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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Shaobing Wu Changmei Wang Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Kunming Yunnan China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCV. It provides a useful exploration for combating Drug Crime.

关键词： data-driven

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Distributed Multi-Agent Resource Allocation Based on Transformer and DRL for Cloud XR Hybrid Content Transmission

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IEEE Internet of Things Journal 2025年

作者： Wang, Zhaocheng Wu, Jun Wang, Rui Li, Ying Tongji University School of Computer Science and Technology Shanghai201804 China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tongji University College of Electronics and Information Engineering Shanghai201804 China Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China Peng Cheng Laboratory Guangdong518055 China

Extended reality (XR) technologies and applications have grown rapidly in recent years. In addition to providing immersive ultra-high-definition (UHD) video, XR also allows for a haptic experience where devices can be remotely manipulated to accomplish tasks. However, varying numbers of XR users accessing the communication system can strain limited spectrum resources, posing challenges in resource allocation. Therefore, this paper studies resource blocks (RBs) allocation problem in a downlink transmission scenario where real-time cloud XR video and haptic contents need to be transmitted simultaneously. We also consider the random variation in the number of XR users and propose an Adaptive Distributed Multi-Agent Deep reinforcement learning (DRL) combined with Transformer (ADMA-DcT) for dynamic RB allocation method. This method addresses the dynamic change in network input dimensions due to user number variability using a state division module and a self-attention mechanism in the encoder module. To our knowledge, this is the first work to study the RB allocation problem in Cloud XR transmission considering simultaneous transmitting of video and haptic services with a dynamically changing user base. Our extensive simulations show that the ADMA-DcT model, end-to-end trained, outperforms other benchmarks in successfully serving a larger number of XR users under varying user number conditions, demonstrating excellent adaptivity and robustness. © 2014 IEEE.

关键词： Multi agent systems

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Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation

arXiv

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arXiv 2025年

作者： Xiang, Wenzhao Liu, Chang Yu, Hongyang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Peng Cheng Laboratory China University of Chinese Academy of Sciences China Department of Electronic Engineering Shanghai Jiao Tong University China

Masked Image Modeling (MIM) has garnered significant attention in self-supervised learning, thanks to its impressive capacity to learn scalable visual representations tailored for downstream tasks. However, images inherently contain abundant redundant information, leading the pixel-based MIM reconstruction process to focus excessively on finer details such as textures, thus prolonging training times unnecessarily. Addressing this challenge requires a shift towards a compact representation of features during MIM reconstruction. Frequency domain analysis provides a promising avenue for achieving compact image feature representation. In contrast to the commonly used Fourier transform, wavelet transform not only offers frequency information but also preserves spatial characteristics and multi-level features of the image. Additionally, the multi-level decomposition process of wavelet transformation aligns well with the hierarchical architecture of modern neural networks. In this study, we leverage wavelet transform as a tool for efficient representation learning to expedite the training process of MIM. Specifically, we conduct multi-level decomposition of images using wavelet transform, utilizing wavelet coefficients from different levels to construct distinct reconstruction targets representing various frequencies and scales. These reconstruction targets are then integrated into the MIM process, with adjustable weights assigned to prioritize the most crucial information. Extensive experiments demonstrate that our method achieves comparable or superior performance across various downstream tasks while exhibiting higher training efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Frequency domain analysis

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ReHyGen: Relational hypergraph enhanced generative aspect sentiment triplet extraction

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Neurocomputing 2025年 639卷

作者： Lin, Zehong Chen, Weibo Xue, Yun Li, Fenghuan South China Normal University Guangdong Foshan528225 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Guangdong Shenzhen518060 China School of Computer Science and Technology Guangdong University of Technology Guangdong Guangzhou510006 China

Aspect Sentiment Triplet Extraction (ASTE) has emerged as a pivotal task in sentiment analysis, focusing on extracting the aspect terms along with the corresponding opinion terms and the expressed sentiments. Recently, generative models have achieved significant success in ASTE task. However, existing generative approaches fail to further model the specific relations within the context for ASTE at the encoding phase, making it difficult to establish the nuanced connections between aspect and opinion terms. Additionally, these approaches rely on simple structured templates at the decoding phase to pair aspect terms with opinion terms, which fails to provide effective relation information for the decoding process. To address the aforementioned issues, we propose ReHyGen, a novel relational hypergraph enhanced framework designed to enhance the relational modeling capabilities of generative ASTE models during both the encoding and decoding phases. Specifically, ReHyGen comprises two core components: the Relational Hypergraph Enhanced Module (RHEM) and the Relational Prompt Module (RPM). RHEM leverages the hypergraph attention network and auxiliary relation classification to capture high-order word interactions and boundary-sensitive word pair relations. RPM incorporates relational information into the decoding phase by providing relation-aware prompts, guiding the generation of more accurate target sequences. Extensive experiments on benchmark datasets demonstrate that our proposed framework significantly improve the performance of generative ASTE models. © 2025

