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SSRN 2023年

作者： Duan, Chenghua Zhou, Wei Zeng, Jun Zhang, Yihao Wen, Junhao School of Bigdata and Software Engineering Chongqing University Daxuecheng South Rd. 55 Chognqing400044 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Moe Chognqing400044 China School of Artificial Intelligence Chongqing University of Technology Chognqing401135 China

Next point-of-interest (POI) recommendation is a crucial part of many location-based service applications. It predicts users’ next visit based on their historical trajectories and current *** solutions primarily focus on sequential modeling of users’ check-in behavior. Unfortunately, most current research overlooks the fact that check-ins are implicit feedback, and inherent noise exists within them. To this end, we propose an approach to cope with noise-learning robust user travel preferences. Specifically, we design a masking strategy for check-ins, which masks the spatio-temporal information of some check-ins in users’ trajectories and forces our model to learn visit preferences without relying on individual inputs, thereby mitigating the bias caused by noisy check-ins. Then, we utilize multi-level geographical information to recommend the next POI more accurately. Additionally, we propose another masking strategy that considers users’ living area and automatically selects the proper strategy based on trajectory features. To the best of our knowledge, this is the first work that investigates the characteristics of noisy check-ins and performs denoising in next POI recommendation. Through extensive experiments, we demonstrate that our model outperforms state-of-the-art approaches and effectively handles data sparsity and noise issues. © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Anchor-Based Discriminative Dual Distribution Calibration for Transductive Zero-Shot Learning

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SSRN 2023年

作者： Zhang, Yi Huang, Sheng Yang, Wanli Tang, Wenhao Zhang, Xiaohong Yang, Dan School of Big Data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Chongqing Shapingba401331 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University No.174 Shazhengjie Chongqing Shapingba400044 China

Zero-shot learning (ZSL) is machine learning task to recognize samples from classes that are not observed during training. Transductive ZSL (TZSL) is a more realistic and effective paradigm that leverages unlabeled unseen data during training to reduce the bias towards seen classes. However, most existing TZSL methods neglect the information gap between visual and semantic spaces, and thus fail to generate distribution-consistent unseen features. To address this issue, we propose a novel TZSL approach named Anchorbased Discriminative Dual Distribution Calibrated Feature Generative Network (AD3C-FGN), which performs anchor-based distribution calibration in both visual and semantic spaces for improving generalization ability of the model. In AD3C-FGN, we adopt conditional generative adversarial network with an unseen discriminator to construct a Y-shape generation model that mitigates the domain shift problem. Moreover, an AD3C module is elaborated for calibrating the distribution of generated and real samples in both visual and semantic spaces with real sample anchors, and also enhance the discriminability of the generated samples. AD3C enforces the generated sample to be closer to its homogenous anchor but farther away from inhomogeneous anchors in both spaces. Extensive experimental results on six popular ZSL benchmarks demonstrate that our method achieves promising performances in different settings. © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Modeling Method for Sampling Zeros of Sampled-data System with Time Delay in Generalized Sample Hold

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IFAC-PapersOnLine 2023年第2期56卷 9799-9805页

作者： Minghui Ou Shan Liang Zhiyong Yang Zhenjie Yan Zhiyong Li School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing 402260 PR China College of Automation Chongqing University Chongqing 400044 PR China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing 400044 PR China. School of Electrical Engineering Southwest Jiaotong University Chengdu 611756 PR China

Unstable zeros are widely known to dramatically curb the performance of controllers and the direct applications of several control algorithms. Time delay is a crucial research topic due to the prevalence of communication and computational delays in the control process. In particular, time delay exacerbates the possibility of continuous-time systems with stable zeros transforming into discrete-time systems with unstable zeros. For continuous-time systems with a relative degree greater than or equal to two, at least one zero of the corresponding discrete-time system converges to either marginally stable or unstable locations as the sampling time approaches zero. This study investigates the sampling zeros of discrete-time systems in the case of a generalized sample hold function (GSHF) with time delay. Moreover, it presents a novel modeling method and stability conditions of the limiting zeros for the discrete-time system. The results demonstrate that the stable properties of zeros for the time delay systems can be preserved in the discretization process when GSHF is applied for signal reconstruction when zero-order hold fails to do so. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method in this study.

关键词： Sampling zeros stability generalized sample hold function (GSHF) time delay. sampled-data system

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Balancing of bike-sharing system via constrained model predictive control 1st

Balancing of bike-sharing system via constrained model predi...

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1st International Conference on Neural computing for Advanced Applications, NCAA 2020

作者： Chen, Yuguang Zeng, Yan Wu, Junjun Li, Qian Wu, Zhou Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China College of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9789811576690

With the development of social urbanization, bike-sharing was born and developed rapidly. Many cities around the world believe that Shared bikes promote environmental protection and development towards sustainable society. In this paper, we consider two problems: bike-sharing system (BSS) redistribution efficiency maximization and system rebalance synchronize. Firstly, we describe BSS dynamic model with a linear form based on graph theory. Then, we propose a quantitative representation to measure the operational efficiency of BSS. We present a model predictive control (MPC) method to solve operational efficiency problem with system constraints. Both the dynamic state and the constraints in the redistribution are considered in MPC algorithm. Then, we verify the effectiveness of our proposed algorithm on different connection type BSS network. According to experimental results, the operational efficiency is maximized and BSS network can reach an equilibrium state during dynamic optimization. Compared to other methods, MPC approach is shown more effective. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Model predictive control

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Efficient Pre-trained Semantics Refinement for Video Temporal Grounding

