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Attention based simplified deep residual network for citywide crowd flows prediction

Frontiers of Computer Science 2021年第2期15卷 51-62页

作者： Genan DAI Xiaoyang HU Youming GE Zhiqing NING Yubao LIU School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006China

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the *** practice,emergency applications often require less training ***,there is a little work on how to obtain good prediction performance with less training *** this paper,we propose a simplified deep residual network for our *** using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar ***,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time *** on the real datasets,we construct a series of experiments compared with the existing *** experimental results confirm the efficiency of our proposed methods.

关键词： crowd flows prediction spatio-temporal data mining attention

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Non-SPF routing algorithm based on ordered semi-group preference algebra

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The Journal of China Universities of Posts and Telecommunications 2017年第6期24卷 14-23页

作者： Zhang Yongtang Fan Bo Department of Computer Science and Technology Guangdong Neusoft Institute Foshan 528225 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China

Layer 2 network technology is extending beyond its traditional local area implementation and finding wider acceptance in provider＇s metropolitan area networks and large-scale cloud data center networks. This is mainly due to its plug-and-play capability and native mobility support. Many efforts have been put to increase the bisection bandwidth in layer 2 network, which has been constrained by the spanning tree protocol （STP） that layer 2 network uses for preventing looping. The recent trend is to incorporate layer 3＇s routing approach into layer 2 network so that multiple paths can be used for forwarding traffic between any source-destination （S-D） node pair. Equal cost multipath （ECMP） is one such example. However, ECMP may still be limited in generating multiple paths due to its shortest path （lowest cost） requirement. In this paper, we consider a non-shortest-path routing approach, called equal preference multipath （EPMP） based on ordered semi group theory, which can generate more paths than ECMP. In EPMP routing, all the paths with different traditionally-defined costs, such as hops, bandwidth, etc., can be determined equally now and thus they become equal candidate paths. By the comparative tests with ECMP, EPMP routing not only generates more paths, provides 15% higher bisection bandwidth, but also identifies bottleneck links in a hierarchical network when different traffic patterns are applied EPMP is more flexible in controlling the number and length of multipath generation. Simulation results indicate the effectiveness of the proposed algorithm. It is a good reference for non-blocking running of big datacenter networks.

关键词： non-SPF routing algorithm algebraic routing equal preference multipath datacenter networks

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Low-rank reduced biquaternion tensor ring decomposition and tensor completion

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Applied Mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of Computer Science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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Demand Learning and Cooperative Deployment of UAV Networks

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Chinese Journal of Electronics 2022年第3期31卷 408-415页

作者： ZHANG Xiao WANG Xuehe XU Xinping ZHAO Yingchao South-Central University for Nationalities Caritas Institute of Higher Education Sun Yat-sen University & Guangdong Key Laboratory of Big Data Analysis and Processing Nanyang Technological University

Unmanned aerial vehicle(UAV) as a powerful tool has found its applicability in assisting mobile users to deal with computation-intensive and delaysensitive applications(e.g., edge computing, high-speed Internet access, and local caching). However, deployment of UAV-aided mobile services(UMS) faces challenges due to the UAV limitation in wireless coverage and energy storage. Aware of such physical limitations, a future UMS system should be intelligent enough to self-plan trajectories and best offer computational capabilities to mobile users. There are important issues regarding the UAV-user interaction, UAV-UAV cooperation for sustainable service provision, and dynamic UMS pricing. These networking and resource management issues are largely overlooked in the literature and this article presents intelligent solutions for cooperative UMS deployment and operation. Mobile users' locations are generally private information and changing over time. How to learn on-demand users' truthful location reporting is important for determining optimal UAV deployment in serving all the users fairly. After addressing the truthful UAV-user interaction issue via game theory, we further study the UAV network sustainability for UMS provision by minimizing the energy consumption cost during deployment and seeking UAV-UAV cooperation. Finally, for profit-maximizing purpose, we analyze the cooperative UAVs' deployment, capacity allocation, and dynamic service pricing.

