutional Visual Attention Network (CVAN) to better focus on the regions of tiny objects. Experimental results demonstrate that the proposed method achieves mAP@.5 accuracy of 53.9% on the VisDrone2021-test-dev dataset and improves by 2.8 % compared to YOLOv7.
Specifics of thе transition то а new model higher education in russia: priorities аnd problems

The article is а chronological and substantive analysis of the stages of formation of the idea of а new Russian level system of higher education, the testing of which began in the fall of 2023. The continuity and novelty of this system, the regulatory framework for its implementation are shown. Iп addition, а description of the main aspects of the new higher education system is given. The positive aspects of its implementation are considered, as well as the risks and currently unresolved substantive, organizational, managerial and other aspects of the upcoming transformations.