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Quantifying Privacy Risks of Behavioral Semantics in Mobile Communication Services

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 1908-1923页

作者： Qiu, Guoying Bai, Tiecheng Tang, Guoming Guo, Deke Li, Chuandong Gan, Yan Zhou, Baoping Shen, Yulong Tarim Univ Coll Cyber Secur Xinjiang 843300 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Data Sci & Analyt DSA Thrust Informat Hub Guangzhou 510220 Guangdong Peoples R China Sun Yat Sen Univ Sch Engn Guangzhou 510220 Guangdong Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Xidian Univ Coll Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Shaanxi Key Lab Network & Syst Secur Xian 710071 Peoples R China

Location-based mobile services, while improving user daily life, also raise significant privacy concerns in the sharing of location data. These trajectories indicate users' traveling behavioural traces with rich semantics derived from open-source information. Behavioral-semantic analysis reveals users' travelling motivations and underlying behavioral patterns. It contributes to attackers launching inferential attacks for behavior prediction, identity identification, or other privacy invasions, even when the location data is protected. It remains open to the issues of behavioral-semantic privacy-risk quantification and privacy-protection evaluation. This paper aims to reveal such semantic privacy risks of user behaviors arising from the publication of location trajectories in mobile scenarios. We formalize user semantic-mobility process to analyze his underlying behavior patterns. Then, we design semantic inference algorithms conditional on the released trajectory to reason about the observation-based likelihood of the user's actual staying and transfer behaviours and behavioural-trace tracking. Extensive experiments with real-world data demonstrate their performance on inference accuracy and semantic similarity, offering a quantification criterion for deploying mobile privacy protection.

关键词： Semantics Privacy Trajectory Protection Motion pictures Data privacy inference algorithms Hidden Markov models Stochastic processes Accuracy Mobile service location privacy protection behavior semantics privacy-risk evaluation & quantification

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DeepTrace: Learning to Optimize Contact Tracing in Epidemic Networks With Graph Neural Networks

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2025年 11卷 97-113页

作者： Tan, Chee Wei Yu, Pei-Duo Chen, Siya Poor, H. Vincent Nanyang Technol Univ Singapore 639798 Singapore Chung Yuan Christian Univ Dept Appl Math Taoyuan City 32023 Taiwan City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

Digital contact tracing aims to curb epidemics by identifying and mitigating public health emergencies through technology. Backward contact tracing, which tracks the sources of infection, proved crucial in places like Japan for identifying COVID-19 infections from superspreading events. This paper presents a novel perspective on digital contact tracing by modeling it as an online graph exploration problem, framing forward and backward tracing strategies as maximum-likelihood estimation tasks that leverage iterative sampling of epidemic network data. The challenge lies in the combinatorial complexity and rapid spread of infections. We introduce DeepTrace, an algorithm based on a Graph Neural Network that iteratively updates its estimations as new contact tracing data is collected, learning to optimize the maximum likelihood estimation by utilizing topological features to accelerate learning and improve convergence. The contact tracing process combines either BFS or DFS to expand the network and trace the infection source, ensuring efficient real-time exploration. Additionally, the GNN model is fine-tuned through a two-phase approach: pre-training with synthetic networks to approximate likelihood probabilities and fine-tuning with high-quality data to refine the model. Using COVID-19 variant data, we illustrate that DeepTrace surpasses current methods in identifying superspreaders, providing a robust basis for a scalable digital contact tracing strategy.

关键词： COVID-19 Resistance Maximum likelihood estimation Pandemics Contact tracing Graph neural networks Data models Real-time systems inference algorithms Topology Digital contact tracing contagion source detection maximum likelihood estimation graph neural network online graph exploration

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Communication-Efficient Distributed Bayesian Federated Learning Over Arbitrary Graphs

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1351-1366页

作者： Wang, Sihua Guo, Huayan Zhu, Xu Yin, Changchuan Lau, Vincent K. N. Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conver Beijing 100876 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Commun Engn Hong Kong 999077 Peoples R China Harbin Inst Technol Sch Informat Sci & Technol Shenzhen 518055 Peoples R China Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Shenzhen Municipal Key Lab AIoT Commun Shenzhen 518055 Peoples R China

