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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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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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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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Spatiotemporal Feature Extraction and Fusion for Longitudinal Alzheimer’s Disease Diagnosis 17th

Spatiotemporal Feature Extraction and Fusion for Longitudina...

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17th International Conference on Brain Informatics, BI 2024

作者： Guan, Zhenghua Yang, Peng Lei, Haijun Yang, Bao Song, Xuegang Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. The brain structure of AD patients progressively atrophies over time. Longitudinal data can capture the changes in imaging over time, providing important information for early detection and diagnosis of the disease, which is not achievable with images from a single time point. We propose a novel longitudinal AD diagnosis model that leverages deformation field data obtained by registering images from adjacent time points. Each pixel in the deformation field represents the displacement vector of the corresponding pixel between two scans, reflecting information about brain tissue atrophy. By effectively extracting both temporal and spatial features from the longitudinal data, our model enhances early AD diagnosis. The input data for the model consists of processed sMRI images and deformation fields calculated between each pair of time points in the longitudinal sMRI data. The model is composed of three modules: Convolutional Long Short-Term Memory (CLSTM), Gate Fusion Long Short-Term Memory (GFLSTM), and an attention module. CLSTM extracts temporal features while preserving spatial features by maintaining the 3D shape of the features, GFLSTM achieves spatiotemporal feature fusion within a cascaded network, and the attention module further enhances feature representation. Our proposed method was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and experiments demonstrate that it outperforms several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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

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

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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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Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

Joint Clustering and Analyze Single Cell Multi-omics Data by...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： An, Congcong Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data. © 2023 IEEE.

关键词： clustering generative adversarial networks single cell multi-omics data unsupervised learning

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Wen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything"to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA's LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS's unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes. © 2023 IEEE.

关键词： Multi-omics Data Pathological Images Segment Anything Survival Analysis

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Reinforcement Learning Application Testing Method based on Multi-attribute Fusion 9

Reinforcement Learning Application Testing Method based on M...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Cai, Lizhi Wang, Jin Chen, Mingang Wang, Jilong School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Reinforcement learning has been successfully applied to assess the reliability of applications, but the existing testing methods based on reinforcement learning have the problems of invalid interactive widgets and difficult training, resulting in low testing efficiency. In order to solve these problems, this paper proposes a lightweight application automation testing method of deep reinforcement learning based on multi-attribute fusion (MARTesting). First, the invalid widgets are removed by the difference operation of the attribute sets of the current and previous state, then the attributes of all widget elements on the page are abstracted into state as the input of the neural network, and a state is accurately determined by fusing the position and text information of page elements, finally combine the novelty of the state and the execution frequency of the action as a reward function. The experimental results on six open source applications show that the MARTesting method proposed in this paper has achieved significant improvements in code coverage and branch coverage compared with existing methods. © 2022 IEEE.

关键词： Reinforcement learning

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