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Dynamic Shortest Path Monitoring in Spatial Networks

Journal of computer science & Technology 2016年第4期31卷 637-648页

作者： Shuo Shang Lisi Chen Zhe-Wei Wei Dan-Huai Guo Ji-Rong Wen State Key Laboratory of Software Development Environment Beijing 100191 China Department of Computer Science China University of Petroleum Beijing 102249 China School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Beijing Key Laboratory of Big-Data Management and Analysis Methods Renmin University of China Beijing 100080 China Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China

With the increasing availab.lity of real-time traffic information, dynamic spatial networks are pervasive nowa- days and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks （DSPM query）. When a traveler aims to a des- tination, his/her shortest path to the destination may change due to two reasons： 1） the travel costs of some edges have been updated and 2） the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons： 1） how to maintain and reuse the existing computation results to accelerate the following computations, and 2） how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

关键词： shortest path dynamic spatial network spatial database location-based service

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THTM: A template matching algorithm based on HOG descriptor and two-stage matching

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AIP Conference Proceedings 2018年第1期1955卷

作者： Yuanjie Jiang Li Ruan Limin Xiao Xi Liu Feng Yuan Haitao Wang State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China Institute of Software Application Technology Guangzhou & Chinese Academy of Sciences Guangzhou 511458 China Space Star Technology CO. Ltd Beijing 10086 China

We propose a novel method for template matching named THTM – a template matching algorithm based on HOG (histogram of gradient) and two-stage matching. We rely on the fast construction of HOG and the two-stage matching that jointly lead to a high accuracy approach for matching. TMTM give enough attention on HOG and creatively propose a twice-stage matching while traditional method only matches once. Our contribution is to apply HOG to template matching successfully and present two-stage matching, which is prominent to improve the matching accuracy based on HOG descriptor. We analyze key features of THTM and perform compared to other commonly used alternatives on a challenging real-world datasets. Experiments show that our method outperforms the comparison method.

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Discriminative dictionary learning sparse coding for person re-identification

Discriminative dictionary learning sparse coding for person ...

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2016 IEEE Intelligent Vehicles Symposium, IV 2016

作者： Hao, Sheng Zhang, Beichen Yan, Huang Zheng, Yanwei Zhang, Xiong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Shenzhen Key Laboratory of Data Vitalization Research Institute in Shenzhen Beihang University ShenZhen518057 China

ISBN: (纸本)9781509018215

Person re-identification is one of the most important issues in intelligent transportation systems. Recently, the widespread availab.lity of cameras and a growing need for public safety have increasingly motivated interest in the problem of person re-identification in multi-camera networks. The main difficulty of person re-identification arises from the variations in human pose, different viewpoint in multi-camera, cluttered background, occlusion, and low image resolution, which lead person re-identification to a challenging problem. This paper presents a method based on sparse coding for person re-identification. To apply sparse coding method, we firstly solve the problem of aligning person images, and to enhance the discrimination of dictionary, a dictionary learning model is added into our method. Experiments on benchmark dataset (CAVIARa, ETZH, i-LIDS) demonstrate that the proposed method outperforms the state-of-the-art approaches. © 2016 IEEE.

关键词： Image resolution

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A high order collab.ration and real time formal model for automatic testing of safety critical systems

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Frontiers of computer science 2015年第4期9卷 495-510页

作者： Jianghua LV ShUong MA Xianjun LI Jiangong SONG State Key Lab of Software Development Environment School of Computer Science Beihang University Beijing 100191 China

The need for safety critical systems （SCS） is both important and urgent, and their evaluation and verification are test-dependent. SCS are usually complex and very large, so manual testing of SCS are infeasible in practice, and develop- ing automatic test approaches for SCS has become an impor- tant trend. This paper defines a formal semantics model for automatic test of SCS, called AutTMSCS, which describes behaviors in SCS testing. The model accommodates the high order collab.ration in real time and temporariness of SCS testing. Testing tasks, test equipment and products under test are abstracted and architected in three layers, and a method for automatic testing is given. Based on extended lab.l tran- sition system （LTS）, the convergency and correctness of the model are proved to demonstrate the computability of the model, indicating that the testing process of SCS can be au- tomatic.