This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined in a communication graph to update the local model weights for minimizing the global loss function. To incorporate the communication graph constraint into the joint posterior distribution, we exploit the fact that the model weights on each device is a function of its local likelihood and local prior and then, the connectivity between adjacent devices is modeled by a Dirac Delta distribution. In this way, the joint distribution can be factorized naturally by a factor graph. Based on the Dirac Delta-based factor graph, we propose a novel distributed approximate Bayesian inference algorithm that combines loopy belief propagation (LBP) and variational Bayesian inference (VBI) for distributed FL. Specifically, VBI is used to approximate the non-Gaussian marginal posterior as a Gaussian distribution in local training process and then, the global training process resembles Gaussian LBP where only the mean and variance are passed among adjacent devices. Furthermore, we propose a new damping factor design according to the communication graph topology to mitigate the potential divergence and achieve consensus convergence. Simulation results verify that the proposed solution achieves faster convergence speed with better performance than baselines.

关键词： Bayes methods Training Convergence inference algorithms Damping Brain modeling Approximation algorithms Optimization Signal processing algorithms Federated learning Bayesian federated learning decentralized consensus optimization message passing

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Interacting Large Language Model Agents Bayesian Social Learning Based Interpretable Models

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IEEE ACCESS 2025年 13卷 25465-25504页

作者： Jain, Adit Krishnamurthy, Vikram Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. While both fields are mature, their application to decision-making involving interacting LLMAs remains unexplored. Motivated by Bayesian sentiment analysis on online platforms, we construct interpretable models and stochastic control algorithms that enable LLMAs to interact and perform Bayesian inference. Because interacting LLMAs learn from both prior decisions and external inputs, they can exhibit bias and herding behavior. Thus, developing interpretable models and stochastic control algorithms is essential to understand and mitigate these behaviors. This paper has three main results. First, we show using Bayesian revealed preferences from microeconomics that an individual LLMA satisfies the necessary and sufficient conditions for rationally inattentive (bounded rationality) Bayesian utility maximization and, given an observation, the LLMA chooses an action that maximizes a regularized utility. Second, we utilize Bayesian social learning to construct interpretable models for LLMAs that interact sequentially with each other and the environment while performing Bayesian inference. Our proposed models capture the herding behavior exhibited by interacting LLMAs. Third, we propose a stochastic control framework to delay herding and improve state estimation accuracy under two settings: 1) centrally controlled LLMAs and 2) autonomous LLMAs with incentives. Throughout the paper, we numerically demonstrate the effectiveness of our methods on real datasets for hate speech classification and product quality assessment, using open-source models like LLaMA and Mistral and closed-source models like ChatGPT. The main takeaway of this paper, based on substantial empirical analysis and mathematical formalism, is that LLMAs act as rationally bounded Bayesian agents that exhibit social learning when interacting. Tradi

关键词： Bayes methods Stochastic processes Signal processing algorithms Signal processing Microeconomics inference algorithms Engines Data models Numerical models Bayesian social learning large language models Bayesian revealed preferences structural results optimal stopping POMDPs self-attention rational inattention model collapse model collapse

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Distributed Learn-to-Optimize: Limited Communications Optimization Over Networks via Deep Unfolded Distributed ADMM

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3012-3024页

作者： Noah, Yoav Shlezinger, Nir Bengurion Univ Negev Sch ECE IL-84105 Beer Sheva Israel

Distributed optimization is a fundamental framework for collaborative inference over networks. The operation is modeled as the joint minimization of a shared objective which typically depends on local observations. Distributed optimization algorithms, such as the distributed alternating direction method of multipliers (D-ADMM), iteratively combine local computations and message exchanges. A main challenge associated with distributed optimization, and particularly with D-ADMM, is that it requires a large number of communications to reach consensus. In this work we propose unfolded D-ADMM, which follows the emerging deep unfolding methodology to enable D-ADMM to operate reliably with a predefined and small number of messages exchanged by each agent. Unfolded D-ADMM fully preserves the operation of D-ADMM, while leveraging data to tune the hyperparameters of each iteration. These hyperparameters can either be agent-specific, aiming at achieving the best performance within a fixed number of iterations over a given network, or shared among the agents, allowing to learn to distributedly optimize over different networks. We specialize unfolded D-ADMM for two representative settings: a distributed sparse recovery setup, and a distributed machine learning learning scenario. Our numerical results demonstrate that the proposed approach dramatically reduces the number of communications utilized by D-ADMM, without compromising on its performance.

