Fundamental schemes to determine disjoint paths for multiple failure scenarios

Disjoint path routing approaches can be used to cope with multiple failure cenarios. This can be achieved using a set of k (k>2) link- (or node-) disjoint path pairs (in single-cost and multi-cost networks). Alternatively, if Shared Risk Link Groups (SRLGs) information is available, the calculation of an SRLG-disjoint path pair (or of a set of such paths) can protect a connection against the joint failure of the set of links in any single SRLG. Paths traversing disaster-prone regions should be disjoint, but in safe regions it may be acceptable for the paths to share links or even nodes for a quicker recovery. Auxiliary algorithms for obtaining the shortest path from a source to a destination are also presented in detail, followed by the illustrated description of Bhandari’s and Suurballe’s algorithms for obtaining a pair of paths of minimal total additive cost. These algorithms are instrumental for some of the presented schemes to determine disjoint paths for multiple failure scenarios.info:eu-repo/semantics/publishedVersio

Efficient Routing Protection Algorithm Based on Optimized Network Topology

Network failures are unavoidable and occur frequently. When the network fails, intra-domain routing protocols deploying on the Internet need to undergo a long convergence process. During this period, a large number of messages are discarded, which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers (ISP). Therefore, improving the availability of intra-domain routing is a trending research question to be solved. Industry usually employs routing protection algorithms to improve intra-domain routing availability. However, existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures, which increases the cost of the network and impedes the methods deployed in practice. To address the issues, this study proposes an efficient routing protection algorithm based on optimized network topology (ERPBONT). ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source purposes. Firstly, the backup path with the minimum path coincidence with the shortest path is described as an integer programming problem. Then the simulated annealing algorithm ERPBONT is used to find the optimal solution. Finally, the algorithm is tested on the simulated topology and the real topology. The experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path, and significantly improves the routing availability

Optimal Link-Disjoint Node-“Somewhat Disjoint” Paths

Network survivability has been recognized as an issue of major importance in terms of security, stability and prosperity. A crucial research problem in this context is the identification of suitable pairs of disjoint paths. Here, “disjointness” can be considered in terms of either nodes or links. Accordingly, several studies have focused on finding pairs of either link or node disjoint paths with a minimum sum of link weights. In this study, we investigate the gap between the optimal node-disjoint and linkdisjoint solutions. Specifically, we formalize several optimization problems that aim at finding minimum-weight link-disjoint paths while restricting the number of its common nodes. We establish that some of these variants are computationally intractable, while for other variants we establish polynomial-time algorithmic solutions. Finally, through extensive simulations, we show that, by allowing link-disjoint paths share a few common nodes, a major improvement is obtained in terms of the quality (i.e., total weight) of the solution

Social Learning Systems: The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems

In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians’ interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN\u27s). This work followed an independent effort to develop an evolutionary knowledge management system, and outlines a model for integrating the wisdom of the crowd into the process of collecting, analyzing, and curating data for dynamical knowledge systems. Throughout, we examine how relational data modeling, automated reasoning, crowdsourcing, and social curation techniques have been exploited to extend the utility of web-based, transactional knowledge management systems, creating a new breed of knowledge-based system in the process: the Social Learning System (SLS). The key questions this work has explored by way of elucidating the SLS model include considerations for 1) how it is possible to unify Web and OSN mining techniques to conform to a versatile, structured, and computationally-efficient ontological framework, and 2) how large-scale knowledge projects may incorporate tiered collaborative editing systems in an effort to elicit knowledge contributions and curation activities from a diverse, participatory audience

wavelet domain inversion and joint deconvolution/interpolation of geophysical data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2003.Includes bibliographical references (leaves 168-174).This thesis presents two innovations to geophysical inversion. The first provides a framework and an algorithm for combining linear deconvolution methods with geostatistical interpolation techniques. This allows for sparsely sampled data to aid in image deblurring problems, or, conversely, noisy and blurred data to aid in sample interpolation. In order to overcome difficulties arising from high dimensionality, the solution must be derived in the correct framework and the structure of the problem must be exploited by an iterative solution algorithm. The effectiveness of the method is demonstrated first on a synthetic problem involving satellite remotely sensed data, and then on a real 3-D seismic data set combined with well logs. The second innovation addresses how to use wavelets in a linear geophysical inverse problem. Wavelets have lead to great successes in image compression and denoising, so it is interesting to see what, if anything, they can do for a general linear inverse problem. It is shown that a simple nonlinear operation of weighting and thresholding wavelet coefficients can consistently outperform classical linear inverse methods in terms of mean-square error across a broad range of noise magnitude in the data. Wavelets allow for an adaptively smoothed solution: smoothed more in uninteresting regions, less at geologically important transitions.(cont.) A third issue is also addressed, somewhat separate from the first two: the correct manipulation of discrete geophysical data. The theory of fractional splines is introduced, which allows for optimal approximation of real signals on a digital computer. Using splines, it can be shown that a linear operation on the spline can be equivalently represented by a matrix operating on the coefficients of a certain spline basis function. The form of the matrix, however, depends completely on the spline basis, and incorrect discretization of the operator into a matrix can lead to large errors in the resulting matrix/vector product.by Jonathan A. Kane.Ph.D

