Plagiarism detection is gaining increasing importance due to requirements for integrity in education. The existing research has investigated the problem of plagrarim detection with a varying degree of success. The literature revealed that there are two main methods for detecting plagiarism, namely extrinsic and intrinsic. This thesis has developed two novel approaches to address both of these methods. Firstly a novel extrinsic method for detecting plagiarism is proposed. The method is based on four well-known techniques namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The LSA application was fine-tuned to take in the stylometric features (most common words) in order to characterise the document authorship as described in chapter 4. The results revealed that LSA based stylometry has outperformed the traditional LSA application. Support vector machine based algorithms were used to perform the classification procedure in order to predict which author has written a particular book being tested. The proposed method has successfully addressed the limitations of semantic characteristics and identified the document source by assigning the book being tested to the right author in most cases. Secondly, the intrinsic detection method has relied on the use of the statistical properties of the most common words. LSA was applied in this method to a group of most common words (MCWs) to extract their usage patterns based on the transitivity property of LSA. The feature sets of the intrinsic model were based on the frequency of the most common words, their relative frequencies in series, and the deviation of these frequencies across all books for a particular author. The Intrinsic method aims to generate a model of author “style” by revealing a set of certain features of authorship. The model’s generation procedure focuses on just one author as an attempt to summarise aspects of an author’s style in a definitive and clear-cut manner. The thesis has also proposed a novel experimental methodology for testing the performance of both extrinsic and intrinsic methods for plagiarism detection. This methodology relies upon the CEN (Corpus of English Novels) training dataset, but divides that dataset up into training and test datasets in a novel manner. Both approaches have been evaluated using the well-known leave-one-out-cross-validation method. Results indicated that by integrating deep analysis (LSA) and Stylometric analysis, hidden changes can be identified whether or not a reference collection exists.

