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IEEE Conference on Wireless Communications and Networking

作者： Tao Zhang Yisheng Zhao Shen Zhang Peng Liu Zhiyi Hu Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Aiming at the problems of low data rate and limited battery power of traditional underwater wireless sensor network (UWSN) based on acoustic communication, a resource allocation strategy for magnetic induction (MI) communication-based UWSN in ocean current scenario is investigated in this paper. Specifically, multiple sensor nodes (SNs) are distributed in seawater at different depths. First, an autonomous underwater vehicle (AUV) is introduced to charge the SNs by magnetic coupling resonant wireless power transfer (MCR-WPT) technology. Then, the SNs transmit data to the AUV through MI communication. The influence of the ocean current on the SNs and AUV is analyzed. A genetic algorithm-based particle swarm optimization algorithm is used to optimize the AUV navigation trajectory. After collecting the data from the SNs, the AUV moves to the position under a surface base station and sends the collected data to the surface base station by MI communication. To minimize system energy consumption, the transmitting power of AUV and SNs are jointly optimized under the constraints of energy causality and transmitting power. The suboptimal solution to the formulated optimization problem is obtained by adopting the improved sparrow search algorithm (ISSA) combining Cauchy variation and reverse learning. Simulation results show that the ISSA has lower system energy consumption than other benchmark methods.

关键词： Energy consumption Sea surface Navigation Magnetoacoustic effects Magnetic resonance Benchmark testing Trajectory Batteries Resource management Optimization

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Location Data Publishing Mechanism in Semi-Trusted Vehicle Networks based on Differential Privacy

Location Data Publishing Mechanism in Semi-Trusted Vehicle N...

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Zhang, Tianhao Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Vehicular ad-hoc networks (VANETs) are essential in mobile ad-hoc network applications, utilizing vehicle location data. However, safeguarding the privacy and security of this data is challenging, especially with untrusted roadside units. To address this, we propose a novel differential privacy-based location data publishing mechanism. It consists of three key components: dynamic group leader selection, data aggregation, and privacy-preserving data publishing. The group leader selection algorithm is based on directional thresholds, hop cycles, and lifespans, assuming semi-trusted vehicles. Additionally, the differential privacy-based algorithm uses map grid segmentation, privacy budget recovery, and a filtering mechanism to ensure location privacy. Comparative experiments show that our scheme outperforms existing models in total hops, group leader lifetime, and mean absolute error. © 2025 SPIE.

关键词： Differential privacy

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Guoshan Liu Hailong Yin Bin Zhu Jingjing Chen Chong-Wah Ngo Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Singapore Management University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset.

关键词： Training Computer vision Accuracy Computational modeling Stochastic processes Predictive models Reliability Faces

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Lightweight 6-bit S-boxes with DPA Resistance

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IEEE Transactions on Network Science and Engineering 2025年

作者： Yan, Liuyan Li, Lang Song, Qingling Hengyang Normal University College of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Lightweight S-boxes have a significant impact on cryptographic algorithms for resource-constrained devices in the Internet of Things (IoT). Previous research on such S-oxes has focused on low area and good cryptographic properties, which neglects the resistance to side-channel analysis, especially differential power analysis (DPA). The reVisited transparency order (VTO) is one of the best indicators for evaluating S-boxes against DPA attacks so far. Therefore, this paper presents a scheme for designing 6-bit S-boxes. The designed S-boxes are suitable for lightweight block ciphers and have a certain ability to resist DPA attacks. Specifically, we first identify 23 3-bit S-boxes with the optimal cryptographic properties and the smallest hardware area through systematic screening. Then, the paper investigates the constructions of lightweight 6-bit S-boxes based on traditional algorithm structures. Finally, a novel structure named F-LM-F is proposed for designing lightweight 6-bit S-boxes by combining Feistel structure and Lai-Massey structure. It has been proven through comparison that the S-boxes under F-LM-F structure achieve fewer fixed points and lower VTO than the 6-bit S-box of BipBip cipher, with a 17.61% reduction in hardware area and a 13.58% decrease in CPU cycles © 2013 IEEE.

