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20th IEEE International Conference on Cognitive informatics and Cognitive Computing, ICCI*CC 2021

作者： Schweikert, Christina Shimojo, Shinsuke Zhang, Zihan Tato, Jonida Hendsey, Rebecca Hsu, D. Frank Mathematics and Science St. John's University Division of Computer Science Queens NY United States California Institute of Technology Division of Biology/Computation and Neural Systems PasadenaCA United States Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Science New YorkNY United States

ISBN: (纸本)9781665421195

Preference choices of a subject when given two human face images is a complex cognitive process. One way to detect a subject's preference is by recording and examining the subject's eye movement gaze sequence before his/her decision. Combinatorial fusion analysis / algorithm (CFA) is a new approach for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD) measure. In this paper, we apply CFA to the study of the eye movement gaze sequences for preference detection. In particular, we use the RSC function to characterize each of the attributes and the CD to measure the diversity between attributes. Our results demonstrate that weighted combination of attributes using diversity strength, computed using average CD's, improves the preference detection. © 2021 IEEE.

关键词： Eye movements

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Mamba vision models: Automated American sign language recognition

Franklin Open

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Franklin Open 2025年 10卷

作者： Altaher, Ali Salem Bang, Chiron Alsharif, Bader Altaher, Ahmed Alanazi, Munid Altaher, Hasan Zhuang, Hanqi Department of Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca Raton 33431 FL United States College of Medicine Ibn Sina University of Medical and Pharmaceutical Science Baghdad Iraq Department of Computer Science and Engineering College of Telecommunication and Information Technical and Vocational Training Corporation Riyadh 12464 Saudi Arabia Electronic Computer Center Al-Nahrain University Jadriya Baghdad Iraq Business Informatics Department College of Business King Khalid University Abha Saudi Arabia Information and Communication Technology Department Baghdad Institute of Technology Middle Technical University Baghdad Iraq

Historically, individuals with hearing impairments have faced significant challenges in effective communication due to the lack of adequate resources. Recent technological advancements have spurred the development of innovative tools aimed at enhancing the quality of life for those with hearing disabilities. This research focuses on the application of Vision Mamba Models for classifying hand gestures representing the American Sign Language (ASL) alphabet, with a detailed comparative analysis of its performance against 13 deep learning architectures. The Vision Mamba Models, namely, Vision Mamba and Remote Sensing Mamba, were trained on a substantial dataset comprising 87,000 images of ASL hand gestures, and through iterative fine-tuning of their architectural parameters, the models’ accuracy and performance were optimized. Experimental results demonstrated that the Mamba Vision Models outperformed all other models previously examined in this context scoring exceptional accuracy rate that exceeded 99.98%, with less architectural complexity. These findings highlight the potential of deep learning technologies, particularly the Mamba vision models, in advancing assistive technologies, offering a sophisticated and highly accurate tool for interpreting ASL hand gestures and promising improved communication and accessibility for individuals with hearing impairments. © 2025 The Authors

关键词： American sign language Deep learning Remote sensing mamba Transfer learning Vision Mamba.

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

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International Conference on Communication and Electronics systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection computer crime Industrial Internet of Things

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Role of 5G and 6G Technologies in Metaverse, Quality of Service Challenges and Future Research Directions

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IEEE Network 2024年

作者： Adil, Muhammad Abulkasim, Hussein Ali, Aitizaz Song, Houbing Farouk, Ahmed Jin, Zhanpeng University at Buffalo The State University of New York Department of Computer Science and Engineering BuffaloNY14260 United States University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates New Valley University Faculty of Science El-Kharga72511 Egypt Asia Pacific University School of Technology Malaysia University of Maryland Department of Information Systems Baltimore County BaltimoreMD21250 United States Hurghada University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada83523 Egypt South China University of Technology School of Future Technology Guangdong Guangzhou511442 China University at Buffalo Department of Computer Science and Engineering BuffaloNY14260 United States