Explorations of knowledge management in a defence engineering environment

This thesis originates from first hand early experiences of the researcher regarding current processes and practices in operation in BAE SYSTEMS Ltd (now referred to hereafter as `the Company'), and recognises the potential for improvement within the realm of knowledge management. The huge volume of internal and external information overwhelms the majority of organisations and knowledge management provides solutions to enable organisations to be effective, efficient, and competitive. The software agent approach and information retrieval technique indicates great potential for effectively managing information. This research seeks to answer the questions of whether software agents can provide the Company with solutions to the knowledge management issues identified in this inquiry and whether they can also be used elsewhere within the organisation to improve other aspects of the business. The research analysis shows that software agents offer a wide applicability across the Company; can be created with relative ease and can provide benefits by improving the effectiveness and efficiency of processes. Findings also provided valuable insight into human-computer-interface design and usability aspects of software agent applications. The research deals with these questions using action research in order to develop a collaborative change mechanism within the Company and a practical applicability of the research findings in situ. Using a pluralistic methodology the findings provide a combination of the subjective and objective views intermittently within the research cycles thereby giving the researchera more holistic view of this research. Little attention has been paid to integrating software agent technologies into the knowledge management processes.This research proposes a software agent application that incorporates: (1) Co-ordination of software agents for information retrieval to manage information gathering, filtering, and dissemination; (2) To promote effective interpretation of information and more efficient processes;(3) Building accurate search profiles weighted on pre-defined criteria; (4) Integrating and organising a Company resource management knowledge-base; (5) Ensuring that the right information gets to the right personnel at the right time; and (6) So the Company can effectively assign the right experts to the right roles within the Company

Replay detection in voice biometrics: an investigation of adaptive and non-adaptive front-ends

Among various physiological and behavioural traits, speech has gained popularity as an effective mode of biometric authentication. Even though they are gaining popularity, automatic speaker verification systems are vulnerable to malicious attacks, known as spoofing attacks. Among various types of spoofing attacks, replay attack poses the biggest threat due to its simplicity and effectiveness. This thesis investigates the importance of 1) improving front-end feature extraction via novel feature extraction techniques and 2) enhancing spectral components via adaptive front-end frameworks to improve replay attack detection. This thesis initially focuses on AM-FM modelling techniques and their use in replay attack detection. A novel method to extract the sub-band frequency modulation (FM) component using the spectral centroid of a signal is proposed, and its use as a potential acoustic feature is also discussed. Frequency Domain Linear Prediction (FDLP) is explored as a method to obtain the temporal envelope of a speech signal. The temporal envelope carries amplitude modulation (AM) information of speech resonances. Several features are extracted from the temporal envelope and the FDLP residual signal. These features are then evaluated for replay attack detection and shown to have significant capability in discriminating genuine and spoofed signals. Fusion of AM and FM-based features has shown that AM and FM carry complementary information that helps distinguish replayed signals from genuine ones. The importance of frequency band allocation when creating filter banks is studied as well to further advance the understanding of front-ends for replay attack detection. Mechanisms inspired by the human auditory system that makes the human ear an excellent spectrum analyser have been investigated and integrated into front-ends. Spatial differentiation, a mechanism that provides additional sharpening to auditory filters is one of them that is used in this work to improve the selectivity of the sub-band decomposition filters. Two features are extracted using the improved filter bank front-end: spectral envelope centroid magnitude (SECM) and spectral envelope centroid frequency (SECF). These are used to establish the positive effect of spatial differentiation on discriminating spoofed signals. Level-dependent filter tuning, which allows the ear to handle a large dynamic range, is integrated into the filter bank to further improve the front-end. This mechanism converts the filter bank into an adaptive one where the selectivity of the filters is varied based on the input signal energy. Experimental results show that this leads to improved spoofing detection performance. Finally, deep neural network (DNN) mechanisms are integrated into sub-band feature extraction to develop an adaptive front-end that adjusts its characteristics based on the sub-band signals. A DNN-based controller that takes sub-band FM components as input, is developed to adaptively control the selectivity and sensitivity of a parallel filter bank to enhance the artifacts that differentiate a replayed signal from a genuine signal. This work illustrates gradient-based optimization of a DNN-based controller using the feedback from a spoofing detection back-end classifier, thus training it to reduce spoofing detection error. The proposed framework has displayed a superior ability in identifying high-quality replayed signals compared to conventional non-adaptive frameworks. All techniques proposed in this thesis have been evaluated on well-established databases on replay attack detection and compared with state-of-the-art baseline systems