Cette thèse vise à fournir aux entrepreneurs une meilleure compréhension de la façon d'améliorer leur performance lors de la collecte de fonds auprès d’investisseurs. Les entrepreneurs ont des difficultés notoires à accéder aux ressources financières et au capital parce qu'ils souffrent d'un aléa de la nouveauté. Cette condition inhérente est due à leur manque de légitimité dans leur marché cible et conduit les investisseurs à les considérer comme intrinsèquement risqués. Les moyens de financement des entrepreneurs ont traditionnellement été l'épargne personnelle, la famille et les amis, les banques ou les investisseurs professionnels. Le financement participatif est apparu comme une alternative à ceux-ci et les chercheurs dans le domaine de la gestion et de l'entrepreneuriat ont pris un grand intérêt à comprendre ses facettes multiples. La majorité de la recherche sur le financement participatif s’est concentrée sur des éléments quantifiables que les investisseurs utilisent pour déterminer la qualité de la startup. Plus la qualité perçue est élevée, plus les investisseurs ont des chances d'investir. Cependant, en complément de ces éléments de qualité, et non abordés par la recherche jusqu’à présent, sont les éléments qualitatifs qui permettent aux projets d’être plus clairs aux yeux des bailleurs de fonds potentiels tout en transmettant des informations en accord avec les attentes de ces mêmes investisseurs. Cette thèse vise à explorer les stratégies que les entrepreneurs peuvent utiliser pour augmenter leur performance dans le financement participatif en comprenant comment les investisseurs donnent du sens aux projets et comment ils les évaluent étant donné la nature de la plateforme utilisée par l'entrepreneur. Cette thèse contribue aux littératures du crowdfunding, de la catégorisation et des plateformes. La thèse explore d'abord comment les entrepreneurs peuvent utiliser les catégories et les stratégies narratives comme des leviers stratégiques pour améliorer leur performance en abaissant le niveau d'ambiguïté de leur offre tout en alignant leurs stratégies narratives aux attentes de la plateforme qu'ils utilisent. Deuxièmement, cette dissertation empreinte un chemin relativement inexploré en fournissant une critique de la relation qui existe entre l’utilisation de plusieurs catégories, l'ambiguïté et la créativité. De plus, la théorie de la catégorisation est enrichie par une analyse approfondie de l'importance des réseaux sémantiques et des images dans le processus de création de sens (« sense
making ») en utilisant une approche empirique nouvelle. Les images sont d'un intérêt particulier étant donné qu'elles ont leur importance à l’origine de la théorie de la catégorisation. Elles sont également traitées par des moyens cognitifs différents de ceux des mots et sont d'une importance vitale dans le monde d'aujourd'hui. Enfin, cette thèse explore la relation entre les plateformes et les récits en théorisant que les premiers sont des types particuliers d'organisations dont l'identité est forgée par leurs parties prenantes internes et externes. L’identité d’une plateforme est vulnérable aux changements tels que les chocs exogènes. Les entrepreneurs doivent apprendre à identifier ces identités ainsi que les changements potentiels afin d'adapter leurs stratégies narratives dans l’espoir d’augmenter leur performance. / This dissertation aims to provide entrepreneurs with a better understanding of how to improve their performance when raising funds from investors. Entrepreneurs have difficulty accessing financial resources and capital because they suffer from a liability of newness. This inherent condition is due to their lack of legitimacy in their target market and leads investors to see them as inherently risky. The traditional means of financing new venture ideas have been through personal savings, family and friends, banks, or professional investors. Crowdfunding has emerged as an alternative to these and scholars in the field of management and entrepreneurship have taken great interest in understanding its multiple facets. Most research in crowdfunding has focused on quantifiable elements that investors use in order to determine the quality of an entrepreneur’s venture. The higher the perceived quality, the higher the likelihood investors have of investing in it. However, orthogonal to these elements of quality, and not addressed in current research, are those qualitative elements that allow projects to become clearer in the eyes of potential funders and transmit valuable information about the venture in a coherent fashion regarding the medium they are raising funds from. This dissertation aims to explore strategies entrepreneurs can use to increase their performance in crowdfunding by understanding how investors make sense of projects and how they evaluate them given the nature of the platform used by the entrepreneur. This thesis contributes to the literature on crowdfunding, categorization, and platforms. The thesis first explores how entrepreneurs can use categories and narrative strategies as strategic levers to improve their performance by lowering the level of ambiguity of their offer while aligning their narrative strategies to the expectations of the platform they use. On a second level, the dissertation provides a deeper understanding of the relation that exists between category spanning, ambiguity, and creativity by addressing this relatively unexplored path. Categorization theory is further enriched through a closer examination of the importance of semantic networks and visuals in the sense making process by using a novel empirical approach. Visuals are of particular interest given they were of seminal importance at the foundation of categorization theory, are processed by different cognitive means than words, and are of vital importance in today’s world. Finally, the dissertation explores the relation between platforms and narratives by theorizing that the former are particular types of organizations whose identity is forged by their internal and external stakeholders. Platform identities are vulnerable to change such as exogenous shocks. Entrepreneurs need to learn how to identify these identities and potential changes in order to tailor their narrative strategies in the hopes of increasing their performance.

In the field of technology, every day new inventions are introduced in the society for promotion human beings life. By virtue of which all the citizens of society are facilitated according to their needs of life style. Most of the people from developed countries are benefited in all ways of life due to their nonstop efforts and struggle in the field of technology. However some are completely denied due to lack of awareness and lacking of technical education in the subject field. The handicapped people are not very well explored in each part of world, especially third world countries by the researchers. So we can claim it, for their weaknesses and so called interest to understand more about the handicapped people worldwide. To understanding about such people will provide us with opportunities to excel in the subfield. There is a big gap between the handicapped and society which causes to cut them off from society. This gap needs to be filled in, while carrying out researches in the desired fields. Our research area is related to ticket reservation machine for the visually impaired people, to make the process easy for them to buy ticket at their own from the ticketing machine. After studying different articles on the subject, it is proved that many inventions have already been done by researchers and others are in progress for helping out the handicapped people. We have presented screen prototypes of the interface of the ticketing machine for the visually impaired people. We collected many hardware devices as one kit which can be use in the machine for the visually impaired users. It will enhance their power of visual sense to perform tasks at their own. There are some principles to follow in building up any system for visually impaired or blind people. If followed these principles in true sense it will facilitate to make new interface designs for interaction with visually impaired or blind users in friendly way. In this way we can overcome the shortcomings between normal and visually impaired people, which will be a great achievement to serve the deprived people.

