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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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An Intelligent Vehicle Control System for Enhancing Road Safety Using Optimal Visible Light Communication Network

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Journal of Optical Communications 2023年第1期44卷 81-94页

作者： Begum, Shaeista Patil, Nagaraj B. Department of Computer Science and Engineering Government Engineering College Karnataka Raichur India

A visible light communication (VLC) provides potential and effective communication paradigm due to the demand of high data-rate applications. VLC networks, consisting of multiple light emitting diodes (LEDs) and it provides the low-cost high data-rate transmission to multiple users simultaneously in indoor environments. VLC has been recently introduced as a secure directional data transmission in vehicle to vehicle to provide an intelligent vehicle control system. However, the performance of this system is mostly affected by the collision of data transmission between different users. In this paper, we introduce an optimal visible light communication (OVLC) network that allows vehicles which have provides collision aware data transmission to improve the chance of transmitting information successively according to the network condition. Firstly, the next forwarding node is selected by the chaotic fish swarm optimization (CFSO) algorithm with the help of vehicle information's such as intensity of light, the distance and speed of neighboring vehicles. The second contribution is to illustrate the congestion control (CC) system for avoiding extra time due to the control packets exchange process. The optimal result is then forward to the source vehicle equipped device, which helps the driver to make a healthy to control vehicle and efficiently avoid or prevent road accidents under different circumstances. The results show that the proposed OVLC network performs very efficient than existing network in terms of quality metrics, such as throughput, delay, packet loss rate, energy consumption and fairness index. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Visible light communication

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An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

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Multimedia Tools and Applications 2025年 1-35页

作者： Alqahtani, Abdullah Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University KSA Al-Kharj Saudi Arabia

In this study, we propose a novel framework for detecting abnormal events in surveillance videos, a critical yet challenging task in security applications. This research introduces a robust and efficient solution for video anomaly detection, offering substantial improvements in surveillance systems' ability to detect abnormal events, thereby contributing to enhanced security measures in public spaces. The proposed framework utilizes a Multiscale Convolutional Autoencoder (MSCAE) that processes inputs from RGB, depth, and optical flow video clips, enhancing the detection accuracy in complex scenes characterized by varying object scales, aspect ratios, and occlusions. To address the challenge of noise and preserve edges in video data, we implement a two-pass bilateral smooth filtering method, which is effective for noise-invariant, edge-preserving image smoothing. For object detection within these complex scenes, an enhanced Faster R-CNN model is employed. This model's performance is further refined through transfer learning on a dataset specifically composed of abnormal event videos. We also introduce significant improvements to the region proposal network (RPN) of the Faster R-CNN, particularly in non-maximum suppression (NMS) and anchor generation techniques, to better detect anomalies in diverse and complex environments. Furthermore, the MSCAE is integrated with Long Short-Term Memory (LSTM) neural networks to classify the detected anomalies, creating an end-to-end solution for video anomaly detection. Hyperparameter optimization for our deep learning models is performed using the Chameleon Swarm Algorithm, ensuring optimal model performance. Our framework was rigorously tested on the CUHK Avenue dataset, where it achieved a remarkable 99.5% accuracy, significantly outperforming existing methods and demonstrating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Optical flows

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Analysis of facial emotion expression in eating occasions using deep learning

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Multimedia Tools and Applications 2023年第20期82卷 31659-31671页

作者： Yildirim, Elif Akbulut, Fatma Patlar Catal, Cagatay Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar

Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems. © 2023, The Author(s).

关键词： Emotion Recognition

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Proactive Crowdsourced Monitoring and Sensing With Expansible Activity Recognition Based on Internet of Things Localization

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IEEE Internet of Things Journal 2025年第11期12卷 17674-17686页

作者： Chen, Lien-Wu Liao, Chun-Wei Liu, Jun-Xian Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan

This article proposes a proactive crowdsourced monitoring and sensing (PCMS) framework with the designed Smart iBeacon device to accurately recognize the activities of an equipped target, exclusively customize the recognition model of a specific target, and actively trigger cooperative tracking of nearby smartphones for an abnormal target based on Internet of Things (IoT) localization. According to our review of relevant research, PCMS is the first framework that provides the following features: 1) coarse-grained and fine-grained features can be extracted to accurately recognize target activities through densely connected convolutional networks with improvement design;2) crowdsourced monitoring and sensing can be actively triggered for a target as the abnormal activity of the target is detected;and 3) deep learning model of target activity recognition can be exclusively customized for a specific target to improve the recognition accuracy based on the dedicated activity data of the target. An Android-based prototype with stationary iBeacon nodes and the Smart iBeacon is implemented to verify the feasibility and superiority of our PCMS framework. Experimental results show that our framework outperforms existing methods and can accurately recognize target activities for abnormal event detection and proactive crowdsourced tracking in a real-time manner. © 2014 IEEE.

