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Attributes-Assisted Joint Contrastive Learning for Person Re-Identification

IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 24672-24684页

作者： Wu, Qingru Zhou, Zhiheng Niu, Chang Liu, Xiaosheng Li, Bo South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Guangdong Peoples R China

Person reidentification (Re-ID) is a crucial technology for intelligent security in Internet of Things (IoT) systems. Recently, unsupervised learning has been widely used for person Re-ID due to its generalization property. However, the effectiveness of commonly used unsupervised clustering methods heavily relies on the quality of the clustered pseudo-labels. Moreover, pedestrian shots in real scenes are prone to factors, such as occlusion. In this article, we propose a novel global and local joint contrastive learning (GLCL) framework based on the memory bank. Specifically, we establish separate memory banks for global and local features, which are updated using global simple samples and local hard samples. The GLCL module helps excavate information from simple and hard samples, aiming to overcome the effects of poor retrieval scenarios, such as background clutter and occlusion. Additionally, we design an attributes-assisted clustering (AAC) module that utilizes pedestrian attributes to refine the clustering results. The AAC module can effectively reduce the impact of pseudo-label noise owing to the supplementary information offered by attributes. Our approach shows improved performance in person Re-ID tasks in complex scenarios, providing a promising solution for intelligent security systems in the IoT. Experimental results demonstrate the superiority of our proposed method.

关键词： Attributes-assisted contrastive learning Internet of Things (IoT) person reidentification (Re-ID) pseudo-label Attributes-assisted contrastive learning Internet of Things (IoT) person reidentification (Re-ID) pseudo-label

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Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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Diverse Visual Question Generation Based on Multiple Objects Selection

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第6期20卷 1-22页

作者： Fang, Wenhao Xie, Jiayuan Liu, Hongfei Chen, Jiali Cai, Yi South China Univ Technol Sch Software Engn Guangzhou Peoples R China SCUT Key Lab Big Data & Intelligent Robot MOE China Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Visual question generation task aims at generating high-quality questions about a given image. To make this tak applicable to various scenarios, e.g., the growing demand for exams, it is important to generate diverse questions. The existing methods for this task control diverse question generation based on different question types, e.g., "what" and "when." Although different question types lead to description diversity, they cannot guarantee semantic diversity when asking the same objects. Research in the field of psychology shows that humans pay attention to different objects in an image based on their preferences, which is beneficial to constructing semantically diverse questions. According to the research, we propose a multi-selector visual question generation (MS-VQG) model that aims to focus on different objects to generate diverse questions. Specifically, our MS-VQG model employs multiple selectors to imitate different humans to select different objects in a given image. Based on these different selected objects, our MS-VQG model can generate diverse questions corresponding to each selector. Extensive experiments on two datasets show that our proposed model outperforms the baselines in generating diverse questions.

关键词： Multimodal visual question generation mixture of experts

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Multi-UAV air combat cooperative game based on virtual opponent and value attention decomposition policy gradient

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Xu, Xiaojie Wang, Yunfan Guo, Xian Huang, Kuihua Zhang, Xuebo Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin Peoples R China Natl Univ Def Technol Coll Syst Engn Lab Big Data & Decis Changsha 410073 Peoples R China

In the multi-unmanned aerial vehicle (UAV) air combat confrontation environment, deriving the cooperative policy of friendly aircraft is still a challenge, owing to the higher-order differential dynamics model of aircraft and the confidence assignment problem in multi-UAV air combat with conflict and cooperation. In this paper, a novel reinforcement learning method that combines virtual opponent and value attention decomposition is proposed. In particular, to reduce the difficulty in training induced by the higher order differential dynamics model, the actions of aircraft are abstracted into actions of the game layer and maneuvering actions of the bottom layer, in which the actions of the game layer are modeled as the pose of the virtual opponent. In the training process, only the policy of the game layer is trained, and the maneuvering policy of the bottom layer is the default policy or the rule-based policy. To address the confidence assignment problem encountered during multi-UAV cooperative training, the total value function of the team is decomposed into individual value functions based on the attention mechanism, and the policy of the game layer is optimized by integrating the individual value into the gradient computation as the baseline. Finally, the algorithm is verified on the dynamic high-fidelity training platform. The results indicate that the algorithm outperforms the state-of-the-art method in typical multi-UAV air combat scenarios such as 4V4, 5V5, and 6V6.