关键词： UAV network sustainability UMS provision local caching path planning delay-sensitive applications future UMS system UAV-UAV cooperation Internet access wireless coverage mobile robots energy storage optimal UAV deployment Internet control engineering computing autonomous aerial vehicles unmanned aerial vehicle dynamic service pricing learning (artificial intelligence) game theory mobile computing UAV-aided mobile services edge computing UMS deployment sustainable service provision resource allocation UAV-user interaction issue on-demand users resource management issues dynamic UMS pricing computation-intensive applications

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Deduplication with blockchain for secure cloud storage 6th

Deduplication with blockchain for secure cloud storage

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6th CCF Academic Conference on big data, CCF big data 2018

作者： Li, Jingyi Wu, Jigang Chen, Long Li, Jiaxing Guangdong University of Technology Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9789811329210

Secure deduplication techniques have been wildly used in cloud storage to save both disk space and network bandwidth. However, traditional schemes store only one copy in cloud which raises problems with data reliability. Meanwhile, the existence of any third party, which is adopted by many deduplication schemes, will bring the risk of being single-pointed. In order to solve the above problem, this paper proposes a deduplication scheme which distributes files to multi servers and records storage informations on blockchain. We present smart contract based protocols without the participants of the central authorities to provide secure deduplication. With safety analysis, we demonstrate that our deduplication scheme is secure and reliable under the definitions specified in the proposed security model with acceptable performance through simulation experiments. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Blockchain

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Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks

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Journal of Computer Science & Technology 2017年第4期32卷 805-813页

作者： Jun Yin Wayne Xin Zhao Xiao-Ming Li School of Electronic Engineering and Computer Science Peking University Beijing 100871 China School of Information Renmin University of China Beijing 100872 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China

Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.

关键词： question answering deep neural network knowledge base

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Low-resource generation of multi-hop reasoning questions 58

Low-resource generation of multi-hop reasoning questions

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Yu, Jianxing Liu, Wei Qiu, Shuang Su, Qinliang Wang, Kai Quan, Xiaojun Yin, Jian School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9781952148255

This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance. Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text. Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text. Since the labeled data is limited and insufficient for training, we propose to learn the model with the help of a large scale of unlabeled data that is much easier to obtain. Such data contains rich expressive forms of the questions with structural patterns on syntax and semantics. These patterns can be estimated by the neural hidden semi-Markov model using latent variables. With latent patterns as a prior, we can regularize the generation model and produce the optimal results. Experimental results on the HotpotQA data set demonstrate the effectiveness of our model. Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements. © 2020 Association for Computational Linguistics

关键词： Semantics

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Learning Human-Written Commit Messages to Document Code Changes

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Journal of Computer Science & Technology 2020年第6期35卷 1258-1277页

作者： Yuan Huang Nan Jia Hao-Jie Zhou Xiang-Ping Chen Zi-Bin Zheng Ming-Dong Tang National Engineering Research Center of Digital Life School of Data and Computer ScienceSun Yat-sen University Guangzhou 510006China School of Information Engineering Hebei GEO UniversityShijiazhuang 050031China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

Commit messages are important complementary information used in understanding code changes. To address message scarcity, some work is proposed for automatically generating commit messages. However, most of these approaches focus on generating summary of the changed software entities at the superficial level, without considering the intent behind the code changes (e.g., the existing approaches cannot generate such message:"fixing 'null' pointer exception"). Considering developers often describe the intent behind the code change when writing the messages, we propose ChangeDoc, an approach to reuse existing messages in version control systems for automatical commit message generation. Our approach includes syntax, semantic, pre-syntax, and pre-semantic similarities. For a given commit without messages, it is able to discover its most similar past commit from a large commit repository, and recommend its message as the message of the given commit. Our repository contains half a million commits that were collected from SourceForge. We evaluate our approach on the commits from 10 projects. The results show that 21.5% of the recommended messages by ChangeDoc can be directly used without modification, and 62.8% require minor modifications. In order to evaluate the quality of the commit messages recommended by ChangeDoc, we performed two empirical studies involving a total of 40 participants (10 professional developers and 30 students). The results indicate that the recommended messages are very good approximations of the ones written by developers and often include important intent information that is not included in the messages generated by other tools.

关键词： commit message recommendation code syntax similarity code semantic similarity code change comprehension

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Self-adaptive Graph Neural Networks for Personalized Sequential Recommendation 1

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28th International Conference on Neural Information processing, ICONIP 2021

作者： Zhang, Yansen Hu, Chenhao Dai, Genan Kong, Weiyang Liu, Yubao Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9783030922702

ISBN: (纸本)9783030922696

Sequential recommendation systems have attracted much attention for the practical applications, and various methods have been proposed. Existing methods based on graph neural networks (GNNs) mostly capture the sequential dependencies on an item graph by the historical interactions. However, due to the pre-defined item graph, there are some unsuitable edges connecting the items that may be weakly relevant or even irrelevant to each other, which will limit the ability of hidden representation in GNNs and reduce the recommendation performance. To address this limitation, we design a new method called Self-Adaptive Graph Neural Networks (SA-GNN). In particular, we employ a self-adaptive adjacency matrix to improve the flexibility of learning by adjusting the weights of the edges in the item graph, so as to weaken the effect of unsuitable connections. Empirical studies on three real-world datasets demonstrate the effectiveness of our proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Graph neural networks

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