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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng D., Niyato D.I., Kim College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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Integration of IOT and optical networks for enhanced connectivity and performance

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Journal of Optical Communications 2025年

作者： Rani, Rajni Tyagi, Sanjive Sharma, Vikas ECE Department KCC Institute of Technology & Management 2B-2C Knowledge Park-III Uttar Pradesh Greater Noida201306 India Department of Computer Science Faculty of Engineering and Technology Swami Vivekanand Subharti University Uttar Pradesh Meerut250005 India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Swami Vivekanand Subharti University Uttar Pradesh Meerut250005 India

The integration of Internet of things (IoT) and optical networks represents a significant evolution in modern communication systems. Optical networks, known for their high bandwidth and low latency, can provide the necessary backbone to support the vast data exchange required by IoT devices. This paper explores the potential of combining IoT with optical networks, analyzing how this convergence can enhance connectivity, increase performance, and tackle the growing demands of future smart environments. Challenges, opportunities, and future directions for this integration are discussed, alongside case studies and applications that showcase the benefits of such an integrated approach. © 2025 Walter de Gruyter GmbH. All rights reserved.

关键词： Internet of things

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CGMatch: A Different Perspective of Semi-supervised Learning

arXiv

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arXiv 2025年

作者： Cheng, Bo Lu, Jueqing Tian, Yuan Zhao, Haifeng Chang, Yi Du, Lan School of Artificial Intelligence Jilin University China International Center of Future Science Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China Faculty of Information Technology Monash University Australia Department of Computer Science Jinling Institute of Technology China

Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approaches achieve impressive successes by combining ideas from both consistency regularization and pseudo-labeling. However, these methods tend to underperform in the more realistic situations with relatively scarce labeled data. We argue that this issue arises because existing methods rely solely on the model’s confidence, making them challenging to accurately assess the model’s state and identify unlabeled examples contributing to the training phase when supervision information is limited, especially during the early stages of model training. In this paper, we propose a novel SSL model called CGMatch, which, for the first time, incorporates a new metric known as Count-Gap (CG). We demonstrate that CG is effective in discovering unlabeled examples beneficial for model training. Along with confidence, a commonly used metric in SSL, we propose a fine-grained dynamic selection (FDS) strategy. This strategy dynamically divides the unlabeled dataset into three subsets with different characteristics: easy-to-learn set, ambiguous set, and hard-to-learn set. By selective filtering subsets, and applying corresponding regularization with selected subsets, we mitigate the negative impact of incorrect pseudo-labels on model optimization and generalization. Extensive experimental results on several common SSL benchmarks indicate the effectiveness of CGMatch especially when the labeled data are particularly limited. Source code is available at https://***/BoCheng-96/CGMatch. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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ADAC: Actor-Double-Attention-Critic for Multi-Agent Cooperation in Mixed Cooperative-Competitive Environments

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Kong, He Xing, Qianli Wang, Qi Niu, Runliang Chen, Hechang Wang, Yu Wang, Shiqi Duan, Zhiyi Chang, Yi Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education School of Artificial Intelligence Jilin Changchun130012 China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Changchun130010 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education College of Computer Science and Technology Jilin Changchun130012 China Jilin University School of Artificial Intelligence Changchun130012 China Ministry of Education Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Changchun130010 China Jilin University College of Computer Science and Technology Jilin Changchun130012 China Inner Mongolia University Department of Computer Science Hohhot010021 China

The cooperation in mixed cooperative-competitive tasks has drawn significant attention in multi-agent deep reinforcement learning. Agents need to cooperate with their teammates while competing against their opponents. However, most existing works treat the cooperative agents and competitive agents equally as they perform the same operation on all the agents. As a result, without distinguishing between cooperative and competitive agents, they may suffer from information disorder in learning an optimally cooperative policy and struggle to decide on the next step action. To address the above issues, we decompose the final Q-value into a weighted combination of three parts: the Q-values of the cooperative agents, the competitive group, and the current agent. A theoretical proof of the correctness of the decomposition is provided. With this decomposition, we are able to consider cooperative and competitive agents separately. Accordingly, we propose a multi-agent actor-critic algorithm called actor-double-attention-critic (ADAC) under centralized training and decentralized execution according to the decomposition. In ADAC, networks with group-specific attention and an attentional weighting network are specially designed. With the designed double-attention structure, ADAC can capture the distributions from different agents and improve cooperation performance. Extensive experiments are conducted in three scenarios with nine settings against six representative methods. The results demonstrate the superiority of the proposed ADAC model against state-of-the-art methods in various mixed cooperative-competitive tasks. © 2000-2011 IEEE.

