Dissertations / Theses on the topic 'Genre features'

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão<br>Made available in DSpace on 2016-01-08T19:31:47Z (GMT). No. of bitstreams: 1 102608.pdf: 7519023 bytes, checksum: 99c53c81079d4b01202f1fd46fd9d8c8 (MD5) Previous issue date: 1995<br>Investigação das conexões existentes entre retórica e discursos disciplinares, através da análise textual de cento e oitenta resenhas acadêmicas em inglês em lingüística, economia e química, e de entrevistas com editores de resenhas de revistas acadêmicas internacionais em cada área. Exame das diferenças e semelhanças existentes entre os movimentos retóricos e os termos de elogio e crítica comumente empregados por resenhadores em cada disciplina para avaliar e descrever novas publicações. As regularidades de função, conteúdo e forma da informação podem definir a existência de um mesmo gênero textual. As variações específicas em cada disciplina sugerem a necessidade de se desenvolver programas de ensino de línguas para fins acadêmicos que explorem as características macroestruturais de gêneros textuais, sem deixar de considerar as idiossincrasias das práticas discursivas em cada disciplina. Concluiu-se que a apropriação de um mesmo gênero textual responde à organização epistemológica da área de conhecimento específica, evidenciando-se assim as conexões entre texto e contexto de produção. A exploração da diversidade de valores e recortes epistemológicos das culturas disciplinares pode contribuir para a formação de leitores e escritores mais críticos em relação às práticas discursivas encontradas em textos acadêmicos em suas respectivas disciplinas.

Social media has become an integral part of the Internet. There have been users across the world sharing content like images, texts, videos, and so on. There is a huge amount of data being generated and it has become a challenge to the social media platforms to group the content for further usage like recommending a video. Especially, grouping videos based on similarity requires extracting features. This thesis investigates potential approaches to extract features that can help in determining the similarity between videos. Features of given videos are extracted using Object Detection and Action Recognition. Bag-of-features representation is used to build the vocabulary of all the features and transform data that can be useful in clustering videos. Probabilistic model-based clustering, Multinomial Mixture model is used to determine the underlying clusters within the data by maximizing the expected log-likelihood and estimating the parameters of data as well as probabilities of clusters. Analysis of clusters is done to understand the genre based on dominant actions and objects. Bayesian Information Criterion(BIC) and Akaike Information Criterion(AIC) are used to determine the optimal number of clusters within the given videos. AIC/BIC scores achieved minimum scores at 32 clusters which are chosen to be the optimal number of clusters. The data is labeled with the genres and Logistic regression is performed to check the cluster performance on test data and has achieved 96% accuracy

Syftet med denna studie är att undersöka hur undervisning med genrepedagogiska drag påverkar elevers argumenterande texter i åk 6. Studien utgår från tre frågeställningar som ligger till grund för undersökningen. Dessa frågor rör vilka genredrag elever använder i argumenterande texter före undervisning, hur undervisningen genomförs och vilka genredrag elever använder i argumenterande texter efter undervisningen. Materialet för studien baseras på observationer av genrepedagogiskt baserad undervisning och elevtextanalyser av argumenterande texter skrivna av elever i årskurs 6. Till grund för studien ligger den sociokulturella teorin där läraren stöttar eleverna i sitt lärande samt cykeln för undervisning och lärande. För att analysera texterna före och efter undervisning används en checklista med genretypiska drag för den argumenterande genren. Studien visar tydligt att före undervisning med genrepedagogiska drag har eleverna svårigheter med att skriva väl fungerande argumenterande texter. Med stöd av undervisning om genredrag i argumenterande texter utvecklar eleverna sitt skrivande och skriver i högre grad texter med genrespecifika drag. Dock visar studien att ytterligare stöttning av läraren och mer tid för att lära sig de genretypiska dragen för en text behövs för att eleverna ska klara av att göra egna kreativa val i sina texter.

Дискурс — це складне і важливе поняття в лінгвістиці. Цей термін є багатозначним, але найвлучніше його можна назвати, «мовою, зануреною в життя», тобто мовлення з усіма залученими до нього екстралінгвістичними факторами. Дискурс відображає у собі реальну картину світу і реагує на її зміни. Через дослідження певного дискурсу можна дослідити зміни у мові та мовленні, які зумовлені різноманітними екстралінгвістичними факторами, тобто зовнішніми умовами, що впливають на сказане, прослідкувати за тенденцією цих змін.<br>Дискурс - это сложное и важное понятие в лингвистике. Этот термин является многозначным, но метко его можно назвать, «языком, погруженным в жизнь», то есть речь со всеми вовлеченными в него экстралингвистическими факторами. Дискурс отражает в себе реальную картину мира и реагирует на ее изменения. Через исследование определенного дискурса можно исследовать изменения в языке и речи, обусловленные различными экстралингвистическими факторами, то есть внешними условиями, влияющими на сказанное, проследить за тенденцией этих изменений.<br>Discourse is a complex and important concept in linguistics. This term is ambiguous, but it can be called "language immersed in life," that is, it is a speech with all extralinguistic factors involved in it. Discourse reflects the real picture of the world and responds to its changes. Through the study of a certain discourse, it is possible to investigate changes in language and speech conditioned by various extralinguistic factors, that is, external conditions that affect what has been said, to track the trend of these changes.

This diploma work deals with music genre recognition using the techniques of Music Information Retrieval. It contains a brief description of the principle of this research area and its subfield called Music Genre Recognition. The following chapter includes selection of the most suitable parameters for describing music genres. This work further characterizes machine learning methods used in this field of research. The next chapter deals with the descriptions of music datasets created for genre classification studies. Subsequently, there is a draft and evaluation of the system for music genre recognition. The last part of this work describes the results of partial parameter analysis, dependence of genre classification accuracy on the amount of parameters and contains a discussion on the causes of classification accurancy for the individual genres.

This study aims to investigate possible distinctions between professional and non-professional written travel texts all treating the same destination: the Norwegian ski resort Trysil. The study will investigate to what extent the different texts correlate with the genre of travel texts, as the travel texts are treated as personal narratives, and how they conform to a given structure for narratives and with guidelines for professional writers. Furthermore, the investigation aims to explore to what extent there are similarities and differences between the texts regarding the given structure. The texts will first be analysed and organized separately by macrorules and a news schema that are constructed specifically for these sorts of texts, in order to reveal their discourse structure, and then compared to each other. As the discourse structure of the different texts is revealed, it is seen that there are certain differences between the two different text types. Finally, seen that the text types differ in their structure, this study will show that despite the fact that journalists write stories, and that non-professional written stories are narratives, they do not share the same structure, and are constructed in different ways.

The thesis deals with the development of Nabokov's treatment of a number of the more common routes and courses which are traditionally supplied by the author to ease the passage of the reader through fiction. It attempts to show how these marked paths and familiar signposts - 'melodrama,' 'totalitarian novel,' 'biography,' 'erotic confession,' 'critical edition,' 'family chronicle, 'mystery story,' and 'autobiographical confession' - emerge in the books as equally misguiding and misguided. The satisfactory application of such labels is demonstrated as becoming progressively more difficult as the novels proceed, with a rising degree of sophistication, to incorporate distinctive combinations of genre features usually considered as mutually exclusive. Further inquiries into the manner of fictional orientation and location encouraged by this regular disappointment of apparently familiar leads and landmarks, however, is increasingly seen to disclose the underlying procedures and desires of the reader to place and confine narrative. The manner in which Nabokov's reader is repeatedly obliged to return to a non-metaphorical 'first base' by way of these false trails, which seemingly point towards an authoritative text, and there to re-examine his own imaginative input is also traced.

