Linear Subspace Learning (LSL) has been widely used in many areas of information processing, such as dimensionality reduction, data mining, pattern recognition and computer vision. Recent years have witnessed several ...
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ISBN:
(纸本)9781479914821
Linear Subspace Learning (LSL) has been widely used in many areas of information processing, such as dimensionality reduction, data mining, pattern recognition and computer vision. Recent years have witnessed several excellent extensions of PCA in LSL. One is the recent L1-norm maximization principal component analysis (L1Max-PCA), which aims at learning linear subspace efficiently. L1Max-PCA simply simulates PCA by replacing the covariance with the so-called L1-norm dispersion in the mapped feature space. However, it is difficult to give an intuitive interpretation. In this paper, a novel subspace learning approach based on sparse dimension reduction is proposed, which enforces the sparsity of the mapped data to better recover cluster structures. The optimization problem is solved efficiently via Alternating Direction Method (ADM). Experimental results show that the proposed method is effective in subspace learning.
The aim of the paper is to consider a game-theoretic properties of strategies available to players in dialogue games. We treat speech acts formulated in a dialogue as moves in a dialogue game and rules for their appro...
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The aim of the paper is to consider a game-theoretic properties of strategies available to players in dialogue games. We treat speech acts formulated in a dialogue as moves in a dialogue game and rules for their appropriateness as rules of this game. We would like to analyze dialogical systems as a Nash-style games, and explore verification possibilities for some properties of such games e.g. dominant strategies and Nash equilibrium. Since automatic verification of such games could be of high complexity, we consider a model checking approach, which is commonly applied method allowing for verification of different systems and their properties. This method is based on solutions, which are trying to deal with the state explosion problem.
A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue ...
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We developed a new computational technique to identify de-centric genetic regulatory relationship candidates. Our technique takes advantages of functional genomics data for the same species under different perturbatio...
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In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for ...
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Mitosis detection poses a major challenge in cell tracking as mitoses are crucial events in the construction of genealogical trees. Making use of typical mitotic patterns that can be seen in phase contrast images of t...
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Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high reco...
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For the design of dependable and efficient wireless sensor networks it is essential to estimate the achievable packet reception rate (PRR) in the deployment environment. Making such estimation is not trivial as packet...
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ISBN:
(纸本)9781479937851
For the design of dependable and efficient wireless sensor networks it is essential to estimate the achievable packet reception rate (PRR) in the deployment environment. Making such estimation is not trivial as packet delivery success depends on the level of interference present in the deployment area. In this work we show that it is possible to obtain a meaningful representation of the expected interference levels at the target location by measuring the probability distribution function of idle period lengths, and use this to estimate PRR before network deployment. We show how a probability distribution function of idle period lengths can be measured using off-the-shelf sensor nodes. We illustrate how to exploit this methodology to estimate PRR in dependence of the used packet length, and show that relatively short measurement periods provide enough data to obtain accurate *** carry out an extensive experimental evaluation showing that Wi-Fi interference can be captured using this method which allows PRR predictions in such Wi-Fi interference setting with an average error of only 3.2%.
A method is proposed for estimating spectral radiances of the surrounding scene from a reflected image on the corneal surface. A digital color camera and the human eyeball are used to develop a simple imaging system w...
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Recently, MoBots or Mobile Botnets have become one of the most critical challenges in mobile communication and cyber security. The integration of Mobile devices with the Internet along with enhanced features and capab...
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Recently, MoBots or Mobile Botnets have become one of the most critical challenges in mobile communication and cyber security. The integration of Mobile devices with the Internet along with enhanced features and capabilities has made them an environment of interest for cyber criminals. Therefore, the spread of sophisticated malware such as Botnets has significantly increased in mobile devices and networks. On the other hand, the Bots and Botnets are newly migrated to mobile devices and have not been fully explored yet. Thus, the efficiency of current security solutions is highly limited due to the lack of available Mobile Botnet datasets and samples. As a result providing a valid dataset to analyse and understand the Mobile botnets has become a crucial issue in mobile security and privacy. In this paper we present an overview of the current available data set and samples and we discuss their advantages and disadvantages. We also propose a model to implement a mobile Botnet test bed to collect data for further analysis.
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