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11th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Li, Jinghua Tian, Pengyu Kong, Dehui Wang, Lichun Wang, Shaofan Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing University of Technology Beijing100124 China College of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116620 China

ISBN: (纸本)9783030322151

Recently, Restricted Boltzmann Machine (RBM) has demonstrated excellent capacity of modelling vector variable. A variant of RBM, Matrix-variate Restricted Boltzmann Machine (MVRBM), extends the ability of RBM and is able to model matrix-variate data directly without vectorized process. However, MVRBM is still an unsupervised generative model, and is usually used to feature extraction or initialization of deep neural network. When MVRBM is used to classify, additional classifiers are necessary. This paper proposes a Matrix-variate Restricted Boltzmann Machine Classification Model (ClassMVRBM) to classify 2D data directly. In the novel ClassMVRBM, classification constraint is introduced to MVRBM. On one hand, the features extracted by MVRBM are more discriminative, on the other hand, the proposed model can be directly used to classify. Experiments on some publicly available databases demonstrate that the classification performance of ClassMVRBM has been largely improved, resulting in higher image classification accuracy than conventional unsupervised RBM, its variants and Restricted Boltzmann Machine Classification Model (ClassRBM). © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Deep neural networks

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Correction to: Industrial-iot-hardware security-improvement using plan load optimization method in cloud

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International Journal of System Assurance engineering and Management 2021年第6期13卷 3108-3108页

作者： Basheer, Shakila Gopu, Magesh Mathew, Rincy Merlin Bivi, Maryam Aysha Prabu, M. Department of Information System College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Software and Systems Engineering Vellore Institute of Technology Vellore India Computer Science Department College of Science and Arts King Khalid University Khamis Mushayt Abha Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science Christ University Bangalore India

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Interactive liver segmentation in CT volumes using fully convolutional networks 11th

Interactive liver segmentation in CT volumes using fully con...

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11th International KES Conference on Intelligent Interactive Multimedia: Systems and Services, KES-IIMSS 2018

作者： Kitrungrotsakul, Titinunt Iwamoto, Yutaro Han, Xian-Hua Wei, Xiong Lin, Lanfen Hu, Hongjie Jiang, Huiyan Chen, Yen-Wei Graduate School of Information Science and Engineering Ritsumeikan University Kyoto Japan Faculty of Science Yamaguchi University Yamaguchi Japan Institute for Infocomm Research Singapore Singapore College of Computer Science and Technology Zhejiang University Hangzhou China Radiology Department Sir Run Run Shaw Hospital Medical School Zhejiang University Hangzhou China Software College Northeastern University Shenyang China

ISBN: (纸本)9783319922300

Organ segmentation is one of the most fundamental and challenging task in computer aided diagnosis (CAD) systems, and segmenting liver from 3D medical data becomes one of the hot research topics in medical analysis field. Graph cut algorithms have been successfully applied to medical image segmentation of different organs for 3D volume data which not only leads to very large-scale graph due to the same node number as voxel number. Slice by Slice liver segmentation method is one of the technique that normally used to solve the memory usage. However, the computation times are increased and reduce the accuracy. In this paper we propose an interactive organ segmentation using fully convolutional networks. The network will perform slice by slice which only 1 slice of seed points in each volume. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 20 CT volumes, focus on liver organ and most of which have tumors inside of the liver, and abnormal deformed shape of liver. Our method can segment with 0.95401 dice accuracy with better than stage-of-the-art methods. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Graphic methods

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Optimal gabor filtering for the inspection of striped fabric

Optimal gabor filtering for the inspection of striped fabric

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Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018

作者： Tong, Le Zhou, Xiaoping Wen, Jiajun Gao, Can College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200072 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518055 China Institute of Textile and Clothing The Hong Kong Polytechnic University Kowloon Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518055 Hong Kong

ISBN: (纸本)9783319996943

As an important part of products’ quality control, automatic fabric inspection has attracted much attention in the past. Compared with manual inspection, automatic inspection can achieve not only more accurate detection results but also a higher efficiency. With the diversified fabric texture and patterns, it is very necessary to develop distinctive detection methods for different types of fabric. In this paper, based on optimal Gabor filters, a novel defect detection model is proposed to address the inspection of striped fabric, which is commonly used in our daily dresses. In the framework of the detection model, Gabor filters perpendicular to the stripe pattern are optimized to minimize the variance of the image but enhance the features of defects. Thereafter, an adaptive thresholding is set to accurately segment the defective image area. The evaluation of the proposed detection model is conducted using samples of the TILDA database. It is revealed that the common fabric defects as well as the pattern variants could be successfully detected through the proposed detection model. © Springer Nature Switzerland AG 2019.

关键词： Gabor filters

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Review on Textual Data Mining for Reviewer Recommendation in Pull-Based Distributed software Development

Review on Textual Data Mining for Reviewer Recommendation in...

