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Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018

作者： Zhong, Zuofeng Wen, Jiajun Gao, Can Zhou, Jie Harbin Institute of Technology Shenzhen Graduate School Shenzhen518055 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518055 China Institute of Textiles and Clothing Hong Kong Polytechnic University Kowloon Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518055 Hong Kong

ISBN: (纸本)9783319996943

Image classification is a challenging issue in pattern recognition due to complex interior structure for data. Meanwhile, the high-dimension data leads to heavy computational burden. To overcome these shortcomings, in this paper, we learn a discriminative projection and representation in a unified framework for image classification task. This method seeks a discriminative representation in a low-dimension space for an image, which enhances the classification accuracy and efficiency. Thus, a projection matrix is learnt by a criterion which demands the minimum of within-class residual and maximum of between-class residual in an iterative procedure. Then all samples are projected into a low-dimension space, and obtain the discriminative representation via L2 regularization. The experimental results demonstrate that the proposed method achieves better classification performances, compared with state-of-the-art sparse representation methods. © Springer Nature Switzerland AG 2019.

关键词： Image classification

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Preface

Smart Innovation, Systems and Technologies

引用

Smart Innovation, Systems and Technologies 2021年 220卷 v页

作者： Voinov, Nikita Schreck, Tobias Khan, Sanowar Higher School of Software Engineering Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria School of Maths Computer Science and Engineering City University of London London United Kingdom

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Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations

arXiv

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arXiv 2020年

作者： Guéhéneuc, Yann Gaël Pimenta, Marcelo Petrillo, Fabio Lacerda, Guilherme University of Vale do Rio dos Sinos Polytechnic School São LeopoldoRS Brazil University of Quebec at Chicoutimi Department of Computer Science & Mathematics Chicoutimi Quebec Canada Federal University of Rio Grande do Sul Institute of Informatics Porto AlegreRS Brazil Concordia University Departement of Computer Science and Software Engineering Montreal Quebec Canada

Refactoring and smells have been well researched by the software-engineering research community these past decades. Several secondary studies have been published on code smells, discussing their implications on software quality,their impact on maintenance and evolution, and existing tools for their detection. Other secondary studies addressed refactoring, discussing refactoring techniques, opportunities for refactoring, impact on quality, and tools support. In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We perform this tertiary review using eight scientific databases, based on a set of five research questions, identifying 40 secondary studies between 1992 and 2018. We organize the main observations and challenges about code smell and their refactoring into: smells definitions, most common code-smell detection approaches, code-smell detection tools, most common refactoring, and refactoring tools. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future k d ll d f t i Copyright © 2020, The Authors. All rights reserved.

关键词： Odors

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STR: Secure computation on additive shares using the share-transform-reveal strategy

arXiv

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

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Application of wavelet denoising method in power battery management system

引用

Paper Asia 2018年第4期COMPENDIUM VOL. 1卷 52-55页

作者： Su, Weixing Liu, Fang School of Computer Science & Software Engineering TIANJIN Polytechnic University Tianjin300387 China

The electric vehicle is a kind of motor vehicle which uses storage battery as the energy source. The different grounds compared with conventional vehicle are that for new energy vehicle, we need to show the remain driving distance, the SOC value of power battery and the power consumption per 100 km. the accuracy of above three statistics effects on the judging ability of driver for the endurance of the vehicle. However the estimation results of these three statistics is influenced by the veracity of the discharge current of storage battery. Consider the above problem, this paper propose an idea that before estimation using the discharge current, we must de-noise by the method based on wavelet analyze, which can improve the veracity of the discharge current and further improve the accuracy of the above three statistics estimation results. © 2018, SHPMedia Sdn Bhd. All rights reserved.

关键词： Electric power supplies to apparatus

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A novel method of android malware detection based on ensemble learning algorithm 8

A novel method of android malware detection based on ensembl...

引用

2018 8th International Workshop on computer science and engineering, WCSE 2018

作者： Zhao, Jingyi Mo, Xiuliang Zheng, Qiao Tianjin Intelligent Computing and Software New Technology Key Laboratory School of computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9789811178610

Android is popular for mobile devices in recent years. As the risk of malware is sharply increasing in Android platform, Android malware detection has become an important research topic. However, the current work shows that the detection of malware still needs to be reformed. Many current results suggest a modification of permissions or a combination of permissions and intents, but Android's fragmentation issues and requiring rooting, hindering the widespread adoption of those methods. Many approaches have been proposed to detect this attack by modifying the Android OS. Existing anti-viruses depend on signature databases that need to be updated from time to time and are unable to detect zero-day malware. The Android Operating system allows inter-application communication through the use of component reuse by using intents. A malware detection model is developed based on ensemble learning algorithms and on the basis of the random forest in WEKA. The method we proposed can take a better efficiency and precision. © Proceedings of 2018 the 8th International Workshop on computer science and engineering, WCSE 2018. All rights reserved.