by Song, Jingyan. / "January 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 110-118). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Recently, large amounts of experimental data for complex biological systems have become available. We use tools and algorithms from machine learning to build data-driven predictive models. We first present a novel algorithm to discover gene sequence motifs associated with temporal expression patterns of genes. Our algorithm, which is based on partial least squares (PLS) regression, is able to directly model the flow of information, from gene sequence to gene expression, to learn cis regulatory motifs and characterize associated gene expression patterns. Our algorithm outperforms traditional computational methods e.g. clustering in motif discovery. We then present a study of extending a machine learning model for transcriptional regulation predictive of genetic regulatory response to Caenorhabditis elegans. We show meaningful results both in terms of prediction accuracy on the test experiments and biological information extracted from the regulatory program. The model discovers DNA binding sites ab intio. We also present a case study where we detect a signal of lineage-specific regulation. Finally we present a comparative study on learning predictive models for motif discovery, based on different boosting algorithms: Adaptive Boosting (AdaBoost), Linear Programming Boosting (LPBoost) and Totally Corrective Boosting (TotalBoost). We evaluate and compare the performance of the three boosting algorithms via both statistical and biological validation, for hypoxia response in Saccharomyces cerevisiae.

No description available.

Within living organisms in the natural world, highly complex systems have evolved over billions of years to carry out the specific synthetic functions required to support and propagate life. Nature's use of biological machines for the synthesis of functional molecules has inspired synthetic chemists from a broad range of specialisms to design artificial molecular machines and systems capable of facilitating non-trivial synthetic tasks. A core strategy employed in attempting to emulate biological machines for synthesis has been to mimic Nature's ability to compartmentalise discrete aspects of a synthetic process. Rotaxanes are favourable architectures around which to design molecular machines as their mechanically-interlocked nature provides the chemist with a unique means by which to achieve compartmentalisation and to control the effective molarity of non-covalently linked components. The research presented in this thesis investigates the design, synthesis and operation of novel, rotaxane-based molecular machines for the non-trivial assembly of individual amino acid building blocks into information-rich oligopeptides. The artificial devices described herein each endeavour to emulate (in a primitive manner) one of Nature's most remarkable machines for synthesis: the ribosome. Information is programmed into these 'synthetic ribosomes' through their careful design and modular assembly; upon operation of the artificial molecular machine, this transcribed information is translated into a pre-defined oligopeptide product. The research presented in this thesis is laid out as follows:Chapter 1 reviews the current state of the art in biomimetic molecular machines and systems capable of promoting non-trivial synthetic tasks;Chapter 2 describes a molecular machine capable of non-proteinogenic oligopeptide synthesis via the sequence-specific assembly of beta-homo amino acid building blocks;Chapter 3 presents a device which operates upon a polymer to assemble individual leucine units into a homo-oligopeptide. This product forms a secondary alpha-helical structure capable of asymmetric organocatalysis in the Juliá-Colonna epoxidation of chalcone derivatives;Chapter 4 details a novel mode of amide-bond-forming catalysis for rotaxane-based molecular machines with a view to assembling an advanced peptidic precursor to Penicillin G.Chapters 2 and 3 are presented as manuscripts which have been compiled for peer-review publication and which represent the collaborative efforts of the Author and the researchers indicated at the beginning of each chapter. The Author's contributions are also outlined at the beginning of each chapter. These manuscripts have been modified only to ensure consistency with the other chapters contained in this thesis.

