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A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets

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Computational Visual Media 2024年第1期10卷 161-186页

作者： Yu Dong Christy Jie Liang Yi Chen Jie Hua School of Computer Science University of Technology SydneySydneyNSW2007Australia Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing100048China

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological ***,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative *** address this issue,we developed a portrait-based visual modeling method called+*** method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological *** applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based *** perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case *** feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor *** adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the *** confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

关键词： visual modeling epidemiological analysis spatiotemporal multidimensional COVID-19

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Study of stability and object tracking of traffic video image for smart cities

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Personal and Ubiquitous Computing 2024年第2期28卷 431-442页

作者： Yongfeng, Xing Luo, Zhong Xian, Zhong School of Computer Science and Technology Wuhan University of Technology 122 Luoshi Road Wuhan430070 China School of Software Nanyang Institute of Technology Nanyang473000 China

In order to solve the problem that image information is not effectively utilized because of unclear traffic video images and random jitter between image sequences, this paper has studied how to achieve stability of traffic video images and proposed an improved Mean Shift algorithm about how to conduct object centroid registration in compensation for the deviations in space localization and on this basis, how to select kernel window width to eliminate the errors in scale positioning. This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications;removes or relieves the impact motion has on imaging, improves the quality of the video image information obtained, automatically adjusts the size of window according to the scale changes of moving object in the image, and effectively enhances the stability and real-time of object tracking. The experiment has proven that the algorithm of this paper is practical to some extent. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Stability

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Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1435-1450页

作者： Sara Charoghchi Zahra Saeidi Samaneh Mashhadi Cryptography and Data Security Laboratory School of Mathematics and Computer ScienceIran University of Science&TechnologyTehranIran

As an effective way to securely transfer secret images,secret image sharing(SIS)has been a noteworthy area of *** in a SIS scheme,a secret image is shared via shadows and could be reconstructed by having the required number of them.A major downside of this method is its noise-like shadows,which draw the malicious users'*** order to overcome this problem,SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then *** most of these schemes,the cover image cannot be recovered without distortion,which makes them useless in case of utilising critical cover images such as military or medical ***,embedding the secret data in Least significant bits of the cover image,in many of these schemes,makes them very fragile to steganlysis.A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is *** order to make the scheme robust to steganalysis,the shadow image is embedded in coefficients of Integer wavelet transform of the cover *** Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to ***,utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.

关键词： hamming code homogeneous linear recursion IWT rook polynomial (t n)-secret image sharing

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Improved safety helmet target detection algorithm of YOLOv8n 24

Improved safety helmet target detection algorithm of YOLOv8n

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14th International Conference on Communication and Network Security, ICCNS 2024

作者： Li, Aiguo Li, Linru School of Computer Science and Technology Xi'an University of Science and Technology Shaanxi Xi'an China School of Computer Science Xi'an University Of Science And Technology Shanxi Xi'an China

ISBN: (纸本)9798400717994

In order to solve the shortcomings of the existing safety helmet detection algorithms in construction sites, tunnels, coal mines and other construction scenarios, it is difficult to detect occluded targets and small targets, and when the model faces too large or too small targets, the detection performance will decline. Tiny targets have fewer pixels, and it is easy to lose information in repeated downsampling, which increases the false detection rate and missed detection rate of helmet detection. In order to solve this problem, a safety helmet detection algorithm based on improved YOLOv8n is proposed. On this basis, the weighted bidirectional feature pyramid network (BiFPN) is introduced. The convolution module is introduced before the first detection head to extract features, and then fused with the P2 detection head and output, which can increase the feature map and increase the receptive field. Finally, the Tanh activation function is added to the GAM attention module to obtain more details and features. while improving the detection ability of small-scale targets, the accuracy and recall rate are improved, and the false detection rate and missed detection rate of the model are reduced. The experimental results on the self-made safety helmet data set show that the accuracy of the BPCG-YOLOv8n model reaches 96.5 percent. Compared with the YOLOv8n model, the average accuracy is improved by 1.1 percentage points, which is superior to other classical detection models in accuracy and has practical application value. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Coal mines

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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A Survey of Edge Caching:Key Issues and Challenges

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Tsinghua science and technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

