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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Yueh-Cheng Chen, Hsin-Yi Hung, Cheng-Jui Chuang, Jen-Hui Hwang, Jenq-Neng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering University of Washington Seattle United States

ISBN: (纸本)9783031781247

Confronting the critical challenge of insufficient training data in the field of complex image recognition, this paper introduces a novel 3D viewpoint transformation technique initially tailored for label recognition. This technique can be used not only for data augmentation by generating synthetic data from any perspective but also transform photos taken from any angle into a frontal view, thereby reducing the complexity of the recognition task. Given the extensive use of wine-related applications with over 20 million users and the continuous publication of wine label datasets, we decided to focus this study on wine labels. This method enhances deep learning model performance by generating visually realistic training samples from a single real-world label image, overcoming the challenges posed by the intricate combinations of text and logos. Unlike classical Generative Adversarial Network (GAN) methods, which fall short in synthesizing such intricate content combinations and require a large amount of training data to become effective, our proposed solution leverages time-tested computer vision and image processing strategies. By using just a single monocular wine label image, we can expand our training dataset, thereby broadening the range of training samples for deep learning applications. This innovative approach to data augmentation circumvents the constraints of limited training resources. We then utilize the augmented training images through the Vision Transformer (ViT) architecture, performing one-shot recognition of existing wine labels in the training classes or future newly collected wine labels unavailable in the training. Experimental results show a significant increase in recognition accuracy over conventional 2D data augmentation techniques, indicating the potential for broader application in various labeling scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wine

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A Review on the Applications of Blockchain-Based Smart Contracts in the Smart Grid 4

A Review on the Applications of Blockchain-Based Smart Contr...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Saleh, Mohammad Alshaikh Khatri, Sunil P. Ghrayeb, Ali Refaat, Shady S. Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science United Kingdom

ISBN: (纸本)9798350306262

Investigations in industrial and computational research emphasize the progress of blockchain-enabled smart contracts due to their resilient features, which include decen-tralised transaction storage, autonomous contract regulation enforcement, and distributed trust creation. Blockchain-powered smart contracts can transform operational frameworks in various sectors aiming for superior service delivery. The advantages this technology brings involve a multitude of applications that span industries as cryptocurrencies, logistics systems, agricultural practices, real estate operations, and energy trading among others. Notably though one crucial emerging application is within smart grids (SG). The decentralization aspect of blockchain is one of the most significant leaps in computing for smart grids. The application of blockchain aligns with the vision of having a decentralized electric grid by distributing renewable energy sources. Therefore, numerous efforts are being conducted to examine the prospects for the relevance of smart contracts and blockchain to the smart grid system. To this end, this paper aims to review the essential elements of blockchain-based smart contracts to emphasize their current capabilities for enhancing the transition to a decentralized smart grid with proposals on future research directions. © 2024 IEEE.

关键词： Smart contract

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

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computers, Materials & Continua 2023年第11期77卷 2385-2406页

作者： Yuting Liu Hongwei Ding Zongshan Wang Gaurav Dhiman Zhijun Yang Peng Hu School of Information Science and Engineering Yunnan UniversityKunming650500China Department of Electrical and Computer Engineering Lebanese American UniversityByblos1401Lebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali140413India Department of Computer Science and Engineering Graphic Era(Deemed to be University)Dehradun248002India Research and Development Department Youbei Technology Co.Ltd.Kunming650500China

The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass *** its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population *** alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called ***,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and ***,an adaptive sine cosine mechanism is embedded to boost the global exploratory ***,the Cauchy mutation is utilized to prevent the loss of population diversity during *** validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test *** outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic *** comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other *** addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic *** analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.

关键词： Metaheuristic algorithms equilibrium optimizer Cauchy mutation robot path planning

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LLM-Powered Multi-Actor System for Intelligent Analysis and Visualization of IEC 61499 Control Systems 50

LLM-Powered Multi-Actor System for Intelligent Analysis and ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Xavier, Midhun Laikh, Tatiana Patil, Sandeep Vyatkin, Valeriy Department of Computer Science Electrical and Space Engineering Lulea Tekniska Universitet Sweden Department of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (纸本)9781665464543

This paper introduces an innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS. By integrating conversational AI technologies, this framework significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. The core of the system comprises specialized LLM actors that interact through a LangGraph-based multiactor framework, addressing various aspects of IEC 61499 control systems including PLC code analysis, SQL query execution, and data visualization. This integration enables operators to interact with the control system using natural language, significantly reducing technical barriers and enhancing the accessibility and usability of complex industrial systems. © 2024 IEEE.

关键词： Control system analysis

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MAINTAINING PLASTICITY IN CONTINUAL LEARNING VIA REGENERATIVE REGULARIZATION 3

MAINTAINING PLASTICITY IN CONTINUAL LEARNING VIA REGENERATIV...

