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International Conference on computer Supported Cooperative Work in Design

作者： Baoxu An Guijuan Zhang Fuwei Li Nianyun Song Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Crowd evacuation simulation can provide guidance for public emergencies or mass casualties which is easy to cause crowding and stampede. Deep reinforcement learning is an effective method for the path planning of crowd evacuation simulation which can reduce the dependence on data and has strong generalization. However, current methods of deep reinforcement learning ignore the consideration of panic emotion which could result in weak authenticity since the panic emotion has a significant impact on crowd evacuation. To address this problem, we propose a novel deep reinforcement learning for the panic crowd evacuation. First, a quadrant based crowd network is constructed to guide the movement of agents according to the change of panic degree in different quadrants. Second, a method of quantifying panic degree is proposed. Through the mean field equation, the dynamic evolution of agent states is realized, and the panic degree in different regions is quantified. Finally, a panic crowd evacuation model based on multi-agent deep deterministic policy gradient (MADDPG) was established. The computated panic degree is introduced into the reward function to recommend a path for multiagent to avoid panic. Experimental results show the effectiveness of our method.

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MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding

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computers, Materials & Continua 2020年第6期63卷 1343-1356页

作者： Dongjie Zhu Yundong Sun Xiaofang Li Haiwen Du Rongning Qu Pingping Yu Xuefeng Piao Russell Higgs Ning Cao School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China School of Astronautics Harbin Institute of TechnologyHarbin150001China School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050018China School of Mathematics and Statistics University College DublinDublinD04 V1W8Ireland School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China

Interactivity is the most significant feature of network data,especially in social *** network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between *** tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction ***,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional ***,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.

关键词： Network embedding network representation learning interactive network data mining

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The functionality on netpad 16

The functionality on netpad

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16th IEEE International Conference on computer science and Education, ICCSE 2021

作者： Li, Ning Li, Chuangzhong Gao, Ying Wang, Ying Zhao, Jietong Zhang, Linjing Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Institute of Guangzhou Software Engineering Guangzhou China Institute of Computing Science and Technology Guangzhou University Guangzhou China Fuzhou Administration Institute Fuzhou China

ISBN: (纸本)9781665414685

Modern information technology-assisted teaching is an important teaching means. In mathematics teaching activities, graphical changes can be vividly displayed through dynamic geometry software (DGS). Function is the basic knowledge in mathematics, and the functionality is the basic component of DGS. NetPad, one of the DGS, has more performance and simpler operation compared with other DGS. In this paper, we introduce the functionality of NetPad. It includes user-defined coordinate system, prompt function, expression input, implicit function, polar equation, parameter equation, function surface etc.. And its operation procedure is simpler to drawing curves dynamic trajectory and exploring function curves intersection. Through a teaching case, the superiority of NetPad in functionality is verified. © 2021 IEEE.

关键词： Functions

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Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions

arXiv

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arXiv 2024年

作者： Zhang, Yifan Zhao, Xinkui Li, Ziying Yin, Jianwei Zhang, Lufei Chen, Zuoning School of Software Technology Zhejiang University Zhejiang Ningbo315048 China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310058 China State Key Laboratory of Mathematical Engineering and Advanced Computing Jiangsu Wuxi214125 China Chinese Academy of Engineering Beijing100088 China

In the era of the Internet of Everything, operating systems (OSs) face unprecedented challenges posed by an evolving application landscape and increasingly heterogeneous hardware ecosystems. This shift toward increasingly dynamic and unpredictable operational contexts presents significant challenges for both OS developers and users. Against this backdrop, the fusion of Artificial Intelligence (AI) with Operating Systems emerges as a critical frontier for innovation. This survey delves into the intricate interplay between AI and OSs, illustrating how existing OS mechanisms combined with AI significantly elevate the performance, security, and efficiency of modern operating systems. We investigate a range of AI methodologies applied to optimize core OS functionalities and clarify the correlation to related studies. Our analysis touches on the existing hurdles and prospective avenues in this interdisciplinary domain, underscoring the imperative for robust and seamless integration of AI capabilities into OS architectures. Through an examination of illustrative case studies and cutting-edge developments, we offer a thorough review of the current status of AI-OS integration, accentuating its pivotal role in steering the evolution of advanced computing paradigms. We also envision the promising prospects of Intelligent Operating Systems, debating how groundbreaking OS designs will usher in novel possibilities and highlight the central role that AI will assume in propelling these next-generation systems forward. This forward-thinking outlook illuminates the profound influence of AI on the foundational elements of computing, heralding the advent of a new epoch characterized by intelligent, self-adapting, and highly adaptive software ecosystems. Copyright © 2024, The Authors. All rights reserved.