Мета: 1. Розглянути особливості поняття антиутопії. 2. Дослідити жанрові особливості романів антиутопій. 3. Описати міждисциплінарний підхід до вивчення художнього перекладу. 4. Охарактеризувати труднощі перекладу романів антиутопій. 5. Проаналізувати використання перекладацьких трансформацій при перекладі роману антиутопії О. Гакслі "Прекрасний новий світ". 6. Охарактеризувати труднощі перекладу інтертекстуальних включень роману антиутопії О. Гакслі "Прекрасний новий світ". Теоретичне значення: систематизація особливостей перекладу роману антиутопії О. Гакслі "Прекрасний новий світ".<br>Цель: 1. Рассмотреть особенности понятия антиутопии. 2. Исследовать жанровые особенности романов антиутопий. 3. Описать междисциплинарный подход к изучению художественного перевода. 4. Охарактеризовать трудности перевода романов антиутопий. 5. Проанализировать использование переводческих трансформаций при переводе романа антиутопии О. Хаксли "О дивный новый мир". 6. Охарактеризовать трудности перевода интертекстуальных включений романа антиутопии О. Хаксли "О дивный новый мир". Теоретическое значение: систематизация особенностей перевода романа антиутопии О. Хаксли "О дивный новый мир".<br>Goal: 1. Consider the features of the concept of anti-utopia. 2. Investigate the genre features of anti-utopia novels. 3. Describe an interdisciplinary approach to the study of literary translation. 4. Describe the difficulties of translating anti-utopian novels. 5. To analyze the use of translation transformations in the translation of O. Huxley's novel "Anti-Utopia" "A Wonderful New World". 6. To characterize the difficulties of translating the intertextual inclusions of O. Huxley's novel "Anti-Utopia" "A Wonderful New World". Theoretical meaning: systematization of the peculiarities of the translation of O. Huxley's anti-utopia novel "A Wonderful New World".

Made available in DSpace on 2016-06-02T20:25:07Z (GMT). No. of bitstreams: 1 3377.pdf: 3546850 bytes, checksum: d15885076635f742d9e61ee253c4d220 (MD5) Previous issue date: 2010-02-26<br>Universidade Federal de Minas Gerais<br>Based on methodological postulates of the Linguistic of corpus and on the genre concepts, proposed by Swales (1990) and Biber (1995), this research intends to describe linguistic traces which are characteristic of historic texts and correlate them to their respective genres, as well as propose a typology of traces so that it is possible to automatically identify the genre. In order to execute the research, the corpus of the Portuguese of the centuries XVI, XVII and XVII of the project Historical Dictionary of the Portuguese in Brazil (program Institutes of the Millennium/CNPq UNESP/Araraquara), which is constituted by 2,459 texts and 7,5 million words has been used. In order to realize a historical description, the study has started from synchronic characteristics obtained from the table of contemporary traces elaborated by Aires (2005). As for the manipulation of the corpus, it has been used the Philologic, the Unitex as well as another tool for the extraction and quantification of traces that has been developed. For the purposes of classification, algorithms available at Weka (Waikato Environment for knowledge Analysis) such as: Naive Bayes, Bayes Net, SMO, Multilayer Perceptron e RBFNetwork, J48, NBTree have been used. The description has been made based on the 62 traces, which include statistics based on a text as a whole and on words, including classes of verbs, pronouns, adverbs as well as discourse markers, expressions and lexical units. It has been concluded that the genres share specific linguistic characteristics. However, they also present their own standards with the use of specific expressions and the frequency of lexical units. Despite the limitations and complications in using a historical corpus, the performance of the classifiers based on the raised traces was satisfactory and the rate of correct classification was 84% and 92%.<br>Com base nos postulados metodológicos da Linguística de Corpus e nos conceitos de gênero, propostos por Swales (1990) e Biber (1995), esta pesquisa pretende descrever traços linguísticos característicos de textos históricos, correlacionando-os a seus respectivos gêneros, e propor uma tipologia de traços de forma que seja possível identificar o gênero de cada texto automaticamente. Para execução da pesquisa foi utilizado o corpus do português dos séculos XVI, XVII e XVIII do projeto Dicionário Histórico do Português do Brasil (programa Institutos do Milênio/CNPq UNESP/Araraquara), constituído por 2.459 textos e 7.5 milhões de palavras. Para realizar uma descrição histórica, partiu-se de características sincrônicas obtidas a partir da tabela de traços contemporâneos elaborada por Aires (2005). No que tange à manipulação do corpus, utilizou-se o Philologic, o Unitex e desenvolveu-se uma ferramenta para extração e quantificação dos traços. Para fins de classificação, foram utilizados os algoritmos disponibilizados no Weka (Waikato Environment for Knowledge Analysis), tais como: Naive Bayes, Bayes Net, SMO, Multilayer Perceptron e RBFNetwork, J48, NBTree. A descrição foi realizada com base em 62 traços, os quais abarcam estatísticas baseadas no texto como um todo e em palavras, incluindo as classes de verbos, pronomes, advérbios, como também marcadores discursivos, expressões e unidades lexicais. Concluiu-se que os gêneros compartilham características linguísticas específicas, porém, também apresentam seus padrões próprios, como o uso de determinadas expressões e a frequência de unidades lexicais. Apesar das limitações e complicações em utilizar um corpus histórico, o desempenho dos classificadores com base nos traços levantados foi satisfatório, com a taxa de acerto 84% e 92% de classificação correta.

Намера студије јесте да се целокупни досадашњи драмски опус Виде Огњеновићсагледа у ширем контексту савремене српске драматургије и књижевне традиције. Овакавзадатак подразумева анализу примењеног литерарно-драматуршког поступка, тематско-мотивских одредница, жанровских и стилских особености, језичког израза,карактеризације ликова, односа према историјским, политичким, идеолошким, културним,социјалним и другим друштвеним феноменима. За реализацију тако дефинисаног циљаистраживања, било је неопходно формирати својеврсни координатни систем. Унутарсистема једна оса представља најзначајније ауторе и њихова репрезентативна дела, који сукључни за развој савремене српске драме од Другог светског рата до прве деценије овогвека, а друга оса означава књижевну традицију као недвосмислену исходишну тачку икреативну инспирацију. Рад је подељен у три органски повезане целине: &bdquo;Савременасрпска драма&rdquo;, &bdquo;Драмски опус Виде Огњеновић&rdquo; и &bdquo;Књижевна традиција као креативнаинспирација<br>Namera studije jeste da se celokupni dosadašnji dramski opus Vide Ognjenovićsagleda u širem kontekstu savremene srpske dramaturgije i književne tradicije. Ovakavzadatak podrazumeva analizu primenjenog literarno-dramaturškog postupka, tematsko-motivskih odrednica, žanrovskih i stilskih osobenosti, jezičkog izraza,karakterizacije likova, odnosa prema istorijskim, političkim, ideološkim, kulturnim,socijalnim i drugim društvenim fenomenima. Za realizaciju tako definisanog ciljaistraživanja, bilo je neophodno formirati svojevrsni koordinatni sistem. Unutarsistema jedna osa predstavlja najznačajnije autore i njihova reprezentativna dela, koji suključni za razvoj savremene srpske drame od Drugog svetskog rata do prve decenije ovogveka, a druga osa označava književnu tradiciju kao nedvosmislenu ishodišnu tačku ikreativnu inspiraciju. Rad je podeljen u tri organski povezane celine: &bdquo;Savremenasrpska drama&rdquo;, &bdquo;Dramski opus Vide Ognjenović&rdquo; i &bdquo;Književna tradicija kao kreativnainspiracija<br>This study is aimed at offering an overview of the entire dramatic oeuvre of VidaOgnjenović to date within the wider context of the contemporary Serbian dramaturgy and literarytradition. This objective entails an analysis of the literary-dramatic procedure used, as well as thethematic-motif determinants, distinctive features in regard to genre and style, linguisticexpression, characterisation, attitude towards historical, political, ideological, cultural, social andother societal phenomena. In order to accomplish the objective of research thus formulated, itwas necessary to form a singular coordinate system. One coordinate axis represents majorauthors and their representative works, which bear crucial significance for the development ofthe contemporary Serbian drama from World War II to the first decade of this century, while theother axis represents literary tradition as an unambiguous point of origin and creative inspiration.The study is divided into three organically related units: &lsquo;Contemporary Serbian drama&rsquo;,&lsquo;Dramatic oeuvre of Vida Ognjenović&rsquo; and &lsquo;Literary tradition as creative inspiration&rsquo;.

