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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...

引用

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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Spatial-temporal Consistency based Crowd Emotion Recognition 26

Spatial-temporal Consistency based Crowd Emotion Recognition

引用

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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Deep reinforcement learning-based panic crowd evacuation simulation 26

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

引用

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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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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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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A Greedy Search Algorithm for Resolving the Lowermost C Threshold in SVM Classification

A Greedy Search Algorithm for Resolving the Lowermost C Thre...

引用

International Conference on Computational Intelligence and Security

作者： Huichuan Duan Naiwen Liu Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

In this paper, the authors present a greedy search algorithm that solves the SVM classification (SVC) problem at the lowermost C end. By combining the SVC asymptotic behavior with empirical results, it can be sure that at sufficiently small cost, a threshold C0, all the minority samples becomes support vectors each with Lagrange multiplier C0, and equal number of majority samples will become support vectors whose Lagrange multipliers are also C0. With this evidence, SVC is transformed into a C-independent combinatorial optimization problem. Taking all the minority inputs as initial support vectors, a greedy algorithm is devised to choose majority class support vectors one by one each with minimal increase to the objective function in its turn. The greedy nature of the algorithm enables finding out the majority support vectors that near or at the majority margin. By taking the found majority support vectors initially and applying the algorithm to the minority class conversely, the support vectors that near the decision boundary are also resolved. Applying linear least squares fitting to both the majority margin and decision boundary, C0 is obtained as a function of kernel parameters. Experimental results show that the proposed algorithm can find C0 almost perfectly.

关键词： Support vector machines Static VAr compensators Kernel Optimization Greedy algorithms Fitting Benchmark testing

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Sequence generation model of traditional Chinese medicine

Sequence generation model of traditional Chinese medicine

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Chengzhi Duan Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

The application of artificial intelligence in the field of traditional Chinese medicine is more and more extensive, and the related research is also more and more. Increasingly, the electronic processing of TCM textual information is becoming more and more important. In this paper, we propose a TCM text-based prescription generation task, intending to be able to implement automatic prescription generation functions based on the sequence-to-sequence (seq2seq) model. Traditional TCM prescriptions are given by the attending physicians, and the prescription recommendation system proposed in this paper achieves a certain degree of accuracy compared to real doctors. After a series of system data training, we improved the accuracy rate to 76%. For FI the value reached the best result of 74% at the existing stage.

关键词： Training Adaptation models Hospitals Transformers Decoding Task analysis Information technology

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Application research of WebSocket technology on web tree component

Application research of WebSocket technology on web tree com...

引用

2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012

作者： Jiang, Feng-Yan Duan, Hui-Chuan School of Information Science and Engineering Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Jinan China Basic Education Department Shandong Normal University Jinan China

ISBN: (纸本)9781467321068

In the development of information systems, Web tree component is an important way to show information, provide a convenient and intuitive information queries, which has become one of the essential features in the information system. At present, Web tree component still has a series of problems in communication efficiency, displaying and updating rate, etc. This paper uses MyEclipse and MySql as development tools to design and implement web tree component based on WebSocket, the component can display and update tree structure dynamicly, Theory analysis and experimental results show that the improved scheme provides a better user experience, comparing with the previous traditional long polling based on AJAX technology, it effectively reduces the amount of data transmission, communication overhead, etc. © 2012 IEEE.

关键词： Information systems

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Named Entity Recognition of Chinese Diabetic Literature by Integrating General Domain Knowledge 4

Named Entity Recognition of Chinese Diabetic Literature by I...

引用

4th IEEE International Conference on computer and Communication Engineering Technology, CCET 2021

作者： Zhang, Duochuan Beijing University of Posts and Telecommunications School of Computer National Demonstrative Software School Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China

ISBN: (纸本)9781665438902

In the field of named entity recognition of diabetes literature, due to the lack of large-scale and high-quality annotation data sets, the traditional named entity recognition technology has the drawback of insufficient language feature extraction and poor generalization ability. In view of the above problems, this paper proposes an entity recognition method that integrates general domain knowledge for naming entity recognition in diabetes literature. This method uses Word2vec algorithm to generate feature level vectors based on Chinese diabetes literature. On this basis, the feature vectors are fused with the feature vectors generated by the pretraining model of BERT on the Chinese Wikipedia corpus, and uses principal component analysis to reduce the redundant feature vectors and enhance the expression ability of feature vectors in the general language scope. Then the context features are extracted by bidirectional long-term and short-term memory network, and finally the entity label sequence is generated by conditional random field decoding. The experiment proves that the model which integrates general domain knowledge is better than other traditional models in the recognition of Chinese diabetes literature, and the highest F1 value is 71.08%. © 2021 IEEE.

关键词： Domain Knowledge

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Text line detection based on mutation analysis

Text line detection based on mutation analysis

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2013 5th International Conference on Computational and Information Sciences, ICCIS 2013

作者： Hou, Dewen Wang, Xichang Liu, Jiang Chen, Xia Key Laboratory for Distributed Computer Software School of Information and Science Engineering Shandong Normal University Shandong China Shandong Oumasoft Co. Ltd Research Institute of Digital Image Processing Shandong China

ISBN: (纸本)9780769550046

A text line detection method based on wavelet transformation and mutation analysis is proposed in this paper. First the character density image is acquired from the input document image by the project function with strips, and then the density image is analyzed by wavelet transformation where the Gaussian or its derivative function is used as the wavelet basis. The local peak positions indicate the line texture which will be continuous and single pixel. Because of the statistical properties of project function and wavelet transformation of mutations signal detection, this algorithm is not sensitive to noise and warped distortions. Experiments show that this algorithm is accurate and robust. © 2013 IEEE.

关键词： Signal analysis

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