关键词： cooperation group-specific attention mixed tasks Multi-agent reinforcement learning

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

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computers in Biology and Medicine 2025年 185卷 109537-109537页

作者： Chundi, Ramesh G., Sasikala Basivi, Praveen Kumar Tippana, Anitha Hulipalled, Vishwanath R. N., Prabakaran Simha, Jay B. Kim, Chang Woo Kakani, Vijay Pasupuleti, Visweswara Rao School of Computer Applications Dayananda Sagar University Bangalore India School of Computer Science and Applications REVA University Rukmini Knowledge Park Bangalore560064 India Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Department of Biotechnology School of Applied Sciences REVA University Rukmini Knowledge Park Bangalore560064 India School of Computing and Information Technology REVA University Rukmini Knowledge Park Bangalore560064 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Abiba Systems CTO and RACE Labs REVA University Rukmini Knowledge Park Bangalore560064 India Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of Integrated System Engineering Inha University 100 Inha-ro Nam-gu Incheon22212 Korea Republic of School of Biosciences Taylor's University Lakeside Campus Selangor Subang Jaya47500 Malaysia Faculty of Earth Sciences Universiti Malaysia Kelantan Campus Jeli Jeli Kelantan17600 Malaysia

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals’ well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects. © 2024 Elsevier Ltd

关键词： Artificial intelligence computer vision Diabetes Deep learning Machine learning

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Evaluating Energy Consumption for Routing Selection using the Multi-Routing Clustering Protocol using Timeslot Transmission in Dynamic Path Selection in Wireless Sensor Networks

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Procedia computer science 2025年 252卷 251-259页

作者： M. Prakash J. Abinesh P. Malarvizhi J. Jeba Emilyn A. Sam Thamburaj D. Vinod Kumar Department of Computer Science and Engineering Knowledge Institute of Technology Salem-637504 Tamil Nadu India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Vinayaka Mission’s College of Physiotherapy Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India Depertment of Biomedical Engineering Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India

Wireless sensor networks (WSNs) are cutting-edge technology that can be used in many fields requiring critical information. However, limited resource constraints, contextual connectivity, and lifecycle requirements drive designers to seek more efficient WSN infrastructures. Unbalanced energy utilization of sensor hubs during information steering in Wireless Sensor Networks (WSN). Accordingly, one of the principal configuration difficulties of remote sensor networks is to limit the energy utilization of sensor hubs. Therefore, many routing schemes are designed to efficiently utilize sensor nodes’ limited Energy. These schemes generally use low-power paths to transmit data. It turns out that using the same path is suboptimal given the network lifetime. To overcome this problem, a new method is introduced that uses a Multi-Route Clustering Protocol Using Timeslot Transmission (MRCP-TTDPS) with dynamic path selection. It consumes Energy on path selection. In the first step, multipath routing is configured to detect path quality, so this method uses Multipath Optimized Routing (MPRS) to send high-quality path data. The second stage uses Cluster-Based Optimal Path Selection (CORS) to establish the best energy path. The third stage develops energy consumption models. The amount of the hubs with the most noteworthy leftover Energy is chosen as the group top of each round. Every standard hub partakes in a group framed by neigh exhausting bunch heads. Every sensor hub sends identified information to the bunch head in each round. The group head sends the data to the base station. The results show that the model is better than the path model regarding path identifier energy utilization and path quality.