L'incessant augment del nombre de seqüències genòmiques, juntament amb <br/>l'increment del nombre de tècniques experimentals de les que es disposa, <br/>permetrà obtenir el catàleg complet de les funcions cel.lulars de <br/>diferents organismes, incloent-hi la nostra espècie. Aquest catàleg <br/>definirà els fonaments sobre els que es podrà entendre millor com els <br/>organismes funcionen a nivell molecular. Al mateix temps es tindran més <br/>pistes sobre els canvis que estan associats amb les malalties. Per tant, <br/>la seqüència en brut, tal i com s'obté dels projectes de seqüenciació de <br/>genomes, no té cap valor sense les anàlisis i la subsegüent anotació de <br/>les característiques que defineixen aquestes funcions. Aquesta tesi <br/>presenta la nostra contribució en tres aspectes relacionats de <br/>l'anotació dels gens en genomes eucariotes.<br/> <br/>Primer, la comparació a nivell de seqüència entre els genomes humà i de <br/>ratolí es va dur a terme mitjançant un protocol semi-automàtic. El <br/>programa de predicció de gens SGP2 es va desenvolupar a partir <br/>d'elements d'aquest protocol. El concepte al darrera de l'SGP2 és que <br/>les regions de similaritat obtingudes amb el programa TBLASTX, es fan <br/>servir per augmentar la puntuació dels exons predits pel programa <br/>geneid, amb el que s obtenen conjunts d'anotacions més acurats <br/>d'estructures gèniques. SGP2 té una especificitat que és prou gran com <br/>per que es puguin validar experimentalment via RT-PCR. La validació de <br/>llocs d'splicing emprant la tècnica de la RT-PCR és un bon exemple de <br/>com la combinació d'aproximacions computacionals i experimentals <br/>produeix millors resultats que per separat.<br/> <br/>S'ha dut a terme l'anàlisi descriptiva a nivell de seqüència dels llocs <br/>d'splicing obtinguts sobre un conjunt fiable de gens ortòlegs per humà, <br/>ratolí, rata i pollastre. S'han explorat les diferències a nivell de <br/>nucleòtid entre llocs U2 i U12, pel conjunt d'introns ortòlegs que se'n <br/>deriva d'aquests gens. S'ha trobat que els senyals d'splicing ortòlegs <br/>entre humà i rossegadors, així com entre rossegadors, estan més <br/>conservats que els llocs no relacionats. Aquesta conservació addicional <br/>pot ser explicada però a nivell de conservació basal dels introns. <br/>D'altra banda, s'ha detectat més conservació de l'esperada entre llocs <br/>d'splicing ortòlegs entre mamífers i pollastre. Els resultats obtinguts <br/>també indiquen que les classes intròniques U2 i U12 han evolucionat <br/>independentment des de l'ancestre comú dels mamífers i les aus. Tampoc <br/>s'ha trobat cap cas convincent d'interconversió entre aquestes dues <br/>classes en el conjunt d'introns ortòlegs generat, ni cap cas de <br/>substitució entre els subtipus AT-AC i GT-AG d'introns U12. Al contrari, <br/>el pas de GT-AG a GC-AG, i viceversa, en introns U2 no sembla ser inusual.<br/> <br/>Finalment, s'han implementat una sèrie d'eines de visualització per <br/>integrar anotacions obtingudes pels programes de predicció de gens i per <br/>les anàlisis comparatives sobre genomes. Una d'aquestes eines, el <br/>gff2ps, s'ha emprat en la cartografia dels genomes humà, de la mosca del <br/>vinagre i del mosquit de la malària, entre d'altres. El programa <br/>gff2aplot i els filtres associats, han facilitat la tasca d'integrar <br/>anotacions de seqüència amb els resultats d'eines per la cerca <br/>d'homologia, com ara el BLAST. S'ha adaptat també el concepte de <br/>pictograma a l'anàlisi comparativa de llocs d splicing ortòlegs, amb el <br/>desenvolupament del programa compi.<br>El aumento incesante del número de secuencias genómicas, junto con el <br/>incremento del número de técnicas experimentales de las que se dispone, <br/>permitirá la obtención del catálogo completo de las funciones celulares <br/>de los diferentes organismos, incluida nuestra especie. Este catálogo <br/>definirá las bases sobre las que se pueda entender mejor el <br/>funcionamiento de los organismos a nivel molecular. Al mismo tiempo, se <br/>obtendrán más pistas sobre los cambios asociados a enfermedades. Por <br/>tanto, la secuencia en bruto, tal y como se obtiene en los proyectos de <br/>secuenciación masiva, no tiene ningún valor sin los análisis y la <br/>posterior anotación de las características que definen estas funciones. <br/>Esta tesis presenta nuestra contribución a tres aspectos relacionados de <br/>la anotación de los genes en genomas eucariotas.<br/> <br/>Primero, la comparación a nivel de secuencia entre el genoma humano y el <br/>de ratón se llevó a cabo mediante un protocolo semi-automático. El <br/>programa de predicción de genes SGP2 se desarrolló a partir de elementos <br/>de dicho protocolo. El concepto sobre el que se fundamenta el SGP2 es <br/>que las regiones de similaridad obtenidas con el programa TBLASTX, se <br/>utilizan para aumentar la puntuación de los exones predichos por el <br/>programa geneid, con lo que se obtienen conjuntos más precisos de <br/>anotaciones de estructuras génicas. SGP2 tiene una especificidad <br/>suficiente como para validar esas anotaciones experimentalmente vía <br/>RT-PCR. La validación de los sitios de splicing mediante el uso de la <br/>técnica de la RT-PCR es un buen ejemplo de cómo la combinación de <br/>aproximaciones computacionales y experimentales produce mejores <br/>resultados que por separado.<br/> <br/>Se ha llevado a cabo el análisis descriptivo a nivel de secuencia de los <br/>sitios de splicing obtenidos sobre un conjunto fiable de genes ortólogos <br/>para humano, ratón, rata y pollo. Se han explorado las diferencias a <br/>nivel de nucleótido entre sitios U2 y U12 para el conjunto de intrones <br/>ortólogos derivado de esos genes. Se ha visto que las señales de <br/>splicing ortólogas entre humanos y roedores, así como entre roedores, <br/>están más conservadas que las no ortólogas. Esta conservación puede ser <br/>explicada en parte a nivel de conservación basal de los intrones. Por <br/>otro lado, se ha detectado mayor conservación de la esperada entre <br/>sitios de splicing ortólogos entre mamíferos y pollo. Los resultados <br/>obtenidos indican también que las clases intrónicas U2 y U12 han <br/>evolucionado independientemente desde el ancestro común de mamíferos y <br/>aves. Tampoco se ha hallado ningún caso convincente de interconversión <br/>entre estas dos clases en el conjunto de intrones ortólogos generado, ni <br/>ningún caso de substitución entre los subtipos AT-AC y GT-AG en intrones <br/>U12. Por el contrario, el paso de GT-AG a GC-AG, y viceversa, en <br/>intrones U2 no parece ser inusual.<br/> <br/>Finalmente, se han implementado una serie de herramientas de <br/>visualización para integrar anotaciones obtenidas por los programas de <br/>predicción de genes y por los análisis comparativos sobre genomas. Una <br/>de estas herramientas, gff2ps, se ha utilizado para cartografiar los <br/>genomas humano, de la mosca del vinagre y del mosquito de la malaria. El <br/>programa gff2aplot y los filtros asociados, han facilitado la tarea de <br/>integrar anotaciones a nivel de secuencia con los resultados obtenidos <br/>por herramientas de búsqueda de homología, como BLAST. Se ha adaptado <br/>también el concepto de pictograma al análisis comparativo de los sitios <br/>de splicing ortólogos, con el desarrollo del programa compi.<br>The constantly increasing amount of available genome sequences, along <br/>with an increasing number of experimental techniques, will help to <br/>produce the complete catalog of cellular functions for different <br/>organisms, including humans. Such a catalog will define the base from <br/>which we will better understand how organisms work at the molecular <br/>level. At the same time it will shed light on which changes are <br/>associated with disease. Therefore, the raw sequence from genome <br/>sequencing projects is worthless without the complete analysis and <br/>further annotation of the genomic features that define those functions. <br/>This dissertation presents our contribution to three related aspects of <br/>gene annotation on eukaryotic genomes.<br/> <br/>First, a comparison at sequence level of human and mouse genomes was <br/>performed by developing a semi-automatic analysis pipeline. The SGP2 <br/>gene-finding tool was developed from procedures used in this pipeline. <br/>The concept behind SGP2 is that similarity regions obtained by TBLASTX <br/>are used to increase the score of exons predicted by geneid, in order to <br/>produce a more accurate set of gene structures. SGP2 provides a <br/>specificity that is high enough for its predictions to be experimentally <br/>verified by RT-PCR. The RT-PCR validation of predicted splice junctions <br/>also serves as example of how combined computational and experimental <br/>approaches will yield the best results.<br/> <br/>Then, we performed a descriptive analysis at sequence level of the <br/>splice site signals from a reliable set of orthologous genes for human, <br/>mouse, rat and chicken. We have explored the differences at nucleotide <br/>sequence level between U2 and U12 for the set of orthologous introns <br/>derived from those genes. We found that orthologous splice signals <br/>between human and rodents and within rodents are more conserved than <br/>unrelated splice sites. However, additional conservation can be <br/>explained mostly by background intron conservation. Additional <br/>conservation over background is detectable in orthologous mammalian and <br/>chicken splice sites. Our results also indicate that the U2 and U12 <br/>intron classes have evolved independently since the split of mammals and <br/>birds. We found neither convincing case of interconversion between these <br/>two classes in our sets of orthologous introns, nor any single case of <br/>switching between AT-AC and GT-AG subtypes within U12 introns. In <br/>contrast, switching between GT-AG and GC-AG U2 subtypes does not appear <br/>to be unusual.<br/> <br/>Finally, we implemented visualization tools to integrate annotation <br/>features for gene- finding and comparative analyses. One of those tools, <br/>gff2ps, was used to draw the whole genome maps for human, fruitfly and <br/>mosquito. gff2aplot and the accompanying parsers facilitate the task of <br/>integrating sequence annotations with the output of homologybased tools, <br/>like BLAST.We have also adapted the concept of pictograms to the <br/>comparative analysis of orthologous splice sites, by developing compi.