关键词： Optimization Distributed databases Deep learning inference algorithms Graph neural networks Vectors Topology Mobile computing Iterative methods Distance learning ADMM deep unfolding distributed optimization

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Probabilistic Topic Modeling With Transformer Representations

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Reuter, Arik Thielmann, Anton Weisser, Christoph Safken, Benjamin Kneib, Thomas Tech Univ Clausthal Inst Math D-38678 Clausthal Zellerfeld Germany BASF D-67056 Ludwigshafen Germany Georg August Univ Gottingen Chair Stat D-37073 Gottingen Germany

The field of topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in natural language processing, however, several successful models that rely on straightforward clustering approaches in transformer-based embedding spaces have emerged and consolidated the notion of topics as clusters of embedding vectors. We propose the transformer-representation neural topic model (TNTM), which combines the benefits of topic representations in transformer-based embedding spaces and probabilistic modeling. Therefore, this approach unifies the powerful and versatile notion of topics based on transformer embeddings with fully probabilistic modeling, as in models such as latent Dirichlet allocation (LDA). We utilize the variational autoencoder (VAE) framework for improved inference speed and modeling flexibility. Experimental results show that our proposed model achieves results on par with various state-of-the-art approaches in terms of embedding coherence while maintaining almost perfect topic diversity. The corresponding source code is available at: https://***/ArikReuter/TNTM.

关键词： Transformers Probabilistic logic Vocabulary Vectors Gaussian distribution Computational modeling Bayes methods inference algorithms Indexes Autoencoders Embedding space latent Dirichlet allocation (LDA) topic model transformer variational autoencoder (VAE)

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ESFormer: A Pillar-Based Object Detection Method Based on Point Cloud Expansion Sampling and Optimised Swin Transformer

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IEEE ACCESS 2025年 13卷 62254-62266页

作者： Zhao, Zhuoxuan Li, Qianchun Hao, Shengyu Huang, Xinyan Zhang, Fan Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Peoples R China China Agr Univ Coll Engn Beijing 100083 Peoples R China

In recent years, with the wide application of autonomous driving, surveillance, and robotics, the demand for accurate object detection in efficient object scenarios has surged. However, traditional object detection methods often face the challenge of difficulty in balancing detection accuracy and processing speed when dealing with the dynamic characteristics of fast-moving objects. To this end, this study proposes an innovative target detection method based on Pillar point cloud, by designing a point cloud enhancement module called the Point Cloud Expansion Sampling Tool (PCES-Tool) and combining it with a Swin Transformer architecture based on optimized patch partition (Swin-T+). to solve the above problems. By adopting the inflated point cloud approach, the method enhances the spatial representation of the object to achieve faster object detection. And Swin Transformer further enhances the model's ability to capture multi-scale features through its hierarchical design, enabling it to recognize objects at different scales and distances. The method is evaluated on the KITTI, NuScenes and WOD standard datasets, and the results show that significant improvements are achieved in the detection accuracy with both three-dimensional intersection and merger ratio (3D IoU) and two-dimensional intersection and merger ratio (2D IoU). The experimental results prove that the method performs well in high-speed detection scenarios, indicating that the combination of the PCES-Tool and Swin Transformer can effectively solve the challenges in point cloud object detection. This study provides important technical support for the future development of real-time object detection systems.

关键词： Point cloud compression Feature extraction Three-dimensional displays Object detection Accuracy Transformers Laser radar Encoding Convolution inference algorithms Point cloud expansion sampling tool swin transformer lidar point loud object detection

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Ancient Wall Painting Restoration Based on Improved Adaptive Sample Block Criminisi Algorithm and CDD Algorithm

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IEEE ACCESS 2025年 13卷 44722-44738页

作者： Wu, Xiujie Fujian Inst Educ Sports & Art Res Dept Fuzhou 350001 Peoples R China

To better protect and restore ancient mural art, this study proposes an improved adaptive sample block Criminisi algorithm to address the shortcomings of traditional manual restoration methods. To enhance the efficiency and quality of image restoration, this study constructs a curvature driven diffusion model based on curvature adaptive parameters and smoothing functions. The experiment showed that compared with the Criminisi and the enhanced Criminisi algorithms, the average PSNR of the research algorithm increased by 12.47% and 7.17%, and the structural similarity index increased by 5.54% and 2.82%, respectively. The subjective average score of this algorithm for image sample restoration was 9.1 points, and the average time for image restoration was 23.25 seconds. Compared with deep residual network and generative adversarial network, the proposed algorithm increases the peak signal-to-noise ratio by 36.59% and 20.42%, respectively, and reduces the image restoration time by 31.92% and 24.80%, respectively. The results indicate that the proposed image restoration method can improve the quality and efficiency of image restoration. This study can provide certain reference value for artificial restoration and has important research significance for the continuation of world history and culture.

