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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

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The New Progress and Methods of Privacy Protection on Medical and Health big data* 14

The New Progress and Methods of Privacy Protection on Medica...

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14th international conference on Software, Knowledge, Information Management and Applications, SKIMA 2022

作者： Wang, Xin Guo, Yating Zhao, Yantao Yu, Hongnian School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi'an China School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom School of Electrical Engineering Yanshan University Qinhuangdao China

ISBN: (纸本)9781665493345

In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of 'Internet plus medical and health' has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected. © 2022 IEEE.

关键词： Digital storage

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Study on Path Planning of Multi-storey Parking Lot Based on Combined Loss Function 18th

Study on Path Planning of Multi-storey Parking Lot Based on ...

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18th international conference on Intelligent computing (ICIC)

作者： Hu, Zhongtian Yan, Jun Wang, Yuli Yang, Changsong Fu, Qiming Lu, Weizhong Wu, Hongjie Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

Path planning research can effectively solve the problem of finding free parking space in multi-storey parking lots. This paper takes advantage of the decision ability of reinforcement learning and the perception ability of deep learning to improve the algorithm based on traditional DQN. On the one hand, Q value is updated with qualification trace;On the other hand, different loss functions are set for the main network and the target network, and the two are combined to improve the accuracy of path planning. The experimental results show that the improved DQN model can accomplish the path planning task of multi-storey parking lot more accurately and efficiently.

关键词： DQN Combined loss function Path planning Artificial intelligence

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A Lightweight Deep Learning Model for Real-time Detection and Recognition of Traffic Signs Images Based on YOLOv5 12

A Lightweight Deep Learning Model for Real-time Detection an...

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12th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery (CyberC)

作者： He, Hui Chen, Qihong Xie, Guoping Yang, Boxiong Li, Shelei Zhou, Bo Gu, Yuye Beijing Normal Univ Adv Inst Nat Sci Zhuhai Peoples R China China Informat Ctr Beijing Peoples R China Univ Sanya Sch Informat & Intelligence Engn Sanya Peoples R China

ISBN: (纸本)9798350331547

The rapid and accurate identification of various road traffic signs is an important research topic in automotive vision systems. Specifically, the correct identification of road signs is an urgent problem requiring effective solutions to facilitate automatic driving. This paper proposes a new approach, PP-LCNet-P2-CT, for the detection and recognition of urban road signs in an automotive vision system using an improved YOLOv5 deep learning model. The main improvement of the PP-LCNet-P2-CT model includes the following: (1) Replacing the YOLOv5 backbone network with the lightweight network PP-LCNet to improve the real-time performance of the detection network;(2) Adding a small target detection head to the detection head to meet the needs of target detection with different scales and mitigate the adverse effects caused by drastic target scale changes;and (3) Integrating the CBAM convolutional block attention model that focuses on target features and the transformer coding block that can capture different local information to ensure the accuracy of lightweight model target detection. The model was tested with the Tsinghua traffic sign dataset, TT100k. The results show that the mAP index of the PP-LCNet-P2-CT model is increased by 29.84% and the FPS is increased by 24.05%, while the number of model parameters is decreased by 32.78% and the GFLOPs decreased by 34.41% compared with the classic YOLOv5 algorithm. The PP-LCNet-P2-CT model allows complex deep learning to be used successfully for unmanned ground vehicles (UGVs) with ordinary computing speeds and high real-time requirements.

关键词： real-time detection and recognition road traffic signs attention mechanism improved deep learning model YOLOv5

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TFS-index-based Multi-keyword Ranked Search Scheme over Cloud Encrypted data 29

TFS-index-based Multi-keyword Ranked Search Scheme over Clou...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Deng, Yinfu Dai, Hua Liu, Yuanlong Li, Zhangchen Yang, Geng Yi, Xun Nanjing University of Post and Telecommunication Nanjing China Nanjing University of Post and Telecommunication Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing China Royal Melbourne Institute of Technology University Melbourne Australia

ISBN: (纸本)9798350330717

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency. © 2023 IEEE.