关键词： Air combat cooperative game Reinforcement learning Value attention decomposition

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WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models 38

WISE: Rethinking the Knowledge Memory for Lifelong Model Edi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Peng Li, Zexi Zhang, Ningyu Xu, Ziwen Yao, Yunzhi Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing. Where the updated knowledge resides in memories is a fundamental question for model editing. In this paper, we find that editing either long-term memory (direct model parameters) or working memory (nonparametric knowledge of neural network activations/representations by retrieval) will result in an impossible triangle-reliability, generalization, and locality can not be realized together in the lifelong editing settings. For long-term memory, directly editing the parameters will cause conflicts with irrelevant pretrained knowledge or previous edits (poor reliability and locality). For working memory, retrieval-based activations can hardly make the model understand the edits and generalize (poor generalization). Therefore, we propose WISE to bridge the gap between memories. In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge. We only edit the knowledge in the side memory and train a router to decide which memory to go through when given a query. For continual editing, we devise a knowledge-sharding mechanism where different sets of edits reside in distinct subspaces of parameters and are subsequently merged into a shared memory without conflicts. Extensive experiments show that WISE can outperform previous model editing methods and overcome the impossible triangle under lifelong model editing of question answering, hallucination, and out-of-distribution settings across trending LLM architectures, e.g., GPT, LLaMA, and Mistral. © 2024 Neural information processing systems foundation. All rights reserved.

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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Learning defense transformations for counterattacking adversarial examples

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NEURAL NETWORKS 2023年第1期164卷 177-185页

作者： Li, Jincheng Zhang, Shuhai Cao, Jiezhang Tan, Mingkui South China Univ Technol Guangzhou Peoples R China PengCheng Lab Shenzhen Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China

Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of DNNs by defending against adversarial examples. Existing defense methods focus on some specific types of adversarial examples and may fail to defend well in real-world applications. In practice, we may face many types of attacks where the exact type of adversarial examples in real-world applications can be even unknown. In this paper, motivated by that adversarial examples are more likely to appear near the classification boundary and are vulnerable to some transformations, we study adversarial examples from a new perspective that whether we can defend against adversarial examples by pulling them back to the original clean distribution. We empirically verify the existence of defense affine transformations that restore adversarial examples. Relying on this, we learn defense transformations to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs. Extensive experiments on both toy and real-world data sets demonstrate the effectiveness and generalization of our defense method. The code is avaliable at https://***/SCUTjinchengli/DefenseTransformer.

关键词： Adversarial examples Classification boundary Defense transformations Affine transformations

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T-Assess: An Efficient data Quality Assessment System Tailored for Trajectory data 51st

T-Assess: An Efficient Data Quality Assessment System Tailor...

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51st International Conference on Very Large data Bases, VLDB 2025

作者： Zhu, Junhao Wang, Tao Hu, Danlei Fang, Ziquan Chen, Lu Gao, Yunjun Li, Tianyi Jensen, Christian S. Zhejiang University China Zhejiang University Zhejiang Key Laboratory of Big Data Intelligent Computing China Aalborg University Denmark

With the widespread use of GPS-enabled devices and services, trajectory data fuels services in a variety of fields, such as transportation and smart cities. However, trajectory data often contains errors stemming from inaccurate GPS measurements, low sampling rates, and transmission interruptions, yielding low-quality trajectory data with negative effects on downstream services. Therefore, a crucial yet tedious endeavor is to assess the quality of trajectory data, serving as a guide for subsequent data cleaning and analyses. Despite some studies addressing general-purpose data quality assessment, no studies exist that are tailored specifically for trajectory data. To more effectively diagnose the quality of trajectory data, we propose T-Assess, an automated trajectory data quality assessment system. T-Assess is built on three fundamental principles: i) extensive coverage, ii) versatility, and iii) efficiency. To achieve comprehensive coverage, we propose assessment criteria spanning validity, completeness, consistency, and fairness. To provide high versatility, T-Assess supports both offline and online evaluations for full-batch trajectory datasets as well as real-time trajectory streams. In addition, we incorporate an evaluation optimization strategy to achieve assessment efficiency. Extensive experiments on fourreal-life benchmark datasets offer insight into the effectiveness of T Assess at quantifying trajectory data quality beyond the capabilities of state-of-the-art data quality systems. © 2025, VLDB Endowment, All rights reserved.