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3rd Conference on Lifelong Learning Agents, CoLLAs 2024

作者： Kumar, Saurabh Marklund, Henrik Van Roy, Benjamin Department of Computer Science Stanford University United States Department of Electrical Engineering Department of Management Science and Engineering Stanford University United States

In continual learning, plasticity refers to the ability of an agent to quickly adapt to new information. Neural networks are known to lose plasticity when processing non-stationary data streams. In this paper, we propose L2 Init, a simple approach for maintaining plasticity by incorporating in the loss function L2 regularization toward initial parameters. This is very similar to standard L2 regularization (L2), the only difference being that L2 regularizes toward the origin. L2 Init is simple to implement and requires selecting only a single hyper-parameter. The motivation for this method is the same as that of methods that reset neurons or parameter values. Intuitively, when recent losses are insensitive to particular parameters, these parameters should drift toward their initial values. This prepares parameters to adapt quickly to new tasks. On problems representative of different types of nonstationarity in continual supervised learning, we demonstrate that L2 Init most consistently mitigates plasticity loss compared to previously proposed approaches. © 2024 CoLLAs. All Rights Reserved.

关键词： Self-supervised learning

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FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression 20

FedSaw: Communication-Efficient Cross-Silo Federated Learnin...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ying, Chen Li, Baochun Li, Bo University of Toronto Department of Electrical and Computer Engineering Canada Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350324334

Cross-silo federated learning (FL) is an emerging approach for institutions to collaboratively train a machine learning model without sharing their siloed data. However, it conventionally requires institutions to centrally store their clients' data, posing a threat to clients' privacy. This paper thus studies a preferable setting to leave data distributed to clients, where traditional FL is used not only across institutions but also among their clients. As this new setting inherits or even exacerbates the major problem of communication inefficiency in the traditional setting, we explore the feasibility of leveraging two compression techniques, pruning and quantization, to improve communication efficiency. Starting by applying off-the-shelf pruning and quantization mechanisms, we observe that they could largely reduce communication overhead with a negligible reduction, sometimes even a slight increase, in training performance. By mathematically analyzing the impact of compression on the performance of the trained model, we find that pruning and quantizing with a proper amount can offset possible performance degradation due to non-i.i.d data. Based on this finding, we propose FedSaw, a new cross-silo FL framework that can improve communication efficiency by adaptively tuning the pruning amount and quantizing updates throughout training. In our extensive evaluation with six benchmark datasets, FedSaw consistently outperformed its state-of-the-art competitors. It decreased the wall-clock training time and communication overhead used for converging to the target accuracy by up to 86.7% and 91.5%, respectively. © 2023 IEEE.

关键词： Efficiency

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Access Management in IoT: Implementing a Secure Access Control System 15

Access Management in IoT: Implementing a Secure Access Contr...

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15th International Conference on Communications, COMM 2024

作者： Neculai, Razvan-Sebastian Bogdan, Ioana-Corina Alexandru, Marian Robu, Dan-Nicolae Transilvania University of Brasov & Eviden Faculty of Electrical Engineering and Computer Science Department of Cybersecurity Braşov Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Braşov Romania

ISBN: (纸本)9798350355956

This paper aims to implement and use the Spring Security framework to secure and authenticate the connection between a web application and the ESP32 device. In the context of a web application, by analyzing the integration of the Spring Security framework, it investigates the ways in which it manages the complex process of authentication and authorization, thus providing a secure and protected connection to an IoT device. Additionally, this study investigates the integration and use of the RTOS operating system to manage physical access in an IoT environment, in particular by implementing the opening of a door using tags. The mechanism described above leads to an additional level of security and control over physical access in an IoT ecosystem. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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Mathematical analysis of AMRes: unlocking enhanced recognition across audio-visual domains

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International Journal of Information Technology (Singapore) 2024年 1-20页

作者： Zoughi, Toktam Deypir, Mahmood Department of Electrical and Computer Engineering Shariaty College Technical and Vocational University (TVU) Tehran Iran Faculty of Computer Engineering Shahid Sattari Aeronautical University of Science and Technology Tehran Iran

This research presents AMRes (Adaptive Windowed Convolutional Neural Network and Multiple Residual Network), a novel method that demonstrates remarkable resilience against overfitting, even in scenarios with limited training data. With meticulous design comprising up to 19 convolutional layers, it enables deep learning models to effectively extract and analyze vital information from input data while preserving it through deep layers. Through rigorous mathematical and empirical validation, the proposed method exhibits adept handling of intra- and inter-speaker variability, efficient training with limited data, and potential applicability beyond speech recognition in image-related applications, showcasing its generality. This paper establishes a solid foundation for advancements in multi-faceted recognition systems. The proposed method excels at proficiently modeling speech signals and achieving highly accurate speech recognition. Thorough evaluations utilizing well-established databases such as Switchboard, Timit, and FarsDAT for speech, and MNIST for image data underscore the method's versatility and efficacy across diverse data contexts. Comparative analysis against state-of-the-art techniques in speech recognition demonstrates the propose method's competitive and often superior performance. Notably, this method significantly reduces phoneme recognition errors by approximately 8% on notable datasets. This research provides a solid foundation for advancing the field and highlights the proposed method's potential in improving recognition systems, encouraging further exploration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Audio-Visual Domains Deep learning Formal Verification Limited Training Data Mathematical Analysis Mathematical Foundations Speech recognition

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