关键词： Cloud computing dictionary partition multikeyword ranked search searchable encryption

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Causal Discovery for Feature Selection in Physical Process-Based Hydrological Systems

Causal Discovery for Feature Selection in Physical Process-B...

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2022 IEEE international conference on big data, big data 2022

作者： Sheth, Paras Liu, Ting Doner, Durmus Deng, Qi Wei, Yuhang Muenich, Rebecca Sabo, John Candan, K. Selcuk Liu, Huan School of Computing and Augmented Intelligence Arizona State University United States School of Sustainable Engineering and the Built Environment Arizona State University United States Future H2O Julie Ann Wrigley Global Futures Laboratory Arizona State University United States Department of River-Coastal Science and Engineering ByWater Institute Tulane University United States

ISBN: (纸本)9781665480451

Physical process-based hydrological models are widely adopted to simulate the water quantity or quality. One of the most commonly used hydrological models is Soil and Water Assessment Tool (SWAT). SWAT models for a large watershed can have over tens of thousands of Hydrological Resource Units (HRUs) which necessitates considerable computational resources. One way to speed up applications of the SWAT model could be to leverage machine learning techniques to identify the crucial features for the prediction task - feature selection. However, majority of the feature selection techniques rely on correlations or some form of a score metric (e.g. mutual information). Furthermore, since correlation does not imply causation, it is important to identify the causal features to improve the prediction accuracy while enhancing the interpretability of machine learning models. However, the SWAT model uses multiple data inputs and features that typically vary by space/HRUs, but may or may not vary over time. This makes it difficult to directly utilize causal discovery models to infer the causal relations. Furthermore, due to the lack of the ground truth causal graph for the SWAT model it is difficult to comment on the validity of the learned causal relations. To overcome these problems, we propose a novel framework that first infers the causal relations for the daily scale of the SWAT data using causal discovery algorithms. Then, it utilizes a community detection module to group similar features together for better interpretability. Finally, it identifies the stable causal relations that appear most often across all the timesteps and leverage them for the prediction of the water quantity. By utilizing only the causal features for the prediction of the target variable can lead to high accuracy as it removes the reliance on spurious correlations. Furthermore, we conduct extensive experiments to validate the effectiveness of the proposed framework along with a real-world case study to

关键词： SWAT

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Efficient Designated-Server Proxy Re-encryption with Keyword Search for big data 8th

Efficient Designated-Server Proxy Re-encryption with Keywor...

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8th international conference on Artificial intelligence and Security , ICAIS 2022

作者： Deng, Miaolei Song, Wenshuai Ma, Mimi Li, Haochen Israr, Muhammad College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Department of Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9783031067600

Against the background of the era of big data and cloud computing, cloud platforms have become the first choice for data storage. While cloud platforms also face serious threats to data security and user privacy. At the same time, users will be faced with the problem of how to efficiently retrieve ciphertext on the cloud. Searchable encryption allows users to directly retrieve ciphertext data on the cloud through keywords, which provides an effective method for solving efficient retrieval of ciphertext on the cloud. As an important branch of searchable encryption technology, proxy re-encryption can realize the sharing of ciphertext among multiple users. However, most of the existing schemes have security shortcomings or functional defects, such as inability to resist keyword guessing attack, and do not support search authority authorization. A new designated-server proxy re-encryption with keyword search scheme was proposed, which not only can resist keyword guessing attack, but also realizes the function of sharing search authority. Finally, we give the performance and security analysis. The experimental results show that our scheme is superior compared with other related schemes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： big data

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High-order Correlation Network for Video Recognition

High-order Correlation Network for Video Recognition

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Dong, Wei Wang, Zhenwei Zhang, Bingbing Zhang, Jianxin Zhang, Qiang Dalian Minzu Univ Sch Comp Sci & Engn Dalian Peoples R China Dalian Minzu Univ Inst Machine Intelligence & Biocomp Dalian Peoples R China Dalian Minzu Univ SEAC Key Lab Big Data Appl Technol Dalian Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

