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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhou, Qinglin Lyu, Lei Liu, Hong Shandong Normal University Jinan China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9781665405270

Achieving autonomous navigation of indoor robots in a mapless environment is a long-standing research problem. Deep Reinforcement Learning (DRL) is widely used for robot navigation by virtue of learning through interaction with the environment. However, the large number of trials for training need lengthy computation times. To address this issue, we propose an innovative DRL model with long-time memory capability for mobile robot's mapless navigation, which can achieve end-to-end navigation based only on laser-ranging data and target location. The long-time memory capability is realized by introducing a memory module based on the special structure of the Long Short-Term Memory (LSTM). The memory module ensures that the model derives information from previous navigation experiences and thus optimizes the model's decision-making. In addition, to enable the model to explore the environment more effectively, we design a novel dual-noise mechanism consisting of Gaussian noise and Ornstein-Uhlenbeck noise. Extensive experiments are conducted on the Gazebo simulation platform and validate that the proposed approach can generate a smoother navigation path and exceed the state-of-the-art performance with less computation cost. © 2022 IEEE.

关键词： Long short-term memory

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Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition 26

Graph Convolutional Network with Long Time Memory for Skelet...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Qi, Yanpeng Pang, Chen Liu, Yiliang Liu, Hong Lyu, Lei Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Shandong Provincial Academy of Education Recruitment and Examination Jinan China

ISBN: (纸本)9798350331684

Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120. © 2023 IEEE.

关键词： Recurrent neural networks

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Data-Driven Crowd Motion Modeling With Group Properties Analysis 25

Data-Driven Crowd Motion Modeling With Group Properties Anal...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Jia, Chuanhua Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

Traditional crowd evacuation simulation methods focus on the analysis of individual behavior in the crowd, but ignore the analysis of potential group properties in the process of crowd motion, which reduces the visual realism of the crowd evacuation simulation process. During crowd motion, crowds cause unconscious self-organization due to common destinations and social relationships, thus forming groups. Considering the influence of group properties on crowd motion is important to improve the visual realism of crowd motion simulation. To address this problem, we propose a crowd motion modeling method for group properties analysis (GPA-CMM). First, we build a data-driven group properties quantification (DGPQ) model to describe the characteristics of group motion accurately. In the model, we divide the crowd into several groups, extract motion properties of the crowd, and quantify the features of intra-group stability and inter-group conflict. Then, in order to analyze their influences of group properties, we build a stability and conflict based crowd motion analysis (SC-CMA) model. Finally, we implement a crowd simulation system based on SC-CMA, visualize the results of the theoretical analysis in a graphical way. The experimental results show that the method can simulate crowd motion more realistically. © 2022 IEEE.

关键词： Behavioral research

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Federated Learning based Path Planning Method for Crowd Evacuation 25

Federated Learning based Path Planning Method for Crowd Evac...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yuan, Zihao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios. © 2022 IEEE.

关键词： Motion planning

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Blockchain-based Crowd-sensing Trust Management Mechanism for Crowd Evacuation 25

Blockchain-based Crowd-sensing Trust Management Mechanism fo...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Jiawen Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

With the continuous urban expansion, the safety of public places has attracted more and more attention. Once an accident occurs and people distrust their surroundings, it will cause congestion, trampling and other events. Individual trust value is particularly important in crowd evacuation. However, it is very difficult to evaluate the individual credibility with existing methods. We propose a blockchain-based crowd-sensing trust management mechanism (B-CSTM) to address this problem. First, we provide a message credibility evaluation mechanism that can evaluate the credibility of messages sent by individuals on spot. Second, we developed a trust calculation method that uses a distributed Evacuation Perception Unit (EPU) to calculate individuals' trust values by perception scores. Finally, we build a blockchain-based trust management mechanism model that uses blockchain to store and query trust values. Through experimental analysis, we visualize the trust results in the evacuation scenario. It is shown that the mechanism is feasible for the management of trust values (collection, calculation and storage, etc.) in crowd evacuation. © 2022 IEEE.