关键词： data quality

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Rational molecular design of P-doped porous carbon material for the VOCs adsorption

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Chinese Journal of Chemical Engineering 2025年第3期79卷 155-163页

作者： Changqing Su Wentao Jiang Yang Guo Guodong Yi Zengxing Li Huan Li Hunan Key Laboratory of Carbon Neutrality and Intelligent Energy School of Resources & Environment Hunan University of Technology and Business Xiangjiang Laboratory School of Advanced Interdisciplinary Studies Research Institute for Big Data & Internet Innovation Hunan University of Technology and Business Industry Training Center Shenzhen Polytechnic University

The objective of this study was to identify and synthesize functional groups for the efficient adsorption of volatile organic compounds（VOCs） through a combination of theoretical calculations,molecular design,and experimental *** density functional theory（DFT） calculation,focusing on the P-containing functional groups,showed that methanol adsorption was dominated by the electrostatic interaction between the carbon surface and methanol,while toluene was mainly trapped through π-π dispersive interaction between toluene molecule and functional group *** experimental results showed the phosphorus-doped carbon materials（PCAC） prepared by directly activating potassium phytate had a phosphorus content of up to 4.5%（atom）,mainly in the form of C—O—P（O）(OH)*** material exhibited a high specific area(987.6 m2·g-1) and a large adsorption capacity for methanol(440.0 mg·g-1) and toluene(350.1 mg·g-1).These properties were superior to those of the specific commercial activated carbon（CAC）sample used for comparison in this *** adsorption efficiencies per unit specific surface area of PCAC were 0.45 mg·g-1m2for methanol and 0.35 mg·g-1·m-2for *** study provided a novel theoretical and experimental framework for the molecular design of polarized elements to enhance the adsorption of polar gases,offering significant advancements over existing commercial solutions.

关键词： Adsorption Density functional theory Doped porous carbon Molecular design VOCs

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Unsupervised Domain Adaption Harnessing Vision-Language Pre-Training

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第9期34卷 8201-8214页

作者： Zhou, Wenlve Zhou, Zhiheng South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510641 Guangdong Peoples R China

This paper addresses two vital challenges in Unsupervised Domain Adaptation (UDA) with a focus on harnessing the power of Vision-Language Pre-training (VLP) models. Firstly, UDA has primarily relied on ImageNet pre-trained models. However, the potential of VLP models in UDA remains largely unexplored. The rich representation of VLP models holds significant promise for enhancing UDA tasks. To address this, we propose a novel method called Cross-Modal Knowledge Distillation (CMKD), leveraging VLP models as teacher models to guide the learning process in the target domain, resulting in state-of-the-art performance. Secondly, current UDA paradigms involve training separate models for each task, leading to significant storage overhead and impractical model deployment as the number of transfer tasks grows. To overcome this challenge, we introduce Residual Sparse Training (RST) exploiting the benefits conferred by VLP's extensive pre-training, a technique that requires minimal adjustment (approximately 0.1%similar to 0.5%) of VLP model parameters to achieve performance comparable to fine-tuning. Combining CMKD and RST, we present a comprehensive solution that effectively leverages VLP models for UDA tasks while reducing storage overhead for model deployment. Furthermore, CMKD can serve as a baseline in conjunction with other methods like FixMatch, enhancing the performance of UDA. Our proposed method outperforms existing techniques on standard benchmarks. Our code will be available at: https://***/Wenlve-Zhou/VLP-UDA.

关键词： Unsupervised domain adaptation vision-language pre-training cross-modal knowledge distillation residual sparse training model deployment

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