关键词： safety critical systems （SCS） test automatictest equipment collab.ration high order calculus LTS realtime

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SEGMENTATION OF LIGHT FIELD IMAGE WITH THE STRUCTURE TENSOR

SEGMENTATION OF LIGHT FIELD IMAGE WITH THE STRUCTURE TENSOR

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IEEE International Conference on Image Processing

作者： Hao Sheng Senyou Deng Shuo Zhang Chao Li Zhang Xiong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467399623

We propose a segmentation model for light field images based on superpixels segmentation and graph-cuts algorithm. Unlike traditional images, which do not offer information for different directions, a light field image encodes space data which can be computed on its epipolar plane images (EPI) with some effective methods. In our work, we analyze the structure of EPI and research the computational process of disparity using EPI. On this basis, we present a new method for computing disparity using the modified structure tensor on EPIs. We further apply the computed disparity lab.ls by fusing RGB images and disparity lab.ls to obtain more detailed over-segmentation. Meanwhile, the modified structure tensor algorithm is used to get more accurate image boundaries, which plays a role in computing disparity features. All these processes are applied in an interactive segmentation model. Our experiments on public data sets demonstrate that the proposed light field image segmentation achieves a higher performance compared with state-of-the-art methods.

关键词： Light field Disparity The structure tensor Image fusion Interactive segmentation Light fields image fusion images Tensor

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ROBUST VISUAL TRACKING USING CORRELATION RESPONSE MAP

ROBUST VISUAL TRACKING USING CORRELATION RESPONSE MAP

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IEEE International Conference on Image Processing

作者： Hao Sheng Kai Lv Jiahui Chen Wei Li State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467399623

In this paper, we address the problem of heavy occlusion where the negative samples contaminate the translation model. In this setting, we decompose the task of tracking into translation and scale estimations of objects. We use hierarchical convolutional features to estimate target position and update translation model, and we use HOG features for the scale filter. In addition, we evaluate the translation's reliability according to the correlation responses map which is the result of correlation detection. Then we propose a new method to update model according to the reliability. Experiments are performed on 28 benchmark sequences with significant scale variations, it shows that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy and robustness.

关键词： Heavy occlusion Negative samples Correlation response Correlation filters Object tracking visual tracking tracking objects correlation detection Translations Translation manufacturing automation protocol MITIGATION ACTION PLANS Scale formation Translation Process

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RELATIVE LOCATION FOR LIGHT FIELD SALIENCY DETECTION

RELATIVE LOCATION FOR LIGHT FIELD SALIENCY DETECTION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hao Sheng Shuo Zhang Xiaoyu Liu Zhang Xiong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781479999897

Light field images, which capture multiple images from different angles of a scene, have been proved that can detect salient regions more effectively. Instead of estimating depth lab.ls from light field images, we proposed to extract relative locations, which can distinguish whether the object is located before the focus plane of the main lens or not, for saliency detection. The relative locations are calculated by comparing raw light field images captured by plenoptic cameras and central views of scenes. The relative locations are then integrated to a modified saliency detection framework to obtain the salient regions. Experimental results demonstrate that the proposed relative locations can help to improve the accuracy of results, which is also efficient. Moreover, the modified framework outperforms the state-of-the-art methods for light field images saliency detection.

关键词： Light field Saliency detection Relative location Raw image Plenoptic camera Light fields multiple image images

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A multi-source tradaboost approach for cross-company defect prediction 28

A multi-source tradaboost approach for cross-company defect ...

