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2019 Conference on Digital Holography and Three-Dimensional Imaging, DH 2019

作者： Zhang, Yaping Wang, Rende Wang, Lin Tsang, Peter Poon, T.-C. Key Lab of Laser Information Processing Technology and Application Kunming University of Science and Technology Kunming650500 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing Jiangsu210094 China Department of Electronic Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

Optical scanning holography (OSH) is a variant of two-pupil heterodyning image processing. We investigate the use of one pupil as a delta function and the other being an annulus as an extended application of OSH. ... 详细信息

ISBN: (纸本)9781943580590

关键词： Holography

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A geometric and fractional entropy-based method for family photo classification

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Expert Systems with Applications: X 2019年 3卷

作者： Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Hu, Tianping Jalab, Hamid A. Ibrahim, Rabha W. Blumenstein, Michael Lu, Tong Ayub, Mohamad Nizam Bin Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University China Faculty of Engineering and IT University of Technology Sydney Australia

Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate. © 2019

关键词： Face recognition

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The role of model checking in software engineering

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Frontiers of computer Science 2018年第4期12卷 642-668页

作者： Anil Kumar KARNA Yuting CHEN Haibo YU Hao ZHONG Jianjun ZHAO Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Software Shanghai Jiao Tong University Shanghai 200240 China Department of Advanced Information Technology Kyushu University Fukuoka 819-0395 Japan

Model checking is a formal verification technique. It takes an exhaustively strategy to check hardware circuits and network protocols against desired properties. Having been developed for more than three decades, model checking is now playing an important role in software engineering for verifying rather complicated software artifacts. This paper surveys the role of model checking in software engineering. In particular, we searched for the related liter- atures published at reputed conferences, symposiums, workshops, and journals, and took a survey of （1） various model checking techniques that can be adapted to software development and their implementations, and （2） the use of model checking at different stages of a software development life cycle. We observed that model checking is useful for soft- ware debugging, constraint solving, and malware detection, and it can help verify different types of software systems, such as object- and aspect-oriented systems, service-oriented applications, web-based applications, and GUI applications including safety- and mission-critical systems. The survey is expected to help human engineers understand the role of model checking in software engineering, and as well decide which model checking technique（s） and/or tool（s） are applicable for developing, analyzing and verifying a practical software system. For researchers, the survey also points out how model checking has been adapted to their research topics on software engineering and its challenges.

关键词： software engineering model checking state-explosion

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A embedding model for text classification

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Expert Systems 2019年第6期36卷 e12460-e12460页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Yi, Jing Zhu, Zhenfang School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Heze University Heze China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China

Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look-up tables, we construct a two-dimensional table that is in the format to represent words in text documents. Finally, the two-dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models. © 2019 John Wiley & Sons, Ltd

关键词： Embeddings

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A Fitted Fuzzy-rough Method for Missing Data Imputation

A Fitted Fuzzy-rough Method for Missing Data Imputation

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Daiwei Li Tianrui Li Haiqing Zhang Abdelaziz Bouras Xi Yu Dan Tang School of Information Science and Technology Southwest Jiaotong University College of Software Engineering Chengdu University of Information Technology Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China College of Software Engineering Chengdu University of Information Technology Chengdu China Computer Science Department Qatar University Doha Qatar College of Information Science and Technology Chengdu University Chengdu China

ISBN: (数字)9781728123486

ISBN: (纸本)9781728123493

Missing data imputation is a fundamental task for reducing uncertainty and vagueness in medical dataset. Fuzzy-rough set has taken very important role to accurate representation original information. This paper proposes Fitted fuzzy-rough imputation algorithms called Fitted FRNNI and Fitted VQNNI by introducing weight coefficients to balance fuzzy similarly relations among training and testing instances. Meanwhile, modification fuzzy decisions of nearest neighbors based on lower/upper approximations are studied. Performance analysis is conducted including classification accuracy analysis, the impact of k parameter and weight coefficient of a and β to evaluate the proposed Fitted FRNNI and VQNNI algorithms. Experimental results on 13 benchmark datasets show that the proposed algorithms outperform current leading algorithms.