关键词： Artificial intelligence

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Abnormal Entity-Aware Knowledge Graph Completion

Abnormal Entity-Aware Knowledge Graph Completion

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Ke Sun Shuo Yu Ciyuan Peng Xiang Li Mehdi Naseriparsa Feng Xia School of Software Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China Institute of Innovation Science and Sustainability Federation University Australia Ballarat Australia Global Professional School Federation University Australia Ballarat Australia

In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service quality in many applications. Although many approaches are proposed to perform knowledge graph completion, they are incapable of handling the abnormal information of knowledge graphs. Therefore, to address the abnormal information issue for the knowledge graph completion task, we design a novel knowledge graph completion framework called ABET, which specially focuses on abnormal entities. ABET consists of two components: a) abnormal entity prediction and b) knowledge graph completion. Firstly, the prediction component automati-cally predicts the abnormal entities in knowledge graphs. Then, the completion component effectively captures the heterogeneous structural information and the high-order features of neighbours based on different relations. Experiments demonstrate that ABET is an effective knowledge graph completion framework, which has made significant improvements over baselines. We further verify that ABET is robust for knowledge graph completion task with abnormal entities.

关键词： Adaptation models Conferences Predictive models Feature extraction Data models Data mining Task analysis

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Resource Consumption for Supporting Federated Learning Enabled Network Edge Intelligence

Resource Consumption for Supporting Federated Learning Enabl...

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IEEE International Conference on Communications Workshops, ICC

作者： Yi– Jing Liu Gang Feng Yao Sun Xiaoqian Li Jianhong Zhou Shuang Qin National Key Laboratory of Science and Technology on Communications Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China James Watt School of Engineering University of Glasgow School of Computer and Software Engineering Xihua University

Federated learning (FL) has recently become one of the hottest focuses in network edge intelligence. In the FL framework, user equipments (UEs) train local machine learning (ML) models and transmit the trained models to an aggregator where a global model is formed and then sent back to UEs, such that FL can enable collaborative model training. In large-scale and dynamic edge networks, both local model training and transmission may not be always successful due to constrained power and computing resources at mobile devices, wireless channel impairments, bandwidth limitations, etc., which directly degrades FL performance in terms of model accuracy and/or training time. On the other hand, we need to quantify the benefits and cost of deploying edge intelligence when we plan to improve network performance by using artificial intelligence (AI) techniques which definitely incur certain cost. Therefore, it is imperative to deeply understand the relationship between the required multiple-dimensional resources and FL performance to facilitate FL enabled edge intelligence. In this paper, we construct an analytical model for investigating the relationship between the accuracy of ML model and consumed network resources in FL enabled edge networks. Based on the analytical model, we can explicitly quantify the trained model accuracy given spatial-temporal domain distribution, available user computing and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis. Our analytical model in this paper provides some useful guidelines for appropriately promoting FL enabled edge network intelligence.

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TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting

arXiv

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arXiv 2024年

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zheng, Jin Ning, Xin Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China Institute of Semiconductors Chinese Academy of Sciences China School of Information and Communication Technology Griffith University Australia RIKEN AIP Japan The University of Tokyo Japan

Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D talking heads. However, due to the difficulty in fitting steep appearance changes, the prevailing paradigm that presents facial motions by directly modifying point appearance may lead to distortions in dynamic regions. To tackle this challenge, we introduce TalkingGaussian, a deformation-based radiance fields framework for high-fidelity talking head synthesis. Leveraging the point-based Gaussian Splatting, facial motions can be represented in our method by applying smooth and continuous deformations to persistent Gaussian primitives, without requiring to learn the difficult appearance change like previous methods. Due to this simplification, precise facial motions can be synthesized while keeping a highly intact facial feature. Under such a deformation paradigm, we further identify a face-mouth motion inconsistency that would affect the learning of detailed speaking motions. To address this conflict, we decompose the model into two branches separately for the face and inside mouth areas, therefore simplifying the learning tasks to help reconstruct more accurate motion and structure of the mouth region. Extensive experiments demonstrate that our method renders high-quality lip-synchronized talking head videos, with better facial fidelity and higher efficiency compared with previous methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise Knowledge graphs Predictive models Fingerprint recognition Transformers

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DeshengNet: An Information Extraction Model for Table in Digital Documents

DeshengNet: An Information Extraction Model for Table in Dig...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Xiangben Hu Jielin Jiang Zhichen Hu Tao Huang Shengjun Xue Xiaolong Xu School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Technology Silicon Lake College Suzhou China

In the scholarly literature, tables carry a huge amount of information. In the traditional information extraction of the table, researcher often spend a lot of manpower to intergrate information. Using computer vision technology can imporve the efficiency of gathering data. However, in the literature of academic, too many negative samples often lead to poor result. In response to the previous problem, this paper proposes DeshengNet based on deep learning table information extraction in digital documents. Firstly, the feature map of literature pictures is obtained through the deep residual network. Then, the multi-scale features are merged with the feature pyramid network. Afterwards, the class box subnet is used for table positioning. Aiming at the problem of too many negative samples in the paper. The focal loss method is used for training. After detection, the spatial features of the table are used for heuristic extraction. The experimental results show that the method proposed in this paper can be applied to the industrial field and serve the researcher.

关键词： Training computer vision Fitting Object detection Information retrieval Portable document format Feature extraction

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