Being nationalist: identity within a post-Ottoman state

The thesis defines and explores three different modalities of nationalism - diagnosis, activism and redemption - in the context of contemporary Bulgaria. Nationalists see a significant divergence between 'who we should be' and 'who we are'. This is accentuated by Bulgarian citizens' experiences of socio-political chaos and uncertainty. The thesis looks at the political rituals which aim to redeem the 'ill' Bulgarian nation, conceived as both post-Ottoman and post-Soviet. It focuses the importance of affect for understanding the relevance of the nation for citizens’ sense of self. I begin by examining the apparatus of production through which the Bulgarian national subject is imbued with a particular character. I consider how it has been constituted historically and how it continues to be moulded by contemporary discourses. I demonstrate that 'being Bulgarian' is nowadays a primarily negative state of being, defined through the discourse of the ill nation. As far as nationalists are concerned, this illness can be cured only through attempting, out of the debris of historical contingency, to renew social structures so that they more closely resemble the ideal. My research focused on one nationalist organisation in Bulgaria which attempted to fulfil this task: VMRO (or IMRO- the Internal Macedonian Revolutionary Movement). I explore how the organisation creates and renews itself as a descendant of the national revival movements of the 19th and early 20th century, and thus as a valid form of contemporary nationalism, while at the same time it fills the role of a modern political party. To heal the nation, VMRO declares a need to be vigilant against further catastrophes and to address the consequences of previous ones. It thus interprets existing social grievances according to specific narratives about the nation’s problems and prescribes redemptive action. VMRO addresses a public which has internalised a sense of being judged by 'the international' (often imagined as 'a dictate'). This is not the 'real' international, but an imagined, power-laden domain. Nationalists engage with this domain by constructing illicit discourses which challenge this nexus of power. In the thesis, I explore how the traditional imperatives of a nationalist organisation - making claims for territories, populations and minority issues - are hybridized by the organisation's dialogic engagement with both 'the international', with citizens' daily concerns and their affective states

Explorations of knowledge management in a defence engineering environment

This thesis originates from first hand early experiences of the researcher regarding current processes and practices in operation in BAE SYSTEMS Ltd (now referred to hereafter as `the Company'), and recognises the potential for improvement within the realm of knowledge management. The huge volume of internal and external information overwhelms the majority of organisations and knowledge management provides solutions to enable organisations to be effective, efficient, and competitive. The software agent approach and information retrieval technique indicates great potential for effectively managing information. This research seeks to answer the questions of whether software agents can provide the Company with solutions to the knowledge management issues identified in this inquiry and whether they can also be used elsewhere within the organisation to improve other aspects of the business. The research analysis shows that software agents offer a wide applicability across the Company; can be created with relative ease and can provide benefits by improving the effectiveness and efficiency of processes. Findings also provided valuable insight into human-computer-interface design and usability aspects of software agent applications. The research deals with these questions using action research in order to develop a collaborative change mechanism within the Company and a practical applicability of the research findings in situ. Using a pluralistic methodology the findings provide a combination of the subjective and objective views intermittently within the research cycles thereby giving the researchera more holistic view of this research. Little attention has been paid to integrating software agent technologies into the knowledge management processes.This research proposes a software agent application that incorporates: (1) Co-ordination of software agents for information retrieval to manage information gathering, filtering, and dissemination; (2) To promote effective interpretation of information and more efficient processes;(3) Building accurate search profiles weighted on pre-defined criteria; (4) Integrating and organising a Company resource management knowledge-base; (5) Ensuring that the right information gets to the right personnel at the right time; and (6) So the Company can effectively assign the right experts to the right roles within the Company.EThOS - Electronic Theses Online ServiceGBUnited Kingdo