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intelligent Development Environment and software Knowledge Graph

Journal of computer Science & technology 2017年第2期32卷 242-249页

作者： Ze-Qi Lin Bing Xie Yan-Zhen Zou Jun-Feng Zhao Xuan-Dong Li Jun Wei Hai-Long Sun Gang Yin Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing 100871 China Beida ( Binhai) Information Research Tianjin 300450 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Engineering Beihang University Beijing 100191 China College of Computer National University of Defense Technology Changsha 410073 China

software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and software knowledge graph -- for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.

关键词： intelligent development environment software big data software knowledge graph semantic search

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Secure Beamforming Design for MISO SWIPT Systems: An Indirectly Optimized Approach

Secure Beamforming Design for MISO SWIPT Systems: An Indirec...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yao Yu Siyu Wang Shumei Liu Lei Guo Zhaolong Ning Shimin Gong Mohammad S. Obaidat School of Computer Science and Engineering Northeastern University Shenyang China Chongqing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Intelligent Systems Engineering Sun Yat-sen University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

关键词： Receivers Interference Signal to noise ratio Transmitters Optimization Security Communication system security

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Deep Supervised Hashing With Anchor Graph

Deep Supervised Hashing With Anchor Graph

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International Conference on computer Vision (ICCV)

作者： Yudong Chen Zhihui Lai Yujuan Ding Kaiyi Lin Waikeung Wong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong China School of Software and Microelectronics Peking University Beijing China Institute of Textiles and Clothing. The Hong Kong Polytechnic University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Recently, a series of deep supervised hashing methods were proposed for binary code learning. However, due to the high computation cost and the limited hardware's memory, these methods will first select a subset from the training set, and then form a mini-batch data to update the network in each iteration. Therefore, the remaining labeled data cannot be fully utilized and the model cannot directly obtain the binary codes of the entire training set for retrieval. To address these problems, this paper proposes an interesting regularized deep model to seamlessly integrate the advantages of deep hashing and efficient binary code learning by using the anchor graph. As such, the deep features and label matrix can be jointly used to optimize the binary codes, and the network can obtain more discriminative feedback from the linear combinations of the learned bits. Moreover, we also reveal the algorithm mechanism and its computation essence. Experiments on three large-scale datasets indicate that the proposed method achieves better retrieval performance with less training time compared to previous deep hashing methods.

关键词： Binary codes Training Data models Neural networks Linear programming Computational modeling Training data

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Image Set Compression with Content Adaptive Sparse Dictionary

Image Set Compression with Content Adaptive Sparse Dictionar...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Qiang Li Jin Wang Jinghua Li Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing CN Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing China College of Computer Science and Technology Dalian University of Technology Dalian China

Image compression plays more and more important role in image processing. Image sparse coding with learned over-complete dictionaries shows promising results on image compression by representing images with dictionary atoms compactly. Within the sparse coding based compression framework, a sparse dictionary is first learned from training images in a predefined image library, and then an image is compressed by representing its non- overlapping image patches as linear combination of very few dictionary atoms, which is called sparse coding. In this paper, we proposed a content adaptive sparse dictionary for image set compression based on sparse coding. For a set of similar images to be compressed, first we divided image patches into DC and AC components. For the AC components, a clustering algorithm is used to get cluster centers. Then a content adaptive dictionary will be learned according to each cluster center. We compared our method with RLS- DLA method and JPEG method to validate the performance of our method, and experimental results show that our method outperforms the comparing methods at high bitrate.

关键词： Dictionaries Image coding Training Machine learning Matching pursuit algorithms Sparse matrices Decoding

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An Adaptive User Preferences Elicitation Scheme for Location Recommendation

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Chinese Journal of Electronics 2016年第5期25卷 943-949页

作者： WANG Fan MENG Xiangwu ZHANG Yujie Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications

User preferences elicitation is a key issue of location recommendation. This paper proposes an adaptive user preferences elicitation scheme based on Collaborative filtering(CF) algorithm for location recommendation. In this scheme, user preferences are divided into user static preferences and user dynamic preferences. The former is estimated based on location category information and historical ratings. Meanwhile, the latter is evaluated based on geographical information and two-dimensional cloud model. The advantage of this method is that it not only considers the diversity of user preferences, but also can alleviate the data sparsity problem. In order to predict user preferences of new locations more precisely, the scheme integrates the similarity of user static preferences, user dynamic preferences and social ties into CF algorithm. Furthermore, the scheme is parallelized on the Hadoop platform for significant improvement in efficiency. Experimental results on Yelp dataset demonstrate the performance gains of the scheme.

