No description available.

Knowledge-based assets play a very important role in the Information Age. How to organize existing knowledge and present to the user properly are important research issue for decision support. Previous literature has indicated that multiple documents can be organized in different ways and different modes of knowledge presentation may result in different learning effects. Typical presentation modes include textual summarization and graphical presentation. The purpose of this thesis is to evaluate whether textual and graphical presentations of a news event may result in different effects for the user. In particular, this study is focused on comparing the textual summary and ontology-base graphical presentation and use the Bloom Theory of Educational Objectives to measure the learning effect of the user An experiment was conducted to assess the knowledge and cognitive process dimension in the Bloom¡¦s theory. We also measured the learning time, system quality, content quality, and overall satisfaction. The result shows that the textual system performed better in learning factual knowledge, and the ontology-base system performed better in learning conceptual and procedural knowledge.

Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to create well formed summaries in literature. One of the newest methods in text summarization is the Latent Semantic Analysis (LSA) method. In this thesis, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores.

Knowledge-based assets play a very important role in the Information Age, and its increasingly influence on organizational competition makes Knowledge Management a hot issue in business research.Content analysis of documents is a core function of knowledge management. In previous research, many techniques have been developed to generate textual summary and/or generating ontology-based episodic knowledge from multipl documents. However, not much research has been done to compare different ways of organizing and presenting knowledge. Since different knowledge presentations may result in different effects on the user, the purpose of this thesis is to develop a method for investigating different document summary and presentation systems. In this research, we have developed an effect measurement method based on the extended Bloom¡¦s Taxonomy of Educational Objectives.More specifically, we proposes evaluation criteria based on memory and cognition of the user. A field experiment was conducted to compare graphical and textual systems. Results indicate that the ontology-based system has significantly superior performance in concept memorizing and procedural memorizing. On the other hand, the textual summary-based system performed better in remembering facts.

Obwohl schon seit den 50er Jahren auf dem Gebiet der Automatischen Textzusammenfassung Forschung betrieben wird, wurden der Nutzen und die Notwendigkeit dieser Systeme erst mit dem Boom des Internets richtig erkannt. Das World Wide Web stellt eine täglich wachsende Menge an Informationen zu nahezu jedem Thema zur Verfügung. Um den Zeitaufwand zum Finden und auch zum Wiederfinden der richtigen Informationen zu minimieren, traten Suchmaschinen ihren Siegeszug an. Doch um einen Überblick zu einem ausgewählten Thema zu erhalten, ist eine einfache Auflistung aller in Frage kommenden Seiten nicht mehr adäquat. Zusätzliche Mechanismen wie Extraktionsalgorithmen für die automatische Generierung von Zusammenfassungen können hier helfen, Suchmaschinen oder Webkataloge zu optimieren, um so den Zeitaufwand bei der Recherche zu verringern und die Suche einfacher und komfortabler zu gestalten. In dieser Diplomarbeit wurde eine Analyse von Extraktionsalgorithmen durchgeführt, welche für die automatische Textzusammenfassung genutzt werden können. Auf Basis dieser Analyse als viel versprechend eingestufte Algorithmen wurden in Java implementiert und die mit diesen Algorithmen erstellten Zusammenfassungen in einer Evaluation verglichen.

This thesis proposes a generalized probabilistic approach to modelling document collections along the combined axes of both semantics and syntax. Probabilistic topic (or semantic) models view documents as random mixtures of unobserved latent topics which are themselves represented as probabilistic distributions over words. They have grown immensely in popularity since the introduction of the original topic model, Latent Dirichlet Allocation (LDA), in 2004, and have seen successes in computational linguistics, bioinformatics, political science, and many other fields. Furthermore, the modular nature of topic models allows them to be extended and adapted to specific tasks with relative ease. Despite the recorded successes, however, there remains a gap in combining axes of information from different sources and in developing models that are as useful as possible for specific applications, particularly in Natural Language Processing (NLP). The main contributions of this thesis are two-fold. First, we present generalized probabilistic models (both parametric and nonparametric) that are semantically and syntactically coherent and contain many simpler probabilistic models as special cases. Our models are consistent along both axes of word information in that an LDA-like component sorts words that are semantically related into distinct topics and a Hidden Markov Model (HMM)-like component determines the syntactic parts-of-speech of words so that we can group words that are both semantically and syntactically affiliated in an unsupervised manner, leading to such groups as verbs about health care and nouns about sports. Second, we apply our generalized probabilistic models to two NLP tasks. Specifically, we present new approaches to automatic text summarization and unsupervised part-of-speech (POS) tagging using our models and report results commensurate with the state-of-the-art in these two sub-fields. Our successes demonstrate the general applicability of our modelling techniques to important areas in computational linguistics and NLP.

