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共 3354 条记录，以下是81-90 订阅

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An advanced data Science Model Based on Big data Analytics for Urban Driving Cycle Construction in China 2020

An Advanced Data Science Model Based on Big Data Analytics f...

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2020 international conference on computing, Networks and Internet of Things, CNIOT 2020

作者： Qin, Yana Yang, Haolin Guo, Mengjie Guo, Meicheng Inner Mongolia University of Technology Inner-Mongolia China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450377713

In recent years, with the rapid growth of car ownership, Chinese road traffic conditions have changed a lot. Governments, enterprises, and the public are increasingly finding that the increasing deviation between the actual fuel consumption and the results of the regulatory certification based on NEDC (New European Driving Cycle). In addition, this deviation has seriously affected the credibility of the government, energy saving and emission reduction of automobiles and environmental pollution. Thus, need to improve urban driving cycle construction methods to adapt the Chinese traffic and automobiles driving cycles. This paper proposes an advanced data science model based on big data analysis for accurate urban driving cycle construction in Chinese cities. In addition, we conduct a lot of data analysis and statistics. Then we design a data preprocessing method for cleaning the noise data to use in driving cycle construction. Extensive experiments and analysis on real-world datasets demonstrate that the proposed methods can significantly reduce the impact of missing and abnormal data on microtrips segmentation, and thus the proposed methods can be used for driving cycle construction in China more accurately. © 2020 ACM.

关键词： data Analytics

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A Glimpse of the Whole: Detecting Few-shot Android Malware Encrypted Network Traffic

A Glimpse of the Whole: Detecting Few-shot Android Malware E...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Wenhao Li Xiao-Yu Zhang Huaifeng Bao Qiang Wang Haichao Shi Zhaoxuan Li Institute of Information Engineering Chinese Academy of Sciences Beijing China

Reversing binary samples is a conventional way to detect Android malware and is limited by the prosperity of code obfuscation. Detecting network traffic generated by Android malware can counter the advanced obfuscation technic and has been intensively studied in recent years. Existing methods mostly require sufficient and balanced training data to construct a satisfactory detector. Unfortunately, the newly emerged Android malware, especially those of new families, is hard to obtain for lack of prior knowledge. Meanwhile, the state-of-the-art few-shot learning approaches are incompetent for task-specific classification. To address the issues, this paper proposes a novel metric-learning framework, namely Path Optimization Prototypical Nets (POPNet), for few-shot Android malware encrypted network traffic classification. POPNet aims to map network traffic onto a high dimensional metric space, using auxiliary traffic from benign android software to augment the representative ability. Path optimization strategies are carefully designed to compress the searching space to obtain a more rational distribution on the linearly separable metric space. Our method achieves state-of-the-art performance on few-shot and zero-shot classification on MalDroid2017 and USTC2016. Additional experiments on Omniglot further prove the generalization of POPNet.

关键词： Smart cities Training data Telecommunication traffic Detectors Extraterrestrial measurements Malware Robustness

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Research on Scientific data Management in Big data Era 20

Research on Scientific Data Management in Big Data Era

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4th international conference on Computer Science and Application Engineering, CSAE 2020

作者： Man, Rui Zhou, Guomin Fan, Jingchao Agricultural Information Institute Chinese Academy of Agricultural Sciences and National Agricultural Science Data Center Beijing China Department of Science and Technology Management Chinese Academy of Agricultural Sciences and National Agricultural Science Data Center Beijing China

ISBN: (纸本)9781450377720

Scientific data is an important strategic resource in the era of big data. Efficient management and wide circulation are the key ways to enhance the value of scientific data resources. With the transformation of the industrial society into the information society, the importance of scientific data management is also increasing all over the world, which continuously promotes the maturity of scientific data management and sharing. In this article, through comprehensive research of scientific data management ideas, policies, practices and results, the analysis summarizes the advanced experience of international scientific data management, for the similar problems and challenges existing in the research in China, puts forward the future a period of time the direction and suggestions on the development of scientific data management: 1. the specification of various kinds of degree of the standardization of scientific data resources;2. To strengthen data mining capacity;3. To strengthen the cultivation of talents in data science;4. To strengthen international cooperation and enhance core competitiveness in the big data era. © 2020 ACM.