关键词： Blockchain

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Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing 25

Collaborative Teams Recruitment Based on Dual Constraints of...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

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

ISBN: (纸本)9781665405270

As crowd sensing tasks become more complex, it is increasingly needed for participants to form teams to collaborate interactively in order to complete tasks more efficiently. It is valuable to fully understand user behavior, the organizational principles of crowd collaboration teams and incorporate them into the user recruitment process. In fact, there is a two-way process of subjective and objective selection for building a team in crowd sensing. From the participant's perspective, the willingness of users to participate in the task is a subjective factor;from the platform's perspective, the trust relationship between users is an objective factor. However, existing work has not well recognized the value of including collaborative teams in the crowd sensing recruitment process and has not considered the willingness and trust relationships between participants simultaneously, which results in poor quality of service (QoS) among recruited users. To address this problem, we propose a willingness and trust-based collaborative team recruitment method (WT-CTRM). First, we construct a willingness network (WN) and a trust network (TN) to describe the interaction of willingness and trust among the collaborated users respectively. Based on these networks, we then predict the unknown willingness and trust relationships between users for collaboration by using graph convolutional networks (GCNs). Finally, we build collaborative teams for the user recruitment process of crowd sensing based on the consensus constraints of the predicted willingness network and trust network. Simulation results show that the recruitment scheme significantly improves the QoS in task scenarios with collaborative requirements. © 2022 IEEE.

关键词： Quality of service

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Learners' States Monitoring Method Based on Face Recognition Technology 12

Learners' States Monitoring Method Based on Face Recognition...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Qiao, Xiaoyi Zheng, Xiangwei Sun, Xiaofang Li, Shuqin Zhang, Yuang School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China

At present, many scholars focus on the learners' states in online learning, however, a complete real-time monitoring system of learners' states has not been proposed. In order to better detect the learning process and provide learners with a basis for self-reflection, this paper describes a learners' states monitoring method based on face recognition technology (LSMFR). A learner monitoring system is developed to collect the learners' states in real-time through external cameras and record the tired blinking, yawning, head posture, and emotional expression of learners respectively. After a comprehensive analysis, the evaluation score of learners' states is obtained. In the running process of the system, information feedback is completed by recording the detection content in real-time and then states of learning effectiveness are generated. After learning for a short time, learners check the data record for self-reflection. The simulation test is carried out on the system and the analysis shows that the system achieves the correct analysis of the learners' states, and generates accurate evaluation scores. © 2022 The Authors. Published by Elsevier B.V.

关键词： face recognition learners' states procedure analysis real-time monitoring

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Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence

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Chinese Journal of Electronics 2023年第4期21卷 636-641页

作者： Hong LIU Yuling SUN Yuanyuan LI School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Modeling and simulating group behaviours have been an active research topic in the field of computer animation and game. This paper presents some novel approaches for supporting entity modeling and path generation in crowd simulation. It analyses related work about crowd simulation first. Then, an entity modeling approach based on CGA (Cellular genetic algorithm) and NURBS (Non uniform relational B splines) technologies is presented. Next, following the analysis to PSO (Particle swarm optimization) and ABC (Artificial bee colony) algorithms, a crowd path generative approach based on ABC- PSO is put forward. After that, a simulating example of crowd cohesion and performance comparison are exhibited for showing the efficiency of the algorithms. Finally, the current work is summarized and an outlook for the future work is given.

关键词： Surface reconstruction Computational modeling Sociology Animation Classification algorithms Surface topography Splines (mathematics)

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on software Engineering and Service Science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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Time series classification based on Unet with 2D transformation

Time series classification based on Unet with 2D transformat...

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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Mingsen Du Yunxia Wang Yanxuan Wei Xiaona Wu Cun Ji School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method.

关键词： Accuracy Time series analysis Transforms Feature extraction Propagation losses Data models Data mining Convolutional neural networks Proposals Internet of Things

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