Essential genes are those whose presence is imperative for an organism's survival, whereas the functions of non-essential genes may be useful but not critical. Abnormal functionality of essential genes may lead to defects or death at an early stage of life. Knowledge of essential genes is therefore key to understanding development, maintenance of major cellular processes and tissue-specific functions that are crucial for life. Existing experimental techniques for identifying essential genes are accurate, but most of them are time consuming and expensive. Predicting essential genes using computational methods, therefore, would be of great value as they circumvent experimental constraints. Our research is based on the hypothesis that mammalian essential (lethal) and non-essential (viable) genes are distinguishable by various properties. We examined a wide range of features of Mus musculus genes, including sequence, protein-protein interactions, gene expression and function, and found 75 features that were statistically discriminative between lethal and viable genes. These features were used as inputs to create a novel machine learning classifier, allowing the prediction of a mouse gene as lethal or viable with the cross-validation and blind test accuracies of ∼91% and ∼93%, respectively. The prediction results are promising, indicating that our classifier is an effective mammalian essential gene prediction method. We further developed the mouse gene essentiality study by analysing the association between essentiality and gene duplication. Mouse genes were labelled as singletons or duplicates, and their expression patterns over 13 developmental stages were examined. We found that lethal genes originating from duplicates are considerably lower in proportion than singletons. At all developmental stages a significantly higher proportion of singletons and lethal genes are expressed than duplicates and viable genes. Lethal genes were also found to be more ancient than viable genes. In addition, we observed that duplicate pairs with similar patterns of developmental co-expression are more likely to be viable; lethal gene duplicate pairs do not have such a trend. Overall, these results suggest that duplicate genes in mouse are less likely to be essential than singletons. Finally, we investigated the evolutionary age of mouse genes across development to see if the morphological hourglass pattern exists in the mouse. We found that in mouse embryos, genes expressed in early and late stages are evolutionarily younger than those expressed in mid-embryogenesis, thus yielding an hourglass pattern. However, the oldest genes are not expressed at the phylotypic stage stated in prior studies, but instead at an earlier time point - the egg cylinder stage. These results question the application of the hourglass model to mouse development.

Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>As the web pages continuously change and their number grows exponentially, the need for genre classification of web pages also increases. One simple reason for this is given by the need to group web pages into various genre categories in order to reduce the complexities of various web tasks (e.g., search). Experts unanimously agree on the huge potential of genre classification of web pages. However, while everybody agrees that genre classification of web pages is necessary, researchers face problems in finding enough labeled data to perform supervised classification of web pages into various genres. The high cost of skilled manual labor, rapid changing nature of web and never ending growth of web pages are the main reasons for the limited amount of labeled data. On the contrary unlabeled data can be acquired relatively inexpensively in comparison to labeled data. This suggests the use of semi-supervised learning approaches for genre classification, instead of using supervised approaches. Semi-supervised learning makes use of both labeled and unlabeled data for training - typically a small amount of labeled data and a large amount of unlabeled data. Semi-supervised learning have been extensively used in text classification problems. Given the link structure of the web, for web-page classification one can use link features in addition to the content features that are used for general text classification. Hence, the feature set corresponding to web-pages can be easily divided into two views, namely content and link based feature views. Intuitively, the two feature views are conditionally independent given the genre category and have the ability to predict the class on their own. The scarcity of labeled data, availability of large amounts of unlabeled data, richer set of features as compared to the conventional text classification tasks (specifically complementary and sufficient views of features) have encouraged us to use co-training as a tool to perform semi-supervised learning. During co-training labeled examples represented using the two views are used to learn distinct classifiers, which keep improving at each iteration by sharing the most confident predictions on the unlabeled data. In this work, we classify web-pages of .eu domain consisting of 1232 labeled host and 20000 unlabeled hosts (provided by the European Archive Foundation [Benczur et al., 2010]) into six different genres, using co-training. We compare our results with the results produced by standard supervised methods. We find that co-training can be an effective and cheap alternative to costly supervised learning. This is mainly due to the two independent and complementary feature sets of web: content based features and link based features.

