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The Action Recognition with Multi-Clue Sparse 3-Dimensional Convolutional Networks 5

The Action Recognition with Multi-Clue Sparse 3-Dimensional ...

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5th International Conference on Computing and Big Data, ICCBD 2022

作者： Man, Hongtao Zou, Deqiang Li, Tuo Inspur Electronic Information Industry Co. Beijing China State Key Laboratory of High-End Server and Storage Technology Beijing China

ISBN: (纸本)9781665457163

The current approaches of deep neural network lack capacity on action recognition, yet ineffective structure for spatiotemporal feature learning, and heavy parameters of architecture are even harsh to optimize. In this paper, we propose multi-clue sparse 3-dimensional convolutional networks(MS3D ConvNet) based on landmark works that aims to make it far known in this field. The MS3D ConvNet enhances channels of input through low, medium, and fast pathways with disparate frames of video, frame sequences considering as temporal clue sampled in terms of variance rate of action. The frames of each pathway are convolved with commonly shared kernels forming cohorts as initial results, and pile up three-way cohorts to the 3D block transmitting to subsequent 3D network. The 3D network is modified by DenseNet architecture with randomly dropping out connections among the layers, and introduces 3D convolutional kernels to deal with temporal information, eventually generating 256-dimension codes of actions. Action recognition employs centers of vector clusters of actions instead of stubborn format, which provide a robust convergence environment. Spontaneously, the MS3D ConvNet fosters capacity of action information and hardly ever sustains heavy amounts of parameters which deserves double profits. This fancy structure also leaves an end-to-end fashion training and a possibility to pre-train parameters. Besides that, we devise tailor strategy based on yolo algorithm to endow disparate factors on pixels in compliance with bounding box frontier, regarded as the means of attention. The MS3D ConvNet has been applied to three public action datasets and a self-defined dataset, which performs advanced steps on accuracy, convergence, and robustness. © 2022 IEEE.

关键词： Deep neural networks

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Adaptive Prediction Of Online Learning Based On Machine Learning Algorithms 12

Adaptive Prediction Of Online Learning Based On Machine Lear...

引用

12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Yin, Qiang Yu, Xiaomei Zhao, Lixiang Li, Yongqin Shandong Normal University School of Information Science and Engineering Jinan250358 China China State Key Laboratory of High-end Server & Storage Technology Jinan250358 China Shandong Normal University Faculty of Education Jinan250014 China

ISBN: (纸本)9798331510633

In recent years, with the advancement of society and the rapid development of internet technology, the popularity of online education has been steadily increasing. Presently, nearly every educational institution worldwide, from elementary schools to universities, has incorporated some level of online teaching. While online instruction offers certain conveniences, it will also be accompanied by the emergence of some problems, given the substantial surge in data related to online learning among students, there is an urgent need for robust data processing methods to forecast their suitability for online education. Previous research underscores the effectiveness of machine learning in tackling diverse data classification tasks. However, there is a scarcity of studies comprehensively comparing various machine learning approaches in the context of online education data. This article utilizes seven distinct, parameter-optimized machine learning approaches to explore a range of performance predictions, spanning both binary and multi-class tasks. During the experimental process, four evaluation metrics and visual aids were employed to assess and compare the methods across these tasks, accompanied by a comprehensive discussion of the experimental results. The results of the experiments reveal that the random forest method demonstrates strong versatility across all selected datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Abnormal Crowd Behavior Detection Based on the Fusion of Macro and Micro Features 26

Abnormal Crowd Behavior Detection Based on the Fusion of Mac...

引用

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

作者： Jiao, Shourong Lu, Dianjie Jia, Chuanhua Liu, Hong Zhang, Guijuan Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

There are two types of research on abnormal crowd behavior detection: micro modeling methods (e.g., crowd pose) and macro modeling methods (e.g., optical flow methods). In crowd evacuation, however, micro methods fail to solve the occlusion problem, while macro methods fail to address issues such as poor real-time performance and non-adaptive motion target detection. To address these issues, we propose a method that combines macro and micro methods to detect abnormal crowd behavior. Firstly, we extract the two-dimensional poses of moving people using the human pose estimation algorithm (OpenPose) and get the corresponding micro motion features. Secondly, we create an optical flow map of the video using the dense optical flow algorithm (Farneback) and a scene activity map by comparing the optical flow sizes between two consecutive frames. Then we derive the entropy change curve, which represents the macro motion features. Finally, we fuse micro and macro motion features and put the fused hybrid feature vectors into our classification network to train our model, and then find the transition time from micro to macro abnormal states in crowd evacuation videos. The experimental findings demonstrate that our proposed method can more accurately and quickly identify the abnormal behaviors that appear in evacuation scenes. © 2023 IEEE.

关键词： Optical flows

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Deep reinforcement learning-based panic crowd evacuation simulation 26

Deep reinforcement learning-based panic crowd evacuation sim...

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

作者： An, Baoxu Zhang, Guijuan Li, Fuwei Song, Nianyun Liu, Hong Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

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. © 2023 IEEE.

