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

共 1361 条记录，以下是871-880 订阅

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Batch Generation of a Satellite big data database and Comparison of LSTM Network Performance on Local Workstations 41

Batch Generation of a Satellite Big Data Database and Compar...

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41st international conference of the Chilean Computer Science Society, SCCC 2022

作者： Trujillo, Roxana Hidalgo, Mauricio Pérez, Ignacio Solar, Mauricio Departamento de Informática U. Técnica Federico Santa María Valparaíso Chile Escuela de Ingeniería U. San Sebastián Santiago Chile Área de Informática U. Santo Tomás Santiago Chile

ISBN: (纸本)9781665456746

In recent years, technology has grown notably in all production areas, although computing has gone hand in hand with the development of new management techniques and handlingof large volumes of data of all kinds, the integration of new hardware structures efficiently organized through highly specialized architectures to work out tasks that demand a high level of computational capacity, time and storage, has become a key point for some work areas,especially for the satellite image processing sector. The data coming from these remote senors has the particularity of being very dense in information and the complete flow of treatment of this from its download and storage, calibration, processing and analysis makes satellite images become a very complex data to manipulate computationally. This requires adequate architectures to carry out this work, also complementing that since the last 5 years the growth in demand for the use of this kind of data has been increasing exponentially due to the multiple functions that are taking place in areas ranging from the management of natural disasters to the modeling of variables using Artificial intelligence which also often require the management of large volumes of very high resolution multispectral images that need storageand processing in real time, with which the capacity of the equipment is very highly forced to its maximum. Finally, this work presents a performance comparison for deep network training (LSTM) where it was determined that the computer equipment that has a GPU and uses CUDAis, at least, 2.5 times more efficient than a standard workstation for the computation of large data volumes at local environments. © 2022 IEEE.

关键词： big data

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Malay lexical simplification model for non-native speaker 5

Malay lexical simplification model for non-native speaker

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5th international conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Omar, Salehah Bakar, Juhaida Abu Nadzir, Maslinda Mohd Harun, Nor Hazlyna Yusoff, Nooraini Sultan Abdul Halim Mu'adzam Shah Polytechnic Department of Information Technology and Communication Kedah Jitra06000 Malaysia Universiti Utara Malaysia Data Science Research Lab School of Computing Kedah Sintok06010 Malaysia Institute for for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia

ISBN: (纸本)9781665495585

Vocabulary is an important language skill that can affect a person's understanding of a sentence. Thus, lexical simplification is the task of converting difficult words into simpler words. It is to make it easier for the reader to understand the sentences. The biggest challenge in lexical simplification is to simplify the words needed without changing the meaning of the sentence. Past studies have shown that there are weaknesses in this task, where simple words are also identified as complex words. This issue has led to the simplification of unnecessary words. The purpose of the study is to produce a complex word identification model for the Malay language into words that are more easily understood by nonnative speakers. Experiments was performed on the appropriate features to obtain the required results. Machine learning was used to ensure the results were more accurate. This study is a novelty in text simplification of the Malay language in the field of Natural Language Processing (NLP) and may be used as a preprocessing tool to improve other tasks in NLP. © 2022 IEEE.

关键词： Learning algorithms

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Energy Efficiency Maximization for UAV and Electric Vehicle Assisted Mobile Edge computing on the Edge Side 8th

Energy Efficiency Maximization for UAV and Electric Vehicle ...

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8th international conference on Artificial intelligence and Security (ICAIS)

作者： Tang, Qiang Li, LinJiang Wang, Jin Kim, Gwang-Jun Tang, Bin Changsha Univ Sci & Technol Sch Comp & Commun Engn Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha 410114 Hunan Peoples R China Chonnam Natl Univ Dept Comp Engn Gwangju 61186 South Korea

ISBN: (纸本)9783031067914;9783031067907

The research on the Internet of Things (IoT) and edge computing, especially Unmanned Aerial Vehicles assisted Mobile Edge computing (UAV assisted MEC) attracted more and more interests of researchers. Nowadays, Electric Vehicles (EV) was introduced into the UAV assisted MEC system to improve system suitability and robustness. In this paper, an UAV and EV assisted MEC system was proposed and a paralled processing was involved with the goal of the edge side energy efficiency maximization, by jointly optimizing the communication scheduling, computing frequency and the trajectory of the UAV. The problem was formulated as a Mixed Integer Non-Liner Programming (MINLP), which was hard to solve. Therefore, the MINLP was divided into three sub-problems by applying the Block Coordinate Descent (BCD) method and solved by using Exhaustive Method (EM) and Successive Convex Optimization (SCO). In addition, a heuristic algorithm was put forward to get the optimization solution. The simulation results showed that our strategy has better performance compared with other benchmarks.

关键词： Mobile edge computing Electric vehicles UAV trajectory

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The Analysis of Commercial Pattern of Cloud computing Platform 22

The Analysis of Commercial Pattern of Cloud Computing Platfo...

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3rd Asia Service Sciences and Software Engineering conference, ASSE 2022

作者： Liu, Haitao Qin, Zhi Ai, Xinxing Tu, Zhigang Zhang, Rongquan Guangxi Artificial Intelligence and Big Data Applying Institute Nanning530201 China Department of Human Resources and Social Security of Guangxi Zhuang Autonomous Region Nanning530022 China Guangxi Iron and Steel Group Co. Ltd. Fangchenggang538000 China Nanchang Randao Science and Technology Co. Ltd. Nanchang330038 China Guangxi University of Finance and Economics Nanning530004 China

ISBN: (纸本)9781450387453

For solving the problem which there have not suitable commercial pattern of cloud computing platform yet, a commercial pattern of cloud computing platform was proposed and constructed. The solving scheme are provided as follows. After explicitly demand surveyed, function analyzed and framework designed, an standard cloud computing platform with local characteristics and IoT think was constructed within 6 months successfully. After a period of time for operating and managing, for better operating the cloud computing platform, pursuing the continous income and excess profit, a creative and practical commercial pattern of cloud computing platform was presented and applied by this paper, that included in data Operation (DO) and Joint Credit Reference (JCR), which could solve the problem that the income of many cloud computing platform are not continuous. Then the performance of cloud computing platform was tentatively evaluated by Markov Random Field theory and Belief Propagation algorithm. Moreover, Cumulative Vote Scheme (CVS) which was also presented by this paper was used to analyze and evaluate the performance of commercial pattern of cloud computing platform. Three different random group samples were used to test and evaluate the performance of commercial pattern by independent practical experiments. Finally, the commercial pattern of cloud computing platform was proved to be very effectiveness by experiment results, and the performance of cloud computing platform was very robustness. © 2022 Association for computing Machinery.

关键词： Cloud computing

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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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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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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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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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