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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Beyond First-Order: A Multi-scale Approach to Finger Knuckle Print Biometrics 18th

Beyond First-Order: A Multi-scale Approach to Finger Knuckl...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Gao, Chengrui Yang, Ziyuan Teoh, Andrew Beng Jin Zhu, Min College of Computer Science Sichuan University Chengdu610045 China School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9789819610679

Recently, finger knuckle prints (FKPs) have gained attention due to their rich textural patterns, positioning them as a promising biometric for identity recognition. Prior FKP recognition methods predominantly leverage first-order feature descriptors, which capture intricate texture details but fail to account for structural information. Emerging research, however, indicates that second-order textures, which describe the curves and arcs of the textures, encompass this overlooked structural information. This paper introduces a novel FKP recognition approach, the Dual-Order Texture Competition Network (DOTCNet), designed to capture texture information in FKP images comprehensively. DOTCNet incorporates three dual-order texture competitive modules (DTCMs), each targeting textures at different scales. Each DTCM employs a learnable texture descriptor, specifically a learnable Gabor filter (LGF), to extract texture features. By leveraging LGFs, the network extracts first and second order textures to describe fine textures and structural features thoroughly. Furthermore, an attention mechanism enhances relevant features in the first-order features, thereby highlighting significant texture details. For second-order features, a competitive mechanism emphasizes structural information while reducing noise from higher-order features. Extensive experimental results reveal that DOTCNet significantly outperforms several standard algorithms on the publicly available PolyU-FKP dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gabor filters

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GAN-Based Federated Adversarial Learning for Enhancing Security Towards Consumer Digital Ecosystems

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IEEE Transactions on Consumer Electronics 2025年

作者： Murmu, Anita Kumar, Piyush Namasudra, Suyel Reddy, M Rajasekhar National Institute of Technology Patna Department of Computer Science and Engineering Patna India Bennett University School of Computer Science Engineering and Technology India National Institute of Technology Department of Computer Science and Engineering Patna India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura India Amrita Vishwa Vidyapeetham Amrita School of Computing Andhra Pradesh India

Federated Adversarial Learning (FAL) maintains the decentralization of adversarial training for data-driven innovations while allowing the collaborative training of a common model to protect privacy facilities. Before sharing with bigger global aggregation, it allows users to change settings locally over many iterations. However, a strong network against attackers in Industry 5.0 towards consumer digital ecosystems is a challenge for adversarial training methodologies. To solve this issue, a novel FAL-based Customized Inequality-Aware Federated Learning (CusIAFL) technique is proposed in this paper for classifying and securing color images. The proposed method reduces the instability brought on by the heterogeneity of the data and optimizes each data sample by understanding the client-label distribution. A unique Pix2Pix Generative Adversarial Network (GAN) algorithm is employed to generate realistic images in the presented research work, while a hybrid approach is used to guarantee consistency in the time series data. This innovative research work is evaluated on various non-medical, consumer electronic, and medical imagery. The experimental results have been evaluated using performance metrics, namely accuracy, entropy, Peak Signal-to-Noise Ratio (PSNR), Hausdorff Distance (HD95), Structural Similarity Index (SSIM), and Mean Square Error (MSE). The results show that the proposed technique outperforms the existing models in terms of security. © 1975-2011 IEEE.

关键词： Generative adversarial networks

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Privacy-Enhanced Federated Expanded Graph Learning for Secure QoS Prediction

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IEEE Transactions on Services Computing 2025年

作者： Zou, Guobing Yan, Zhi Hu, Shengxiang Gan, Yanglan Zhang, Bofeng Chen, Yixin Shanghai University School of Computer Engineering and Science Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai201620 China Shanghai Polytechnic University School of Computer and Information Engineering Shanghai201209 China Washington University Department of Computer Science and Engineering St. LouisMO63130 United States

Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Secondly, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework named Privacy-Enhanced Federated Expanded Graph Learning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service invocations. © 2025 IEEE.

关键词： Federated learning

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Idempotency in Service Mesh: For Resiliency of Fog-Native Applications in Multi-Domain Edge-to-Cloud Ecosystems 15

Idempotency in Service Mesh: For Resiliency of Fog-Native Ap...