关键词： Reinforcement learning

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Spatial-temporal Consistency based Crowd Emotion Recognition 26

Spatial-temporal Consistency based Crowd Emotion Recognition

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

作者： Zhao, Chuanmiao Zhang, Guijuan Lv, Lei Liu, Hong Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

In crowd evacuation scenarios, it is an effective way to ensure evacuation safety by recognizing real crowd emotions and then taking measures such as emotional infection to reduce crowd panic. However, factors such as distance, exposure, angle, and occlusion can lead to incomplete face expression information collection, thus failing to accurately identify crowd emotions. Therefore, it is still a very challenging problem to recognize individual emotions in the case of incomplete face expression information collection, and thus accurately identify crowd emotions during evacuation. To solve this problem, we propose a spatial-temporal consistency-based crowd emotion recognition method to accurately identify the real emotions of the crowd. First, for video frames that can capture the complete facial expression information, we use the residual network to accurately identify the individual emotion values in each frame. For video frames that cannot capture the complete facial expression information, we propose an individual emotion calculation model based on spatial-temporal consistency to calculate the individual emotion values in each frame. Second, we define the crowd panic level and obtain the real crowd emotion by calculation. Finally, we implement an end-to-end crowd panic emotion recognition system to verify our method. The experimental results show that the method can accurately calculate the crowd panic level, which is important for guiding crowd evacuation. © 2023 IEEE.

关键词： Emotion Recognition

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Risk Region-based Prediction Model for the Epidemic Spreading 26

Risk Region-based Prediction Model for the Epidemic Spreadin...

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

作者： Li, Fuwei Lu, Dianjie An, Baoxu Zhang, Guijuan Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

Studying the spread process of epidemics in the crowd is a significant topic, which can help to take measures in advance to avoid epidemic spread and reduce the losses of life and property of the general public. However, this is a challenging problem that how to capture the spatio-temporal characteristics of the epidemic spreading. In this paper, we propose a risk region-based prediction model to characterize the spread feature. First, we propose a risk region-based spread model to describe the spatial relationship between individuals by dividing risk regions and assigning different spread characteristics to different risk regions. Second, we develop a spatio-temporal point process-based infection intensity quantification method to describe the variation of epidemic spread characteristics over time in different risk regions in the form of intensity. Then, we propose a long short-term memory (LSTM) based intensity function prediction method to solve the corresponding intensity and predict the spread process by the spatio-temporal point process (STPP). Finally, we implement an epidemic spreading simulation platform to verify our method and visualize the experimental results. The proposed model is expected to provide guidance for predicting the spread of epidemics. © 2023 IEEE.

关键词： Long short-term memory

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An Epidemic Model Based on Intra- and Inter-group Interactions 26

An Epidemic Model Based on Intra- and Inter-group Interactio...

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

作者： Geng, Wencong Zhang, Guijuan Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately. © 2023 IEEE.

关键词： COVID-19

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Singular Value Decomposition Based Pedestrian Trajectory Prediction 26

Singular Value Decomposition Based Pedestrian Trajectory Pre...

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

作者： Wang, Heng Zhang, Guijuan Liu, Hong Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory. © 2023 IEEE.

关键词： Singular value decomposition

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Multifunctional Module Design Based on Hybrid CMOS-Memristor Logic Circuit

Multifunctional Module Design Based on Hybrid CMOS-Memristor...

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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Ji, Chao Li, Tuo Zou, Xiaofeng State Key Laboratory of High-End Server & Storage Technology China Shandong Inspur Artificial Intelligence Research Institute Co. Ltd Jinan China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

With the rapid development of computer technology, logical circuits play an important role in many fields. Semiconductor transistors, as the basic logic circuit unit of modern computer hardware structure, also encounter more and more technical limitations in the highly integrated field such as computer chips. In order to continue Moore's Law effectively, higher requirements need to be put forward for the basic logic unit. The emergence of memristor opens up a new way to explore advanced computing architecture. This paper presents a MRL- based logic circuit in which input and output logic is provided by high and low voltage. Logic gate circuit elements such as XOR, NAND, NOR are implemented in conjunction with CMOS inverters. Through the construction of basic ratio logic gate, the adder and multiplier is simplified, and the result of signal simulation using LTspice software is completely correct. Finally, a multifunctional logic module is innovatively designed. © 2022 IEEE.

关键词： Timing circuits

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Operator Optimization Oriented Person Re-Identification

Operator Optimization Oriented Person Re-Identification

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2022 International Conference on Machine Vision, Automatic Identification and Detection, MVAID 2022

作者： Cao, Qichun Wang, Binqiang Dong, Gang Hu, Kekun Yang, Hongbin Electronic Information Industry Co. Ltd. Beijing100085 China State Key Laboratory of High-End Server & Storage Technology Beijing100085 China

Using FPGA to accelerate the application of convolutional neural networks has attracted more and more attention. However, the design technology of the FPGA accelerator cannot follow up the latest achievements of the convolutional neural networks, nor can it consider optimizing the complex network structure combined with the characteristics of hardware to improve the accuracy of the network and simplify the implementation of hardware. To address these issues, in this paper, we adopt the development idea of algorithm-hardware co-design, compress the structure and optimize the operator for a state-of-the-art lightweight person re-identification network, and design a new network model SGCNet (Shift Gaussian Convolution Network). Specifically, SGCNet is obtained by structure optimization and operator replacement according to the optimization degree of the operator in the FPGA hardware environment. For convolution operation, only 1 × 1 kernel convolution operator is adopted in SGCNet, while spatial convolution is replaced by a more effective shift operation. SGCNet is more concise and clear in the operation types and complex structure of the network. Experimental evaluations demonstrate that SGCNet's top-5 accuracy of 92.7% on ImageNet and R1 accuracy of 74.6% on CUHK03 of person re-identification data set, is higher than CMSNet network. © Published under licence by IOP Publishing Ltd.

关键词： Field programmable gate arrays (FPGA)

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