The popularity of Metaverse technology has grown in recent years due to its ability to connect real-world and virtual-world objects to establish a social ecology paradigm, in which various applications collaborate towards a common objective, such as the digital transformation of the world. However, this emerging technology faces significant challenges in terms of security, scalability, interoperability, and communication, which can impact Quality of Service (QoS) metrics like latency, throughput, handover with mobility, communication cost, platform integration, and data privacy, etc. The literature contains several survey papers to familiarize readers, students, researchers, and enterprise stakeholders with these challenges. However, we did not find a single comprehensive review paper that discusses how security, scalability, interoperability, and communication affect the QoS metrics in the Metaverse, especially in the context of 5G and 6G technologies. Therefore, our aim is to provide an overview of the latest literature by considering the requirements of Metaverse technology in the era of futuristic communication technologies such as 5G and 6G technologies. Moreover, we identified and analyzed the contributions and limitations of current literature to highlight the key research challenges that require the attention of the research community to ensure their redressal. Furthermore, we acknowledge that this is the first survey paper that provides an overview of the QoS metrics of future 5G and 6G-enabled Metaverse technology under one umbrella. © 1986-2012 IEEE.

关键词： 5G mobile communication systems

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X2-Softmax: Margin Adaptive Loss Function for Face Recognition

arXiv

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arXiv 2023年

作者： Xu, Jiamu Liu, Xiaoxiang Zhang, Xinyuan Si, Yain-Whar Li, Xiaofan Shi, Zheng Wang, Ke Gong, Xueyuan School of Intelligent Systems Science and Engnieering Jinan University Guangdong Province China College of Information Science and Technology Jinan University Guangdong Province China Department of Computer and Information Science Faculty of Science and Technology University of Macau China

Learning the discriminative features of different faces is an important task in face recognition. By extracting face features in neural networks, it becomes easy to measure the similarity of different face images, which makes face recognition possible. To enhance the neural network’s face feature separability, incorporating an angular margin during training is common practice. State-of-the-art loss functions CosFace and ArcFace apply fixed margins between weights of classes to enhance the inter-class separation of face features. Since the distribution of samples in the training set is imbalanced, similarities between different identities are unequal. Therefore, using an inappropriately fixed angular margin may lead to the problem that the model is difficult to converge or the face features are not discriminative enough. It is more in line with our intuition that the margins are angular adaptive, which could increase with the angles between classes growing. In this paper, we propose a new angular margin loss named X2-Softmax. X2-Softmax loss has adaptive angular margins, which provide the margin that increases with the angle between different classes growing. The angular adaptive margin ensures model flexibility and effectively improves the effect of face recognition. We have trained the neural network with X2-Softmax loss on the MS1Mv3 dataset and tested it on several evaluation benchmarks to demonstrate the effectiveness and superiority of our loss function. The experimental code and trained model are published in https://***/xujiamu123/X2-Softmax/tree/main. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Companion Recognition System for Amnesia Affected Persons

Companion Recognition System for Amnesia Affected Persons

引用

Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： G Ignisha Rajathi R Devin James N Raja Balaji Subhash R Hari Krishnan G Yogeeshwaran Dept of Information Technology Manipal Institute of Technology Bengaluru Engineering Manipal India Dept of CSE Sri Krishna College of Engineering Coimbatore India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Dept of CSE and Business Systems Sri Krishna College of Engineeirng and Technology Coimbatore India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Recogni is an AI-based application designed for the purpose of enhancing the quality for individuals who are suffering from amnesia and other memory-related challenges. This application addresses key challenges in memory management, daily task organization, and social integration through features like geofencing reminders, family sharing, and interactive memory aids and turns out to be a good companion for them. To protect user data, recogni’s progressive development integrates advanced functions such as location-based notes, voice-controlled reminders, and multi-language support which are all reinforced by robust security measures. Wearable device integration, predictive analytics, and emergency assistance, are promised to be added in the ongoing development, making Recogni a versatile tool for memory care [1].

关键词： Measurement Data security Memory management Refining Collaboration Transforms Speech recognition Organizations Wearable devices Predictive analytics

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Impact of Neural Network Depth on Split Federated Learning Performance in Low-Resource UAV Networks

Impact of Neural Network Depth on Split Federated Learning P...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Houda Hafi Bouziane Brik Miloud Bagaa Adlen Ksentini NTIC Faculty Abdelhamid Mehri University Constantine Algeria Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE Universite du Quebec a Trois-Rivieres Trois-Rivieres QC Canada EURECOM Sophia-Antipolis France

