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24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Zeng, Gailun Guo, Jianxiong Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9789819615278

Bandit Convex Optimization (BCO) is an imperative analysis framework when dealing with sequential decision-making problems. Considering to balance the computational cost and bounds of regrets, in this paper, we propose a hybridized algorithm of one- and two-point bandit convex models in non-stationary environments and use a more general performance measure dynamic regret, which records the cumulative difference between function loss and a feasible comparator sequence during the time horizon T. The path length of a comparator sequence PT reveals the non-stationarity of environments. Our proposed algorithm builds an upper bound of dynamic regret O((1+PT)1/2[β(λT)1/2+((1-λ)T)3/4]), where the parameter λ can dynamically adjust the bound guarantee to balance the computational cost in real applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Comparators (optical)

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Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking

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IEEE Transactions on Big data 2025年

作者： Wang, Mingjie Guo, Jianxiong Li, Sirui Xiao, Dingwen Tang, Zhiqing Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China

Deep neural networks have significantly advanced person re-identification (ReID) applications in the realm of the industrial internet, yet they remain vulnerable. Thus, it is crucial to study the robustness of ReID systems, as there are risks of adversaries using these vulnerabilities to compromise industrial surveillance systems. Current adversarial methods focus on generating attack samples using misclassification feedback from victim models (VMs), neglecting VM's cognitive processes. We seek to address this by producing authentic ReID attack instances through VM cognition decryption. This approach boasts advantages like better transferability to open-set ReID tests, easier VM misdirection, and enhanced creation of realistic and undetectable assault images. However, the task of deciphering the cognitive mechanism in VM is widely considered to be a formidable challenge. In this paper, we propose a novel inconspicuous and controllable ReID attack baseline, LCYE (Look Closer to Your Enemy), to generate adversarial query images. Specifically, LCYE first distills VM's knowledge via teacher-student memory mimicking the proxy task. This knowledge prior serves as an unambiguous cryptographic token, encapsulating elements deemed indispensable and plausible by the VM, with the intent of facilitating precise adversarial misdirection. Further, benefiting from the multiple opposing task framework of LCYE, we investigate the interpretability and generalization of ReID models from the view of the adversarial attack, including cross-domain adaption, cross-model consensus, and online learning process. Extensive experiments on four ReID benchmarks show that our method outperforms other state-of-the-art attackers with a large margin in white-box, black-box, and target attacks. The source code can be found at https://***/MingjieWang0606/LCYE-attack_reid. © 2015 IEEE.

关键词： Adversarial machine learning

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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Boundary-Aware Feature Fusion With Dual-Stream Attention for Remote Sensing Small Object Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Song, Jingnan Zhou, Mingliang Luo, Jun Pu, Huayan Feng, Yong Wei, Xuekai Jia, Weijia Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Heavy Rainfall Res Ctr China Wuhan 430074 Peoples R China Beijing Normal Univ Zhuhai BNU UIC Inst Artificial Intelligence & Future Netw Zhuhai 519087 Peoples R China BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Zhuhai 519087 Guangdong Peoples R China

Detecting small objects in remote sensing images poses significant challenges to the field of computer vision, primarily stemming from the complexity of backgrounds, limitations in pixel resolution, and information loss during the feature fusion process. While general object detection has significantly advanced in recent years, remote sensing small object detection remains an unsolved problem, with existing frameworks struggling to achieve high performance at small scales. In this article, we propose a novel framework called the boundary-aware feature fusion network (BAFNet), which significantly enhances the model's ability to represent and locate small objects precisely within complex remote sensing scenarios. First, a dual-stream attention fusion module captures complementary foreground and background cues through bidirectional context modeling. Jointly attending to objects and their surroundings enhances discriminative power for distinguishing small objects. Additionally, we incorporate a boundary-aware branch to better preserve crucial detailed information vital for small-scale objects. This auxiliary component supervises the fusion of contextual semantics and spatial information, aiding in retaining critical boundary details that are prone to loss during cross-layer feature fusion. We conducted experiments on the challenging ai-TOD, VisDrone, DIOR, and LEVIR-Ship datasets. The results demonstrate the superiority of our approach over other state-of-the-art (SOTA) object detection methods, particularly in terms of precisely identifying small objects within remote sensing images. The code is available at https://***/ooo1128/BAFNet.

关键词： Feature extraction Object detection Remote sensing Detectors Accuracy Semantics Training Neck Attention mechanisms Artificial intelligence Boundary-aware feature fusion small object detection

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MTMD: multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Wang, Mingjie Tian, Juanxi Zhang, Mingze Guo, Jianxiong Jia, Weijia BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Gonzaga University School of Engineering and Applied Sciences SpokaneWA99258 United States Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability. Recent advancements in machine learning technologies have showcased their efficacy in discerning authentic profit signals within the realm of stock trend forecasting, predominantly employing temporal data derived from historical stock price patterns. Nevertheless, the inherently volatile and dynamic characteristics of the stock market render the learning and capture of multi-scale temporal dependencies and stable trading opportunities a formidable challenge. This predicament is primarily attributed to the difficulty in distinguishing real profit signal patterns amidst a plethora of mixed, noisy data. In response to these complexities, we propose a multi-Scale Temporal Memory Learning and Efficient Debiasing (MTMD) model. This innovative approach encompasses the creation of a learnable embedding coupled with external attention, serving as a memory module through self-similarity. It aims to mitigate noise interference and bolster temporal consistency within the model. The MTMD model adeptly amalgamates comprehensive local data at each timestamp while concurrently focusing on salient historical patterns on a global scale. Furthermore, the incorporation of a graph network, tailored to assimilate global and local information, facilitates the adaptive fusion of heterogeneous multi-scale data. Rigorous ablation studies and experimental evaluations affirm that the MTMD model surpasses contemporary state-of-the-art methodologies by a substantial margin in benchmark datasets. © 2025 IEEE.

