To manage the information flood in the Internet, we usually navigate specific information using the provided search engines. Search engines are convenient but with limited functions. For example, it is impractical and impossible to browse through the entire collected information for us to gain an overall picture about what the navigated information stands for. To do so, we need an appropriate approach to automatically extracting concepts from the navigated information to assist users to easily and quickly gain the primary understanding toward a topic that interests users. In this research, we propose an approach to extracting concepts from the navigated web information and detecting the concept changes over time. It basically includes two stages. In the first stage, information is decomposed into paragraphs and they are clustered with key terms identified through the aid of latent semantic indexing method. Concepts are represented in the form of paragraph summary and associated key terms, which allows the user to easily comprehend what they describe. The second stage is to adaptively modify the concept structure to detect concept changes. With new information added, the concepts could be merging, splitting, or even emerging with time. Three experiments are conducted in this research to verify the proposed approach. Results of the first and second experiments show both high recall and high precision that matches the predefined concept categories. The last one is an illustrated real case application on the tsunami event. It shows that we can easily grasp different concepts of the tsunami reports and realize their changes by using our approach. The feasibility of employing our approach is thus justified.

No description available.

Software testing is an important factor of the software development process. Integration testing is an important and expensive level of the software testing process. Unfortunately, since the developers have limited time to perform integration testing and debugging and integration testing becomes very hard as the combinations grow in size, the chain of calls from one module to another grow in number, length, and complexity. This research is about providing new methodology for integration testing to reduce the number of test cases needed to a significant degree while returning as much of its effectiveness as possible. The proposed approach shows the best order in which to integrate the classes currently available for integration and the external method calls that should be tested and in their order for maximum effectiveness. Our approach limits the number of integration test cases. The integration test cases number depends mainly on the dependency among modules and on the number of the integrated classes in the application. The dependency among modules is determined by using an information retrieval technique called Latent Semantic Indexing (LSI). In addition, this research extends the mutation testing for use in integration testing as a method to evaluate the effectiveness of the integration testing process. We have developed a set of integration mutation operators to support development of integration mutation testing. We have conducted experiments based on ten Java applications. To evaluate the proposed methodology, we have created mutants using new mutation operators that exercise the integration testing. Our experiments show that the test cases killed more than 60% of the created mutants.

This study investigates a semi-automatic method for creation of topical labels representing the topical concepts in information objects. The method is called rotated latent semantic indexing (rLSI). rLSI has found application in text mining but has not been used for topical labels generation in digital libraries (DLs). The present study proposes a theoretical model and an evaluation framework which are based on the LSA theory of meaning and investigates rLSI in a DL environment. The proposed evaluation framework for rLSI topical labels is focused on human-information search behavior and satisfaction measures. The experimental systems that utilize those topical labels were built for the purposes of evaluating user satisfaction with the search process. A new instrument was developed for this study and the experiment showed high reliability of the measurement scales and confirmed the construct validity. Data was collected through the information search tasks performed by 122 participants using two experimental systems. A quantitative method of analysis, partial least squares structural equation modeling (PLS-SEM), was used to test a set of research hypotheses and to answer research questions. The results showed a not significant, indirect effect of topical label type on both guidance and satisfaction. The conclusion of the study is that topical labels generated using rLSI provide the same levels of alignment, guidance, and satisfaction with the search process as topical labels created by the professional indexers using best practices.

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2010

Document clustering automatically organizes a document collection into distinct groups of similar documents on the basis of their contents. Most of existing document clustering techniques deal with monolingual documents (i.e., documents written in one language). However, with the trend of globalization and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for multilingual document clustering (MLDC). Motivated by its significance and need, this study designs a Latent Semantic Indexing (LSI) based MLDC technique. Our empirical evaluation results show that the proposed LSI-based multilingual document clustering technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision.

Thesis (M.S.)--Duquesne University, 2006. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p.38-40) and index.

In an age of zero-day exploits and increased on-line attacks on computing infrastructure, operational security practitioners are becoming increasingly aware of the value of the information captured in log events. Analysis of these events is critical during incident response, forensic investigations related to network breaches, hacking attacks and data leaks. Such analysis has led to the discipline of Security Event Analysis, also known as Log Analysis. There are several challenges when dealing with events, foremost being the increased volumes at which events are often generated and stored. Furthermore, events are often captured as unstructured data, with very little consistency in the formats or contents of the events. In this environment, security analysts and implementers of Log Management (LM) or Security Information and Event Management (SIEM) systems face the daunting task of identifying, classifying and disambiguating massive volumes of events in order for security analysis and automation to proceed. Latent Semantic Mapping (LSM) is a proven paradigm shown to be an effective method of, among other things, enabling word clustering, document clustering, topic clustering and semantic inference. This research is an investigation into the practical application of LSM in the discipline of Security Event Analysis, showing the value of using LSM to assist practitioners in identifying types of events, classifying events as belonging to certain sources or technologies and disambiguating different events from each other. The culmination of this research presents adaptations to traditional natural language processing techniques that resulted in improved efficacy of LSM when dealing with Security Event Analysis. This research provides strong evidence supporting the wider adoption and use of LSM, as well as further investigation into Security Event Analysis assisted by LSM and other natural language or computer-learning processing techniques. / LaTeX with hyperref package / Adobe Acrobat 9.54 Paper Capture Plug-in

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Limited research has been conducted with regards to the applicability of topic extraction techniques in Sociology. Addressing the modern methodological opportunities, and responding to the skepticism with regards to the absence of theoretical foundations supporting the use of text analytics, I argue that Latent Semantic Analysis (LSA), complemented by other text analysis techniques and multivariate techniques, can constitute a unique hybrid method that can facilitate the sociological interpretations of web-based textual data. To illustrate the applicability of the hybrid technique, I developed two case studies. My first case study is associated with the Sociology of media. It focuses on the topic extraction and sentiment polarization among partisan texts posted on two major news sites. I find evidence of highly polarized opinions on comments posted on the Huffington Post and the Daily Caller. The highest polarizing topic was associated with a commentator’s reference on Hoodies in the context of the Trayvon Martin’s incident. My findings support contemporary research suggesting that media pundits frequently use tactics of outrage to provoke polarization of public opinion. My second case study contributes to the research domain of the Sociology of knowledge. The hybrid method revealed evidence of topical divides and topical “bridges” in the intellectual landscape of the British and the American sociological journals. My findings confirm the theoretical assertions describing Sociology as a fractured field, and partially support the existence of more globalized topics in the discipline.