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arXiv 2024年

作者： Svystun, Serhii Melnychenko, Oleksandr Radiuk, Pavlo Savenko, Oleg Sachenko, Anatoliy Lysyi, Andrii Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi29016 Ukraine Department of Computer Science Khmelnytskyi National University Khmelnytskyi29016 Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil46020 Ukraine Department of Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom26600 Poland

The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model’s accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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可信联邦学习进化优化算法综述

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engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization Data-driven optimization Bayesian optimization

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms

Efficient Resource Management in IoTs Using Evolutionary and...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Keshav Dev Gupta Korhan Cengiz Charu Awasthi Siva Akash Ramini Shyamapriya Chatterjee M Pavithra Department of Computer Science Engineering Poornima College of Engineering jaipur India College of Information Technology University of Fujairah UAE Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Department of information technology JSS academy of Technical Education Noida India Department of School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh Department of Information Technology Narula Institute of Technology Kolkata West Bengal Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems.

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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Cryptography, informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Roni Reza Abdullah Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Informatics Engineering National Institute of Science and Technology Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.

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Online Resource Allocation for User Experience Improvement in Heterogeneous MEC Systems

Online Resource Allocation for User Experience Improvement i...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weiya Ni Jingwen Tong Liqun Fu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Mobile edge cloud (MEC) has emerged as a critical technology for enabling low-latency and real-time mobile device applications. However, an efficient resource allocation framework for improving the user experience in MEC with heterogeneous users is still missing, especially considering the recent sparks of AI-generated content applications. This paper proposes a double-closed-loop online resource allocation (DORA) framework for user experience improvement. This framework employs inner and outer loops to construct the optimal online allocation strategy and recommend a suitable strategy for different types of users, respectively. Based on the DORA framework, we put forth OR2A-HetU, an Online Resource Recommendation and Allocation algorithm for Heterogeneous Users, to solve this resource allocation problem. The OR2A-HetU algorithm proceeds sequentially and can converge to the optimal solution when the time horizon is sufficiently large. The numerical results show that the proposed algorithm outperforms the baseline algorithms, and the user complaint rate decreases from 48.8% to 27.5% when the available resources increase.

关键词： Multi-access edge computing User experience Stability analysis Real-time systems Mobile handsets Resource management Sparks Low latency communication Optimization Numerical stability

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization

A Framework for Democratizing Open-Source Decision-Making us...

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Computing and informatics (ICCI), International Conference on

作者： Khalifa H. Afifi Khaled M. Fouad Heba K. Aslan Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computers and Engineering New Mansoura University Egypt Informatics Department Electronics Research Institute (ERI) Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairro Egypt

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general.

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms 15

Enhancing Security and Scalability of Metaverse with Blockch...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Goyal, Aarti Rajawat, Kavita Raboaca, Maria Simona Verma, Chaman Mihaltan, Traian Candin School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Mind Intellect Technology Indore India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (纸本)9798350321388

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world. © 2023 IEEE.

关键词： Scalability

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Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong

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Current Medical Science 2022年第1期42卷 226-236页

作者： Zi-xiao WANG James NTAMBARA Yan LU Wei DAI Rui-jun MENG Dan-min QIAN Department of Medical Informatics School of MedicineNantong UniversityNantong 226001China Department of Computer Science College of Engineering and Computing SciencesNew York Institute of TechnologyNew York 10023USA Department of Computer Science College of Overseas EducationNanjing University of Posts and TelecommunicationsNanjing 210023China Department of Epidemiology School of Public HealthNantong UniversityNantong 226019China Artificial Intelligence Laboratory Center De Montfort University of LeicesterLeicester LE1 9BHUnited Kingdom

Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

关键词： influenza prediction data-driven Support Vector Machine Discriminant Analysis Ensemble Classifier

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lucas Ribeiro Hélder P. Oliveira Xiao Hu Tania Pereira INESC TEC INESC TEC - Institute for Systems and Computer Engineering Technology and Science FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP - Faculty of Science University of Porto Porto Portugal Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Nell Hodgson Woodruff School of Nursing Emory University Atlanta USA INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.

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Accidental Face Recognition and Detection Using Machine Learning

Accidental Face Recognition and Detection Using Machine Lear...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ashish Sharma Piyansh Agrawal Krishan Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura UP India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.

关键词： Resistance Technological innovation Road accidents Social networking (online) Smart cities Face recognition Roads

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