关键词： international cooperation

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A Deep Autoencoder Based Outlier Detection for Time Series

A Deep Autoencoder Based Outlier Detection for Time Series

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2020 IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2020

作者： Wang, Jin Miao, Fang You, Lei Fan, Wenjie School of Information Science and Technology Chengdu University Chengdu China Institute of Big Data Chengdu University Chengdu China

ISBN: (纸本)9781728198736

Time series outlier detection is an important topic in data mining, having significant applications in reality. Due to the complexity and dynamics of time series, it is quite difficult to detect outlier in time series. Particularly, influenced by outside factors, time series are usually unpredictable, accompanied with concept drift. Recently, recurrent neural network has been used to identify time series outlier, and demonstrated great potential. However, RNN usually uses deterministic state transition structure, which cannot characterize the variability of high-dimensional time series. This paper proposes to incorporate latent variables into RNN, aiming to catch the time series variability as much as possible. In particular, our method combines RNN and variation auto-encoder framework. We evaluate our method with several real datasets, and demonstrate that our method has superior detecting performance. © 2020 IEEE.

关键词： Time series

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Transformative computing for Distributed Services Management Protocols 34th

Transformative Computing for Distributed Services Management...

引用

34th international conference on advanced Information Networking and Applications, AINA 2020

作者： Ogiela, Lidia Takizawa, Makoto Ogiela, Urszula Department of Cryptography and Cognitive Informatics Pedagogical University of Krakow Podchorążych 2 Street Kraków30-084 Poland Department of Advanced Sciences Hosei University 3-7-2 Kajino-cho Tokyo Koganei-shi184-8584 Japan Pedagogical University of Krakow Podchorążych 2 Street Kraków30-084 Poland

ISBN: (纸本)9783030440404

This paper describes new solutions for distributed services management protocols. The new idea will be based on use transformative computing methodology in services securing algorithms. Such solutions will be based on registration and analysis of different external signals, and semantic evaluation of their meaning. Evaluation of proposed techniques will be described, as well as possible application for information/data and services management. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Analysis Dropout Situation of Business Computer Students at University of Phayao 2nd

Analysis Dropout Situation of Business Computer Students at ...

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22nd international conference on Interactive Collaborative Learning (ICL) / 48th IGIP international conference on Engineering Pedagogy - Impact of the 4th Industrial Revolution on Engineering Education

作者： Nuankaew, Pratya Nuankaew, Wongpanya Phanniphong, Kanakarn Fooprateepsiri, Rerkchai Bussaman, Sittichai Univ Phayao Phayao Thailand Rajabhat Mahasarakham Univ Maha Sarakham Thailand Rajamangala Univ Technol Tawan Ok Chon Buri Thailand

ISBN: (纸本)9783030402747;9783030402730

This paper aims to analyze the situation of dropout of business computer students at University of Phayao. The composition of the goal consists of 3 main points, including summarizing the situation from the past to the present, statistical analysis and machine learning analysis. data collection is 1,888 students from the Department of Business Computer at University of Phayao from the academic year 2001-2016. The research tools are percentages, decision tree, cross-validation methods, and confusion matrix performance. The results showed that the dropout rate of learners in business computer program tended to increase even though the number of new students decreased. It was found that the academic results had a significant influence on dropout, which most of the students who dropped out were obviously in their first academic year. Which, the model received is a high-performance prediction level with an accuracy of 87.21%. It was found that factors affecting the dropout consisted of seven courses: 221110, 221120, 001103, 128221, 005171, 122130 and 128221. Based on research findings, it represents a situation that has entered into a crisis in which the stakeholders need to focus on the above problems.

关键词： Student dropout prediction Analysis dropout situation Educational data mining Personal learning pathway

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Hybrid Algorithm and Forecasting Technology of Network Public Opinion Based on BP Neural Network 9th

Hybrid Algorithm and Forecasting Technology of Network Publi...

