Today, the advent of networking technologies and computer hardware have enabled more and more inexpensive PCs, various mobile devices, smart phones, PDAs, sensors and cameras to be linked to the Internet with better connectivity. In recent years, we have witnessed the emergence of several instances of distributed applications, providing infrastructures for social interactions over large-scale wide-area networks and facilitating the ways users share and publish data. User generated data today range from simple text files to (semi-) structured documents and multimedia content. With the emergence of Semantic Web, the number of features (associated with a content) that are used in order to index those large amounts of heterogenous pieces of data is growing dramatically. The feature sets associated with each content type can grow continuously as we discover new ways of describing a content in formulated terms. As the number of dimensions in the feature data grow (as high as 100 to 1000), it becomes harder and harder to search for information in a dataset due to the curse of dimensionality and it is not appropriate to use naive search methods, as their performance degrade to linear search. As an alternative, we can distribute the content and the query processing load to a set of peers in a distributed Peer-to-Peer (P2P) network and incorporate high-dimensional distributed search techniques to attack the problem. Currently, a large percentage of Internet traffic consists of video and music files shared and exchanged over P2P networks. In most present services, searching for music is performed through keyword search and naive string-matching algorithms using collaborative filtering techniques which mostly use tag based approaches. In music information retrieval (MIR) systems, the main goal is to make recommendations similar to the music that the user listens to. In these systems, techniques based on acoustic feature extraction can be employed to achieve content-based music similarity search (i.e., searching through music based on what can be heard from the music track). Using these techniques we can devise an automated measure of similarity that can replace the need for human experts (or users) who assign descriptive genre tags and meta-data to each recording and solve the famous cold-start problem associated with the collaborative filtering techniques. In this work we explore the advantages of distributed structures by efficiently distributing the content features and query processing load on the peers in a P2P network. Using a family of Locality Sensitive Hash (LSH) functions based on p-stable distributions we propose an efficient, scalable and load-balanced system, capable of performing K-Nearest-Neighbor (KNN) and Range queries. We also propose a new load-balanced indexing algorithm and evaluate it using our Java based simulator. Our results show that this P2P design ensures load-balancing and guarantees logarithmic number of hops for query processing. Our system is extensible to be used with all types of multi-dimensional feature data and it can also be employed as the main indexing scheme of a multipurpose recommendation system. / Graduate

This thesis deals with the nature and definition of phonological similarity and shows that, when similarity plays a role in the motivation of phonological processes, it is evaluated over abstract, phonological features and not purely phonetic properties. Empirical evidence for this position is drawn from the domain of consonant harmony. Typological studies of consonant harmony (Hansson 2001, Rose and Walker 2004) have argued that segments which interact in consonant harmony processes must be highly similar to one another. This thesis provides analyses of a range of consonant harmony processes and demonstrates that, in each case, the notion of similarity needed in order to determine participating segments is evaluated over contrastive feature specifications. Contrastive specifications are established according to language-specific feature hierarchies (Jackobson and Halle 1956, Dresher 2003, forthcoming) with some features taking scope over others. Languages analyzed in some detail include Bumo Izon, Kalabari Ijo, Hausa, Dholuo, Anywa, Tzutujil and Aymara. Two definitions of similarity are proposed in order to account for two sets of cases. In one set of consonant harmony processes, interacting segments are similar in the sense that they constitute the natural class of segments contrastively specified in the harmonic feature. In another set of cases, participating segments must be similar according to the following definition; they must differ in only a single marked and contrastive feature specification.

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Distributed denial of service attacks (or DDoS) are a common occurrence on the internet and are becoming more intense as the bot-nets, used to launch them, grow bigger. Preventing or stopping DDoS is not possible without radically changing the internet infrastructure; various DDoS mitigation techniques have been devised with different degrees of success. All mitigation techniques share the need for a DDoS detection mechanism. DDoS detection based on traffic self-similarity estimation is a relatively new approach which is built on the notion that undis- turbed network traffic displays fractal like properties. These fractal like properties are known to degrade in presence of abnormal traffic conditions like DDoS. Detection is possible by observing the changes in the level of self-similarity in the traffic flow at the target of the attack. Existing literature assumes that DDoS traffic lacks the self-similar properties of undisturbed traffic. We show how existing bot- nets could be used to generate a self-similar traffic flow and thus break such assumptions. We then study the implications of self-similar attack traffic on DDoS detection. We find that, even when DDoS traffic is self-similar, detection is still possible. We also find that the traffic flow resulting from the superimposition of DDoS flow and legitimate traffic flow possesses a level of self-similarity that depends non-linearly on both relative traffic intensity and on the difference in self-similarity between the two incoming flows.

Thesis (Ph. D.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (M.S.)--Western Washington University, 2009. / Includes bibliographical references (leaves 33-35). Also issued online.

Thesis (Ph. D.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph. D.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph. D. )--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 73-75).

Thesis (M. A.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (leaves 42-48). Also available via the World Wide Web.