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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

Journal of Computer science & Technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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FPS Killer - A Moving Target Detection Algorithm in FPS Game Based on Yolov3 Frame and Gradient Descent Algorithm 2

FPS Killer - A Moving Target Detection Algorithm in FPS Game...

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2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Huang, Jiayu Liu, Aofan School of Computing Jiangsu Vocational College of Commerce Jiangsu China School of Computing & Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350370188

With the continuous development of the economic level of the society, the game industry is getting more and more extensive development among young people. As one of the mainstreams in the game industry, FPS games are warmly welcomed by the majority of players. In order to ensure the fairness of the game, anti-cheat technologies have emerged, such as 'BattlEye' and 'ValveAnti-Cheat'. This article applies the current popular computer vision technology to FPS games and proposes a cheating technology that cannot be detected by traditional anti-cheating methods. However, this project will only be used for academic research and discussion, and it is forbidden to use it for commercial sales and esports. © 2024 IEEE.

关键词： Object detection

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A Progressive Skip Reasoning Fusion Method for Multi-Modal Classification 24

A Progressive Skip Reasoning Fusion Method for Multi-Modal C...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qian Liang, Xinyan Qian, Yuhua Cui, Zhihua Wen, Jie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798400706868

In multi-modal classification tasks, a good fusion algorithm can effectively integrate and process multi-modal data, thereby significantly improving its performance. Researchers often focus on the design of complex fusion operators and have proposed numerous fusion operators, while paying less attention to the design of feature fusion usage, specifically how features should be fused to better facilitate multi-modal classification tasks. In this article, we propose a progressive skip reasoning fusion network (PSRFN) to make some attempts to address this issue. Firstly, unlike most existing multi-modal fusion methods that only use one fusion operator in a single stage to fuse all view features, PSRFN utilizes the progressive skip reasoning (PSR) block to fuse all views with a fusion operator at each layer. Specifically, each PSR block utilizes all view features and the fused features from the previous layer to jointly obtain the fused features for the current layer. Secondly, each PSR block utilizes a dual-weighted fusion strategy with learnable parameters to adaptively allocate weights during the fusion process. The first level of weighting assigns weights to each view feature, while the second level assigns weights to the fused features from the previous layer and the fused features obtained from the first level of weighting in the current layer. This strategy ensures that the PSR block can dynamically adjust the weights based on the actual contribution of features. Finally, to enable the model to fully utilize feature information from different levels for feature fusion, the skip connections are adopted between PSR blocks. Extensive experiment results on six real multi-modal datasets show that a better usage for fusion operator is indeed able to improve performance. © 2024 ACM.

关键词： Classification (of information)

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Counseling Responses for Mental Health Forum Questions with Early Maladaptive Schema Prediction 27

Counseling Responses for Mental Health Forum Questions with ...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Gollapalli, Sujatha Das Ang, Beng Heng Du, Mingzhe Ng, See-Kiong Institute of Data Science National University of Singapore Singapore Integrative Sciences and Engineering Programme National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University China

ISBN: (纸本)9781643685489

State-of-the-art Large Language Models (LLMs) have shown remarkable capabilities for general Question Answering (QA) tasks. However, their practical use for answering mental health questions has been limited due to the missing link between LLM-generated answer responses and well-established theories and guiding principles from Psychology and Counseling. We present a first step in this direction with STeer, an AI-based method that supports Schema Therapy-enabled responses for mental health questions on community QA forums. STeer uses Early Maladaptive Schemas (EMSs), a fundamental concept from Schema Therapy that characterizes "self-defeating, unhealthy patterns of thought and behavior" in individuals, to effectively prompt state-of-the-art LLMs to generate specific, theoretically-grounded, empathetic counseling responses to mental health questions. We present EMSRank, a novel method based on the Personalized PageRank algorithm, to automatically predict the EMSs from mental health forum question texts. We show that EMSRank is computationally scalable and can be further combined with textual entailment to obtain high precision, explainable EMS labels for mental health forum questions. To address the current lack of annotated datasets, we also leveraged on EMSRank to create a first-of-its-kind, large dataset of about 23K EMS-annotated mental health questions from three diverse, currently operating, peer-support community forums for mental health. With the global rise in mental health issues, our work is a timely step towards enabling the use of AI-based assistive tools for counseling support on mental health community forums. © 2024 The Authors.

关键词： Electronic health record

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A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 990-1004页

作者： Xiaohua Ge Qing-Long Han Jun Wang Xian-Ming Zhang IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science and School of Data Science City University of Hong KongChina

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external *** central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction *** this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle ***,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is *** is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is *** simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

关键词： Adaptive control collision avoidance distributed formation control multi-vehicle systems neural networks obstacle avoidance repulsive potential

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Classification recognition for traffic dangerous driving behavior

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第Speical issue期1卷 115-124页

作者： Yang, X.W. School of Information and Communication Engineering Shanxi Electronic Science and Technology Institute Linfen041000 China School of Data Science and Technology North University of China Shan Xi Taiyuan030051 China

The precise classification recognition of dangerous driving behaviors can effectively reduce traffic accidents and improve road safety. To address the challenges of complex and dynamic traffic environments where driving behaviors can vary significantly’ an innovative method for classification recognizing for dangerous driving behaviors has been developed’ utilizing an enhanced k-means clustering algorithm. Wavelet decomposition is aPPlied to denoise the image data of driving behaviors. The refined data is then effectively categorized using the improved k-means clustering technique to discern distinct driving actions. Convolutional neural networks are employed to extract and classify critical action areas’ enabling precise identification of hazardous driving behaviors. Test results demonstrate that this designed method achieves a maximum classification recognition rate of 98% for dangerous driving behaviors’ with shorter recognition processing time. © 2024, Aracne Editrice. All rights reserved.

关键词： K-means clustering

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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English-to-Low-Resource Translation: A Multimodal Approach for Hindi, Malayalam, Bengali, and Hausa 9

English-to-Low-Resource Translation: A Multimodal Approach f...

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9th Conference on Machine Translation, WMT 2024

作者： Hatami, Ali Banerjee, Shubhanker Arcan, Mihael Buitelaar, Paul McCrae, John Philip Insight SFI Research Centre for Data Analytics Data Science Institute University of Galway Ireland ADAPT Centre Data Science Institute University of Galway Ireland Lua Health Galway Ireland School of Computer Science University of Galway Ireland

ISBN: (纸本)9798891761797

Multimodal machine translation leverages multiple data modalities to enhance translation quality, particularly for low-resourced languages. This paper uses a multimodal model that integrates visual information with textual data to improve translation accuracy from English to Hindi, Malayalam, Bengali, and Hausa. This approach employs a gated fusion mechanism to effectively combine the outputs of textual and visual encoders, enabling more nuanced translations that consider both language and contextual visual cues. The model’s performance was evaluated against the text-only machine translation model based on BLEU, ChrF2 and TER. Experimental results demonstrate that the multimodal approach consistently outperforms the text-only baseline, highlighting the potential of integrating visual information in low-resourced language translation tasks. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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