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The dynamic allocation model for the resources of cloud services delivery networks

Jisuanji Xuebao/Chinese Journal of computers 2011年第12期34卷 2305-2318页

作者： Shi, Pei-Chang Wang, Huai-Min Yin, Gang Liu, Xue-Ning Yuan, Xiao-Qun Shi, Dian-Xi National Laboratory for Parallel and Distributed Processing School of Computer and Science National University of Defense Technology Changsha 410073 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Electronics and Information Engineering Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

Cloud Services Delivery Networks (CSDN) constructs a layer distributed server overlay over the Internet, which uses the way to the nearest and on-demand approach providing services to end users. Facing the scale and diversification of the resource demand characteristics of the Internet cloud services, CSDN forms different logical sub-server overlay for different kinds of cloud services. However, most servers and bandwidth resources of CSDN are used to deliver the streaming and downloading kind of cloud services, and the dynamic allocation of their delivery resource is the main research emphasis in this paper. This paper first models the problem to be a multi-dimensional facility location problem, according to the two characteristics: the memory resource and bandwidth resource of this kind of application are the bottleneck resource;the hot contents of this kind of application can be delivered using the Peer-to-Peer mechanisms. After the model analyzed and its NP-Complete proved, we then propose a heuristic algorithm. Finally, using the service delivery cost savings as the performance metrics, while the actual system's operation trace is as the input, the effectiveness of the algorithm are comprehensively assessed.

关键词： Heuristic algorithms

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A robust algorithm for iris localization based on radial symmetry and circular integro differential operator

A robust algorithm for iris localization based on radial sym...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zuhong Zhou Peng Yao Zhenquan Zhuang Jun Li University of Science and Technology Hefei China Department of Computer Vision and Image Processing Institute for Infocomm Research Singapore

Locating an iris is important in an iris recognition system. Previous studies have shown good performance when an iris image is clear. However, if an iris image is occluded by eyelids, eyelashes and reflection, the accuracy and robustness of localization will decrease significantly. This paper proposed a new algorithm to locate an iris in a coarse-to-fine manner: firstly, a modified radial symmetry transform was used to find the possible pupil region; secondly, an enhanced circular integro operator was adopted to obtain the precise iris location. Experimental results on CASIA V3.0 iris database showed that proposed algorithm has a more accurate and robust performance than the previous methods, especially when occlusion of eyelids, eyelashes and reflection occurred. A real-time system was able to be achieved by the coarse-to-fine strategy as well.

关键词： Iris recognition Eyelashes Eyelids Pixel Iris Transforms Robustness

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LATENT SEMANTIC ANALYSIS IN SOUND EVENT DETECTION

LATENT SEMANTIC ANALYSIS IN SOUND EVENT DETECTION

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European Signal processing Conference

作者： Annamaria Mesaros Toni Heittola Anssi Klapuri Department of Signal Processing Tampere University of Technology Queen Mary University of London Electronic Engineering & Computer Science

This paper presents the use of probabilistic latent semantic analysis (PLSA) for modeling co-occurrence of overlapping sound events in audio recordings from everyday audio environments such as office, street or shop. Co-occurrence of events is represented as the degree of their overlapping in a fixed length segment of polyphonic audio. In the training stage, PLSA is used to learn the relationships between individual events. In detection, the PLSA model continuously adjusts the probabilities of events according to the history of events detected so far. The event probabilities provided by the model are integrated into a sound event detection system that outputs a monophonic sequence of events. The model offers a very good representation of the data, having low perplexity on test recordings. Using PLSA for estimating prior probabilities of events provides an increase of event detection accuracy to 35%, compared to 30% for using uniform priors for the events. There are different levels of performance increase in different audio contexts, with few contexts showing significant improvement.

关键词： History Event detection Hidden Markov models Data models Context Acoustics Semantics

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A new method of tank detection for SAR images

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Journal of Convergence Information technology 2011年第11期6卷 441-449页

作者： Hu, Huozhen Tian, Jinwen Dai, Guangming Wang, Maocai National key laboratory of science and technology on multi-spectral information processing Huazhong university of Science and technology Wuhan Hubei China Department of Computer Science China university of Geosciences Wuhan Hubei 430074 China

The tank is one of important military targets. Tanks detection is the study focus of the synthetic aperture radar(SAR) image processing currently. But there may be many false alarms existed in the detection result with most of the traditional tank detection methods affected by the SAR speckle. A new method of tank detection for SAR images based on the features of SAR images is put forward by this paper. It uses Gauss low-pass filtering to smooth the original image and the geometric active contour model based on prediction theory metric to realize automatic segmentation. It detects candidate targets by removing little connected regions. Finally, it further removes the false alarm targets based on the grey characteristic of the shadow regions. Experimental results indicate that the method can effectively and rightly detect tanks of SAR images. Moreover, it is insensitive to the initial contour.

关键词： Synthetic aperture radar

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Multi-dimensional evolutionary feature synthesis for content-based image retrieval

Multi-dimensional evolutionary feature synthesis for content...

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IEEE International Conference on Image processing

作者： Serkan Kiranyaz Jenni Pulkkinen Turker Ince Moncef Gabbouj Department of Signal Processing Tampere University of Technology Finland Faculty of Computer Science Izmir University of Economics Izmir Turkey

Low-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characteristics or signatures. However, especially the (low-level) features, which can be extracted automatically usually lack the discrimination power needed for accurate description of the image content and may lead to a poor retrieval performance. In order to efficiently address this problem, in this paper we propose a multi- dimensional evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operators so as to synthesize highly discriminative set of features in an optimal dimension. The optimality therein is sought by the multi-dimensional particle swarm optimization method along with the fractional global-best formation technique. Clustering and CBIR experiments where the proposed feature synthesizer is evolved using only the minority of the image database, demonstrate a significant performance improvement and exhibit a major discrimination between the features of different classes.

