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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoguang Gu Yongdong Zhang Dongming Zhang Jintao Li Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Local features have been widely used in many computer vision related researches, such as near-duplicate image and video retrieval. However, the storage and query cost of local features become prohibitive on large-scale database. In this paper, we propose a representative local features mining method to generate a compact but more effective feature subset. First, we do an unsupervised annotation for all similar images(or frames in video) in the database. Second, we compute a comprehensive score for every local feature. The score function combines the robustness and discrimination. Finally, we sort all the local features in an image by their scores and the low-score local features can be removed. The selected local features are robust and discriminative, which can guarantee the better retrieval quality than using full of the original feature set. By our method, the number of local features can be significantly reduced and a large amount of storage and computational cost can be saved. The experimental results show that we can use 30% of the features to get a better query performance than that of full feature set.

关键词： Databases Robustness Vectors Visualization Feature extraction Binary codes Complexity theory

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Elastic and Efficient Virtual Network Provisioning for cloud-Based Multi-tier Applications

Elastic and Efficient Virtual Network Provisioning for Cloud...

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International Conference on Parallel processing (ICPP)

作者： Meng Shen Ke Xu Fan Li Kun Yang Liehuang Zhu Lei Guan Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Department of Computer Science Tsinghua University P. R. China School of Computer Science & Electronic Engineering University of Essex UK The ISN National Key Lab XiDian University P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467375887

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

关键词： Bandwidth Elasticity cloud computing Algorithm design and analysis Load modeling Resource management

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Threshold-Based Secure and Privacy-Preserving Message Verification in VANETs

Threshold-Based Secure and Privacy-Preserving Message Verifi...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Wei Gao Mingzhong Wang Liehuang Zhu Xiaoping Zhang Language Information Processing and Cloud Computing Application Beijing Institute of Technology National Key Lab of Vehicular Transmission China North Vehicle Research Institute

ISBN: (纸本)9781479965144

Messages spreading inside vehicular ad hoc networks (VANETs) generally need to achieve the property of verifiability and content integrity, while preserving user privacy. Otherwise, VANETs will either fall into chaos, or prevent users from embracing it. To achieve this goal, we propose a protocol, which contains a priori and posteriori countermeasures, to guarantee these features. The a priori process firstly verifies that each message is sent by a vehicle only once. Then it collects and checks whether the count of the message exceeds the threshold value to improve the trustworthiness of the message. The posteriori process verifies the integrity of the message, ensuring it is unchanged during transmission between the vehicle and the road side unit. The privacy is preserved by applying group signature. In case of disruptive events, the proposed solution can trace back to the source vehicle which generates the message.

关键词： Vehicles Privacy Authentication Protocols Public key Servers

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Generalized Unsupervised Manifold Alignment 14

Generalized Unsupervised Manifold Alignment

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Proceedings of the 28th International Conference on Neural information processing Systems - Volume 2

作者： Zhen Cui Hong Chang Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China and School of Computer Science and Technology Huaqiao University Xiamen China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

In this paper, we propose a Generalized Unsupervised Manifold Alignment (GU-MA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserving criteria, as well as the feature comparability of the corresponding points in the mutual embedding space. The main benefits of this model include: (1) simultaneous discovery and alignment of manifold structures; (2) fully unsuper-vised matching without any pre-specified correspondences; (3) efficient iterative alignment without computations in all permutation cases. Experimental results on dataset matching and real-world applications demonstrate the effectiveness and the practicability of our manifold alignment method.

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A multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis in the frequency domain

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2014年 215卷 603-616页

作者： Chen, Guang Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Inf. Processing and Cloud Computing App. Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

Brain-computer interface (BCI) is a new way for man-machine interaction with wide applications, in which steady-state visual evoked potentials (SSVEP) is a promising option. However, many characteristics of SSVEP show great user variation. So parameter optimization and channel selection for each subject were applied to improve the performance of BCI. These optimizations limit the practical applicability of the SSVEP-based BCI. The use of a canonical correlation analysis (CCA) method for multi-channel SSVEP in the time domain detection showed highly increased detection accuracy, but it is sensitive to the noise when the stimulate frequency is low. In this paper, a method of CCA in the frequency domain is presented for classifying multi-channel SSVEPs. First overlapping average is conducted on the original training signals. Then fast Fourier transform (FFT) is used to transform the signals from time domain to frequency domain to produce the reference data. Finally, according to the correlation coefficients of the new data and the references in the frequency domain, the SSVEP is classified. The experimental results show the enhanced accuracy of our method when applied to low stimulate frequencies. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Brain computer interface

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Image reconstruction of concrete based on Filtered Backprojection method using ultrasonic time of flight data

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Computer Modelling and New Technologies 2014年第6期18卷 72-78页

作者： Fan, Honghui Zhu, Hongjin Han, Qingbang School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Sichuan Province Key Laboratory of Bridge non-destructive testing and engineering computing Zigong Sichuan China Key Laboratory of Cloud Computing Intelligent Information Processing of Changzhou City Changzhou Jiangsu China College of IOT Engineering Hohai University Jiangsu China

This research aims to recognize the defect of concrete materials using an ultrasonic computed tomography imaging technique. Filtered Backprojection method was used to reconstruct concrete images in this paper. Ultrasonic time of flight data was measured to reconstruct computer tomography images. 306 data paths were obtained in total by manual scanning for one computer tomography image. We examined the effect of the interpolation data as the density of time of flight data has a considerable effect on image quality. The feasibility of concrete reconstruction system and time of flight data interpolation were examined in detail using numerical and concrete phantoms.

