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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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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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Adaptive fusion of structure and attribute guided polarized communities search

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Frontiers of computer science 2024年第1期18卷 255-257页

作者： Fanyi YANG Huifang MA Wentao WANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Key Lab of Education Blockchain and Intelligent Technology(Ministry of Education) Guangxi Normal UniversityGuilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

1 Introduction and main contributions The exploration of polarized communities,which consist of two antagonistic subgraphs and include a set of query nodes,is a crucial task in community search on signed *** existing methods either predominantly rely on topological structure while disregarding node attributes or tend to prioritize the global identification of all polarized ***,they fail to consider two crucial ***,integrating node attributes with network structure can enhance the search quality for polarized communities in attributed signed networks by leveraging complementary ***,global criteria-based polarized community detection aims to identify all polarized communities,neglecting personalized analyses centered around individual users.

关键词： polarized structure integrating

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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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Compresso: Latency-Aware Transmission of Compressed IoT Measurement Data Over SDN

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IEEE Internet of Things Journal 2025年第12期12卷 20462-20472页

作者： Feng, Wendi Dou, Xintan Taherkordi, Amir Cheng, Bo Zhang, Wei Beijing Information Science and Technology University College of Computer Science Beijing102206 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Oslo Department of Informatics Oslo0316 Norway

Measurement data obtained from "things" in the Internet of Things (IoT) faces challenges in efficient transmission due to the low-bandwidth data transmission link. We observe that measurement data are fixed in size and format, and low-entropy in the time domain, indicating that compression can be benefited. Rather than employing a single compression algorithm as advocated in existing literature, we argue that optimal transmission can be achieved by jointly considering compression overheads and network status, where software-defined networking (SDN) is employed to enforce network statistics and packet forwarding. This article presents a new paradigm that achieves optimal transmission of SDN-empowered compressed measurement data. We formulate the problem as an optimization problem and prove its nonpolynomial hardness time complexity. Due to this complexity, we introduce Compresso, a heuristic algorithm that efficiently solves the problem. We conduct rigorous simulations, and the results demonstrate the efficiency of the new paradigm and Compresso, i.e., attaining comparable performance to the optimal solution with 50% time usage reduction. © 2014 IEEE.

关键词： Data transfer

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A Program Logic for Concurrent Randomized Programs in the Oblivious Adversary Model 34th

A Program Logic for Concurrent Randomized Programs in the Ob...

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34th European Symposium on Programming, ESOP 2025, which was held as part of the International Joint Conferences on Theory and Practice of software, ETAPS 2025

作者： Fan, Weijie Liang, Hongjin Feng, Xinyu Jiang, Hanru State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China Beijing Institute of Mathematical Sciences and Applications Beijing Beijing101408 China

ISBN: (纸本)9783031911170

Concurrent randomized programs in the oblivious adversary model are extremely difficult for modular verification because the interaction between threads is very sensitive to the program structure and the execution steps. We propose a new program logic supporting thread-local verification. With a novel "split" mechanism, one can split the state distribution into smaller partitions, and the reasoning can be done based on each partition independently, which allows us to avoid considering different execution paths of branch statements simultaneously. The logic rules are compositional and are natural extensions of their sequential counterparts. Using our program logic, we verify four typical algorithms in the oblivious adversary model. © The Author(s) 2025.

关键词： Model checking

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TSCMR:Two-Stage Cross-Modal Retrieval 9th

TSCMR:Two-Stage Cross-Modal Retrieval

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Chen, Zhihao Wang, Hongya School of Computer Science and Technology Donghua University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783031466731

Currently, large-scale vision and language models has significantly improved the performances of cross-modal retrieval tasks. However, large-scale models require a substantial amount of computing resources, so the execution of these models on devices with limited resources is challenging. Thus, it is paramount to reduce the model size and minimize computing costs of a model without sacrificing its performance. In this paper, we improved TERAN by dividing cross-modal retrieval into two stages: image-text coarse-grained matching and image-text fine-grained matching. Specifically, we present a novel approach called Two-Stage Cross-Modal Retrieval network(TSCMR). To reduce model size after model training, our approach utilized a new knowledge distillation method for Transformer-based models. Experiments have shown that our approach maintains a performance comparable to TERAN on the MS-COCO 1K test set, while being 2x smaller and 3.1x faster on inference. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Distillation

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A Function-Aware Mimic Defense Theory and Its Practice

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China Communications 2024年第8期21卷 192-210页

作者： He Jiajun Yuan Yali Liang Sichu Fu Jiale Zhu Hongyu Cheng Guang School of Mathematics Southeast UniversityNanjing 211189China School of Cyber Science and Engineering Southeast UniversityNanjing 211189China School of Artificial Intelligence Southeast UniversityNanjing 211189China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China

In recent years,network attacks have been characterized by diversification and scale,which indicates a requirement for defense strategies to sacrifice generalizability for higher *** the latest theoretical achievement in active defense,mimic defense demonstrates high robustness against complex *** study proposes a Function-aware,Bayesian adjudication,and Adaptive updating Mimic Defense(FBAMD)theory for addressing the current problems of existing work including limited ability to resist unknown threats,imprecise heterogeneous metrics,and over-reliance on relatively-correct *** incorporates three critical ***,the common features of executors’vulnerabilities are obtained from the perspective of the functional implementation(i.e,input-output relationships extraction).Secondly,a new adjudication mechanism considering Bayes’theory is proposed by leveraging the advantages of both current results and historical ***,posterior confidence can be updated regularly with prior adjudication information,which provides mimic system *** experimental analysis shows that FBAMD exhibits the best performance in the face of different types of attacks compared to the state-of-the-art over real-world *** study presents a promising step toward the theo-retical innovation of mimic defense.

关键词： Bayesian theory confidence functional implementation mimic defense

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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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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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