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An optimal differentially private data release mechanism with constrained error

Frontiers of Computer Science 2022年第1期16卷 187-189页

作者： Hao WANG Zhengquan XU Xiaoshan ZHANG Xiao PENG Kaiju LI Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismCollege of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing400065China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430079China College of Computer Science Chongqing UniversityChongqing400044China

1 Introduction and main contributions In data-driven applications,such as location based services(LBSs),disease surveillance and social networks,etc.,information fusion is necessary for data owners to obtain better *** the aggregated data may contain individuaFs sensi-tive ***,privacy preserving data fusion has become a substantial issue in data aggregating and mining[1,2].

关键词： LBS release optimal

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Research on knowledge Graph Construction for Emergency Resource Management 12

Research on Knowledge Graph Construction for Emergency Resou...

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12th International Symposium on Project Management, ISPM 2024

作者： Zhang, Kehong Wang, Min Zhou, Chenjie School of Information Engineering and Artificial Intelligence Lanzhou University of Finance and Economics Gansu Lanzhou730030 China Gansu Provincial Key Laboratory of Intelligent Business China "Belt and Road" Intellectual Property Protection and Data Utilization Industrial Research Institute China

ISBN: (纸本)9798331302344

Emergency resource management is a very important part of the public affairs emergency management system, once the emergence of public emergencies requires timely and efficient emergency rescue response, and rapid emergency rescue capabilities must rely on scientific and reasonable emergency resource management scheduling. Therefore, this paper addresses the problem of emergency resource management scheduling, the application of knowledge mapping technology to build an emergency resource decision-making system, combining the government, enterprises and other social forces, collaborative cooperation, to ensure a rapid response in the event of emergencies, and improve the efficiency of emergency rescue. In this paper, we obtain the original text from the emergency management website through web crawlers and label the entity relationship types to complete the construction of the dataset;on this basis, the entity relationships are objectively displayed through the Neo4j graph database to complete the creation of the knowledge graph of emergency resource scheduling. The constructed knowledge graph is reliable and can provide a scientific program for emergency management scheduling. © 2024 ISPM. All Rights Reserved.

关键词： knowledge graph

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NcoVis: A Visual Analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education

arXiv

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arXiv 2024年

作者： Shao, Pengyang Yang, Yonghui Gao, Chen Chen, Lei Zhang, Kun Zhuang, Chenyi Wu, Le Li, Yong Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group Zhejiang Hangzhou China

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students’ proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion. Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD. © 2024, CC BY.

关键词： knowledge graph

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A Highly Selective Wideband Filtering Stacked DDPA 4

A Highly Selective Wideband Filtering Stacked DDPA

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Tian, Kang Zhang, JiaSheng La, Dongsheng Northeastern University at Qinhuangdao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Qinhuangdao066000 China An Hui Ming Bian Electronic Science & Technology Co. Ltd. China

ISBN: (纸本)9798350363586

In this paper, a compact and highly selective stacked filtering dense dielectric patch (DDP) antenna (DDPA) is proposed. By placing a pair of thin DDP with high dielectric constant along the y axis on the DDP with low dielectric constant, the resonant frequency of the TE12 mode is adjusted to approach low frequency and form a passband with the TE11 mode. By loading a U-shaped microstrip feeding line on the microstrip feeding line, the bandwidth can be further broadened and the TE11, TE11 + δ and TE12 modes can be successfully excited in the passband. The radiation nulls are generated by the U-shaped microstrip line and non-radiative TE12 mode, respectively. By adjusting the length of the U-shaped microstrip line and the size of DDP with high dielectric constant, the independent adjustment of radiation nulls can be achieved. Finally, the proposed filtering antenna achieves a relative bandwidth of 11.66 % (4.04-4.54 GHz). The average realized gain and peak gain are 6.89 dBi and 7.1 dBi, respectively. The realized gain in the passband is very flat, and the fluctuation is less than 0.7 dBi. The proposed antenna has the advantages of compactness, high selectivity and flat gain, and it can be applied to modern satellite communication and wireless communication systems. © 2024 IEEE.

关键词： Bandwidth

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A Color Image Encryption Scheme Based on Block Compressive Sensing and Chaotic Systems 2

A Color Image Encryption Scheme Based on Block Compressive S...

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2nd International Conference on Applied Statistics, Computational Mathematics, and Software engineering, ASCMSE 2023

作者： Tian, Junfeng Chen, Keke Key Laboratory of Big Data Analysis and Processing of Henan Province Henan University Kaifeng475004 China College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng475004 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The traditional compressive sensing theory does not take full advantage of the differences in image block information features for sampling, and the measurement matrix does not adequately measure the image based on the image information features. To solve these problems, we design a color image encryption scheme based on block compressive perception and chaotic systems. First, the color image is divided into three channels of RGB and into 8*8 blocks respectively. Then, a zigzag transform is applied to each block so that each pixel value is evenly distributed. Each block is sparsified by the discrete cosine transform, and the measurement matrix generated by the Chen chaotic system is sampled to compress the image blocks to CR times the original block size according to different compression ratios (CR). The resulting chunked compressed samples are further distributed across the pixel values of the image using a cross-channel confusion dislocation method. Finally, the size of the pixel values is changed by a dissimilarity operation. The experimental results show that the scheme can balance the security of image information transmission and the integrity of reception, and has good resistance to various attacks. © 2023 SPIE.