关键词： Interpolation

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High-through put Identification of Protein Disulfide Bonds from Complex Samples

High-through put Identification of Protein Disulfide Bonds f...

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中国化学会首届全国质谱分析学术研讨会

作者： Shan Lu Sheng-Bo Fan Bing Yang Jia-Ming Meng Long Wu Kun Zhang En-Zhi Shen Chun-Qing Song Rui-Xiang Sun Si-Min He Meng-Qiu Dong College of Life Science Beijing Normal University National Institute of Biological Sciences Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

Correct disulfide bond formation is essential for the activity and stability of numerous proteins of essential biological functions such as secreted signaling proteins and cell surface *** protein disulfide bonds typically requires a large amount of highly purified proteins,and much relies on arduous sample preparation and manual data *** we report a robust and sensitive method for identifying disulfide bonds in *** the new method,we successfully mapped all 74 disulfide bonds from a mixture often purified proteins and hundreds of disulfide bonds from an *** periplasmic fraction,a *** mitochondrial fraction,and human whole-cell *** biological experiments verified nine out of nine randomly selected,newly identified disulfide bonds in *** proteins.

关键词： High-through put Identification of Protein Disulfide Bonds from Complex Samples

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HAMMING EMBEDDING WITH FRAGILE BITS FOR IMAGE SEARCH

HAMMING EMBEDDING WITH FRAGILE BITS FOR IMAGE SEARCH

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IEEE International Conference on Image processing

作者： Dongye Zhuang Dongming Zhang Jintao Li Ke Lv Qi Tian University of Chinese Academy of Sciences Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Department of Computer Science University of Texas at San Antonio

ISBN: (纸本)9781479957521

Recently, several binary descriptors are proposed, which represent interest points in image using binary codes. In these binary feature schemes, two descriptors are considered as a match, if the Hamming distance between them is below a threshold. Applying Hamming distance to measure the similarity between binary descriptors can extremely promote the computational efficiency. However, our experimental results presents that there exists a large number of bits in the binary feature vector cannot maintain the robustness while image conditions change. Rather than ignore the impacts of those unstable bits, we take into account the difference of robustness among the feature bits and propose a novel similarity measurement, which called the Fragile Bit Ratio (FBR). FBR is used in binary feature matching to measure how two features differ. High FBRs are associated with genuine matches between two binary features and low FBRs are associated with impostor ones. Based on this metric, we propose a new binary feature matching scheme to fuse the Hamming distance and Fragile Bit Ratio. In our approach, we match the descriptors using the Hamming distance threshold roughly, and then filtered by the Fragile Bits Ratio to refine the candidate set. In experiments, using Fragile Bits Radio can effectively remove the false matches and highly improve the accuracy of image search. Furthermore, our method can easily be integrated into the other well-established binary features schemes.

关键词： Image Search Hamming Embedding Binary Features

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A Survey of Commonsense Knowledge Acquisition

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Journal of Computer Science & Technology 2013年第4期28卷 689-719页

作者：臧良俊曹聪曹亚男吴昱明曹存根 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences University of the Chinese Academy of Sciences The Second Lab Institute of Information Engineering Chinese Academy of Sciences

Collecting massive commonsense knowledge （CSK） for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects： CSK acquisition task （what CSK is acquired from where）, technique used （how can CSK be acquired）, and CSK evaluation methods （how to evaluate the acquired CSK）. In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.

关键词： commonsense knowledge knowledge acquisition knowledge representation and reasoning

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Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews

Ratable Aspects over Sentiments: Predicting Ratings for Unra...

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IEEE International Conference on Data Mining (ICDM)

作者： Wenjuan Luo Fuzhen Zhuang Xiaohu Cheng Qing He Zhongzhi Shi Renren Inc Beijing China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing CAS China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479943012

Most existing rat able aspect generating methods for aspect mining focus on identifying and rating aspects of reviews with overall ratings, while huge amount of unrated reviews are beyond their ability. This drawback motivates the research problem in this paper: predicting aspect ratings and overall ratings for unrated reviews. To solve this problem, we novelly propose a topic model based on Latent Dirichlet Allocation with indirect supervision. Compared with the previous bag-of-words representation of review documents, we utilize the quad-tuples of (head, modifier, rating, entity) to explicitly model the associations between modifiers and ratings. Specifically, our solution for aspect mining in unrated reviews is decomposed into three steps. Firstly, rat able aspects are generated over sentiments from training reviews with overall ratings. Afterwards, inference of aspect identification and rating for unrated reviews are provided. Finally, overall ratings are predicted for unrated reviews. Under this framework, aspect and sentiment associations are captured in the form of joint probabilities through a generative process. The effectiveness of our approach is testified on a real-world dataset crawled from Trip Advisor http://***/, and extensive experiments show that our method significantly outperforms state-of-the-art methods.

关键词： Training Feature extraction Gaussian distribution Inference algorithms Equations Mathematical model Numerical models

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