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Preparing software Quality Assurance Professionals: Metamorphic Exploration for Machine Learning

Preparing Software Quality Assurance Professionals: Metamorp...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Sen Yang Dave Towey Zhi Quan Zhou T.Y. Chen Faculty of Engineering & Information Technologies University of Sydney Australia School of Computer Science University of Nottingham Ningbo China People’s Republic of China School of Computing and Information Technology University of Wollongong Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

computer science (CS) subjects have been rapidly growing in popularity, and demand for CS education and training has put increasing pressure on teaching resources in higher education (HE) and elsewhere. HE in the People's Republic of China (PRC) has also been developing, with one product of this evolution being Sino-foreign HE institutions (SfHEIs). Much of the popularity growth for CS can be linked to the growth of CS-based technology and innovation, especially in the form of Artificial Intelligence (AI) and Machine Learning (ML). AI/ML-based innovation has been forecast to offer increases in quality of life for consumers. However, AI/ML systems face a challenge for software quality assurance (SQA): They are so-called “untestable systems” - identifying the correctness of AI/ML system outputs or behaviour may not be feasible. Preparing SQA professionals to be able to ensure AI/ML SQA will require innovative and creative education and training. An SQA approach called metamorphic testing (MT) has a proven track record of alleviating the oracle problem, and has great potential as a testing methodology for AI/ML systems. Metamorphic exploration (ME) is a new addition to the MT literature, and involves developing the user's understanding of the system under study. This paper reports on experiences at an SfHEI of using ME and MT to test an AI/ML system.

关键词： Testing Clustering algorithms software algorithms computer science Training Artificial intelligence

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Dynamic characteristics of charging effects on the dielectric constant due to E-beam irradiation:a numerical simulation

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Plasma Science and technology 2018年第3期20卷 1-9页

作者： Guobao FENG Wanzhao CUI Lu LIU National Key Lab of Science and Technology on Space Microwave China Academy of Space TechnologyXi'an 710000People's Republic of China School of Electronic and Information Engineering Xi'an Jiaotong UniversityXi'an 710049People's Republic of China School of Computer Science and Engineering Xi'an University of TechnologyXi'an 710048People's Republic of China

A series of synthetic variations of material intrinsic properties always come with charging phenomena due to electron beam *** effects of charging on the dielectric constant will influence the charging dynamic in *** this paper,we propose a numerical simulation for investigating the dynamic characteristics of charging effects on the dielectric constant due to electron beam *** scattering process between electrons and atoms is calculated considering elastic and inelastic collisions via the Rutherford model and the fast secondary electron model,*** charge drift due to E-field,density gradient caused diffusion,charges trap by material defect,free electron and hole neutralization,and variation in the internal dielectric constant are considered when simulating the transport *** dynamics of electron and hole distributions and charging states are demonstrated during E-beam *** a function of material nonlinear susceptibility and primary energy,the dynamics of charging states and dielectric constants are then presented in the charging *** is found that the variation in the internal dielectric constant is more with respect to the depth and irradiation *** with a larger nonlinear susceptibility corresponds a faster charging *** addition,the effective dielectric constant and the surface potential have a linear relationship in the charging ***,with shrinking charging affect range,the situation with a higher energy primary electron comes with less dielectric constant *** proposed numerical simulation mode of the charging process and the results presented in this study offer a comprehensive insight into the complicated charging phenomena in electron irradiation related fields.