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Training without sharing data is one of the drivers that makes Federated Learning (FL) more attractive, compared to centralized approaches. However, requiring each learner to train the full model may not be efficient, particularly for devices with restricted resources, such as those available in Unmanned Aerial Vehicles (UAVs). To address this issue, a variation of FL technique, specifically Split Federated Learning (SFL), has recently been proposed. Unlike FL, the key concept of SFL is to divide the layers of the neural network among the involved learners. Therefore, each individual client will train only a segment of the model (submodel) rather than the entire model. Clearly, this technique, besides data privacy, optimizes the utilization of computational resources, reduces client-side training time, and enhances model privacy. However, there are questions that require answers: How should we split the model? Shall we systematically divide it in half, or is there a more optimal approach? In this line of thought, this paper provides a detailed analysis of possible splitting schemes of a power consumption prediction model for UAV s. First, the SFL-enabled model is presented. Second, an experimental analysis is conducted in which different splitting alternatives are made and numerically analyzed to examine the influence of network layering on split federated learning performance.

关键词： Training Performance evaluation Wireless communication Data privacy Federated learning Computational modeling Neural networks

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A Novel Spatial–Temporal Analysis Approach to Pedestrian Groups Detection

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Procedia computer science 2022年 207卷 2364-2373页

作者： Claudia Cavallaro Giuseppe Vizzari Department of Mathematics and Computer Science University of Catania Viale Andrea Doria 5 Catania 95125 Italy Department of Informatics Systems and Communication University of Milano-Bicocca Viale Sarca 336/14 Milano 20126 Italy

The growing availability of geo–referred data describing human behaviour, at different scales and levels of granularity, represents an opportunity for the development and application of data analysis algorithms, whose usage can range from security, to traffic, to architectural design and planning, and even marketing. Focusing on pedestrian generated trajectories, the presence of groups within an analyzed population can influence overall dynamics, from microscopic perspective, and it can provide significant indications. Several approaches for video footage analyses are available, but they generally focus on microscopic features of videos and trajectories and they are generally not suited to scale to the analysis of relatively large datasets of trajectories. The present work proposes a novel approach to spatial–temporal analysis of pedestrian trajectories aimed at detecting groups of pedestrians within large datasets and having minimal assumptions on the nature of these groups.

关键词： Groups Analysis of pedestrian trajectories Clustering

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Modeling Prototypical Preference Behavior and Diversity using Rank Score Characteristic Functions 21

Modeling Prototypical Preference Behavior and Diversity usin...

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21st IEEE International Conference on Cognitive informatics and Cognitive Computing, ICCI*CC 2022

作者： Schweikert, Christina Shimojo, Shinsuke Glasser, Hannah Hendsey, Rebecca Alsaber, Rami Hsu, D. Frank St. John's University QueensNY United States California Institute of Technology Division of Biology and Biological Engineering / Computation and Neural Systems PasadenaCA United States Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Science New YorkNY United States

ISBN: (纸本)9781665490849

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling. © 2022 IEEE.

关键词： Eye movements

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Routing of Data Between the Nodes in Mobile Adhoc Networks using Machine Learning Modelling

Routing of Data Between the Nodes in Mobile Adhoc Networks u...

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2022 International Conference on Electronics and Renewable systems, ICEARS 2022

作者： Ramesh Babu, P Ranjan Nayak, Nihar Meenakshi, K Das, Saswata Selvakanmani, S. Ananda Kumar, K S Wollega University College of Engineering Technology Department of Computer Science Nekemte Ethiopia Department of Mca Andhra Pradesh Chittoor India Department of Mathematics Bengaluru India Cognizant Technology Solutions India Pvt. Ltd. West Bengal Kolkata India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Dilla University School of Computing and Informatics College of Engineering and Technology Snnpr Dilla Ethiopia

ISBN: (纸本)9781665484251

The routing of data between the packets in mobile adhoc networks has severe limitations that involves communication and computational overhead, which has to be reduced for effective transmission of data packets. In this paper, we develop a machine learning assisted routing mechanism that has three different modules to serve the purpose of reduced overhead during routing. The study uses genetic algorithm to resolve the routing constraints between the source and destination nodes. The modelling is specifically designed to overcome the limitations associated with the generation of overheads. The simulation is conducted to test the efficacy of the models against various existing mechanisms. The results of simulation shows that the proposed method achieves higher rate of packet delivery rate, throughput and reduced delay. © 2022 IEEE.

关键词： Machine learning

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