关键词： Wireless Sensor Networks (WSN) Multi-Route clustering Protocol using Timeslot Transmission in Dynamic path selection (MRCP-TTDPS) Energy data transfer multipath routing Quality path sensor nodes

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Rethinking Polyp Segmentation from the Perspectives of Matching Views and Seeking Camouflage

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Multimedia Systems 2025年第3期31卷 1-17页

作者： Jiang, Zhengfang Chen, Haipeng Yang, Yongping Liu, Xianzhu Lyu, Yingda College of Software Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Department of Colorectal Anal Surgery The Second Hospital of Jilin University Changchun130022 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China College of Opto-Electronic Engineering Changchun University of Science and Technology Changchun130022 China Public Computer Education and Research Center Jilin University Changchun130012 China

Accurate segmentation of polyps from colonoscopy images is of great significance for the prevention of colorectal cancer. Despite notable progress, polyp segmentation remains a challenging task due to the diversity of polyp size and shape, as well as the camouflage property of polyps caused by their high similarity to surrounding tissues. To address these challenges, inspired by human perceptual experience in finding objects, we propose a multi-perspective collaborative network (MPCNet), innovatively identifying the potential polyps from the perspectives of matching views and seeking camouflage. Firstly, from the former perspective, a global–local aware module is proposed to provide various feature receptive fields to match suitable views for polyps of different sizes and shapes. Then, from the latter perspective, a texture enhancement module (TEM) and an interaction refinement module (IRM) are proposed to mine and complement clues essential for breaking the camouflage. Specifically, TEM mines texture-related clues and amplifies the subtle texture differences between polyps and their background, while IRM complements boundary clues and further optimizes feature representation through the interaction between boundaries and regions, thereby generating accurate segmentation results with complete regions and fine boundaries. Extensive experiments on five benchmark polyp datasets show that our MPCNet outperforms other state-of-the-art methods. In particular, we achieve a mean Dice of 81.7% and 82.4% on the most challenging datasets CVC-ColonDB and ETIS, respectively, which are significantly higher than competing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image segmentation

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External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT

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Abdominal Radiology 2025年 1-10页

作者： Kirpalani, Anish Viel, Théo Hu, Zixuan Lin, Hui Ming Hermans, Sebastiaan Gomez, David Moreland, Robert Mathur, Shobhit Jhaveri, Aaditeya Wu, Matthew Vlachou, Paraskevi A. Tafur, Monica Sejdić, Ervin Colak, Errol Department of Medical Imaging St. Michael’s Hospital Unity Health Toronto Toronto Canada Department of Medical Imaging Temetry Faculty of Medicine University of Toronto Toronto Canada Li Ka Shing Knowledge Institute Unity Health Toronto Toronto Canada NVIDIA Paris France The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto Toronto Canada Division of General Surgery St. Michael’s Hospital Unity Health Toronto Toronto Canada Department of Surgery Temetry Faculty of Medicine University of Toronto Toronto Canada Institute for Medical Science University of Toronto Toronto Canada Institute of Health Policy Management and Evaluation University of Toronto Toronto Canada North York General Hospital Toronto Canada

Purpose: This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geographically and temporally distinct external dataset. Method: A single-center retrospective study was conducted using an external dataset comprising 1216 CT scans (608 positive and 608 negative for splenic injuries). The ML model, trained on the RSNA Abdominal Traumatic Injury CT (RATIC) dataset, employs a multi-component pipeline including 2D MaxVit, 2.5D CoatNet with LSTM for study-level predictions. Model performance was evaluated using sensitivity, specificity, PPV, NPV, accuracy, F1 score, and AUC. Results: The ML model achieved an AUC of 0.931 (95% CI: 0.917, 0.945) for binary classification of splenic injuries, with an accuracy of 0.849 (95% CI: 0.827, 0.868), sensitivity of 0.747 (95% CI: 0.711, 0.780), and specificity of 0.951 (95% CI: 0.930, 0.965). For high-grade splenic injuries, the model achieved an AUC of 0.950 (95% CI: 0.932, 0.968), accuracy of 0.928 (95% CI: 0.912, 0.941), sensitivity of 0.719 (95% CI: 0.643, 0.784), and specificity of 0.958 (95% CI: 0.944, 0.968). Conclusion: The ML model shows strong, reliable performance and generalizability in detecting and grading splenic injuries on CT scans. This supports its potential clinical application, particularly for quick and accurate diagnosis in splenic trauma patients, and highlights the value of RSNA AI challenges in advancing clinical research and applications in medical imaging. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： AAST Artificial intelligence Computed tomography (CT) External validation Machine learning Splenic injury Trauma

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UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

arXiv

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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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