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arXiv 2019年

作者： Liao, Hao Wu, Xingtong Zhou, Mingyang Yeung, Chi Ho National Engineering Laboratory for big data computing systems Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Department of Science and Environmental Studies Education University of Hong Kong Hong Kong

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn;the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical mechanics

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Employment Recommendation Algorithm Based on Ensemble Learning

Employment Recommendation Algorithm Based on Ensemble Learni...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Yubei Lin Yinhua Huang Pingping Chen School of Software South China University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China

The user's filtering costs and time costs were increased because of a large number of search results in the context of big data technology. Therefore, personalized recommendation technology is needed to improve the utilization ratio of information resources, which carries out accurate and efficient information filtering. This paper focuses on the employment recommendation algorithm hoping to maximize the true intention of job applicants in the recommended job list. As an important tool in machine learning, ensemble learning can improve the prediction accuracy and adaptability effectively in job recommendation algorithms. The main work of this paper is to carry out some research on the algorithm concept related to recommendation algorithm and gradient promotion decision tree algorithm based on recommendation algorithm, and build an employment recommendation algorithm. The employment recommendation algorithm is evaluated by the TOPN list experiment.

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Fault detection capabilities of combinatorial testing and random testing for Boolean-specifications

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International Journal of Performability engineering 2019年第11期15卷 2952-2961页

作者： Wang, Ziyuan Li, Yang Gu, Xueqing Zheng, Xiaojia Yu, Min School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Software Testing Engineering Laboratory of Jiangsu Province Nanjing211169 China Tongda College Nanjing University of Posts and Telecommunications Yangzhou225127 China

The problem of fault detection capability of combinatorial testing has drawn a lot of attention. People conducted many experiments on different subjects to compare fault detection capabilities of combinatorial testing and random testing. However, previous confusing results can hardly answer the question of whether combinatorial test suite detects more faults than random test suite. To answer this question more trustfully, we conducted an experiment on general-form Boolean-specifications. In our experiment, fault detection frequencies and fault detection ratios in combinatorial testing, where test suites are generated by some classic combinatorial test generation algorithms, are collected by the repeated running of combinatorial testing. Moreover, fault detection probabilities in random testing are obtained by theoretical analysis. By comparing fault detection frequencies, ratios, and probabilities, our experimental results suggest that combinatorial testing has a little advantage of fault detection capability over random testing. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Fault detection

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Multi-Feature-Based Decision Fusion Framework for Hyperspectral Imagery Classification

Multi-Feature-Based Decision Fusion Framework for Hyperspect...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sen Jia Junjian Xian College of Computer Science and Software Engineering Shenzhen University China

Classification of a hyperspectral image (HSI) is a very active topic in remote sensing, which has practical applications in many fields, In this paper, a multitask sparse logistic regression method based on multi-feature and decision fusion approach (MTSLR-MPGF) is proposed for cross-scene hyperspectral image classification. Specifically, the Gabor Features with certain orientations and morphology feature are utilized and used for hyperspectral imagery classification. Next, we used feature fusion methods in order to produce an accurate thematic map based on the remote sensed hyperspectral image classification. The extensive experiments on two real hyperspectral data sets have demonstrated superior performance of the proposed MTSLR-MPGF approach over the state-of-the-art methods in terms of overall accuracy and average accuracy.

关键词： Training Hyperspectral imaging Feature extraction Geoscience Logistics

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Deep learning based monocular depth prediction: Datasets, methods and applications

arXiv

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arXiv 2020年

作者： Li, Qing Zhu, Jiasong Liu, Jun Cao, Rui Li, Qingquan Jia, Sen Qiu, Guoping College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Urban Informatics Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Urban Smart Transportation and Safety Maintenance Shenzhen University Shenzhen518060 China Shenzhen TCL Industrial Research Institute Co. Ltd. Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Estimating depth from RGB images can facilitate many photometric computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we first introduce the datasets for depth estimation, and then give a comprehensive introduction of the methods from three perspectives: supervised learning-based methods, unsupervised learning-based methods, and sparse samples guidance-based methods. In addition, downstream applications that benefit from the progress have also been illustrated. Finally, we point out the future directions and conclude the paper. Copyright © 2020, The Authors. All rights reserved.

关键词： Image reconstruction

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Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

arXiv

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arXiv 2019年

作者： Hou, Xianxu Sun, Ke Shen, Linlin Qiu, Guoping College of Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Key Laboratory of Spatial Information Smarting Sensing and Services Shenzhen University Shenzhen China School of Computer Science University of Nottingham Nottingham United Kingdom

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction. Keywords: Image Generation, Facial Attributes, Generative model, VAE, GAN Copyright © 2019, The Authors. All rights reserved.