关键词： charging effect dielectric constant E-beam irradiation numerical simulation

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Neighborhood linear discriminant analysis

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Pattern Recognition 2022年 123卷

作者： Fa Zhu Junbin Gao Jian Yang Ning Ye College of Information Science and Technology Nanjing Forestry University Nanjing 210037 PR China Discipline of Business Analytics University of Sydney Business School University of Sydney NSW 2006 Australia PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and the Jiangsu Key Lab of Image and Video Understanding for Social Security the School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China

Linear Discriminant Analysis (LDA) assumes that all samples from the same class are independently and identically distributed (i.i.d.). LDA may fail in the cases where the assumption does not hold. Particularly when a class contains several clusters (or subclasses), LDA cannot correctly depict the internal structure as the scatter matrices that LDA relies on are defined at the class level. In order to mitigate the problem, this paper proposes a neighborhood linear discriminant analysis (nLDA) in which the scatter matrices are defined on a neighborhood consisting of reverse nearest neighbors. Thus, the new discriminator does not need an i.i.d. assumption. In addition, the neighborhood can be naturally regarded as the smallest subclass, for which it is easier to be obtained than subclass without resorting to any clustering algorithms. The projected directions are sought to make sure that the within-neighborhood scatter as small as possible and the between-neighborhood scatter as large as possible, simultaneously. The experimental results show that nLDA performs significantly better than previous discriminators, such as LDA, LFDA, ccLDA, LM-NNDA, and l 2 , 1 -RLDA.

关键词： Linear discriminant analysis Reverse nearest neighbors Neighborhood linear discriminant analysis Multimodal class

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Long short term memory model for analysis and forecast of PM2.5 4th

Long short term memory model for analysis and forecast of PM...

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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Yan, Leiming Zhou, Min Wu, Yaowen Yan, Luqi Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9783030000059

Atmospheric PM2.5 is a pollutant that has a major impact on the atmospheric environment and human health. Based on LSTM, we construct two prediction models, Stack LSTM and Encoder-Decoder, and evaluate the prediction performance of the model through four years of meteorological data training and testing models in Nanjing, Beijing, and Sanya. In the experiment, using the meteorological factors, contaminant factors, seasonal factors, and the normalized results of PM2.5 as input, the daily average PM2.5 concentration is predicted from 1 to 3 days. Experimental results show that the LSTM model has better performance than Random Forest and Encoder-Decoder. Using Nanjing as an example, comparing the forecast results of Nanjing PM2.5 with the data released by the environmental authorities, it is found that the value of the PM2.5 concentration predicted by the LSTM model is very close to the value of PM2.5 monitored by Nanjing’s environmental authorities. In prediction of PM2.5 for three consecutive days, the Root Mean Square Error (RMSE) of the LSTM model is only 18.96. Under the LSTM model, the prediction result of the three cities are better than other prediction models, which shows that the LSTM model has a good adaptability in predicting the PM2.5 concentration. © Springer Nature Switzerland AG 2018.

关键词： Long short-term memory

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Fast Classification Algorithms via Distributed Accelerated Alternating Direction Method of Multipliers

Fast Classification Algorithms via Distributed Accelerated A...

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IEEE International Conference on Data Mining (ICDM)

作者： Huihui Wang Shunmei Meng Yiming Qiao Jing Zhang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Technology School of Software Nanjing University of Posts and Telecommunications Nanjing China

Distributed machine learning has gained lots of attention due to the rapid growth of data. In this paper, we focus regularized empirical risk minimization problems, and propose two novel Distributed Accelerated Alternating Direction Method of Multipliers (D-A2DM2) algorithms for distributed classification. Based on the framework of Alternating Direction Method of Multipliers (ADMM), we decentralize the distributed classification problem as a global consensus optimization problem with a series of sub-problems. In D-A2DM2, we exploit ADMM with variance reduction for sub-problem optimization in parallel. Taking global update and local update into consideration respectively, we propose two acceleration mechanisms in the framework of D-A2DM2. In particular, inspired by Nesterov's accelerated gradient descent, we utilize it for global update to further improve time efficiency. Moreover, we also introduce Nesterov's acceleration for local update, and develop the corrected local update and symmetric dual update to accelerate the convergence with only a little change in the computational effort. Theoretically, D-A2DM2 has a linear convergence rate. Empirically, experimental results show that D-A2DM2 converge faster than existing distributed ADMM-based classification, and could be a highly efficient algorithm for practical use.

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