Thesis details a cybercrime classification framework stemming from a mixed methodological approach, which is both top down and bottom up and is designed to be multidisciplinary and adaptable across sectors.<br>Master by Research of Mathematical Sciences<br>Variation in scam classification is regularly identified as a primary cause of discrepancy in victim report data resulting in unsuccessful scam identification and insufficient rates of interception by law enforcement, which results in the low prosecution rate of scammers. The result of such discrepancies lead to complex concerns, such as the under reporting of scam incidence, and reduced rates of successful follow up by investigative and enforcement agencies consequential to difficulties in making correct referrals. Without a shared and common lexicon of scam labels and descriptions, communication between investigative agencies and cross-border cooperation is obstructed. With no compatible comprehension of the scam lexicon, timely progression in scam-case management leading to the identification, tracking and interception of scammer communications cannot be realised. Ambiguities leading to interpretational impedances are aiding scammers by enabling their scams in cross-jurisdictional and multi-national platforms. If the wide variety of known scam types could be condensed to recognisable and traceable instances, the business models that scammers use could be identified and future scamming events predicted, monitored, and interrupted. Following a mixed methodology, this research aims to address some of these concerns. This is achieved by clustering scam descriptions and partitioning them into scam types, called scam genres. The result of which reveals homogeneous groups of scam cases and allows for the assessment of the effectiveness of using static features in identifying scam types. Second to this, identification of the most suitable model for reducing scam cases into the fewest number of clusters with the least number of scam cases within in each cluster at an accuracy level of at least 95% is achieved. Through the use of hierarchical clustering, this research grouped publically available scams into homogeneous clusters of scam genres. Two-hundred and seventy-seven scams from 38 separate categories of scam classification were condensed into as few as 7-clusters of scam genre. Following a mixed methodological, grounded theoretical approach and using discriminant function analysis, 82 static features were derived from the 277 scam descriptions analysed. Of the 82 static features derived, it was concluded that only 68 significantly predicted scam type and explained 95% of the total variation found in scam case assignment. The most significant static features determined to be crucial to any scamming campaign and useful in identifying the type of scam genre a scam case belongs to were; what the scam offered, the role of the victim, the goal of the scammer and the method of scam introduction. The results of this research provide empirical evidence of the inconsistent use of definitions across jurisdictions in scam descriptions, and will contribute to the development of a uniform lexicon of scamming terminology as well as become foundational to further research on the impact of scams for law enforcement, the public and private sector, the community and the individual.

The aim of this study is to investigate how five boys in grade 3 of compulsory school structure and use language when they write narrative texts retelling and assessing personally experienced events. This is studied by interpreting how the boys use genre steps that are typical of the structure of personal narrative texts and by detecting and naming different linguistic features in the texts. The material in the study consists of five boys’personal reports and the method, which is based on the theories of genre pedagogy (see Johansson &amp; Sandell Ring 2012:28ff, 223f), comes from systemic functional linguistics. A central finding of the study is that the boys structure their texts in varying combinations of genre steps which commonly occur in texts of a narrative kind. Another central finding is that the language in the boys’ texts consists of many verbs and verb groups which explain that someone is acting or doing something, different discourse connectives to indicate time, along with expressions for emotions and descriptions of experiences or objects.

The last 15-20 years have seen an explosion in genome sequencing projects producing an unprecidented amount of data which supports the Identification and mapping of many genes. However, it does not describe their roles within the cell and functional genomics has attempted to discover uncover what these genes do with respect to their biochemical function, (sub)cellular localisation and processes m which they work. However, large proportions of genes within genomes still remain functionally uncharacterised, typically one third of a genome. Common homology-based approaches are arguably at their limit, and it is timely to consider the potential for alternative techniques to inform on gene function.

In a normal microarray classification problem there will be many genes, on the order of thousands, and few samples, on the order of tens. This necessitates a massive feature space reduction before classification can take place. While much time and effort has gone into evaluating and comparing the performance of different classifiers, less thought has been spent on the problem of efficient feature space reduction. There are in the microarray classification literature several widely used heuristic feature reduction algorithms that will indeed find small feature subsets to classify over. These methods work in a broad sense but we find that they often require too much computation, find overly large gene sets or are not properly generalizable. Therefore, we believe that a systematic study of feature reduction, as it is related to microarray classification, is in order. In this thesis we review current feature space reduction algorithms and propose a new, mixed model algorithm. This mixed-modified algorithm uses the best aspects of the filter algorithms and the best aspects of the wrapper algorithms to find very small yet highly discriminant gene sets. We also discuss methods to evaluate alternate, ambiguous gene sets. Applying our new mixed model algorithm to several published datasets we find that our new algorithm outperforms current gene finding methods.

The computer classification of musical audio can form the basis for systems that allow new ways of interacting with digital music collections. Existing music classification systems suffer, however, from inaccuracy as well as poor scalability. Feature selection is a machine-learning tool that can potentially improve both accuracy and scalability of classification. Unfortunately, there is no consensus on which feature selection algorithms are most appropriate or on how to evaluate the effectiveness of feature selection. Based on relevant literature in music information retrieval (MIR) and machine learning and on empirical testing, the thesis specifies an appropriate evaluation method for feature selection, employs this method to compare existing feature selection algorithms, and evaluates an appropriate feature selection algorithm on the problem of musical genre classification. The outcomes include an increased understanding of the potential for feature selection to benefit MIR and a new technique for optimizing one type of classification-based system.

This thesis explores the ideas of feature learning and sparse coding for Music Information Retrieval (MIR). Sparse coding is an algorithm which aims to learn new feature representations from data automatically. In contrast to previous work which uses sparse coding in an MIR context the concept of supervised sparse coding is also investigated, which makes use of the ground-truth labels explicitly during the learning process. Here sparse coding and supervised coding are applied to two MIR problems: classification of musical genre and recognition of the emotional content of music. A variation of Label Consistent K-SVD is used to add supervision during the dictionary learning process. In the case of Music Genre Recognition (MGR) an additional discriminative term is added to encourage tracks from the same genre to have similar sparse codes. For Music Emotion Recognition (MER) a linear regression term is added to learn an optimal classifier and dictionary pair. These results indicate that while sparse coding performs well for MGR, the additional supervision fails to improve the performance. In the case of MER, supervised coding significantly outperforms both standard sparse coding and commonly used designed features, namely MFCC and pitch chroma.

Mitochondria contain their own genetic system, encoding key subunits of the oxidative phosphorylation system. These subunits are expressed by an organelle-specific gene expression machinery. This work revealed a number of fundamental aspects of mitochondrial gene expression and provides evidence that this process is organized in a unique and organelle-specific manner which likely evolved to optimize protein synthesis and assembly in mitochondria. Most importantly, improving the experimental handling of ribosomes we could show that mitochondrial ribosomes are organized in large assemblies that we termed MIOREX complexes. Ribosomes present in these complexes organize gene expression by recruiting multiple factors required for post-transcriptional steps. In addition, we could reveal mechanisms by which ribosome-interactor complexes modulate and coordinate the expression and assembly of the respiratory chain subunits. For example we showed that the Cbp3-Cbp6 complex binds to the ribosome in proximity to the tunnel exit to coordinate synthesis and assembly of cytochrome b. This location perfectly positions Cbp3-Cbp6 for direct binding to newly synthesized cytochrome b and permits Cbp3-Cbp6 to establish a feedback loop that allows modulation of cytochrome b synthesis in response to assembly efficiency. Likewise the interaction of the membrane-anchor proteins Mba1 and Mdm38 with the tunnel exit region enables them to participate in the translation of the two intron-encoding genes COX1 and COB in addition to their role in membrane insertion.  In summary, work presented in this thesis shows that mitochondrial gene expression is a highly organized and regulated process. The concepts and technical innovations will facilitate the elucidation of many additional and important aspects and therefore contribute to the general understanding of how proteins are synthesized in mitochondria.<br><p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>

While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis.