关键词： Learning systems

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Frameworks for querying databases using natural language: A literature review

arXiv

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arXiv 2019年

作者： Dar, Hafsa Shareef Ikramullah Lali, M. Ul Din, Moin Malik, Khalid Mahmood Bukhari, Syed Ahmad Chan Dept. of Software Engineering University of Gujrat Punjab50700 Pakistan Dept. of Computer Science University of Gujrat Punjab50700 Pakistan Computer Science and Engineering Department Oakland University 2200 N. Squirrel Rd RochesterMI48309 United States Division of Computer Science Mathematics and Science College of Professional Studies St. John's University New York United States

A Natural Language Interface (NLI) facilitates users to pose queries to retrieve information from a database without using any artificial language such as the Structured Query Language (SQL). Several applications in various domains including healthcare, customer support and search engines, require elaborating structured data having information on text. Moreover, many issues have been explored including configuration complexity, processing of intensive algorithms, and popularity of relational databases, due to which translating natural language to database query has become a secondary area of investigation. The emerging trend of querying systems and speech-enabled interfaces revived natural language to database queries research area., The last survey published on this topic was six years ago in 2013. To best of our knowledge, there is no recent study found which discusses the current state of the art translations frameworks for natural language for structured and non-structured query languages. In this paper, we have reviewed 47 frameworks from 2008 to 2018. Out of 47, 35 were closely relevant to our work. SQL based frameworks have been categorized as statistical, symbolic and connectionist approaches. Whereas, NoSQL based frameworks have been categorized as semantic matching and pattern matching. These frameworks are then reviewed based on their supporting language, scheme of their heuristic rule, interoperability support, dataset scope and their overall performance score. The findings stated that 70% of the work in natural language to database querying has been carried out for SQL, and NoSQL share 15%, 10% and 5% of languages like SPAROL, CYPHER and GREMLIN respectively. It has also been observed that most of the frameworks support English language only. Copyright © 2019, The Authors. All rights reserved.

关键词： Pattern matching

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Transfer learning for lung cancer computed tomography diagnosis 8

Transfer learning for lung cancer computed tomography diagno...

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018

作者： Jiang, Yunpeng Shinichi, Yoshida Che, Xiangjiu College of Software Jilin University China Graduate School of Engineering Kochi University of Technology Japan School of Information Kochi University of Technology Japan College of Computer Science and Technology Jilin University China

We propose an application of the transfer learning strategy of VGG16 to lung cancer CT diagnosis and conduct the experiment to clarify the performance of detection. Contributions of this paper are following three works, i.e., 1. put forward a new multi-channel fusion method to make the CT images available for VGG16 input, 2. compare the difference between the two methods, analyze the successful reason, and certify the advantage of transfer learning, and 3. optimize the model by using fine-tune and fully convolution methods to reduce more weight parameters, get a good convergence result and improve the diagnosis accuracy. We show that the result is better than training without transfer and the proposed methods can be extended for other medical imaging tasks. The proposed method and idea of the transfer learning for computer-aided diagnosis can be useful to make a development in medical diagnosis domain. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.

关键词： computer aided diagnosis

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Research of vehicle cooperative adaptive cruise control system based on deep reinforcement learning 8

Research of vehicle cooperative adaptive cruise control syst...

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作者： Li, Xiang Yoshida, Shinichi Liu, Yanheng College of Software Jilin University China Graduate School of Engineering Kochi University of Technology Japan School of Information Kochi University of Technology Japan College of Computer Science and Technology Jilin University China

With the development of the Internet of Vehicles, the application of Advanced Driver Assistance Systems(ADAS) is becoming more and more extensive. As one of ADAS, Adaptive Cruise Control(ACC) technology has been widely used in automobiles. The Cooperative Adaptive Cruise Control(CACC) is a system which longitudinal vehicles constitute a platoon to realize automatic driving of the following vehicles in the platoon through Vehicle to Vehicle communication. In this paper, we made a CACC controller used deep reinforcement learning method to solve platoon control problem of CACC system. After agents joins the CACC platoon, our controller automatically adjusts the speed of the vehicle to maintain a safe distance to achieve automatic driving. In our controller, deep neural network is used to estimate the value function to figure out the best strategy of the controller. The experimental results show our controller could keep the CACC longitudinal platoon in a short safe distance while maintaining stability. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.

关键词： Controllers

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Optimally compressed digital content delivery using short message service 14th

Optimally compressed digital content delivery using short me...

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14th International Conference on Information Technology - New Generations, ITNG 2017

作者： Khan, Muhammad Fahad Mushtaq, Mubashar Saleem, Khalid Department of Computer Sciences Quaid-i-Azam University Islamabad Pakistan Department of Software Engineering Foundation University Islamabad Pakistan Department of Computer Science Forman Christian College A Chartered University Lahore Pakistan

ISBN: (纸本)9783319549774

Researchers are devising new ways for robust digital content delivery in situations where telecommunication signal strength is very low, especially during natural disasters. In this paper, we present research work targeting two dimensions: (a) We selected the IANA standard for digital content classification, 20 types in 5 categories;applied and compared five different lossless compression schemes (LZW, Huffman coding, PPM, Arithmetic Coding, BWT and LZMA) on these 20 data types;(b) A generic prototype application which encodes (for sending) and decodes (on receiving) the compressed digital content over SMS. Sending digital contents via SMS over satellite communication is achieved by converting digital content into text;apply lossless compression on the text and transmit the compressed text by using SMS. Proposed method does not require Internet Service and also not requires any additional hardware in existing network architecture to transmit digital contents. Results show that overall PPM compression method offers best compression ratio (0.63) among all compression schemes Thus PPM reduces the SMS transmission saving up to 43%, while LZW performs the least with 17.6%. © Springer International Publishing AG 2018.

关键词： Satellite communication systems

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