Although bug tracking systems are fundamental to support virtually any software development process, they are currently suboptimal to support the needs and complexities of large communities. This dissertation first presents a study showing empirical evidence that the traditional interface used by current bug tracking systems invites much noise—unreliable, unuseful, and disorganized information—into the ecosystem. We find that noise comes from, not only low-quality contributions posted by inexperienced users or from conflicts that naturally arise in such ecosystems, but also from the difficulty of fitting the complex bug resolution process and knowledge into the linear sequence of comments that current bug tracking systems use to collect and organize information. Since productivity in bug tracking systems relies on bug reports with accessible and realible information, this leaves contributors struggling to work on and to make sense of the dumps of data submitted to bug reports and, thus, impacting productivity. Next generation bug tracking systems should be more than a tool for exchanging unstructured textual comments. They should be an ecosystem that is tailored for collaborative knowledge building, leveraging the power of the masses to collect reliable and useful information about bugs, providing mechanisms and incentives to verify the validity of such information and mechanisms to organize such information, thus, facilitating comprehension and reasoning. To bring bug tracking systems towards this vision, we present three orthogonal approaches aiming at increasing the usefulness and realiability of contributions and organizing information to improve understanding and reasoning. To improve the usefulness and realibility of contributions we propose the addition of game mechanisms to bug tracking systems, with the objective of motivating contributors to post higher-quality content. Through an empirical investigation of Stack Overflow we evaluate the effects of the mechanisms in such a collaborative software development ecosystem and map a promissing approach to use game mechanisms in bug tracking systems. To improve data organization, we propose two complementary approaches. The first is an automated approach to data organization, creating bug report summaries that make reading and working with bug reports easier, by highlighting the portions of bug reports that expert developers would focus on, if reading the bug report in a hurry. The second approach to improve data organization is a fundamental change on how data is collected and organized, eliminating comments as the main component of bug reports. Instead of comments, users contribute informational posts about bug diagnostics or solutions, allowing users to post contextual comments for each of the different diagnostic iiior solution posts. Our evaluations with real bug tracking system users find that they consider the bug report summaries to be very useful in facilitating common bug tracking system tasks, such as finding duplicate bug reports. In addition, users found that organzing content though diagnostic and solution posts to significanly facilitate reasoning about and searching for relevant information. Finally, we present future directions of work investigating how next generation bug tracking systems could combine the use of the three approaches, such that they benefit from and build upon the results of the other approaches. Next generation bug tracking systems should be more than a tool for exchanging unstructured textual comments. They should be an ecosystem that is tailored for collaborative knowledge building, leveraging the power of the masses to collect reliable and useful information about bugs, providing mechanisms and incentives to verify the validity of such information and mechanisms to organize such information, thus, facilitating comprehension and reasoning. To bring bug tracking systems towards this vision, we present three orthogonal approaches aiming at increasing the usefulness and realiability of contributions and organizing information to improve understanding and reasoning. To improve the usefulness and realibility of contributions we propose the addition of game mechanisms to bug tracking systems, with the objective of motivating contributors to post higher-quality content. Through an empirical investigation of Stack Overflow we evaluate the effects of the mechanisms in such a collaborative software development ecosystem and map a promissing approach to use game mechanisms in bug tracking systems. To improve data organization, we propose two complementary approaches. The first is an automated approach to data organization, creating bug report summaries that make reading and working with bug reports easier, by highlighting the portions of bug reports that expert developers would focus on, if reading the bug report in a hurry. The second approach to improve data organization is a fundamental change on how data is collected and organized, eliminating comments as the main component of bug reports. Instead of comments, users contribute informational posts about bug diagnostics or solutions, allowing users to post contextual comments for each of the different diagnostic iiior solution posts. Our evaluations with real bug tracking system users find that they consider the bug report summaries to be very useful in facilitating common bug tracking system tasks, such as finding duplicate bug reports. In addition, users found that organzing content though diagnostic and solution posts to significanly facilitate reasoning about and searching for relevant information. Finally, we present future directions of work investigating how next generation bug tracking systems could combine the use of the three approaches, such that they benefit from and build upon the results of the other approaches.