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International Conference for Convergence of Technology (I2CT)

作者： Raveen Savinda Rathnayake Guhanathan Poravi College of Computer Science and Engineering University of Westminster London UK Department of Software Engineering Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

Distributed software development process has dramatically changed over the last decade due to the integration of social collaborative development environment. The pull-based software development methodology made its mark in the open source distributed development as it is a convenient and effective system to organise collaborative contribution. Code reviews for software projects have been a best practice in software engineering. With the emerge of pull-based software development methodology, code reviewers faced difficulty in reviewing the contributions because of the higher number of incoming pull requests. In order to address this problem, reviewer recommendation systems have been implemented. In these systems, textual data mining techniques have been applied. This paper focuses on identifying the different approaches in terms of textual data mining used in the domain of the reviewer recommendations in pull-based software development and identifies their drawbacks and room for improvement. This paper contains the initial part of ongoing research and in the future, we hope to use this knowledge to come up with a solution that addresses the identified drawbacks and the identified improvements.

关键词： software Information retrieval Collaboration Support vector machines Text mining software engineering

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The Pinkas Dataset

The Pinkas Dataset

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International Conference on Document Analysis and Recognition

作者： Berat Kurar Barakat Jihad El-Sana Irina Rabaev Ben-Gurion University of the Negev Department of Computer Science Ben-Gurion University of the Negev Software Engineering Department Shamoon College of Engineering

In historical document image processing, datasets account for a significant part of any research, and are crucial for the diversity and abundance of experimental results, which contribute to the development of new algorithms to meet the new challenge. Moreover, they are very important for benchmarking processing algorithms. Numerous publicly available document image datasets of different languages have been emerged. However, current segmentation and recognition performances are nearly saturated with respect to the present publicly available datasets. As such, collecting and labelling historical document images is a burden on historical document image processing researchers. This paper introduces a public historical document image dataset, Pinkas dataset, with new challenges to open room for improvement and identify strengths and weaknesses of available processing algorithms. It is the first dataset in medieval handwritten Hebrew and fully labeled at word, line and page level by an expert of historical Hebrew manuscripts. Pinkas dataset contributes to the diversity of benchmarking standards. In this paper we present meta features of Pinkas dataset and apply recent word spotting algorithms to analyze the room for improvement in terms of performance.

关键词： Image segmentation Benchmark testing Text analysis Databases Document image processing Layout Degradation

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The computer Vision-based Tolerancing Callout Detection Model

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Procedia CIRP 2020年 92卷 134-139页

作者： Xin Deng Tukun Li Yuanping Xu Yanlong Cao Chao Kong Errui Zhang School of Software Engineering Chengdu University of Information Technology Chengdu China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH UK Shandong Industrial Technology Research Institute of Zhejiang University Shandong 277500 China College of Engineering and Computer Science Australia National University Canberra ACT 2600 Australia

Tolerancing symbols play an important role in mechanical product drawings, and they directly determine the functions, mating properties, interchangeability and working life of geometrical products. A symbolic tolerancing callout containing a set of symbols represents a set of pre-ordered operations with attributes. It includes an amount of knowledge from the drawing and standard documents, which is often reconstructed manually by engineers. Thus, at the same time, a symbolic tolerancing callout makes it difficult for the end-user to understand and interpret these callouts manually. To this end, this study puts forward a tolerancing callout detection model via the use of off-the-shelf costumer-grade cameras on current mobile devices for extracting and recognizing tolerancing callout blocks and symbols in them intelligently. This model has four core components: image preprocessing, callout location and extraction, symbol and character segmentation, and deep learning-based symbol recognition. The image preprocessing component is developed to remove the interferences on the target technical drawings through the corresponding morphological methods. This study proposes a novel solution on callout block locations and extractions in callout intensive scenarios since the callout locations and extractions can directly affect the accuracy of symbol and character recognitions. Then, Huff Transform and improved projection methods have been devised to symbol and character segmentations. Finally, this study constructed a convolutional neural network (CNN) to train a symbol recognition model. The experimental results show that the proposed model gains applicability on intelligent callout extractions and the corresponding symbol recognitions.

关键词： Tolerancing symbols Callout location extraction Symbol character segmentation CNN

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BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

arXiv

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arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

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Consumer product preference prediction towards online shopping

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： S Deepa R Suguna P Sathishkumar C Jamunadevi E N Vidhya Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai India

Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.

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How Do API Selections Affect the Runtime Performance of Data Analytics Tasks?

How Do API Selections Affect the Runtime Performance of Data...

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IEEE International Conference on Automated software engineering (ASE)

作者： Yida Tao Shan Tang Yepang Liu Zhiwu Xu Shengchao Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science & Engineering Southern University of Science and Technology Shenzhen China School of Computing & Digital Technologies Teesside University UK

As data volume and complexity grow at an unprecedented rate, the performance of data analytics programs is becoming a major concern for developers. We observed that developers sometimes use alternative data analytics APIs to improve program runtime performance while preserving functional equivalence. However, little is known on the characteristics and performance attributes of alternative data analytics APIs. In this paper, we propose a novel approach to extracting alternative implementations that invoke different data analytics APIs to solve the same tasks. A key appeal of our approach is that it exploits the comparative structures in Stack Overflow discussions to discover programming alternatives. We show that our approach is promising, as 86% of the extracted code pairs were validated as true alternative implementations. In over 20% of these pairs, the faster implementation was reported to achieve a 10x or more speedup over its slower alternative. We hope that our study offers a new perspective of API recommendation and motivates future research on optimizing data analytics programs.

关键词： Data analysis Task analysis Libraries Runtime Data mining Python Programming

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