关键词： Image restoration Maintenance engineering Painting Image segmentation Filling Noise measurement Manuals inference algorithms Cultural differences Wavelet transforms Adaptive sample block Criminisi algorithm CDD algorithm ancient murals digital image restoration technology

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A Single-Stage Photovoltaic Module Defect Detection Method Based on Optimized YOLOv8

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IEEE ACCESS 2025年 13卷 27805-27817页

作者： Gao, Yihong Pang, Chengxin Zeng, Xinhua Jiang, Pengyi Shanghai Univ Elect Power Shanghai 200000 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China NYU New York NY 10012 USA

Defect detection in photovoltaic (PV) modules presents significant challenges. In the presence of inconspicuous or small-scale defects, downsampling operations can cause features to vanish, and computational complexity increases as the model deepens, resulting in reduced detection accuracy, higher latency, and challenges in deploying the model on edge devices with limited computational resources. In response, this article proposes a single-stage object detection model based on YOLOv8, the PSA-PVdetector (PSA-det). The core innovation of PSA-det is the novel Partial Spatial Attention (PSA) mechanism, which integrates Partial Convolution (PConv) with Spatial Attention (SA) to optimize feature extraction and reduce computational overhead. For small-scale defect detection, a Multi-Channel Feature Fusion (MCFF) detection head is designed to enable finer-grained center prediction. Additionally, ShapeIoU is employed as the bounding box regression metric, enhancing the classical IoU by incorporating bounding box shape, thereby improving defect localization accuracy. Experimental results demonstrate that PSA-det achieves an mAP50 of 87.2% on the Panel-2 dataset and 72.0% on the Solar dataset. On the Nvidia Jetson Xavier NX DK platform, PSA-det achieves an inference latency as low as 2.6 ms, effectively balancing accuracy and real-time performance.

关键词： Feature extraction Defect detection Head YOLO Convolution Accuracy Inspection Computational modeling inference algorithms Termination of employment YOLOv8 photovoltaic defect detection edge deployment partial convolution spatial attention

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ViTeGNN: Towards Versatile Inference of Temporal Graph Neural Networks on FPGA

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第3期36卷 502-519页

作者： Zhou, Hongkuan Zhang, Bingyi Kannan, Rajgopal Busart, Carl Prasanna, Viktor K. Univ Southern Calif Los Angeles CA 90007 USA DEVCOM Army Res Off Los Angeles CA 90089 USA DEVCOM Army Res Lab Adelphi MD 20783 USA

Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs, outperforming other methods in many high-impact downstream tasks. However, achieving high-performance TGNN inference in production environments is challenging because TGNN models suffer from high computation complexity and intrinsic temporal data dependency that hinders data parallelism. In addition, real-world TGNN applications have different latency and throughput requirements. This work presents ViTeGNN, a versatile TGNN inference solution for memory-based TGNNs on FPGAs. ViTeGNN performs algorithm-model-architecture co-design to meet the latency and throughput requirements of real-world TGNN applications. Besides the vanilla inference mode ViTeGNN-bal that updates embeddings for nodes interacting with others, we propose ViTeGNN-lat and ViTeGNN-thpt, optimized for latency and throughput. Our model optimizations include a lightweight method to compute attention scores and a related temporal neighbor pruning strategy to reduce computation and memory accesses. These are holistically coupled with key hardware optimizations that leverage the FPGA hardware. We propose a novel hardware module to execute the complex neighbor update process efficiently. To ensure similar accuracy vis-a-vis the original model, the simplified models are trained using the knowledge distillation technique. We propose a unified hardware design that supports all of these three inference modes without FPGA reconfiguration. Enabled by our flexible hardware architecture, we further propose ViTeGNN-auto, which automatically selects the best inference mode at runtime based on latency and throughput requirements, guided by our accurate performance model. We evaluate the performance of the proposed hardware accelerator on five real-world datasets. ViTeGNN-bal reduces the computation complexity by an average of 62% and memory accesses by an average of 36% with only

关键词： Computational modeling Field programmable gate arrays Throughput inference algorithms Optimization Accuracy Parallel processing Heuristic algorithms Graph neural networks Vectors FPGA hardware architecture temporal graph neural network

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