Gene expression microarray datasets often consist of a limited number of samples relative to a large number of expression measurements, usually on the order of thousands of genes. These characteristics pose a challenge to any classification model as they might negatively impact its prediction accuracy. Therefore, dimensionality reduction is a core process prior to any classification task. This dissertation introduces the iterative feature perturbation method (IFP), an embedded gene selector that iteratively discards non-relevant features. IFP considers relevant features as those which after perturbation with noise cause a change in the predictive accuracy of the classification model. Non-relevant features do not cause any change in the predictive accuracy in such a situation. We apply IFP to 4 cancer microarray datasets: colon cancer (cancer vs. normal), leukemia (subtype classification), Moffitt colon cancer (prognosis predictor) and lung cancer (prognosis predictor). We compare results obtained by IFP to those of SVM-RFE and the t-test using a linear support vector machine as the classifier in all cases. We do so using the original entire set of features in the datasets, and using a preselected set of 200 features (based on p values) from each dataset. When using the entire set of features, the IFP approach results in comparable accuracy (and higher at some points) with respect to SVM-RFE on 3 of the 4 datasets. The simple t-test feature ranking typically produces classifiers with the highest accuracy across the 4 datasets. When using 200 features chosen by the t-test, the accuracy results show up to 3% performance improvement for both IFP and SVM-RFE across the 4 datasets. We corroborate these results with an AUC analysis and a statistical analysis using the Friedman/Holm test. Similar to the application of the t-test, we used the methodsinformation gain and reliefF as filters and compared all three. Results of the AUC analysis show that IFP and SVM-RFE obtain the highest AUC value when applied on the t-test-filtered datasets. This result is additionally corroborated with statistical analysis. The percentage of overlap between the gene sets selected by any two methods across the four datasets indicates that different sets of genes can and do result in similar accuracies. We created ensembles of classifiers using the bagging technique with IFP, SVM-RFE and the t-test, and showed that their performance can be at least equivalent to those of the non-bagging cases, as well as better in some cases.

Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two major approaches to dimensionality reduction in genomic analysis and biomarker identification studies are: Feature extraction, creating new features by combining existing ones based on a mapping technique; and feature selection, choosing an optimal subset of all features based on an objective function. In this dissertation, we show how our innovative reduction schemes effectively reduce the dimensionality of DNA gene expression data to extract biologically interpretable and relevant features which result in enhancing the biomarker identification process. To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) subtypes classification, we extract molecular features from MD microarray data by constructing sub-networks using a novel integrative scheme which utilizes protein-protein interaction (PPI) network, functional gene sets information and mRNA profiling data. The workflow includes three major steps: First, by combining PPI network structure and gene-gene co-expression relationship into a new distance metric, we apply affinity propagation clustering (APC) to build gene sub-networks; secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information; finally, based on the constructed sub-network and gene set features, we apply multi-class support vector machine (MSVM) for MD sub-type classification and highlight the biomarkers contributing to the sub-type prediction. The experimental results show that our scheme could construct sub-networks that are more relevant to MD than those constructed by the conventional approach. Furthermore, our integrative strategy substantially improved the prediction accuracy, especially for those ‘hard-to-classify' sub-types. Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proven incorrect and applying uniform weight in the pathway analysis may not be an adequate approach for tasks like molecular classification of diseases, as genes in a functional group may have different differential power. Hence, we propose to use different weights for the pathway analysis which resulted in the development of four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight. To help us understand our MD expression data better and derive scientific insight from it, we have explored a suite of visualization tools. Particularly, for selected top performing MD sub-networks, we displayed the network view using Cytoscape; functional annotations using IPA and DAVID functional analysis tools; expression pattern using heat-map and parallel coordinates plot; and MD associated pathways using KEGG pathway diagrams. We also performed weighted MD pathway analysis, and identified overlapping sub-networks across different weight schemes and different MD subtypes using Venn Diagrams, which resulted in the identification of a new sub-network significantly associated with MD. All those graphically displayed data and information helped us understand our MD data and the MD subtypes better, resulting in the identification of several potentially MD associated biomarker pathways and genes.<br>Ph. D.

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases.

Abstract The aims of this study were to investigate the clinical features, the endoscopic and histological abnormalities of ileocolonic and gastroduodenal mucosa and immunogenetic background of microscopic colitis (MC) and its subtypes collagenous colitis (CC) and lymphocytic colitis (LC). 30 patients with CC and 54 with LC were examined with different control groups used according to the study. The mean age at diagnosis was in the sixties in both CC and LC, with a female preponderance in both Autoimmune conditions such as celiac diseased (CD) were common in MC. Bronchial asthma associated with LC. Lactose intolerance associated with MC but colonic diverticulosis was rare. Ileal histological changes were common in MC. Focal gastritis did not associate with MC. Lymphocytic gastritis was found only in LC. Gastric endoscopic erosions were more prevalent in CC than in LC. The age at diagnosis of MC was higher in H. pylori positive than negative patients. The patients with MC had shorter duodenal villi than controls even when patients with CD were excluded. HLA-DR3-DQ2 haplotype and TNF2 allele carriage were more frequent in patients with MC compared to controls. The genotype GG of IL-6-174 was more prevalent in MC compared to the controls. IL-6 genotype did not associate with the serum IL-6 concentration. The concentration of IL-6 was higher in patients with CC than in LC. In conclusion, in addition to colonic typical inflammation, histological abnormalities were detected also in gastric, duodenal and ileal mucosa. CD was common in MC, but there was no association with specific types of gastritis. HLA association was found in MC. Polymorphism in the proinflammatory IL-6-174 gene displayed a possible association with MC. Although CC and LC share many clinical features, the differences in the occurrence of immune conditions, gastric abnormalities and IL-6 response point to differences in their pathogenesis<br>Tiivistelmä Tutkimuksen tavoitteena oli tutkia mikroskooppisen koliitin sekä sen alaryhmien, kollageenikoliitin ja lymfosyyttisen koliitin kliinisiä piirteitä, mahalaukun ja ohutsuolen limakalvon muutoksia sekä immunogeneettistä taustaa. Tutkimukseen osallistui 30 kollageeni- ja 54 lymfosyyttikoliittipotilasta sekä verrokkeja. Sekä kollageenikoliitti että lymfosyyttinen koliitti diagnosoitiin keskimäärin 50&#8211;60 v iässä, ja molemmissa tautiryhmissä naisia oli enemmän kuin miehiä. Autoimmuunisairaudet kuten keliakia olivat yleisiä liitännäissairauksia. Astmaa esiintyi lymfosyyttistä koliittia sairastavilla verrokkeja enemmän. Laktoosi-intoleranssi oli yleistä, mutta paksusuolen divertikuloosia oli harvoin mikroskooppista koliittia sairastavilla potilailla. Ileumin muutokset olivat yleisiä. Mikroskooppinen koliitti ei assosioitunut fokaaliseen gastriittiin. Lymfosyyttigastriittia todettiin vain lymfosyyttisessä koliitissa. Mahalaukun eroosioita esiintyi enemmän kollageenikoliitissa kuin lymfosyyttisessa koliitissa. Mikroskooppinen koliitti ilmeni iäkkäämpänä niillä, joilla todettiin helikobakteeri. Pohjukaissuolen suolinukka oli keliakiasta riippumatta matalampaa kuin verrokeilla. HLA-DR3-DQ2 haplotyyppiä, TNF-2 alleelia ja IL-6-174-GG genotyyppiä esiintyi enemmmän mikroskooppista koliittia sairastavilla potilailla kuin verrokeilla. IL-6 genotyyppi ei vaikuttanut seerumin IL-6-pitoisuuteen. IL-6 pitoisuus oli korkeampi kollageenikoliitissa kuin lymfosyyttisessä koliitissa. Havainnot osoittavat, että mikroskooppisessa koliitissa limakalvomuutoksia on paksusuolen lisäksi myös muualla mahasuolikanavassa. Keliakia on tavallinen liitännäistauti. HLA-DR3-DQ2 on yleinen mikroskooppista koliittia sairastavilla myös ilman keliakiaa. IL-6-174-GG genotyypin yleisyys viittaa siihen, että tämä polymorfismi saattaa altistaa mikroskooppiselle koliitille. Vaikka kollageenikoliitti ja lymfosyyttinen koliitti ovat kliinisesti samankaltaisia sairauksia, erot tautiassosiaatioissa, mahan limakalvon muutoksissa ja seerumin IL-6-tasoissa viittaavat erilaisiin syntymekanismeihin

