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STR: Spatio-temporal trajectory representation learning with dual-focus encoder for whole trajectory similarity computation

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Information Fusion 2025年 123卷

作者： Li, Mengqiu Niu, Xinzheng Zhu, Jiahui Fournier-Viger, Philippe Wu, Youxi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China school of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Artificial Intelligence Hebei University of Technology Tianjin China

In the field of trajectory data mining, trajectory similarity computation is a key issue associated with trajectory representation learning. Current methods of trajectory similarity computation often represent trajectory data of varying lengths as vectors with uniform dimension. However, existing trajectory representations fall short of comprehensively calculating trajectory similarity: first, they focus solely on spatial information, while overlooking the temporal information of the trajectory, and second, they represent the temporal and spatial information separately. To address these limitations, we propose a novel Spatio-temporal Trajectory Representation method, named STR, aimed at capturing both the features of individual trajectories and the relationships between them in a three-dimensional spatio-temporal region from the perspectives of both trajectory points and whole trajectories, and improving the accuracy of trajectory similarity metrics. STR consists of two main modules: (i) a trajectory feature tokenization module, which considers the uneven density distribution of trajectory points in the three-dimensional space to build a hierarchical structure that considers space and time as a whole, and extracts spatio-temporal features to construct a token sequence;(ii) a dual-focus trajectory encoder learns the features of a given trajectory and those of similar trajectories, thereby enabling the consideration of relationships between whole trajectories. Moreover, a density-based whole trajectory similarity metric is designed to find similar trajectories. Experiments were conducted on three public datasets, and the accuracy of the trajectory representations obtained with STR is significantly greater than for existing state-of-the-art baselines on a variety of distance metrics. © 2025 Elsevier B.V.

关键词： Dual-focus trajectory encoder Representation learning Spatio-temporal trajectory Trajectory similarity

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Conjugate Surrogate for Expensive Multiobjective Optimization

Conjugate Surrogate for Expensive Multiobjective Optimizatio...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Qi-Te Yang Liu-Yue Luo Xin-Xin Xu Chun-Hua Chen Hua Wang Jun Zhang Zhi-Hui Zhan School of Computer Science and Engineering South China University of Technology Guangzhou P. R. China College of Artificial Intelligence Nankai University Tianjin P. R. China School of Computer Science and Technology Ocean University of China Qingdao P. R. China School of Software Engineering South China University of Technology Guangzhou P. R. China Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne Australia Hanyang University Ansan South Korea

The Kriging surrogate (KS) has been widely used in surrogate-assisted multiobjective evolutionary algorithms (SAMOEAs) for solving expensive multiobjective optimization problems (EMOPs). Typically, when tackling an M-objective EMOP, a KS consists of M Kriging models, in which each model is used to approximate one objective function to replace the expensive fitness evaluation. Since such a KS is only efficient in solving low-dimensional EMOPs, the dimension reduction method has been adopted to construct the reduction surrogate (RS) to reduce training costs. However, both KS and RS can only approximate the mapping from variables to different objectives (i.e., objective function) but ignore the potential relationship between objectives. For practical applications, it is necessary to take into account the mapping between different objectives for the reliability of the surrogate. Therefore, we for the first time propose the concept of the conjugate surrogate (CS) and construct a simple CS to realize the approximated mapping from objectives to objectives. Different from KS or RS, all models in CS are conjugate symbiosis. In collaboration with RS, CS can not only benefit the light training cost, but also improve the convergence speed. Compared with five state-of-the-art SAMOEAs, the CS-assisted algorithm shows its effectiveness and competitiveness in solving EMOPs.