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15th International Conference on Cloud Computing and Services science, CLOSER 2025

作者： Whitaker, Matthew Volckaert, Bruno Al-Naday, Mays School of Computer Science and Electrical Engineering University of Essex United Kingdom Ghent University - imec IDLab Department of Information Technology Gent Belgium

ISBN: (纸本)9789897587474

Resilient operation of cloud-native applications is a critical requirement to service continuity, and to fostering trust in the cloud paradigm. So far, service meshes have been offering resiliency to a subset of failures. But, they fall short in achieving idempotency for HTTP POST requests. In fact, their current resiliency measures may escalate the impact of a POST request failure. Besides, the current tight control over failures - within central clouds - is being threatened by the growing distribution of applications across heterogeneous clouds. Namely, in moving towards a fog-native paradigm of applications. This renders achieving both idempotency and request satisfaction for POST microservices a non-trivial challenge. To address this challenge, we propose a novel, two-pattern, resiliency solution: Idempotency and Completer. The first is an idempotency management system that enables safe retries, following transient network/infrastructure failure. While the second is a FaaS-based completer system that enables automated resolution of microservice functional failures. This is realised through systematic integration and application of developer-defined error solvers. The proposed solution has been implemented as a fog-native service, and integrated over example service mesh Consul. The solution is evaluated experimentally, and results show considerable improvement in user satisfaction, including 100% request completion rate. The results further illustrate the scalability of the solution and benefit in closing the current gap in service mesh systems. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Hypertext systems

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Reconfigurable Intelligent Surface-Assisted Beam Management for AI Signal Classification in mmWave Networks

Reconfigurable Intelligent Surface-Assisted Beam Management ...

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2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Shtaiwi, Eyad Sklivanitis, George Pados, Dimitris A. The Center for Connected Autonomy and AI The Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

ISBN: (纸本)9781946815200

Millimeter wave (mmWave) communications are vulnerable to blockages and node mobility due to the highly directional signal beams. Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Automatic modulation classification (AMC) could improve the transmission efficiency of mmWave wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment, under the condition of unknown prior information, such as CSI, thus improving spectral efficiency and reducing the power consumption in NextG wireless communications systems. Deep learning (DL) has been widely used for AMC in sub-6 GHz wireless networks due to its capacity for feature learning directly on raw radio front-end data. © 2025 USNC-URSI.

关键词： Image coding

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The connectivity carcass of a vertex subset in a graph: both odd and even case 8

The connectivity carcass of a vertex subset in a graph: both...

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8th SIAM Symposium on Simplicity of Algorithms, SOSA 2025

作者： Baswana, Surender Pandey, Abhyuday Department of Computer Science & Engineering IIT Kanpur Kanpur208016 India Tower Research Capital LLC New York10013 United States

ISBN: (纸本)9781611978315

Let G = (V, E) be an undirected unweighted multi-graph and S ⊆ V be a subset of vertices called the Steiner set. A set of edges with the least cardinality whose removal disconnects S, that is, there is no path between at least one pair of vertices from S, is called a Steiner mincut for S or simply an S-mincut. Connectivity Carcass is a compact data structure storing all S-mincuts in G announced by Dinitz and Vainshtein in an extended abstract [8] in 1994. The complete proof of various results of this data structure for the simpler case when the value of S-mincut is odd appeared in the year 2000 [10]. Over the last couple of decades, there have been attempts towards the proof for the case when the value of S-mincut is even [5], but none of them met a logical end. We present the following results. 1. We present the first complete, self-contained, and peer-reviewed exposition of the connectivity carcass which covers both even and odd cases of value of S-mincut. 2. We derive the results using an alternate and much simpler approach. In particular, we derive the results using submodularity of cuts and its generalizations - a well-known property of graphs expressed using a simple inequality. 3. We also show how the connectivity carcass can be helpful in efficiently answering some basic queries related to S-mincuts using some additional insights. Copyright © 2025 by SIAM.