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Li, Ao Liu, Huijun Zhu, Yiqing Ge, Yongxin Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing 400044 China Chongqing University School of Big Data and Software Engineering Chongqing 401331 China Chongqing University College of Computer Science Chongqing 401331 China Chongqing University National Elite Institute of Engineering Chongqing 401331 China

Video Temporal Grounding (VTG) is a fine-grained video understanding task that aims to ground the relevant video moments corresponding to given language queries. Most existing approaches utilize powerful Vision-Language Models (VLMs), augmented with additional network architectures or specialized modules to supplement temporal reasoning capabilities. Despite achieving impressive performance, these approaches tend to overlook a critical issue that pre-trained visual and textual representations are not specifically optimized for VTG. Particularly, such representations often suffer from inter-modal semantic misalignment referring to the inconsistency between different modalities, and intra-modal semantic confusion, caused by insufficient discriminability within visual modality. To address these limitations, we propose an efficient semantics refinement framework built upon pre-trained models, featuring two core components. First, the Modal Knowledge Bidirectional Propagation (MKBP) component promotes inter-modal semantic alignment via bidirectional enrichment of textual and visual semantics, exploiting their complementary strengths without introducing additional parameters. Second, the Content Context Contrast Learning (C3L) component alleviates intra-modal semantic confusion within visual modality by bringing query-specific visual features closer while separating irrelevant ones. Comprehensive experiments on six benchmark datasets demonstrate the superior performance of our proposed methods. © 1991-2012 IEEE.

关键词： Contrast learning Knowledge propagation Semantic refinement Video temporal grounding Vision-Language Models

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Self-Supervised Commonsense Knowledge Learning for Document-Level Relation Extraction

SSRN

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SSRN 2022年

作者： Li, Rongzhen Zhong, Jiang Xue, Zhongxuan Dai, Qizhu Li, Xue College of Computer Science Chongqing University Chongqing400044 China Key Laboratory of Dependable Service Computing in Cyber-Physical-Society Ministry of Education Chongqing University Chongqing400044 China School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

Compared to sentence-level relation extraction, practical document-level relation extraction (DocRE) is a more challenging task for which multi-entity problems need to be resolved. It aims at extracting relationships between two entities over multiple sentences at once while taking into account significant cross-sentence features. Learning long-distance semantic relation representation across sentences in a document, however, is a widespread and difficult task. To address this problem, this paper proposes a self-supervised commonsense-enhanced DocRE method, called SCDRE, without external knowledge. First, we introduce self-supervised learning to represent commonsense knowledge of each entity in an entity pair based on the commonsense entailed text. Second, we convert the cross-sentence entity pairs into anonymous entity pairs with coreference commonsense replacement. Finally, we perform semantic relation representation learning on the anonymous entity pairs and automatically convert them into target entity pairs. We examined our model on three publicly accessible datasets, DocRED, DialogRE and MPDD, and the results show that it performs significantly better than strong baselines by 2.03% F1, and commonsense knowledge has an important contribution to the DocRE by the ablation experimental analysis. © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Contextual-Semantic-Aware Linkable Knowledge Prediction in Stack Overflow via Self-Attention

Contextual-Semantic-Aware Linkable Knowledge Prediction in S...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Zhaolin Luo Ling Xu Zhou Xu Meng Yan Yan Lei Can Li Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665425889

In Stack Overflow, a question and its answers are defined as a knowledge unit. These knowledge units can be linked together for different purposes, which typically subdivided into four classes: Duplicate, Directly linkable, Indirectly linkable, and Isolated. Developers usually use these linkable knowledge units to search for more targeted information. Prior studies have found that deep learning or SVM technique can effectively predict the class of linkable knowledge units. However, they focus on short-distance semantic relationship but fail to capture global information (semantic relationship between a word and all the words in the same knowledge unit) and ignore joint semantics (semantic relationship between a word with all the words in different knowledge units). To address the issues, we propose a Self-Attention-based contextual semantic aware Linkable Knowledge prediction model (SALKU). SALKU leverages self-attention to pay attention to all the words in a knowledge unit and fully capture the global information needed for each word, then utilizes a variant of self-attention to extract joint semantics between two knowledge units. Experiment results on an existing dataset show that SALKU out-performs the state-of-the-art approaches CNN, Tuning SVM, and Soft-cos SVM in terms of three metrics, respectively. Additionally, SALKU is faster than the three baseline approaches.

关键词： Support vector machines Measurement Knowledge engineering Deep learning Semantics Predictive models Software reliability

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Extended Abstract of Graph4Web: A Relation-Aware Graph Attention Network for Web service Classification

Extended Abstract of Graph4Web: A Relation-Aware Graph Atten...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kunsong Zhao Jin Liu Zhou Xu Xiao Liu Lei Xue Zhiwen Xie Yuxuan Zhou Xin Wang School of Computer Science Wuhan University Wuhan China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) China School of Big Data and Software Engineering Chongqing University Chongqing China School of Information Technology Deakin University Geelong Australia Department of Computing The Hong Kong Polytechnic University Hong Kong China College of Engineering and Computer Science Syracuse University Syracuse USA

Software reuse, as a means to develop new software products with similar functions by virtue of existing software components, has become a popular way during the software development process. In particular, as the service-oriented architecture became popular, web services turned into an indispensable part in modem software development Web services provide a basic composition with high cohesion and loose coupling to support responses among heterogeneous software components, which is the valuable resources for software reuse. The popular web service repositories, such as Programmable Web, contain a mass of web services for beginners and developers to choose from. Nevertheless, the large number of web services also makes it difficult to select the suitable services. Thus, the key to reuse software components lies in how to find appropriate web services from repositories to meet developers requirements in specific application scenarios.

关键词： Couplings Web services Modems service-oriented architecture Software reusability

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