Automatic literary genre classification presents a challenging task for Natural Language Processing (NLP) systems, mainly because literary texts have deeper levels of meanings, hold distinctive themes, and communicate certain messages and emotions. We conduct a study where we experiment with building literary genre classifiers based on emotions in novels, to investigate the effects that features pertinent to emotions have on models of genre prediction. We begin by performing an analysis of emotions describing emotional composition and density in the dataset. The experiments are carried out on a dataset consisting of novels categorized in eight different genres. Genre prediction models are built using three algorithms: Random Forest, Support Vector Machine, and k-Nearest Neighbor. We build models based on emotion-words counts and emotional words in a novel, and compare them to models of commonly used features, the bag-of-words and the TF-IDF features. Moreover, we use a feature selection dimensionality reduction procedure on the TF-IDF feature set and study its impact on classification performance. Finally, we train and test the classifiers on a combination of the two most optimal emotion-related feature sets, and compare them on classifiers trained and tested on a combination of bag-of-words and the reduced TF-IDF features. Our results confirm that: using features of emotional content in novels improves classification performance a 75% F1 compared to a bag-of-words baseline of 71% F1; TF-IDF feature filtering method positively impacts genre classification performance on literary texts.

The Way of the Warrior, Realising the Mythic Warrior-hero in the Action genre and in Australian Cinema, is Creative Practice research that pursues detailed analysis of the warrior-hero in Australian and, action genre cinema narratives. Warrior-hero archetypes are employed in the original Australian feature screenplay Behold a Pale Horse in order to address whether it is possible to synthesise the Warrior-hero archetype with the tropes, codes and conventions of the Action genre in an Australian context and create an original screenplay with the potential for both critical and commercial success?

Gene therapy for inherited diseases affecting the hematopoietic system requires transplantation and engraftment of a high number of autologous genetically engineered hematopoietic stem cells (HSCs). In the context of gene therapy for nonfatal diseases, as thalassemia, a favorable balance between the extent of conditioning of the host bone marrow (BM) and the adequate dose of transduced HSCs needs to be achieved. For adult thalassemic patients, the issue of HSC source is crucial as the minimal target dose poses a challenge for the use of steady-state BM. Peripheral blood (PB) mobilized CD34+ cells represent a valid alternative, but intrinsic characteristics of thalassemic patients (splenomegaly and thrombophilia) dictate caution in the choice of full dose G-CSF as mobilizing agent. The recently available mobilizing agent Plerixafor, which reversibly inhibits the stromal cell-derived factor1/CXC-motif receptor-4 (SDF-1–CXCR4) interaction within the BM microenvironment could represent an attractive alternative. We characterized HSCs mobilized from adult patients affected by transfusion dependent β-thalassemia by Plerixafor (Thal Plx PB) or by the combination of G-CSF + Plerixafor (Thal G+Plx PB), as collaborative study between Hematology-BMT Unit and TIGET, at San Raffaele Hospital in Milan. In addition, we had the unique opportunity to compare Plerixafor-mobilized PB cells with BM CD34+ cells pre- and post-Plerixafor treatment, derived from the same patient. CD34+ cells purified from patients’ leukoaphereses were analyzed for their biological and functional properties, subpopulations composition and expression profile. In vivo reconstitution potential and lymphomyeloid differentiation were tested following transplantation in NOD/SCID/IL2rγnull (NSG) mice. We performed comparative studies using thalassemic patient-derived samples and also BM and G-CSF-mobilized CD34+ cells from healthy donors, in order to unravel whether different sources might determine intrinsic molecular and functional features of HSCs. We showed that Plerixafor mobilizes high-quality HSCs able to provide stable long-term hematopoietic engraftment in NSG mice. Moreover, the quiescence status of these cells correlates with the enriched scid-repopulating cell frequency, in comparison to the other sources. The insights into the transcriptional program reveal the molecular machinery underlying “stemness” features of cells derived from different sources, defining their specific functional properties. Noteworthy, CD34+ cells exposed to Plerixafor and harvested from the BM acquire an intermediate signature between resident and circulating cells, suggesting an effect of this agent on HSC function. Mobilized CD34+ cells isolated from thalassemic samples were also efficiently transduced with the GLOBE lentiviral vector carrying the β-globin gene, and showed stable transgene expression. The availability of high numbers of Thal Plx PB and Thal G+Plx PB CD34+ cells allowed us to test in vitro different transduction protocols and also to evaluate the reconstitution potential of transduced cells in NSG mice. These experiments allowed to optimize culture conditions and to confirm the maintenance of engraftment and repopulation capacities of the mobilized cells also after in vitro manipulation. Our studies uncovered previously unknown unique HSC properties shaped by their origin and illuminate the choice of different transplantation strategies accordingly to the clinical need. The availability of new sources of HSCs, superior in terms of CD34+ cell yield, transduction efficiency and biological features to BM, indeed, would have a significant impact on the feasibility and efficacy of gene therapy.

This work defines useful features for the classification of symbolically encoded music into 14 classical genres namely chorale, symphony, etude, fugue, prelude, contrafactum, sonata, mazurka, motet, sonatina, waltze, concerto, Gregorian chant and scherzo. Features are based on Music Theory and grouped into seven categories: distances in the harmonic mobius strip, distances on the line of fifths, scale, rhythmic syncopation and meter, polyphony measurements, duration and instrumentation. Features are extracted and ranked combining 5 filter-based methods. Six Machine Learning algorithms are defined for classification: three Support Vector Machines, one Bayesian network, the C4.5 and random forests. Using nested cross-validation for training and testing and considering all the features, the Bayesian network classifier yields 84.10% empirical accuracy. The FEATUROMETRE process measures the usefulness of the feature subsets in an approach similar to wrapper methods, conveying relevant information to domain experts. Another experiment measures the usefulness and accuracy of features individually and by category using FEATUROMETRE. Grouping the music pieces by their period, the measured accuracy with the random forest classifier in the second experiment reaches 89.81%.