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28th International Conference on software engineering and Knowledge engineering, SEKE 2016

作者： Yu, Xiao Liu, Jin Fu, Mandi Ma, Chuanxiang Nie, Guoping Chen, Xu State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Educational Informationalization Engineering Research Center of HuBei Province Wuhan China School of Mathematics Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)189170639X

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to withincompany (WC) data may decrease the prediction model performance. To address such issues, this paper introduces Multi-Source TrAdaBoost algorithm, an effective transfer learning approach to perform CCDP. The core idea of our approach is that: 1) employ limited amount of lab.led WC data to weaken the impact of irrelevant CC data;2) import knowledge not from one but from multiple sources to avoid negative transfer. The experimental results indicate that: 1) our proposed approach achieves the best overall performance among all tested CCDP approaches;2) only 10% lab.led WC data is enough to achieve good performance of CCDP by using our proposed approach. Copyright © 2016 by KSI Research Inc. and Knowledge Systems Institute Graduate school.

关键词： Forecasting

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ScalaRDF: A Distributed, Elastic and Scalab.e In-Memory RDF Triple Store

ScalaRDF: A Distributed, Elastic and Scalable In-Memory RDF ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chunming Hu Xixu Wang Renyu Yang Tianyu Wo State Key Laboratory of Software Development Environment (NLSDE) School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781509053827

The Resource Description Framework (RDF) andSPARQL query language are gaining increasing popularity andacceptance. The ever-increasing RDF data has reached a billionscale of triples, resulting in the proliferation of distributed RDFstore systems within the Semantic Web community. However, theelasticity and performance issues are still far from settled inface of data volume explosion and workload spike. In addition, providers face great pressures to provision uninterrupted reliablestorage service whilst reducing the operational costs due to avariety of system failures. Therefore, how to efficiently realizesystem fault tolerance remains an intractable problem. In this paper, we introduce ScalaRDF, a distributed and elastic in-memoryRDF triple store to provision a fault-tolerant and scalab.e RDFstore and query mechanism. Specifically, we describe a consistenthashing protocol that optimizes the RDF data placement, dataoperations (especially for online RDF triple update operations)and achieves an autonomously elastic data re-distribution in theevent of cluster node joining or departing, avoiding the holisticoscillation of data storage. In addition, the data store is ableto realize a rapid and transparent failover through replicationmechanism which stores in-memory data replica in the next hashhop. The experiments demonstrate that query time and updatetime are reduced by 87% and 90% respectively compared to otherapproaches. For an 18G source dataset, the data redistributiontakes at most 60 seconds when system scales out and at most 100seconds for recovery when nodes undergo crash-stop failures.

关键词： Resource description framework Protocols Indexes Monitoring Dictionaries Distributed databases Database languages

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Multilinear hyperplane hashing

Multilinear hyperplane hashing

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2016 IEEE Conference on computer Vision and Pattern Recognition, CVPR 2016

作者： Liu, Xianglong Fan, Xinjie Deng, Cheng Li, Zhujin Su, Hao Tao, Dacheng State Key Lab of Software Development Environment Beihang University Beijing China School of Electronic Engineering Xidian University Xi'an China Department of Computer Science Stanford University StanfordCA United States QCIS and FEIT University of Technology Sydney Australia

ISBN: (纸本)9781467388511

Hashing has become an increasingly popular technique for fast nearest neighbor search. Despite its successful progress in classic pointtopoint search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up applications like active learning with SVMs. Existing hyperplane hashing methods enable the fast search based on randomly generated hash codes, but still suffer from a low collision probability and thus usually require long codes for a satisfying performance. To overcome this problem, this paper proposes a multilinear hyperplane hashing that generates a hash bit using multiple linear projections. Our theoretical analysis shows that with an even number of random linear projections, the multilinear hash function possesses strong locality sensitivity to hyperplane queries. To leverage its sensitivity to the angle distance, we further introduce an angular quantization based learning framework for compact multilinear hashing, which considerably boosts the search performance with less hash bits. Experiments with applications to large-scale (up to one million) active learning on two datasets demonstrate the overall superiority of the proposed approach.

关键词： Nearest neighbor search

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