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9th international Workshop of advanced Manufacturing and Automation, IWAMA 2019

作者： Zhang, Ronghua Liu, Changzheng Ma, Hongliang Intelligent and Distributed Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shihezi University Shihezi832000 China

ISBN: (纸本)9789811523403

Based on the hybrid algorithm and BP neural network, the forecasting technology of network public opinion was studied. After detailed introduction of the research background and significance, the concept and development of hybrid algorithm, Hybrid algorithm is analyzed in detail. The user network public opinion classification model under the clustering algorithm is established. Then the data mining experiment of Sina Weibo users’ network public opinion is conducted. Using an improved BP neural network algorithm, unsupervised user network clustering and evaluation of the results, finally, the user’s Internet public opinion is divided into six categories. The characteristic preferences of each user group are analyzed, with recommendations provided for operation recommendation and promotion. © Springer Nature Singapore Pte Ltd 2020.

关键词： data mining

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mining defects of result-sensitive function based on information entropy 6th

Mining defects of result-sensitive function based on informa...

引用

6th international conference on Artificial Intelligence and Security,ICAIS 2020

作者： Chen, Lin Yang, Chunfang Liu, Fenlin Gong, Daofu Yin, ZhongXu Zhengzhou Science and Technology Institute Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811581007

Result-sensitive function is a typical type of security-sensitive function. The misuse of result-sensitive functions often leads to a lot kinds of software defects. Existing defect detection methods based on code mining for result-sensitive functions usually require a gived security rule or an inferred security rule as input. Based on the principle of consistency, we propose a defect detection method based on information entropy. Firstly, the feature vector about usage of function is extracted from every function instance. Then, the information entropy is introduced to measure the abnormal degree of the feature vector. The function instances with high degree of abnormality is regarded as dangerous instances. Experiments show that the proposed method can effectively detect dangerous instances of security defects without a gived security rule. © Springer Nature Singapore Pte Ltd 2020.

关键词： Defects

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Matrix-Based data Security in Cloud computing Using advanced Cramer-Shoup Cryptosystem

Matrix-Based Data Security in Cloud Computing Using Advanced...

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Springer international conference on Emerging Technologies in data mining and Information Security (IEMIS)

作者： Sirajudeen, Y. Mohamed Anitha, R. Sri Venkateswara Coll Engn DST SERB Cloud Res Lab Sriperumbudur 602117 Tamil Nadu India

ISBN: (纸本)9789811319518;9789811319501

Cloud computing allows users to store data in a distributed cloud storage. So the data are stored in different geographical locations which will predominantly increase the vulnerability of the data. The natural way to safeguard the data from the intruders is to encrypt the data before storing in the cloud. This paper proposes an advanced Cramer-Shoup Cryptosystem to resolve the security threats in cloud environment. The proposed encryption method withstands well against the adaptive chosen cipher text attack (CCA2). While considering high velocity, encryption method has to work as fast as possible to improve the efficiency. The proposed ACS Cryptosystem supports batch encryption and decryption to increase the efficiency and reduces the computation overhead. The experimental analysis for the proposed method is carried out and observed that the proposed encryption technique improves the security of the data and results are compared with existing security algorithms.

关键词： Cloud security advanced cramer-shoup cryptosystem Cloud encryption data security

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FabricGene: A Higher-Level Feature Representation of Fabric Patterns for Nationality Classification 16th

FabricGene: A Higher-Level Feature Representation of Fabric ...

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16th international conference on advanced data mining and Applications (ADMA)

作者： Yu, Shuang Li, Xiongfei Wang, Hancheng Zhang, Xiaoli Chen, Shiping Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China CSIRO Data61 Sydney NSW 2016 Australia

ISBN: (纸本)9783030653903;9783030653897

Nationality fabric classification is a significant work to promote the protection work of fabric patterns and further reveal its unique connotation and inheritance rules in big data era. Thus, how to ascertain the feature representation of fabric patterns becomes a primary problem. This paper presents a high-level feature representation for fabric patterns for nationality classification, called FabricGene, which improves the semantic expression ability of the fabric pattern features. In fabric patterns, each FabricGene represents a complete abstract concept including the external shape and connotation characteristics. We evaluate the performance of FabricGenes and basic geometric primitives to illustrate the effectiveness of FabricGenes in nationality classification. Five widely used classification algorithms are applied to classify the fabric patterns by learning from training data with 12 groups of FabricGenes and 11 groups of basic geometric primitives respectively. The results demonstrate that the FabricGenes perform more effectively and stably in nationality classification than the basic geometric primitives. Namely, the FabricGenes can express the fabric patterns' nationality features more accurately.

关键词： Fabric pattern Feature representation FabricGene Basic geometric primitive Nationality classification

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