关键词： Emotion Recognition

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Decentralized Federated Learning in LEO Satellite-based IoE Communications: Latency Optimization under Reliability Constraints

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IEEE Internet of Things Journal 2025年

作者： Qin, Pengxiang Li, Qiao Xu, Dongyang Xi’an Jiaotong University School of Information and Communication Engineering Xi’an710049 China Rocket Force University of Engineering Laboratory of Intelligent Control Xi’an710000 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710126 China

Artificial intelligence-empowered low earth orbit (LEO) satellite networks have the great potential to provide robust and ubiquitous communications capabilities for Internet of Everything (IoE) services. Due to the intrinsic high decentralization, it is expected for LEO satellite nodes to deploy decentralized federated learning (DFL) for model training collaboratively while preserving data privacy. However, the challenges lie in the latency management of the DFL process under communications reliability constraints. To address the issue, we propose a novel DFL framework that incorporates halving and doubling to balance the load of networks. Considering the dependency of aggregation, the latency of DFL clients is recursively derived. Under constraints of energy consumption and network reliability, an optimization problem is formulated that aims to minimize the mean latency, solved using the particle swarm optimization algorithm. Additionally, we introduce a latency-aware scheduling strategy to further improve DFL latency-efficiency by leveraging overlapping inter-satellite links among DFL clients. Simulation results show that the proposed methods significantly accelerate the DFL process by up to 16.2% and enhance its efficiency compared to the alternatives. © 2014 IEEE.

关键词： Federated learning

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Ambiguity Function Analysis and Optimization of Frequency-Hopping MIMO Radar with Movable Antennas

arXiv

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arXiv 2025年

作者： Chen, Xiang Zhao, Ming-Min Li, Min Li, Liyan Zhao, Min-Jian Wang, Jiangzhou College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China School of Information Science and Engineering Southeast University Nanjing211119 China

In this paper, we propose a movable antenna (MA)enabled frequency-hopping (FH) multiple-input multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width in the angular domain is characterized. We discover that this minimum width is related to the antenna size, the antenna number, and the target angle. Meanwhile, we present lower bounds of the ambiguity function in the Doppler and delay domains, and show that the impact of the antenna size on the radar performance in these two domains is very different from that in the angular domain. Moreover, the performance enhancement brought by MAs exhibits a certain trade-off between the main lobe width and the side lobe peak levels. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method, and show that its performance is very close to the baseline. Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate that MAs can reduce the main lobe width and suppress the side lobe levels of the ambiguity function, thereby enhancing radar performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Frequency hopping

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Leakage Source Parameter Estimation Based on TDLAS Sensor 25

Leakage Source Parameter Estimation Based on TDLAS Sensor

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and control Engineering

作者： Xinkai Zhang Jigong Li Jing Yang Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798400712647

Toxic/hazardous gas leakage seriously jeopardizes public safety and the ecological environment, and the use of a UAV equipped with a TDLAS gas sensor to quickly estimate the location of the gas leakage source and the intensity of the leakage at the early stage of the gas leakage, and take timely measures to prevent the expansion of the accident can be effectively prevented. In this paper, the objective function based on the TDLAS gas sensor is constructed by using the position information of the UAV in the process of tracking the plume, the estimated ambient wind speed/direction information, the collected TDLAS measurements, and the Gaussian plume diffusion model, and then the simulated annealing algorithm is used to find the optimization of the objective function, and to solve for the leakage source location and the intensity of the leakage. The simulation results show that the success rate of the proposed method is about 86.7%, the average error of the estimated location is about 1.79 m, and the average error of the leakage intensity is 0.077, which can effectively estimate the location of the leakage source and the leakage strength.

关键词： Gaussian plume diffusion model

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

引用

IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations

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Patterns 2025年第5期6卷

作者： Zhou, Pengfei Min, Weiqing Fu, Chaoran Jin, Ying Huang, Mingyu Li, Xiangyang Mei, Shuhuan Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou China Zhongke Shenjian Technology Co. Ltd. Suzhou China

Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary cultures, there is a growing demand for food intelligence to enable tasks like recipe recommendations and diet-disease correlation discovery. To address this, we introduce the food-oriented large language model (LLM) FoodSky, which offers fine-grained perception and reasoning on food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky significantly outperforms general-purpose LLMs on the Chinese National Chef Examination and Dietetic Examination, achieving an accuracy of 83.3% and 91.2%, respectively. Beyond enhancing culinary creativity and promoting healthier eating patterns, FoodSky aims to establish a new benchmark for domain-specific LLMs in addressing real-world food-related challenges. © 2025 The Author(s)

关键词： Food ingredients

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Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE controllers Under External Uncertainties

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

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