Thermal paints are paints that exhibit a number of permanent colour changes at various temperatures. Rolls-Royce, a producer of gas turbine engines, use thermal paints to map the surface heat distribution over components in gas turbine engines. Engine components are coated with thermal paints and built into engines. The engine is run which heats the components, and hence the paints. This results in a colour distribution over the surface of the painted components. This project aims to generate predictions for the temperature that the thermal paints applied to gas turbine engines have reached during engine operation. Training models are built using Raman spectra taken from known temperature paint samples. Raman spectra from the painted engine components are tested in these training models to generate temperature predictions. The known temperature paint samples are heated in an oven, while the paints applied to engine component are heated in a gas turbine engine. This leads to differences in the spectra of the known temperature paints and the engine run paints, complicating the training model. This thesis presents a method for classifying the spectra from the known temperature paints samples and the unknown temperature engine samples in such a way that meaningful predictive models can be built.

Cheung Pik Shan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 79-82). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Named Entity Translation Matching --- p.2 / Chapter 1.2 --- Mining New Translations from News --- p.3 / Chapter 1.3 --- Thesis Organization --- p.4 / Chapter 2 --- Related Work --- p.5 / Chapter 3 --- Named Entity Matching Model --- p.9 / Chapter 3.1 --- Problem Nature --- p.9 / Chapter 3.2 --- Matching Model Investigation --- p.12 / Chapter 3.3 --- Tokenization --- p.15 / Chapter 3.4 --- Hybrid Semantic and Phonetic Matching Algorithm --- p.16 / Chapter 4 --- Phonetic Matching Model --- p.22 / Chapter 4.1 --- Generating Phonetic Representation for English --- p.22 / Chapter 4.1.1 --- Phoneme Generation --- p.22 / Chapter 4.1.2 --- Training the Tagging Lexicon and Transformation Rules --- p.25 / Chapter 4.2 --- Generating Phonetic Representation for Chinese --- p.29 / Chapter 4.3 --- Phonetic Matching Algorithm --- p.31 / Chapter 5 --- Learning Phonetic Similarity --- p.37 / Chapter 5.1 --- The Widrow-Hoff Algorithm --- p.39 / Chapter 5.2 --- The Exponentiated-Gradient Algorithm --- p.41 / Chapter 5.3 --- The Genetic Algorithm --- p.42 / Chapter 6 --- Experiments on Named Entity Matching Model --- p.43 / Chapter 6.1 --- Results for Learning Phonetic Similarity --- p.44 / Chapter 6.2 --- Results for Named Entity Matching --- p.46 / Chapter 7 --- Mining New Entity Translations from News --- p.48 / Chapter 7.1 --- Metadata Generation --- p.52 / Chapter 7.2 --- Discovering Comparable News Cluster --- p.54 / Chapter 7.2.1 --- News Preprocessing --- p.54 / Chapter 7.2.2 --- Gloss Translation --- p.55 / Chapter 7.2.3 --- Comparable News Cluster Discovery --- p.62 / Chapter 7.3 --- Named Entity Cognate Generation --- p.64 / Chapter 7.4 --- Entity Matching --- p.66 / Chapter 7.4.1 --- Matching Algorithm --- p.66 / Chapter 7.4.2 --- Matching Result Production --- p.68 / Chapter 8 --- Experiments on Mining New Translations --- p.69 / Chapter 9 --- Experiments on Context-based Gloss Translation --- p.72 / Chapter 9.1 --- Results on Chinese News Translation --- p.73 / Chapter 9.2 --- Results on Arabic News Translation --- p.75 / Chapter 10 --- Conclusions and Future Work --- p.77 / Bibliography --- p.79 / A --- p.83 / B --- p.85 / C --- p.87 / D --- p.89 / E --- p.91 / F --- p.94 / G --- p.95

Guo Ping. / "March 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 166-182). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.