关键词： Undirected graphs

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Smart Playground for Electricity Generation Using Swing 8

Smart Playground for Electricity Generation Using Swing

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Mandal, Lopa Devang, Prathiksha Fathima, Hafiza Ahmed, Syed Faraz Duddukuri, Sridhar Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Information Technology Bangalore India

ISBN: (纸本)9798331520762

The world's energy demand is rapidly increasing due to rise in population. Energy generation is mainly dependent on fossil fuels, nuclear energy and renewable energy resources. Therefore, there is a need for a cleaner method to generate electricity which can act as a substitute for the existing methods. This paper proposes an innovative method of generating electricity using play equipment like swings. The motion of the swing has been used in the present work to move the shaft back and forth which will drive the sprockets and transfer the momentum to the spur gear arrangement. This runs the dynamo and generates electricity, and it can then be stored in batteries or kinetic energy storing equipment and then used later. The proposed method minimizes environmental impact, reduces energy consumption and promotes sustainability. © 2025 IEEE.

关键词： Energy utilization

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Unified Disease Prediction System Using Machine Learning Techniques 3

Unified Disease Prediction System Using Machine Learning Tec...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Verma, Shalini Rallan, Reema Singh, Teekam Kumar, Naresh Lovely Professional University Department of Computer Application Punjab India Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University Dehradun India Maya Devi University Computer Science Department Dehradun India

ISBN: (纸本)9798331542375

This innovative research uses datasets from Kaggle to leverage Machine Learning (ML) techniques to develop a complete unified disease prediction system. With its comprehensive coverage of diabetes, heart disease, and Parkinson's disease, the dataset offers a strong basis for predictive modelling. Support Vector Machines (SVM) are used in the study for Parkinson's and diabetes predictions, while logistic regression is used for heart disease predictions. The system's reliability and usability are improved by this varied computational approach, which guarantees nuanced forecasts for any medical condition. The popular area of interest is the nexus between new technology and healthcare, focusing on the critical role in data-driven decision-making, which plays an important role in preventative medicine. The research highlights the value of early diagnosis and preventative healthcare actions in addition to advancing healthcare technologies. By merging intuitive machine learning algorithms. For heart disease prediction using logistic regression model exhibited 81.96% accuracy, while SVM system for diabetes and Parkinson's disease exhibited 77.27 and 87.17%, respectively. The open-source Streamlit platform integration provides an intuitive user interface for the integrated module. This project's technology integration is an innovative way to build a more robust and adaptable healthcare system that promotes early detection and preventative treatment of prevalent health issues. © 2025 IEEE.

关键词： Neurodegenerative diseases

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A Wireless Fully Implantable DBS Device for Closed-Loop Neuromodulation Applications

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IEEE Sensors Journal 2025年第9期25卷 15331-15344页

作者： Su, Kangyu Qiu, Zhang Xu, Kedi Xu, Jian Wang, Yueming Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University MOE Frontier Science Center for Brain Science and Brain-Machine Integration Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou310013 China Zhejiang University State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Closed-loop deep brain stimulation (DBS) is an emerging technology in the field of neuromodulation, which can provide effective treatment methods for intractable neurological disorders. To address the faced challenges, this article presents a high-precision closed-loop DBS device based on a custom-designed neuromodulation integrated circuit (CNIC) fabricated in a 180-nm bipolar-CMOS-DMOS (BCD) process. Also, three new techniques have been developed to optimize system performance. First, to improve system stability, a frequency shaping & quick blanking amplifier (FSQBA)-based analog front-end is proposed to extend system dynamic range (DR). Second, both stimulation artifacts (SAs) suppression scheme and closed-loop control algorithm are developed to achieve real simultaneous recording and stimulation. Third, a redundant-crossfire (RXF) technique is designed to enhance current precision without consuming much extra area. Bench-top measurements show that the designed system features a wide DR of 112 dB, a short saturation recovery time of 100 μs, and a high stimulation current resolution of 13.1-bit. Besides, animal experiments demonstrate that the designed system is highly suitable for long-term implantation applications, thus providing accurate and reliable closed-loop control for neuromodulation therapies. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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