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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BHONEM:Binary High-Order Network Embedding Methods for Net worked-Guarantee Loans

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Journal of computer science & Technology 2019年第3期34卷 657-669页

作者： Da-Wei Cheng Yi Tu Zhen-Wei Ma Zhi-Bin Niu Li-Qing Zhang Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Mathematical Science Shanghai Jiao Tong University.Shanghai 200240China School of Computer Software Tianjin UniversityTianjin 300354China

Networked-guarantee loans may cause systemic risk related concern for the government and banks in *** prediction of the default of enterprise loans is a typical machine learning based classification problem, and the networked guarantee makes this problem very difficult to solve. As we know, a complex network is usually stored and represented by an adjacency matrix. It is a high-dimensional and sparse matrix, whereas machine-learning methods usually need lowdimensional dense feature representations. Therefore, in this paper, we propose a binary higher-order network embedding method to learn the low-dimensional representations of a guarantee network. We first set vertices of this heterogeneous economic network by binary roles (guarantor and guarantee), and then define high-order adjacent measures based on their roles and economic domain knowledge. Afterwards, we design a penalty parameter in the objective function to balance the importance of network structure and adjacency. We optimize it by negative sampling based gradient descent algorithms,which solve the limitation of stochastic gradient descent on weighted edges without compromising efficiency. Finally, we test our proposed method on three real-world network datasets. The result shows that this method outperforms other start-of-the-art algorithms for both classification accuracy and robustness, especially in a guarantee network.

关键词： networked-guarantee loan high-order network embedding representative learning gradient descent

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Personalized tourism recommendation algorithm integrating tag and emotional polarity analysis

Personalized tourism recommendation algorithm integrating ta...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Peng Yuan Qiuxia Chen Zhifeng Wang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan Liaoning China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen Guangdong China

ISBN: (纸本)9798350309720

The matrix factorization algorithm has achieved good results in the field of scenic spot recommendation, but there are still many problems. Most of the studies only refer to the user’ s score of the tourist attraction to represent the user ’s overall evaluation of the attraction, thus ignoring the correlation between the online review text information of the attraction and the attraction, which eventually leads to the low accuracy of the entire recommendation system. This paper proposes a personalized tourism recommendation algorithm that integrates scenic spot labels and sentiment polarity analysis. On the basis of constructing feature labels for various scenic spots, the sentiment polarity analysis of user comments is carried out through natural language text information processing, and implicit emotions are given to the labels contained in scenic spots. Secondly, it is introduced into the factor vector of matrix decomposition model algorithm, and finally the potential relationship between users and feature labels and scenic spots is deeply explored. Through the user ’s preference for emotional feature labels, the user ’s approximate rating value for attractions is predicted. Experiments show that compared with the user-based collaborative filtering scenic spot recommendation algorithm, the average absolute error (MAE) and root mean square error (RMSE) are reduced by 70.28 % and 65.23 % respectively. Compared with the tag-based collaborative filtering attraction recommendation algorithm, MAE and RMSE are reduced by 65.02 % and 63.93 %, respectively. Compared with the matrix factorization recommendation algorithm of scenic spot labels, MAE and RMSE are reduced by 34.02% and 29.93 % respectively. To sum up the performance of the algorithm is significantly higher than the existing attractions recommended algorithm can be more accurate to provide users with recommended options.

关键词： Correlation Collaborative filtering Natural languages Information processing Prediction algorithms Approximation algorithms Matrix decomposition

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TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

arXiv

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arXiv 2022年

作者： Lv, Pei Pei, Xinming Ren, Xinyu Zhang, Yuzhen Li, Chaochao Xu, Mingliang The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China The State Key Laboratory of CAD&CG Zhejiang University Hangzhou China Network engineering Henan University China Computer science and technology The State Key Laboratory of CAD&CG Zhejiang University Hangzhou China Software engineering Zhengzhou University China Software engineering Henan Polytechnic University Jiaozuo China The School of Information Engineering Zhengzhou University Zhengzhou China

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent’s movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others’ motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method. © 2022, CC BY-NC-SA.

关键词： Motion planning

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Ethical Principles and Governance Technology Development of AI in China

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engineering 2020年第3期6卷 302-309页

作者： Wenjun Wu Tiejun Huang Ke Gong State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China Chinese Institute of New Generation Artificial Intelligence Development Strategie Nankai UniversityTianjin 300071China

Ethics and governance are vital to the healthy and sustainable development of artificial intelligence(AI).With the long-term goal of keeping AI beneficial to human society,governments,research organizations,and companies in China have published ethical guidelines and principles for AI,and have launched projects to develop AI governance *** paper presents a survey of these efforts and highlights the preliminary outcomes in *** also describes the major research challenges in AI governance research and discusses future research directions.