关键词： Location recommendation Adaptive User static preferences User dynamic preferences

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Hierarchical-Based Object Detection with Improved Locality Sparse Coding

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Chinese Journal of Electronics 2016年第2期25卷 290-295页

作者： ZHU Suguo DU Junping REN Nan LIANG Meiyu Beijing Key Laboratory of Intelligent Telecommunication software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.

关键词： Hierarchical detection Sparse coding Classification Object detection

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key technique research on desktop virtualization in cloud environment

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International Journal of Modeling, Simulation, and Scientific Computing 2017年第2期8卷 232-240页

作者： Zhuohui Shen Liqin Guo Baocun Hou Hongyan Quan ShuangShuang Zhou College of Computer Science and Software Engineering East China Normal University P.R.China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering P.R.China Beijing Aerospace Smart Manufacturing Technology Development Co.Ltd P.R.China

Desktop Virtualization is an essential problem in cloud manufacturing to make full use of shared resources so as to meet the practical application *** this paper,an efficient virtualization solution for desktop transferring is presented for sharing *** strategy is not limited to transfer desktops between virtual machines(VMs),transmission between virtual and physical machines is also achieved.A three-layer systemic structure based on web environment is proposed in the *** employ the application program library Libvirt and kernel-based virtual machine(KVM)technique to manage VMs with dynamic customization scheme,which gains better virtualization per***,we present two different schemes for desktop graphics *** is based on the transport protocol virtual network computing(VNC),and the other adopts the simple protocol for independent computing environment(SPICE),which both achieve more satisfactory *** validity of our scheme is demonstrated on some experimental results and systemic qualitative evaluation is also provided.

关键词： Virtualization KVM VNC VM SPICE.

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Parallel medical imaging for intelligent medical image analysis: Concepts, methods, and applications

arXiv

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arXiv 2019年

作者： Gou, Chao Shen, Tianyu Zheng, Wenbo Xue, Huadan Yu, Hui Ji, Qiang Jin, Zhengyu Wang, Fei-Yue School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Radiology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China School of Creative Technologies University of Portsmouth PortsmouthPO1 2DJ United Kingdom Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Qingdao Academy of Intelligent Industries Qingdao266000 China

Data-driven artificial intelligence technologies have made much progress in medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the closed-loop optimization based on parallel execution, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decisions. Furthermore, we illustrate the preliminary implementation of PMI method through the case studies of mammogram analysis and skin lesion image analysis. Experimental results on several public medical image datasets demonstrate the effectiveness of proposed PMI. © 2019, CC BY.

关键词： Medical imaging

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Framework for adaptive computation offloading in IoT applications 17

Framework for adaptive computation offloading in IoT applica...

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Chen, Shihong Liu, Bichun Chen, Xing Zhang, Ying Huang, Gang College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Institute of Software School of Electronics Engineering and Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

ISBN: (纸本)1595930361

The internet of things (IoT) attracts great interest in many application domains concerned with monitoring and control of physical phenomena. IoT applications try to provide more and more functionality and then they inevitably become so complex as to make the limits of devices worse, which may lead to poor performance of applications. Computation offloading is a promising way to improve the performance of an IoT application by executing some parts of the application on remote devices or servers. However, supporting such capability is not easy for application developers due to (1) adaptability: IoT applications often face changes of runtime environments so that the adaptation on offloading is needed. (2) effectiveness: when the device context changes, it needs to dynamically decide the deployment plan of computation tasks, and the reduced execution time must be greater than the network delay and extra overheads caused by offloading. This paper proposes a framework which supports IoT applications with adaptive computation offloading capability. First, a design pattern is proposed to enable an application to be computation offloaded on-demand. Second, an estimation model is presented to automatically decide the deployment plan for offloading. Third, a framework is implemented to support the design pattern and the estimation model. A thorough zxevaluation on the real-world application is proposed, and the results show that our approach can help reduce execution time by over 45% in most scenarios.

关键词： computation offloading

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Opinion Leader Mining of Social Network Combined with Hierarchical Sentiment Analysis

Opinion Leader Mining of Social Network Combined with Hierar...

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2017中国智能自动化会议(CIAC 2017)

作者： Hang Ye Junping Du Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications

1 Introduction Nowadays,with the booming of internet,social networks are more and more popular in daily communications around the *** can post ideas and be the source of information at any time in anywhere on the social *** is of great value to do research about social networks with mass ***,because of the unrestraint and anonymity,the authenticity of the information on social

关键词： Opinion Leader Mining of Social Network Combined with Hierarchical Sentiment Analysis SSD

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