关键词： Feature extraction Image databases Synthesizers Vectors Genetic algorithms Indexes

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Evolutionary method for finding communities in bipartite networks

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Physical Review E 2011年第6期83卷 066120-066120页

作者： Weihua Zhan Zhongzhi Zhang Jihong Guan Shuigeng Zhou Department of Computer Science and Technology Tongji University 4800 Cao’an Road Shanghai 201804 China School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China

An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework—detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

关键词： Community structure Modularity Azanza Network TWO-COMPONENT

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Representation of Multiple-Valued Bent Functions Using Vilenkin-Chrestenson Decision Diagrams

Representation of Multiple-Valued Bent Functions Using Vilen...

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International Symposium on Multiple-Valued Logic

作者： Stanislav Stankovic Milena Stankovic Jaakko T. Astola Department of Signal Processing Tampere University of Technology Tampere Finland Department of Computer Science Faculty of Electronics University of Niš Nis Serbia

Bent functions are a class of discrete functions which exhibit the highest degree of nonlinearity. As such bent functions form an essential part of cryptographic systems. Original concept of bent functions defined in GF(2) can be extended to multiple-valued case. Multiple-valued bent functions are defined in therms of properties of their Vilenkin-Chrestenson spectra. Decision diagrams are a method of compact representation of discrete functions. Special types of decision diagrams have been introduced for various types of discrete functions. In this paper we demonstrate how Vilenkin-Chrestenson decision diagrams can be used for efficient representation of multiple-valued bent functions.

关键词： Transforms Input variables Cryptography Spectral analysis Topology Decision trees Silicon

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Implementation and evaluation of an SCA-resistant embedded processor 1

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10th IFIP Conference on Smart Card Research and Advanced Applications, CARDIS 2011

作者： Tillich, Stefan Kirschbaum, Mario Szekely, Alexander University of Bristol Computer Science Department Merchant Venturers Building Woodland Road BS8 1UB Bristol United Kingdom Institute for Applied Information Processing and Communications Graz University of Technology Inffeldgasse 16a A-8010 Graz Austria

ISBN: (数字)9783642272578

ISBN: (纸本)9783642272561

Side-channel analysis (SCA) attacks are a threat for many embedded applications which have a need for security. With embedded processors being at the very heart of such applications, it is desirable to address SCA attacks with countermeasures which "naturally" fit deployment in those processors. This paper describes our work in implementing one such protection concept in an ASIC prototype and our results from a practical evaluation of its security. We are able to demonstrate that the basic principle of limiting the "leaking" portion of the processor works rather well to reduce the side-channel leakage. From this result we can draw valuable conclusions for future embedded processor design. In order to minimize the remaining leakage, the security concept calls for the application of a secure logic style. We used two concrete secure logic styles (iMDPL and DWDDL) in order to demonstrate this increase in security. Unfortunately, neither of these logic styles seems to do a particularly good job as we were still able to attribute SCA leakage to the secure-logic part of the processor. If a better suited logic style can be employed we believe that the overall leakage of the processor can be further reduced. Thus we deem the evaluated security concept as a viable method for protecting embedded processors. © 2011 IFIP International Federation for Information processing.

关键词： Side channel attack

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Collective network of evolutionary binary classifiers for content-based image retrieval

Collective network of evolutionary binary classifiers for co...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Serkan Kiranyaz Stefan Uhlmann Jenni Pulkkinen Moncef Gabbouj Turker Ince Department of Signal Processing Tampere University of Technology Tampere Finland Faculty of Computer Science Izmir University of Economics Izmir Turkey

The content-based image retrieval (CBIR) has been an active research field for which several feature extraction, classification and retrieval techniques have been proposed up to date. However, when the database size grows larger, it is a common fact that the overall retrieval performance significantly deteriorates. In this paper, we propose collective network of (evolutionary) binary classifiers (CNBC) framework to achieve a high retrieval performance even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be trained incrementally. The CNBC framework basically adopts a “Divide and Conquer” type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. In such an evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale re-training or re-configuration. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its scalability; and a significant performance improvement is achieved over traditional retrieval techniques.

关键词： Training Neurons Accuracy Feature extraction Image databases Search problems Scalability

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A robust keyword spotting system for Persian conversational telephone speech using feature and score normalization and ARMA filter

A robust keyword spotting system for Persian conversational ...

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IEEE GCC Conference & Exhibition

作者： Akram Shokri Shima Tabibian Ahmad Akbari Babak Nasersharif Jahanshah Kabudian Audio and Signal Processing Lab Computer Engineering Department Iran University of Science and Technology Iran Computer Engineering Department Faculty of Engineering University of Guilan Rasht Iran Research Center for Intelligent Signal Processing Tehran Iran

Keyword spotting (KWS) refers to detection of a limited number of given keywords in speech utterances. In this paper, we evaluate a robust keyword spotting system based on hidden markov models for speaker independent Persian conversational telephone speech. Performance of base line keyword spotter is improved by means of normalizing features using cepstral mean and variance normalization (CMVN) and cepstral gain normalization (CGN). And better performance is gained by applying auto-regressive moving average (ARMA) filter on normalized features. Experimental results show that although all these methods improve keyword spotting performance, CMVN and ARMA (MVA) processing of PLP features works much better on our Persian conversational telephone speech database and 41% improvement to baseline system is achieved at false alarm (FA) rate equal to 8.6 FA/KW/Hour.

关键词： Hidden Markov models Speech Computational modeling Mel frequency cepstral coefficient Filtering Speech recognition

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