How to model global video representation is an important research content of video recognition. Among current convolutional neural network(CNN) based methods, only using first-order representations (i.e. global average pooling) has limitations in capturing spatiotemporal features of videos. Recent studies have shown that high-order statistics are more suitable to model complex feature distributions. To better characterize the spatiotemporal structure for video recognition, we propose a novel High-order Correlation Network (HoCNet) in this work, the core of which is to explore high-order video representations through correlation computation and covariance pooling. HoCNet leverages the correlation module to obtain complex temporal dynamic information of frames via computing dot product of features in the fixed sliding window of two adjacent frames. As an approximate high-order calculation, the correlation module can be inserted into any stage of the deep network to model high-order representations in various spatial resolutions. Additionally, a robust high-order pooling module, i.e., iterative matrix square root normalization of covariance pooling (iSQRT-COV), is also introduced at the end of the network, and this further boosts modeling complex spatiotemporal distributions of video features. Experiments conducted on four widely used video benchmarks demonstrate the effectiveness of HoCNet, which achieves the comparable performance with the state-of-the-art models.

关键词： video recognition covariance pooling second-order correlation deep network

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Human-Machine Interaction for Monitoring COVID-19 Internet data in Russia and the World 15th

Human-Machine Interaction for Monitoring COVID-19 Internet D...

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15th APCA international conference on Automatic Control and Soft computing (CONTROLO)

作者： Levashkin, Sergei Zakharova, Oksana Ivanov, Konstantin Povolzhskiy State Univ Telecommun & Informat Artificial Intelligence Lab Samara 443090 Russia

ISBN: (纸本)9783031100475;9783031100468

We develop a human-machine interaction via dashboard for COVID-19 data visualization in the regions of Russia and the world. In particular, it includes an adaptive-compartmental multi-parametric model of the epidemic spread, which is a generalization of the classical SEIR models;and a module for visualizing and setting the parameters of this model according to epidemiological data, implemented in a dashboard. data for testing have been collected since March 2020 on a daily basis from open Internet sources and placed on a "data farm" (an automated system for collecting, storing and pre-processing data from heterogeneous sources) hosted on a remote server. The combination of the proposed approach and its implementation in the form of a dashboard with the ability to conduct visual numerical experiments and compare them with real data allows most accurately tune the model parameters thus turning it into an intelligent system to support a decision-making. That is a small step towards Industry 5.0.

关键词： Human-machine interaction Industry 5.0 big data algorithm data visualization Optimization of the decision-making process Dashboard COVID-19 spread mathematical model

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big data Analysis for Anti-Money Laundering: A Case of Open Source Greenplum Application 19th

Big Data Analysis for Anti-Money Laundering: A Case of Ope...

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19th international conference on Web Information Systems and Applications, WISA 2022

作者： Hu, Chaochen Li, Ran Li, Chao Miao, Hengshuo Yang, Zongyou Zhang, Tengda Tsinghua University Beijing China Communication University of China Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing AgileCentury Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9783031203084

For the anti-money laundering regulations of financial institutions, it is important to do the timely big data analysis to continuously optimize the comprehensiveness, correctness and effectiveness of transaction monitoring models. And at the same time, how to reduce the cost investment for the solution is also important. The traditional solutions are usually based on Hadoop family, which is costly both in technical stack building and in code programming. And sometimes, although these costly solutions can be borne, the response time cannot be borne. This paper is an application case to build the solution on open source distributed database Greenplum. By utilizing the share-nothing MPP structure and the software ecosystem of Greenplum, a solution of high efficient big data analysis through large scale parallel computing is designed. After ETL processing by the tools such as gpfdist, we use advanced SQL features to support the calculation of several key transaction monitoring statistical indicators for anti-money laundering. The test cases show that, through this way, we can not only reduce the cost in technical stack building and in code programming, but also get the analysis results in just few seconds. Thus, this is a feasible solution for the similar big data analysis tasks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th international conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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