With the rapid growth in the prevalence of digital video in the world comes the need for efficient and effective management of such information. The field of content-based video indexing and retrieval aims to achieve this through the automatic recognition of the structure and content of video data and the indexing of both low level formative features and high level semantic features. Two of the main problems facing this field are what low level features can be used, and how to 'bridge the semantic gap' between low level features and high level understanding of a video sequence. In this thesis we propose a new method that can successfully automatically classify video shots into broadly defined video genres. The classification serves as the first step in the indexing of a video sequence and its consequent retrieval from a large database by partitioning the database into more manageable sub-units according to genre, e.g. sport, drama, scenery, news reading. As the transmission and storage of digital video is commonly in a compressed format (e.g. the MPEG-1, -2, and -4 standards), it is therefore efficient for any processing to occur in the compressed domain. In this thesis video files compressed in the MPEG-1 format are considered, although the majority of methods presented can be easily adapted for use with MPEG-2 and MPEG-4 formats. For indexing purposes an MPEG-1 file contains spatial information in DCT coefficients and temporal information using motion vectors. The reliability of the MPEG motion vectors is evaluated using a spatial block activity factor estimated from DCT coefficients, to discard the vectors which do not represent the true motion within a video sequence. The thesis also presents a robust camera motion estimation technique, based on Least Median-of-Squares regression, to minimise the influence of the outliers due to object motion and wrongly predicted motion vectors. The results produced by the proposed technique show a significant improvement in the sensitivity to object motion when compared to those produced by an M-estimator technique. Robust motion intensity metrics are also presented for camera and object motion, calculated from the estimated camera model and the MPEG motion vector field after the filtering of unreliable vectors. A novel metric based on the activity factor used in the motion vector field filtering called activity power flow is introduced to effectively capture the spatio-temporal evolution of scenes through a video shot. These shot-based, low-level, global features represent both the spatial content of a shot, and the motion in a shot, both due to movement of the camera, and also of objects. The thesis also compares several machine learning techniques to transform low level visual features into high level semantics, in particular Radial Basis Function (RBF) networks with a focus on a tree-based RBF network. In this network, the result of a binary classification tree is used to configure and to initialise the structure of the RBF network. Video shots in a database are classified into four video genres: Sport, News, Scenery, and Drama. This is believed to be the first shot based video classification algorithm and the first method which uses only compressed domain features. Experimental results show that this method is both efficient, as processing is undertaken in the compressed domain, and effective, providing a classification accuracy which is comparable, where possible, to previous techniques. For the genre set {sport, news, cartoon, commercial, music} the best classification accuracies seen in previous works are 83.1% [131] using just visual features and 87% [133] using combined audio and visual features compared with a classification accuracy of 83.6% presented in this thesis. For the genre set (sport, news, cartoon, drama, music } previous work [134] reported classification accuracies of 72.0% for visual features and 88.8% using combined audio and visual features compared with a classification accuracy of 86.2 % in this thesis.

碩士<br>國立雲林科技大學<br>資訊工程研究所<br>100<br>In this paper, we propose a movie genre classification system using a meta-heuristic optimization algorithm called Self-Adaptive Harmony Search (i.e., SAHS) to select local features for corresponding movie genres. Then, each one-against-one Support Vector Machine (i.e., SVM) classifier is fed with the corresponding local feature set and the majority voting method is used to determine the prediction of each movie. Totally, we extract 277 features from each movie trailer, including visual and audio features. However, no more than 25 features are used to discriminate each pair of movie genres. The experimental results show that the overall accuracy reaches 91.9%, and this demonstrates more precise features can be selected for each pair of genres to get better classification results.

博士<br>國立清華大學<br>資訊工程學系<br>101<br>A music piece usually consists of a sequence of sound events which represent both short-term and long-term temporal information. However, in the task of automatic music genre classification, most text-categorization-based approaches only capture temporal local dependencies (e.g., unigram and bigram-based occurrence statistics) to represent music contents. In this dissertation, we propose to use time constrained sequential patterns (TSPs) as effective features for music genre classification. First of all, an automatic language identification technique is performed to tokenize each music piece into a sequence of hidden Markov model indices. Then TSP mining is applied to music sequences to discover genre-specific TSPs, followed by the computation of occurrence frequencies of TSPs in each music piece. Finally, these occurrence frequencies are feed into support vector machines (SVMs) to perform the classification task. Experiments conducted on two widely used datasets, GTZAN and ISMIR2004Genre, show that the proposed method can discover more discriminative temporal structures and achieve a better recognition accuracy than the unigram and bigram-based statistical approach. In addition, we also propose another music genre/mood classification system which combines both short-term frame based timbre features and the long-term modulation spectral analysis of timbre features for SVMs. This proposed system won the first place of the MIREX 2011 music mood classification task. In our submission, we performed the modulation spectral analysis on short-term timbre features to extract long-term modulation features. However, two operations in this analysis are likely to smooth out useful modulation information, which may degrade the classification performance. The first one is to take the averaging of modulation spectrograms extracted from texture windows (each of which is composed of timbre features extracted from hundreds of frames) to create a representative modulation spectrogram for a music clip. The second one is to compute the mean and standard deviation of modulation spectral contrast/valley matrices (these two matrices are computed from the representative modulation spectrogram) to obtain a compact feature vector for a music clip. To avoid smoothing out modulation information, in this dissertation, we propose the use of a two-dimensional representation of acoustic frequency and modulation frequncy to compute joint frequency features. These joint frequency features, including acoustic-modulation spectral contrast/valley (AMSC/AMSV), flatness measure and crest measure (AMSFM/AMSCM), are then computed from modulation spectra of each joint frequency subband. By combining the proposed features, together with the modulation spectral analysis of MFCC, and statistical descriptors of short-term timbre features, this new set of features outperforms our MIREX 2011 submission on four other genre/mood datasets.

碩士<br>國立清華大學<br>電機工程學系<br>94<br>It is hard to achieve the goal that making computer understand a film content, because there are too many events may happen in a film. Since the difficult the film content in computer understanding, we first experiment how the low level visual features can affect a film. Based on some low level visual features have been proposed, we presents a framework for the classification of feature films into genres in this thesis. Our current domain of study is the movie preview. A film preview often emphasizes the theme of a film and hence provides suitable information for classification, on the other hand, a preview is more easy to get than a whole movie. In our approach, we classify movies into three broad categories: Action, Dramas, or thriller films. In our experiment, four computable video features (average shot length, color variance, motion content and lighting key) are analysis that how well these features for classify films into genres. On the other hand, we proposed another features called visual effect to distinguish drama or non-drama films. Classification Tree and Neural network are used after analysis of these low level visual features, with the features distribution. After our experiment, we found that our approach can also be broadened for other potential applications including the building and updating of video databases with minimal human intervention, scene understanding, browsing and retrieval of videos on the Internet and video libraries.

碩士<br>國立臺南大學<br>資訊工程學系碩士班<br>98<br>This paper proposes a hierarchical music genre classification method integrating both temporal and non-temporal features of music. First of all, music genres are devided into two primary classes based on the difference of global timbre. Then, the concept of audio words are applied to convert the music into sequence of audio words. A transition probability model is derived for each genre by estimating n-grams from the training data and the maximum likelihood estimation is utilized to classify the music. To improve the accuracy, we propose a feature selection method based on typical-driven refinement. This method enhances the performance by excluding confusing training samples. Experimental results show that the proposed method can improve the accuracy of music genre classification by 4%.

博士<br>國立清華大學<br>資訊工程學系<br>103<br>Increased availability of large-scale datasets has attracted increased academic and industrial attention to large-scale learning. Concurrently, huge growth in demand for smart phones has had a commensurate impact on related industries such as wafer manufacturing and mobile application industries. In the wafer manufacturing industry, increased demand has driven efforts to increase wafer production capacity, in part by reducing failure rates. Wafer map failure pattern recognition (WMFPR), an application of machine vision, can be used to automatically classify wafers, thus assisting engineers in identifying root causes of failure and thus increasing wafer yield. In the mobile application industry, increased demand for online music distribution has driven interest in music genre classification (MGC), which is an application of machine hearing, can facilitate music organization and music recommendation for online music services. However, reduced yet discriminative feature representations are still needed for these two large-scale learning applications. By contrast to conventional approaches, we consider an alternate approach for designing visual features for WMFPR and MGC. To validate system performance, we collected the world's largest public wafer map dataset (WM-811k) for WMFPR, and applied the world's largest benchmark dataset (MASD) for MGC. Experimental results show that the proposed visual features can considerably improve recognition rates. Furthermore, TSMC has adopted the proposed WMFPR method, while the proposed MGC method won the MIREX music genre classification contests from 2011 to 2013, indicating the robustness of the proposed methods.