关键词： Profitability

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Modeling and Parameter Identification of Micro-positioning System Based on the aitken Expedited Method

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Gu, Ya Wang, Luyao You, Chengtian Wang, Qing-Guo The College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai201418 China The Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China The Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China

The hysteresis of the positioning platform exhibits rate-dependence. To address this issue, The combined modeling approach for hysteresis, integrating the improved gradient descent method, is proposed. This method incorporates a hysteresis operator at the input of a hybrid radial basis function-autoregressive (RBF-ARX) model. Subsequently, parameter identification is conducted using the gradient descent algorithm based on decomposition identification technology (DGD). Furthermore, the aitken expedited method is introduced into the DGD to mitigate the impact of the learning rate on the algorithm’s convergence. Finally, the performance of the proposed method is validated through simulation and application on a micro-positioning platform, indicating significant improvements in both convergence speed and parameter estimation accuracy. © 1963-2012 IEEE.

关键词： Gradient methods

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QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Yang Wu, Yuan Xing, Huanlai Feng, Li Wang, Tian Jia, Weijia Southwest Jiaotong University School of Computer and Artificial Intelligent Chengdu611756 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

Augmented reality (AR) enhances human perception by overlaying digital information in the real environment and can be applied to various fields such as education, healthcare, and industry. To relieve the computing pressure of resourcelimited AR devices, edge assisted AR has attracted significant attention in recent years. It enables AR devices to offload the computation-intensive environment analysis tasks to the edge server and to download the point-based 3D virtual objects for rendering. Although this method can effectively reduce the processing latency, it may introduce a long transmission latency. To address this issue, this work proposes a semantic communication empowered edge assisted AR to reduce the quality of experience (QoE)-aware latency. To this end, the AR devices first utilize an image encoder to extract semantic features of source images. Due to the importance difference of extracted features and limited communication resources, the feature selection and bandwidth allocation are explored to improve transmission efficiency. In addition, when the edge server completes the generation of pointbased 3D virtual objects, it employs a transformer-based encoder to mask a proportion of points and learn a high-level representation of unmasked points. In this way, low-complexity semantic information of 3D objects is sent to the AR devices, which can reduce the transmission overhead of 3D objects. Moreover, the mask ratio of the point encoder is further optimized to balance the reconstructed quality and transmission latency of 3D objects. Therefore, this work aims to minimize the overall QoE-aware latency of edge assisted AR by jointly optimizing the semantic feature selection, mask ratio, and bandwidth allocation. A convexbased improved Harris Hawks optimization algorithm is designed to solve the formulated problem. Experimental results demonstrate that our semantic communication empowered edge assisted AR can effectively reduce the overall latency and outperform

关键词： Augmented reality

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Control Design for Transient Performance

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 3-7页

作者： Qing-Guo Wang Institute of Artificial Intelligence and Future Networks Beijing Normal University Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International CollegeZhuhai 519087China

CONTROL systems are everywhere – chemical plants,energy systems, manufacturing, homes and buildings,automobiles and trains, medical devices, cellular telephones and internet, aircraft and spacecraft. Recent developments in these fields bring up the systems with the unprecedented scope, scale and complexity such as cyber-physical and human systems, and the required control task becomes more challenging than ever [1]。

关键词： telephone automobile everywhere

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Federated multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2026-2039页

作者： Wang, Mingjie Guo, Jianxiong Jia, Weijia BNU-HKBU United International College Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China BNU-HKBU United International College Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China

Federated learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects the parameters of local models without accessing their local data. However, a significant challenge in FL is handling the heterogeneity of local data distribution, which often results in a drifted global model that is difficult to converge. To address this issue, current methods employ different strategies, such as knowledge distillation, weighted model aggregation, and multitask learning. These approaches are referred to as asynchronous FL, as they align user models either locally or posthoc, where model drift has already occurred or has been underestimated. In this article, we propose an active and synchronous correlation approach to address the challenge of user heterogeneity in FL. Specifically, our approach aims to approximate FL as standard deep learning by actively and synchronously scheduling user learning pace in each round with a dynamic multiphase curriculum. A global curriculum is formed by an autoregressive autoencoder that integrates all user curricula on the server. This global curriculum is then divided into multiple phases and broadcast to users to measure and align the domain-agnostic learning pace. Empirical studies demonstrate that our approach outperforms existing asynchronous approaches in terms of generalization performance, even in the presence of severe user heterogeneity. © 2023 IEEE.

关键词： Curricula

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