关键词： Android (operating system)

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Video compression coding via colorization: A Generative Adversarial Network (GAN)-based approach

arXiv

引用

arXiv 2019年

作者： Pan, Zhaoqing Yuan, Feng Lei, Jianjun Kwong, Sam School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Computer Science City University of Hong Kong Hong Kong

Under the limited storage, computing and network bandwidth resources, the video compression coding technology plays an important role for visual communication. To efficiently compress raw video data, a colorization-based video compression coding method is proposed in this paper. In the proposed encoder, only the video luminance components are encoded and transmitted. To restore the video chrominance information, a generative adversarial network (GAN) model is adopted in the proposed decoder. In order to make the GAN work efficiently for video colorization, the generator of the proposed GAN model adopts an optimized MultiResUNet, an attention module, and a mixed loss function. Experimental results show that when compared with the H.265/HEVC video compression coding standard using all-intra coding structure, the proposed video compression coding method achieves an average of 72.05% BDBR reduction, and an average of 4.758 dB BDPSNR increase. Moreover, to our knowledge, this is the first work which compresses videos by using GAN-based colorization, and it provides a new way for addressing the video compression coding problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Finding interesting cleaning rules from dirty data

Finding interesting cleaning rules from dirty data

引用

作者： Peng, Nan Wang, Hongya Ding, Wencheng Xiao, Yingyuan Liu, Zhenyu School of Computer Science and Technology Donghua University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Shanghai Key Laboratory of Computer Software Testing and Evaluation Shanghai China

ISBN: (纸本)9781538636749

Data cleaning is an important problem and data quality rules are the most promising way to address it. Previous researches have focused on constraints, such as functional dependencies (FDs), conditional functional dependencies (CFDs), fixing rules, and editing rules. These rules can be provided by experts, or discovered by mining algorithms and then experts verify. But in the condition of dirty dataset, it is highly unlikely that useful FDs could be discovered. If there are no useful cleaning rules, the user can not capture and correct errors in data. In this paper, we develop a system that is able to find interesting rules from dirty data. There is no hope to mine significant functional dependencies directly by the TANE algorithm because of the existence of errors in data. Thus, we discover approximate functional dependencies as candidate cleaning rules with the improved TANE algorithm. The number of candidate rules is very large, making it expensive for the user verification. Since not all candidate rules make sense in practice, we design two kinds of ranking methods: the support-based ranking and the comprehensive score ordering. Our ranking methods usually put the most useful rules ahead. Experimental results show that our sorting methods can effectively help the user find useful cleaning rules. © 2017 IEEE.

关键词： Data Cleaning Data Quality Rules Functional Dependencies

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Stock technical analysis system based on real-time stream processing

Stock technical analysis system based on real-time stream pr...

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作者： Ding, Wencheng Wang, Hongya Peng, Nan Xiao, Yingyuan Liu, Zhenyu School of Computer Science and Technology Donghua University Shanghai China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Shanghai Key Laboratory of Computer Software Testing and Evaluation Shanghai China

ISBN: (纸本)9781538636749

Nowadays a large number of applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Among numerous tools that support these scenarios, the Esper engine is one of the most excellent and practical ones. In this paper, we utilize the Esper engine and a series of financial models to design and implement Stock Technical Analysis System Based on Real-time Stream Processing(STAS). Several general financial models, such as the capital asset pricing model, Candlestick chart, KDJ index and MACD index, are introduced to fulfill the requirements from different users. All these models or indexes are supported by the Esper engine which has a superiority in real-time processing. In this system, users can choose their favorite stocks based on annualized income and visualize these selected stocks in the form of data list, graph and analysis report. © 2017 IEEE.

关键词： Esper Engine Real-time Stream Processing Stock Technical Analysis

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Microblog text clustering based on BK-means algorithm 8

Microblog text clustering based on BK-means algorithm

引用

2018 8th International Workshop on computer science and engineering, WCSE 2018

作者： Li, Qianru Mo, Xiuliang Wang, Chundong Tianjin Intelligent Computing and Software New Technology Key Laboratory School of Computer Science and Engineering Tianjin university of technology Tianjin300384 China

ISBN: (纸本)9789811178610

In recent years, the increasing popularity of social media such as WeChat and Weibo has facilitated the communication among people. However, due to the characteristics like large scale, fast propagation, low quality and diverse modalities of social short texts, the short text clustering faces the challenge of sparse features, high dimension and noise interference. The traditional clustering method based on vector space model is not good for short text data processing. With the improvement of K-means algorithm, this paper proposes a short-text clustering algorithm named BK-means which alleviates the effect of data sparseness. Firstly, we preprocess the wordset by means of word segmentation, stop-of-word and other operations, then extract the biterm using the BTM to model the document, and get the document-topic, the topic-word distribution matrix. Finally, we use the proposed BK-means algorithm to cluster short texts of documents represented by vectors. Experiments on the short text data of Sina Weibo have proved that the short text clustering algorithm based on BK-means is superior to the traditional one, and both the F-measure and the purity are improved. © Proceedings of 2018 the 8th International Workshop on computer science and engineering, WCSE 2018. All rights reserved.

关键词： K-means clustering

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