With technological advances digital video and imaging are becoming more and more relevant. Medical, remote-learning, surveillance, conferencing and home monitoring are just a few applications of these technologies. Along with compression, there is now a need for analysis and extraction of data. During the days of film and early digital cameras the processing and manipulation of data from such cameras was transparent to the end user. This transparency has been decreasing and the industry is moving towards `smart users' - people who will be enabled to program and manipulate their video and imaging systems. Smart cameras can currently zoom, refocus and adjust lighting by sourcing out current from the camera itself to the headlight. Such cameras are used in the industry for inspection, quality control and even counting objects in jewelry stores and museums, but could eventually allow user defined programmability. However, all this will not happen without interactive software as well as capabilities in the hardware to allow programmability. In this research, compression, expansion and detail extraction from videos in the surveillance arena are addressed. Here, a video codec is defined that can embed contextual details of a video stream depending on user defined requirements creating a video summary. This codec also carries out motion based segmentation that helps in object detection. Once an object is segmented it is matched against a database using its shape and color information. If the object is not a good match, the user can either add it to the database or consider it an anomaly. RGB vector angle information is used to generate object descriptors to match objects to a database. This descriptor implicitly incorporates the shape and color information while keeping the size of the database manageable. Color images of objects that are considered `safe' are taken from various angles and distances (with the same background as that covered by the camera is question) and their RGB vector angle based descriptors constitute the information contained in the database. This research is a first step towards building a compression and detection system for specific surveillance applications. While the user has to build and maintain a database, there are no restrictions on the size of the images, zoom and angle requirements, thus, reducing the burden on the end user in creating such a database. This also allows use of different types of cameras and doesn't need a lot of up-front planning on camera location, etc.

With widespread availability of low-cost devices capable of photo shooting and high-volume video recording, we are facing explosion of both image and video data. The sheer volume of such visual data poses both challenges and opportunities in machine learning and computer vision research. In image classification, most of previous research has focused on small to mediumscale data sets, containing objects from dozens of categories. However, we could easily access images spreading thousands of categories. Unfortunately, despite the well-known advantages and recent advancements of multi-class classification techniques in machine learning, complexity concerns have driven most research on such super large-scale data set back to simple methods such as nearest neighbor search, one-vs-one or one-vs-rest approach. However, facing image classification problem with such huge task space, it is no surprise that these classical algorithms, often favored for their simplicity, will be brought to their knees not only because of the training time and storage cost they incur, but also because of the conceptual awkwardness of such algorithms in massive multi-class paradigms. Therefore, it is our goal to directly address the bigness of image data, not only the large number of training images and high-dimensional image features, but also the large task space. Specifically, we present algorithms capable of efficiently and effectively training classifiers that could differentiate tens of thousands of image classes. Similar to images, one of the major difficulties in video analysis is also the huge amount of data, in the sense that videos could be hours long or even endless. However, it is often true that only a small portion of video contains important information. Consequently, algorithms that could automatically detect unusual events within streaming or archival video would significantly improve the efficiency of video analysis and save valuable human attention for only the most salient contents. Moreover, given lengthy recorded videos, such as those captured by digital cameras on mobile phones, or surveillance cameras, most users do not have the time or energy to edit the video such that only the most salient and interesting part of the original video is kept. To this end, we also develop algorithm for automatic video summarization, without human intervention. Finally, we further extend our research on video summarization into a supervised formulation, where users are asked to generate summaries for a subset of a class of videos of similar nature. Given such manually generated summaries, our algorithm learns the preferred storyline within the given class of videos, and automatically generates summaries for the rest of videos in the class, capturing the similar storyline as in those manually summarized videos.

Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2018-04-19T18:44:37Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2018-04-23T16:27:14Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) / Made available in DSpace on 2018-04-23T16:27:14Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) luis_henrique_goncalves_aguiar.pdf: 1963486 bytes, checksum: ce8ee9274d520386492773d2e289f109 (MD5) Previous issue date: 2017 / Considerando a grande quantidade de informa??es textuais dispon?veis atualmente, principalmente na web, est? se tronando cada vez mais dif?cil o acesso e a assimila??o desse conte?do para o usu?rio. Nesse contexto, torna-se necess?rio buscar tarefas capazes de transformar essa grande quantidade de dados em conhecimento ?til e organizado. Uma alternativa para amenizar esse problema, ? reduzir o volume de informa??es dispon?veis a partir da produ??o de resumos dos textos originais, por meio da sumariza??o autom?tica (SA) de textos. A sumariza??o autom?tica de textos consiste na produ??o autom?tica de resumos a partir de um ou mais textos-fonte, de modo que o sum?rio contenha as informa??es mais relevantes deste. A avalia??o de resumos ? uma tarefa importante no campo da sumariza??o autom?tica de texto, a abordagem mais intuitiva ? a avalia??o humana, por?m ? onerosa e improdutiva. Outra alternativa ? a avalia??o autom?tica, alguns avaliadores foram propostos, sendo a mais conhecida e amplamente usada ? a medida ROUGE (Recall-Oriented Understudy for Gisting Evaluation). Um fator limitante na avalia??o da ROUGE ? a utiliza??o do sum?rio humano de refer?ncia, o que implica em uma restri??o do idioma e dom?nio, al?m de requerer um trabalho humano demorado e oneroso. Diante das dificuldades encontradas na avalia??o de sum?rios autom?ticos, o presente trabalho apresenta o modelo Cassiopeia como um novo m?todo de avalia??o. O modelo ? um agrupador de textos hier?rquico, o qual consiste no uso da sumariza??o na etapa do pr?-processamento, onde a qualidade do agrupamento ? influenciada positivamente conforme a qualidade da sumariza??o. As simula??es realizadas neste trabalho mostraram que a avalia??o realizada pelo modelo Cassiopeia ? semelhante a avalia??o realizada pela ferramenta ROUGE. Por outro lado, a utiliza??o do modelo Cassiopeia como avaliador de sum?rios autom?ticos evidenciou algumas vantagens, sendo as principais; a n?o utiliza??o do sum?rio humano no processo de avalia??o, e a independ?ncia do dom?nio e do idioma. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017. / Considering the large amount of textual information currently available, especially on the web, it is becoming increasingly difficult to access and assimilate this content to the user. In this context, it becomes necessary to search for tasks that can transform this large amount of information into useful and organized knowledge. The solution, or at least an alternative, to moderate this problem is to reduce the volume of information available, from the production of abstracts of the original texts, through automatic summarization (SA) of texts. The Automatic Summarization of texts consists of the automatic production of abstracts from one or more source texts, which the summary must contain the most relevant information of the source text. The evaluation of abstracts is an important task in the field of automatic text summarization, the most intuitive approach is human evaluation, but it is costly and unproductive. Another alternative is the automatic evaluation, some evaluators have been proposed, and the most widely used is the ROUGE (Recall-Oriented Understudy for Gisting Evaluation). A limiting factor in ROUGE's evaluation is the use of the human reference summary, which implies a restriction of language and domain, as well as requiring time-consuming and expensive human work. In view of the difficulties encountered in the evaluation of automatic summaries, this paper presents the Cassiopeia model as a new evaluation method. The model is a hierarchical text grouper, which consists of the use of the summarization in the stage of the pre-processing, where the quality of the grouping is influenced positively according to the quality of the summarization. The simulations performed in this work showed that the evaluations performed by Cassiopeia in comparison to the ROUGE tool are similar. On the other hand, the use of the Cassiopeia model as an automatic summarization evaluator showed some advantages, the main ones are; being the non-use of the human abstract in the evaluation process, and the independent of the domain and the language.