关键词： Side channel attack

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Classifying retinal diseases via pyramid vision graph convolutional network for optical coherence tomography images

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Biomedical Optics Express 2025年第6期16卷 2312-2326页

作者： Jin Qian Lei Tao Changhao Gong Jun Xu Yuemei Luo Jiangsu Key Laboratory of Intelligent Medical Image Computing (IMIC) School of Artificial Intelligence Nanjing University of Information Science and Technology 210044 Nanjing China

Recent advancements have seen a significant focus on using deep neural networks for classifying retinal diseases in optical coherence tomography (OCT) images. However, traditional deep neural networks treat images as grid or sequential structures, limiting their flexibility in capturing irregular and complex objects, resulting in suboptimal performance in practical applications. To address this issue, we propose a novel visual neural network model with a pyramid structure, called pyramid vision graph convolutional networks (PVGCN). This model enhances the correlations between structures by segmenting images into multiple nodes and connecting the nearest nodes. Specifically, it consists of two core components: 1) vision graph block and 2) pyramid structure. The vision graph block, composed of a grapher block and a feed-forward network (FFN), uses graph convolution methods to divide the image into multiple regions, treating them as nodes and representing the image as graph data. The graph constructed based on nodes can capture relationships between nodes without positional restrictions, better representing the irregular structure of retinal tissue. The FFN module improves the over-smoothing phenomenon in the grapher stage, enabling more accurate classification. The pyramid structure decomposes OCT images into a series of sub-images at different scales, integrating features at different scales to obtain a comprehensive feature representation of retinal hierarchical structure information. This structure can replace the extraction of higher-dimensional features in a large model by integrating features at different scales, significantly reducing the number of parameters. We conducted extensive experiments on two different datasets. The experimental results show that the proposed PVGCN achieved accuracies of 0.9954 and 0.9787 on the two datasets, respectively, surpassing existing methods. Additionally, the model demonstrated recognition capabilities comparable to those of

关键词： Computed tomography Imaging techniques Magnetic resonance imaging Neural networks Optical networks Segmentation

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Throughput Improvement for RIS-Empowered Wireless Powered Anti-Jamming Communication Networks (WPAJCN)

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IEEE Transactions on information Forensics and Security 2025年 20卷 4622-4637页

作者： Chu, Zheng Chieng, David Foong Kwong, Chiew Jin, Huan Zhu, Zhengyu Huang, Chongwen Yuen, Chau University of Nottingham Ningbo China Next Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Ningbo China School of Computer Science Ningbo315100 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore

In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing the Riemannian Manifold Optimization (RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency. © 2005-2012 IEEE.

关键词： Lagrange multipliers

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Gao, Shihao Li, Caoshuo Mao, Cunli Zhang, Xulong Qu, Xiaoyang Jin, Taisong Wang, Jianzong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Ping An Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph's semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches. © 2025 IEEE.

关键词： Contrastive learning Graph representation Self-supervised learning

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Vehicle Pseudonym Change Mechanism Based on Dynamic Game

Vehicle Pseudonym Change Mechanism Based on Dynamic Game

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2024 International Conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Wang, Junren Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

intelligent Transportation Systems (ITS), particularly vehicular ad-hoc networks, play a crucial role in improving road safety and traffic efficiency. However, transmitting vehicle location and identity data poses significant privacy risks, potentially allowing attackers to exploit this information. In this study, we developed a user-centric privacy model based on information entropy, enabling vehicles to monitor real-time location privacy levels. We also proposed a decision algorithm for pseudonym changes, dynamically identifying a cooperative set of vehicles for pseudonym alteration. Comparative experiments using SUMO and NS-3 simulations show that our scheme enhances location privacy while reducing pseudonym change frequency. This research provides valuable theoretical and technical foundations for ITS. © 2025 SPIE.

关键词： Vehicular ad hoc networks

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of Computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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UAV 3D Path Planning Based on Improved Chimp Optimization Algorithm

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Computers, Materials and Continua 2025年第3期83卷 5679-5698页

作者： Wenli Lei Xinghao Wu Kun Jia Jinping Han School of Physics and Electronic Information Yan’an University Yan’an 716000 China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an 716000 China

Aiming to address the limitations of the standard Chimp Optimization Algorithm (ChOA), such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle (UAV) path planning, this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm (IChOA). First, this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints, transforming the path planning problem into an optimization problem with multiple constraints. Second, this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed. Finally, this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons, which effectively balances global exploration and local exploitation, significantly optimizing the algorithm’s search performance. To validate the effectiveness of the IChOA algorithm, this paper conducts experimental comparisons with eight different intelligent algorithms. The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning. It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.

关键词： UAV path planning chimp optimization algorithm chaotic mapping adaptive weighting

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