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International Conference on computer Supported Cooperative Work in Design

作者： Chuanmiao Zhao Guijuan Zhang Lei Lv 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

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.

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Deep reinforcement learning-based panic crowd evacuation simulation

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

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

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

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International Conference on computer Supported Cooperative Work in Design

作者： Wencong Geng Guijuan Zhang 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

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.

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

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

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International Conference on computer Supported Cooperative Work in Design

作者： Shourong Jiao Dianjie Lu Chuanhua Jia Hong Liu Guijuan Zhang 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

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.

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Fusion of Machine Learning for Teaching Case Research on Algorithm Course 11

Fusion of Machine Learning for Teaching Case Research on Alg...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Hu, Lisha Hu, Chunyu Hebei University of Economics and Business Institute of Information Technology Shijiazhuang China School of Computer Science and Technology China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9781665406796

'Algorithm Design and Analysis' is a professional compulsory course for computer undergraduates. A solid grasp of the content of the course is of great importance for students to engage in relevant positions after graduation such as algorithm engineers or to further study. Nowadays, a large number of application problems need to be solved by machine learning algorithms. In fact, the underlying implementation details of many machine learning algorithms are also derived from these basic algorithms. However, there are few descriptions related to machine learning algorithms in current algorithm courses. In the process of teaching basic algorithms, if automatic association with machine learning algorithms can be realized, knowledge will root in the core knowledge base of students, thereby realizing continuous extension of knowledge. Based on this, this paper effectively associates divide and conquer and greedy algorithms with the machine learning representative - - decision tree algorithm, so as to improve the students' analogy of relevant contents and knowledge and draw inferences from one instance. © 2021 IEEE.

关键词： Decision trees

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A Dueling-DDPG Architecture for Mobile Robots Path Planning Based on Laser Range Findings 18th

A Dueling-DDPG Architecture for Mobile Robots Path Planning ...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zhao, Panpan Zheng, Jinfang Zhou, Qinglin Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030891879

Planning an obstacle-free optimal path presents great challenges for mobile robot applications, the deep deterministic policy gradient (DDPG) algorithm offers an effective solution. However, when the original DDPG is applied to robot path planning, there remains many problems such as inefficient learning and slow convergence that can adversely affect the ability to acquire optimal path. In response to these concerns, we propose an innovative framework named dueling deep deterministic policy gradient (D-DDPG) in this paper. First of all, we integrate the dueling network into the critic network to improve the estimation accuracy of Q-value. Furthermore, we design a novel reward function by combining the cosine distance with the Euclidean distance to improve learning efficiency. Our proposed model is validated by several experiments conducted in the simulation platform Gazebo. Experiments results demonstrate that our proposed model has the better path planning capability even in the unknown environment. © 2021, Springer Nature Switzerland AG.

关键词： Robot applications

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Learners’ States Monitoring Method Based on Face Recognition Technology

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Procedia computer Science 2022年 202卷 172-177页

作者： Xiaoyi Qiao Xiangwei Zheng Xiaofang Sun Shuqin Li Yuang Zhang School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 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.

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

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Research on the Method of Emotion Contagion in Virtual Space Based on SIRS 15th

Research on the Method of Emotion Contagion in Virtual Space...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Liu, Heng Hong, Xiao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9789811625398

There is emotional contagion in virtual space, and the process of emotional contagion is also affected by emotional recurrence. However, the existing emotional contagion model does not consider the impact of individual emotional recurrence on emotional contagion in the virtual space. Therefore, the emotional contagion process cannot be simulated. The paper proposes a method of recurrent emotional contagion in virtual space. First, we construct an individual emotional recurrence contagion model based on SIRS (Susceptible-Infected-Recovered-Susceptible), we propose emotional recurrence contagion rules, and consider the impact of emotional recurrence in the virtual space on the emotional contagion process;secondly, we construct a virtual space emotional recurrence contagion model (REC) and solve it to simulate the process of emotional recurrence contagion. Third, we build a WS small-world network based on REC simulation. Provide REC (Recurrent emotional contagion model) simulation process in WS small-world network to verify the rationality of the REC. Finally, the REC is simulated to show the simulation effect of emotional recurrence contagion. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Small-world networks

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

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International Symposium on Parallel and distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance Computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： Performance evaluation Scientific computing High performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms computer architecture Machine learning Matrix decomposition

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GCMNet: Gated Cascade Multi-scale Network for Crowd Counting 18th

GCMNet: Gated Cascade Multi-scale Network for Crowd Counting

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zheng, Jinfang Zhao, Panpan Xie, Jinyang Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030893620

With the rapid development of convolutional neural networks, many CNN-based methods have emerged and made promising progress in the field of crowd counting. However, dealing with extremely scale variation remains a challenging but attractive issue. In this paper, we propose an innovative Gated Cascade Multi-scale Network (GCMNet) to tackle with the issue by taking full advantage of the representation of multi-scale features in a multi-level network. First of all, we implement such an idea by obtaining rich contextual information with a multi-scale contextual information enhancement module. Then, considering the pixel-level image detail information that is lost during the successive feature extraction process, we propose a hopping cascade module to refine this detail information. However, naively refining all the detail information is sub-optimal. Therefore, a gated information selection delivery module is designed to adaptively control the delivery of information between multi-level features. Combined with our proposed module, our method can effectively generate high-quality crowd density maps. The superiority of our method over current methods is demonstrated through extensive experiments on four challenging datasets. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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