关键词： AI ethical principles AI governance technology Machine learning Privacy Safety Fairness

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MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

arXiv

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arXiv 2023年

作者： Guo, Jingcai Xu, Yuanyuan Xu, Wenchao Zhan, Yufeng Sun, Yuxia Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Computer Science Jinan University Guangzhou510632 China Guangdong Provincial Key Laboratory of High-Performance Computing Guangzhou510275 China

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Concretely, we first generate a malware image for each observed sample based on their numeric features using our proposed numeric encoder with a re-designed multi-scale CNN structure, which can better explore their statistical and spatial correlations. Besides, we propose to organize tokenized malware features into a sentence for each sample considering its behaviors and dynamics, and utilize language models as the textual encoder to transform it into a representable and computable textual vector. Such parallel multi-modal encoders can fuse the above two components to enhance the feature diversity. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated subspace, i.e., discriminative and exclusive spaces, with contrastive sampling and ρ-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and cont

关键词： Modal analysis

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Accessible or Not? An Empirical Investigation of Android App Accessibility

arXiv

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arXiv 2022年

作者： Chen, Sen Chen, Chunyang Fan, Lingling Fan, Mingming Zhan, Xian Liu, Yang College of Intelligence and Computing Tianjin University China Monash University Australia College of Cyber Science Nankai University China The Hong Kong University of Science and Technology Hong Kong The Hong Kong Polytechnic University Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

Mobile apps provide new opportunities to people with disabilities to act independently in the world. Following the law of the US, EU, mobile OS vendors such as Google and Apple have included accessibility features in their mobile systems and provide a set of guidelines and toolsets for ensuring mobile app accessibility. Motivated by this trend, researchers have conducted empirical studies by using the inaccessibility issue rate of each page (i.e., screen level) to represent the characteristics of mobile app accessibility. However, there still lacks an empirical investigation directly focusing on the issues themselves (i.e., issue level) to unveil more fine-grained findings, due to the lack of an effective issue detection method and a relatively comprehensive dataset of issues. To fill in this literature gap, we first propose an automated app page exploration tool, named Xbot, to facilitate app accessibility testing and automatically collect accessibility issues by leveraging the instrumentation technique and static program analysis. Owing to the relatively high activity coverage (around 80%) achieved by Xbot when exploring apps, Xbot achieves better performance on accessibility issue collection than existing testing tools such as Google Monkey. With Xbot, we are able to collect a relatively comprehensive accessibility issue dataset and finally collect 86,767 issues from 2,270 unique apps including both closed-source and open-source apps, based on which we further carry out an empirical study from the perspective of accessibility issues themselves to investigate novel characteristics of accessibility issues. Specifically, we extensively investigate these issues by checking 1) the overall severity of issues with multiple criteria, 2) the in-depth relation between issue types and app categories, GUI component types, 3) the frequent issue patterns quantitatively, and 4) the fixing status of accessibility issues. Finally, we highlight some insights to the community and h

关键词： Application programs

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A Compatible ECG Diagnosis Cloud Computing Framework and Prototype Application 20

A Compatible ECG Diagnosis Cloud Computing Framework and Pro...

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6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

作者： Zhang, Zhen Li, Hongqiang Gong, Zheng Jin, Rize Chung, Tae-Sun School of Computer Science and Technology Tiangong University Tianjin300387 China Dept. of Software Ajou University Suwon16499 Korea Republic of School of Electronics and Information Engineering Tiangong University Tianjin300387 China

ISBN: (纸本)9781450377089

The ECG signal analysis and diagnosis algorithms have been studied for decades. There are some state of art algorithms that have been developed. In this paper, we proposed a compatible ECG automatic diagnosis Cloud Computing framework in order to integrate these exist algorithms. On the other hand, there are many studies regarding the IoT based health diagnosis system. But there are few of that aiming at the personal use health monitor and diagnose. Basing on our proposed framework, users can diagnose their heart health status by themselves conveniently anywhere and anytime through the mobile application. The ECG character automatic classification computing algorithm is compatible for Python and MATLAB by introducing the hybrid programming technic on the cloud computing side. So that, it is easy for researchers to integrate their developed algorithm into this framework to build an application quickly. We developed a prototype application as well to verify the availability of this framework. © 2020 ACM.

关键词： Python

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