关键词： Cryptography

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Research on a Technology of Mail Anti-virus Gateway for Industrial Internet

Research on a Technology of Mail Anti-virus Gateway for Indu...

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2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Wu, Yuhong Lv, Yunshan Zhang, Ling School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350314670

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system;then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency. © 2023 IEEE.

关键词： Network security

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RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning 6

RECC: A Relationship-Enhanced Content Caching Algorithm Usin...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Ren, Jiarui Zhang, Haiyan Zhou, Xiaoping Zhu, Menghan Ningxia University School of Information Engineering Yinchuan China Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

ISBN: (纸本)9781665453110

Mobile edge caching (MEC) is a promising technology to alleviate traffic congestion in the network. Current studies explored deep reinforcement learning (DRL)-based MEC methods. These methods consider the dynamics of the request size to maximize the cache hit rate. However, they usually ignored the potential request relationships among contents. Two contents with a strong relationship are usually requested sequentially. Inspired by this assumption, this paper proposes a relationship-enhanced content caching algorithm using DRL, named RECC. Our RECC infers user preferences by mining the request relationships among contents. In this work, the relationships are modeled as request sequences, and the request features are learned by using graph embedding. These features will be used as input of state in our DRL-based algorithm. We utilize the Wolpertinger architecture to solve the limitation of large discrete action space. The simulation results indicate that our RECC outperformed the traditional cache policies and state-of-the-art DRL-based method in cache hit rate. Furthermore, the proposed RECC has advantages in long-term stability in the environment where content popularity changes dynamically, and also has a higher cache hit rate when handling the requests with number changes dynamically. © 2022 IEEE.

关键词： Reinforcement learning

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A Review of Atmospheric Electricity Research in china from 2019 to 2022

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Advances in Atmospheric Sciences 2023年第8期40卷 1457-1484页

作者： Weitao LYU Dong ZHENG Yang ZHANG Wen YAO Rubin JIANG Shanfeng YUAN Dongxia LIU Fanchao LYU Baoyou ZHU Gaopeng LU Qilin ZHANG Yongbo TAN Xuejuan WANG Yakun LIU Shaodong CHEN Lyuwen CHEN Qingyong LI Yijun ZHANG State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Laboratory of Lightning Physics and Protection Engineering Chinese Academy of Meteorological SciencesBeijing 100081China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041China Chinese Academy of Sciences Key Laboratory of Geospace Environment School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei 230026China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200030China Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510641China Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong UniversityBeijing 100044China Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China

Atmospheric electricity is composed of a series of electric phenomena in the *** advances in atmospheric electricity research conducted in china have been achieved in recent *** this paper,the research progress on atmospheric electricity achieved in china during 2019-22 is reviewed focusing on the following aspects:(1)lightning detection and location techniques,(2)thunderstorm electricity,(3)lightning forecasting methods and techniques,(4)physical processes of lightning discharge,(5)high energy emissions and effects of thunderstorms on the upper atmosphere,and(6)the effect of aerosol on lightning.

关键词： atmospheric electricity thunderstorm lightning lightning detection lightning physics

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CCAC: Contrastive Learning with Channel Attention and Contour Loss for Brain Image Registration

CCAC: Contrastive Learning with Channel Attention and Contou...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Ying Shi, Dantong Wang, Jiaqi Xiang, Jinhai Zhang, Wen Huazhong Agricultural University College of Informatics Wuhan China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Wuhan China

ISBN: (纸本)9798350337488

In medical image analysis, image registration plays a key role in establishing the deformation field between image pairs. A good deformation model is essential for high-quality estimates. However, most existing approaches still face great challenges in extracting better image features and generating detailed information on deformation fields. In this paper, we present a brain image registration model based on contrastive learning with channel attention and contour loss (CCAC). To integrate lower-level features more effectively, we propose a channel attention-based skip connection to obtain weight information of features at different encoder layers. To enhance the feature consistency of semantics in different stages, we then introduce multi-stage contrastive learning to capture semantics at different stages. Moreover, in order to reduce the influence caused by the disparities of inter-image gray value in registered images, contour feature loss is employed. Both qualitative and quantitative experiments on the popular LPBA40 dataset demonstrate that our method can achieve much better performance than the baseline methods. © 2023 IEEE.

关键词： Brain MR image Channel attention Contour features Medical image registration Multistage contrastive learning

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