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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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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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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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Unveiling the Intricacies of Cyber Harassment Intentions on Social Media Platforms 3

Unveiling the Intricacies of Cyber Harassment Intentions on ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Shraya, Bejgum Reddy Dopathi, Hari Charan Anitha, G. Pattanaik, Balachandra Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Wallaga University Nekemte Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350381931

Cyberbullying is a severe issue that impacts teens and adults alike. Errors like hopelessness and suicide have resulted from it. The demand for material on social media platforms to be regulated is growing. In the work that follows, machine learning and natural language processing are used to construct a model to identify cyberbullying in text data. The program leverages data from two different types of cyberbullying: personal attacks on Wikipedia forums and tweets containing hate speech. To determine the optimal approach, three feature extraction techniques and four classifiers are examined. The method produces more than 80% accurate data from Wikipedia and more than 90% correct data from Twitter. © 2024 IEEE.

关键词： Machine learning

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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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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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Assessing the Suitability of Software Tools for System-Theoretic Process Analysis of Nuclear Instrumentation and Control Systems 29

Assessing the Suitability of Software Tools for System-Theor...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： King, Akira Ovsiannikova, Polina Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350361230

Modernization of currently operational nuclear power plants is becoming increasingly important to maintain their performance and safety. Ensuring the safety of newer Instrumentation and Control (I&C) systems used in modernization efforts requires hazard analysis techniques suitable for the analysis of complex and software-heavy systems. System-Theoretic Process Analysis (STPA) has proven to be a suitable hazard analysis method for these complex I&C systems, however, its practical use is still often limited by its labor-intensive and time-consuming nature, partially due to the limitations of the tools used to perform the analysis: common Office tools such as Microsoft Excel or Visio. Conducting an STPA analysis could be simpler and more attractive with software tools specific to the method. This work introduces the requirements for these software tools and lays the foundation for further work, in which software tools will be evaluated against these requirements. © 2024 IEEE.

关键词： Nuclear power plants

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Behaviors Speak More: Achieving User Authentication Leveraging Facial Activities via mmWave Sensing 24

Behaviors Speak More: Achieving User Authentication Leveragi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Jiang, Chenxu Yu, Sihan Fu, Jingjing Lin, Chun-Chih Zhu, Huadi Ma, Xiaolong Li, Ming Guo, Linke Department of Electrical and Computer Engineering Clemson University ClemsonSC United States Department of Computer Science and Engineering The University of Texas at Arlington ArlingtonTX United States

ISBN: (纸本)9798400706974

Human faces have been widely adopted in many applications and systems requiring a high-security standard. Although face authentication is deemed to be mature nowadays, many existing works have demonstrated not only the privacy leakage of facial information but also the success of spoofing attacks on face biometrics. The critical reason behind this is the failure of liveness detection in biometrics. This work advances most biometric-based user authentication schemes by exploring dynamic biometrics (human facial activities) rather than traditional static biometrics (human faces). Inspired by observations from psychology, we propose the mmFaceID to leverage humans' dynamic facial activities when performing word reading for achieving robust, highly accurate, and effective user authentication via mmWave sensing. By addressing a series of technical challenges of capturing micro-level facial muscle movements using a mmWave sensor, we build a neural network to reconstruct facial activities via estimated expression parameters. Then, unique features can be extracted to enable robust user authentication regardless of relative distances and orientations. We conduct comprehensive experiments on 23 participants to evaluate mmFaceID in terms of distances/orientations, length of word lists, occlusion, and language backgrounds, demonstrating an authentication accuracy of 94.7%. We also extend our evaluation in a real IoT scenario. By speaking real IoT commends, the average authentication accuracy can reach up to 92.28%. © 2024 Copyright is held by the owner/author(s).

关键词： Biometrics

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A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

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IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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