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QAR Data Imputation Using Generative Adversarial Network with Self-Attention Mechanism

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Big Data Mining and Analytics 2024年第1期7卷 12-28页

作者： Jingqi Zhao Chuitian Rong Xin Dang Huabo Sun School of Computer Science and Technology Tiangong UniversityTianjin 300387China Institute of Aviation Safety China Academy of Civil Aviation Science and TechnologyBeijing 100028China

Quick Access Recorder(QAR),an important device for storing data from various flight parameters,contains a large amount of valuable data and comprehensively records the real state of the airline ***,the recorded data have certain missing values due to factors,such as weather and equipment *** missing values seriously affect the analysis of QAR data by aeronautical engineers,such as airline flight scenario reproduction and airline flight safety status ***,imputing missing values in the QAR data,which can further guarantee the flight safety of airlines,is *** data also have multivariate,multiprocess,and temporal ***,we innovatively propose the imputation models A-AEGAN("A"denotes attention mechanism,"AE"denotes autoencoder,and"GAN"denotes generative adversarial network)and SA-AEGAN("SA"denotes self-attentive mechanism)for missing values of QAR data,which can be effectively applied to QAR ***,we apply an innovative generative adversarial network to impute missing values from QAR *** improved gated recurrent unit is then introduced as the neural unit of GAN,which can successfully capture the temporal relationships in QAR *** addition,we modify the basic structure of GAN by using an autoencoder as the generator and a recurrent neural network as the *** missing values in the QAR data are imputed by using the adversarial relationship between generator and *** introduce an attention mechanism in the autoencoder to further improve the capability of the proposed model to capture the features of QAR *** mechanisms can maintain the correlation among QAR data and improve the capability of the model to impute missing ***,we improve the proposed model by integrating a self-attention mechanism to further capture the relationship between different parameters within the QAR *** results on real datasets demonstrate that the model can rea

关键词： multivariate time series data imputation self-attention Generative Adversarial Network(GAN)

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Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

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computers, Materials & Continua 2023年第9期76卷 2649-2665页

作者： Jingyao Liu Qinghe Feng Jiashi Zhao Yu Miao Wei He Weili Shi Zhengang Jiang School of Computer and Information Engineering Chuzhou UniversityChuzhou239000China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China Zhongshan Institute of Changchun University of Science and Technology ZhongshanChina School of Intelligent Engineering Henan Institute of TechnologyXinxiang453003China

The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the *** study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with ***,the data were *** optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance ***,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning ***,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease *** this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.

关键词： Classification COVID-19 deep learning segmentation unsupervised learning weakly supervised

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Stackelberg Game-Based Joint Computing Resource Allocation and Task Offloading Method in Edge Computing

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9704-9716页

作者： Chai, Yuan Zeng, Xiao-Jun Chen, Quan Cheng, Lianglun Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China University of Manchester Department of Computer Science ManchesterM13 9PL United Kingdom

Edge computing (EC) has emerged as an important technology to support the low-delay request of massive devices nowadays. Task offloading is an essential part in EC because it can influence the use of network resources and network performance dramatically. Most existing task offloading works are only from the view of users. To effectively considering the features and objectives of both users and edge nodes from their different perspectives, a Stackelberg game-based joint computing resource allocation and task offloading method is proposed in this paper. For the nature in EC where edge nodes and users play different roles, the problem is formulated as a bi-level optimization model with multiple leaders and multiple followers. The edge nodes can be seen as leaders and the users are followers. When jointly allocating computing resource and offloading tasks, edge nodes and users have different objectives. The objective of edge nodes is to achieve the most revenue and least energy cost, and the objective of users is to obtain short delay, consume little energy and pay less. Further, considering the particular features of EC, unlike existing Stackelberg game-based task offloading research, we focus on the computing resource allocation rather than pricing. The edge nodes decide the amount of computing resources to be allocated to each user. The users will then react according to such allocation to decide task offloading strategies. Interference, delay, energy, and payoff are all considered. Evolutionary optimization method BLEAQ-II is applied to solve the designed Stackelberg game-based task offloading model. Numerical results have shown the effectiveness of the proposed method. © 1967-2012 IEEE.

关键词： Mobile edge computing

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