关键词： Smartphones

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AI-based real-time hand gesture-controlled virtual mouse

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 258-267页

作者： Kasar, Manisha Kavimandan, Pranoti Suryawanshi, Trupti Abbad, Sudarshana Department of Computer Science and Engineering Bharati Vidyapeeth Deemed to be University College of Engineering Pune India Pune India

Hand gesture control technology is an emerging alternative to conventional input devices like keyboards and mice, enabling users to interact with computers through natural hand movements. Although, many researchers worked on gesture recognition and its challenges, yet wide range of gestures has not been addressed. In this direction, the author proposed a novel gesture-controlled virtual mouse system that uses hand gestures and hand tip detection with a webcam or internal camera for computer navigation that accurately recognizes various gestures, including cursor movement, left and right clicks, double clicks, scrolling, drag and drop, multiple item selection, volume control. To deal with this real-world problem, the authors proposed a solution for limited space situations and assist individuals with hand impairments who cannot use a physical mouse. Furthermore, it surpasses the limitations of traditional mice, such as mechanical wear and tear, adaptability to different environments, and restricted functionalities. By leveraging hand gestures and tip detection through a camera proposed system offers an efficient and adaptable approach to operating a PC mouse. The proposed methodology uses computer vision and AI for the accurate detection of gestures, even in challenging environments. It also offers a comprehensive set of gestures for various actions and functions. ©, Engineers Australia.

关键词： Cameras

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Enhanced ore classification through optimized CNN ensembles and feature fusion

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Iran Journal of computer Science 2025年第2期8卷 491-509页

作者： Yurdakul, Mustafa Uyar, Kübra Taşdemir, Şakir Computer Engineering Department Kirikkale University Kırıkkale Türkiye Computer Engineering Department Alanya Alaaddin Keykubat University Antalya 07425 Türkiye Computer Engineering Department Selcuk University Konya Türkiye

Ore is a type of natural stone that contains economically valuable minerals or metals. Accurate classification of ore minerals is crucial for improving operational efficiency in mining, reducing environmental impacts, and determining market value. Traditional methods for classifying ores are often time-consuming, labor-intensive, and error-prone. Therefore, computer-aided systems offer a significant advantage in this field. In this study, various efficient Deep Learning (DL) approaches are utilized for the detection of ore types. Within the scope of the study, four different experiments (transfer learning, feature extraction and classification with SVM, feature selection with optimization algorithms, and ensemble methods) are conducted, and the methods are compared in terms of classification metrics. As a result of the experimental case studies, high accuracy rates between 95 and 98% are achieved. The most successful method is the ensemble method, weighted by grid search. The ensemble model, which combined AlexNet, VGG16, and Xception models, achieves remarkable results with an overall accuracy of 98.11%, precision of 98.18%, recall of 98.11%, and f1-score of 98.11% on the publicly available Ore Images Dataset (OID). This study demonstrates that efficient DL approaches can classify ores with very high accuracy and have significant potential applications in the mining industry. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： CNN Ensemble learning Feature fusion Feature selection Optimization Ore classification

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Enhanced Wi-Fi-Li-Fi system

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 183-189页

作者： Gowtham, M. Department of Computer Science and Engineering The National Institute of Engineering Karnataka Mysuru India

Today's world is fully dependent on data. Data are individual packets or units of information which on process leads to a useful information which intend helps in decision making. So these data are to be shared among the multiple user devices and we come across some of the most commonly used transmission media. Currently, Wireless Fidelity (Wi-Fi) is dominating over other media because of its ease of remote accessing methods without any physical connection. But these do have some loopholes, as they have limited speed of transmission or very low bit rate. So in order to overcome these drawbacks we come up with an emerging concept where the speed of transmission can be enhanced up to 100 Gbps with the help of light as the source of transmission, called Light-Fidelity (Li-Fi). Li-Fi range is of large bandwidth that enables the communication in radiofrequency areas and shows data transmission efficiently. By using both Wi-Fi and Li-Fi we propose a new system, the hybrid Wi-Fi-Li-Fi system. © 2023 Inderscience Enterprises Ltd.

关键词： Decision making

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Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving

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SN computer Science 2024年第5期5卷 1-16页

作者： Yigit, Gulsum Amasyali, Mehmet Fatih Department of Computer Engineering Yildiz Technical University Istanbul Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey

Math Word Problem (MWP) solving presents a challenging task in Natural Language Processing (NLP). This study aims to provide MWP solvers with a more diverse training set, ultimately improving their ability to solve various math problems. We propose several methods for data augmentation by modifying the problem texts and equations, such as synonym replacement, rule-based: question replacement, and rule based: reversing question methodologies over two English MWP datasets. This study extends by introducing a new in-context learning augmentation method, employing the Llama-7b language model. This approach involves instruction-based prompting for rephrasing the math problem texts. Performance evaluations are conducted on 9 baseline models, revealing that augmentation methods outperform baseline models. Moreover, concatenating examples generated by various augmentation methods further improves performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data augmentation In-context learning Llama-7b Math word problem solving Question answering

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