Users share private information on the web through a variety of applications, such as email, instant messaging, social media, and document sharing. Unfortunately, recent revelations have shown that not only is users' data at risk from hackers and malicious insiders, but also from government surveillance. This state of affairs motivates the need for users to be able to encrypt their online data.In this dissertation, we explore how to help users encrypt their online data, with a special focus on securing email. First, we explore the design principles that are necessary to create usable, secure email. As part of this exploration, we conduct eight usability studies of eleven different secure email tools including a total of 347 participants. Second, we develop a novel, paired-participant methodology that allows us to test whether a given secure email system can be adopted in a grassroots fashion. Third, we apply our discovered design principles to PGP-based secure email, and demonstrate that these principles are sufficient to create the first PGP-based system that is usable by novices. We have also begun applying the lessons learned from our secure email research more generally to content-based encryption on the web. As part of this effort, we develop MessageGuard, a platform for accelerating research into usable, content-based encryption. Using MessageGuard, we build and evaluate Private Facebook Chat (PFC), a secure instant messaging system that integrates with Facebook Chat. Results from our usability analysis of PFC provided initial evidence that our design principles are also important components to usable, content-based encryption on the Web.

The audio signals can provide rich semantic cues for analyzing multimedia content, so audio information has been recently used for content-based multimedia indexing and retrieval. Due to growing amount of audio data, demand for efficient retrieval techniques is increasing. In this thesis work, we propose a complete, scalable and extensible audio based content management and retrieval system for news broadcasts. The proposed system considers classification, segmentation, analysis and retrieval of an audio stream. In the sound classification and segmentation stage, a sound stream is segmented by classifying each sub segment into silence, pure speech, music, environmental sound, speech over music, and speech over environmental sound in multiple steps. Support Vector Machines and Hidden Markov Models are employed for classification and these models are trained by using different sets of MPEG-7 features. In the analysis and retrieval stage, two alternatives exist for users to query audio data. The first of these isolates user from main acoustic classes by providing semantic domain based fuzzy classes. The latter offers users to query audio by giving an audio sample in order to find out the similar segments or by requesting expressive summary of the content directly. Additionally, a series of tests was conducted on audio tracks of TRECVID news broadcasts to evaluate the performance of the proposed solution.

As the Internet became widespread all over the world, people started to share great amount of data on the web and almost every people joined different data networks in order to have a quick access to data shared among people and survive against the information overload on the web. Recommender systems are created to provide users more personalized information services and to make data available for people without an extra effort. Most of these systems aim to get or learn user preferences, explicitly or implicitly depending to the system, and guess &ldquo / preferable data&rdquo / that has not already been consumed by the user. Traditional approaches use user/item similarity or item content information to filter items for the active user / however most of the recent approaches also consider the trustworthiness of users. By using trustworthiness, only reliable users according to the target user opinion will be considered during information retrieval. Within this thesis work, a content boosted method of using trust data in recommender systems is proposed. It is aimed to be shown that people who trust the active user and the people, whom the active user trusts, also have correlated opinions with the active user. This results the fact that the rated items by these people can also be used while offering new items to users. For this research, www.epinions.com site is crawled, in order to access user trust relationships, product content information and review ratings which are ratings given by users to product reviews that are written by other users.

Data analysis has become a very important area for both companies and researchers as a consequence of the technological developments in recent years. Companies are trying to increase their profit by analyzing the existing data about their customers and making decisions for the future according to the results of these analyses. Parallel to the need of companies, researchers are investigating different methodologies to analyze data more accurately with high performance. Recommender systems are one of the most popular and widespread data analysis tools. A recommender system applies knowledge discovery techniques to the existing data and makes personalized product recommendations during live customer interaction. However, the huge growth of customers and products especially on the internet, poses some challenges for recommender systems, producing high quality recommendations and performing millions of recommendations per second. In order to improve the performance of recommender systems, researchers have proposed many different methods. Singular Value Decomposition (SVD) technique based on dimension reduction is one of these methods which produces high quality recommendations, but has to undergo very expensive matrix calculations. In this thesis, we propose and experimentally validate some contributions to SVD technique which are based on the user and the item categorization. Besides, we adopt tags to classical 2D (User-Item) SVD technique and report the results of experiments. Results are promising to make more accurate and scalable recommender systems.

This thesis proposes the design, development and evaluation of a hybrid video recommendation system. The proposed hybrid video recommendation system is based on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm in which relations between users are used to make recommendations. Adsorption is used to generate the base recommendation list. In order to overcome the problems that occur in pure collaborative system, content based filtering is injected. Content based filtering uses the idea of suggesting similar items that matches user preferences. In order to use content based filtering, first, the base recommendation list is updated by removing weak recommendations. Following this, item similarities of the remaining list are calculated and new items are inserted to form the final recommendations. Thus, collaborative recommendations are empowered considering item similarities. Therefore, the developed hybrid system combines both collaborative and content based approaches to produce more effective suggestions.

Over the years, exemplar-based methods have yielded significant improvements over their model-based counterparts in image synthesis applications. Notably, texture synthesis algorithms using an exemplar-based approach have shown success where traditional stochastic methods failed. As an illustrative example, I will present an exemplar-based approach that yields substantial benefits for user-guided terrain synthesis using Digital Elevation Models (DEMs). This success is realized through exploitation of structural properties of natural terrain. In addition to their proliferation in the image synthesis domain, as annotated image datasets become increasingly available, exemplar-based methods are also gaining in popularity for image analysis applications. This thesis addresses the intersection between exemplar-based analysis and the problem of content-based image retrieval (CBIR). A basic problem in CBIR is the process by which the search criteria are refined by the user through the manipulation of returned exemplars. Exemplar-based analysis is particularly well-suited to query refinement due to its interpretability and the ease with which it can be incorporated into an interactive system. I investigate this connection in the domain of Computer-Assisted Diagnosis (CAD) of dermatological images. I demonstrate that exemplar-based approaches in CBIR can be effective for diagnosing pigmented skin lesions (PSLs). I will present an exemplar-based algorithm for segmenting PSLs in dermatoscopic images. In addition, I will present a generalized representation of dermoscopic features for detection and matching. This representation not only leads to an exemplar-based PSL diagnosis scheme, but it also enables us to realize interactive region-of-interest retrieval, which includes a relevance feedback mechanism to facilitate more flexible query-by-example analysis. Finally, I will assess the benefit of this CBIR-CAD approach through both quantitative evaluations and user studies.

Research conducted in South Africa indicates that many South African students who register for undergraduate study each year are under-prepared for university education and that many of these English Second Language students also have low levels of reading ability. This has an adverse effect on their chances of academic success. These students very often become part of the "revolving door syndrome". In order to meet the reading needs of students in the 21" century, educators are pressed to develop effective instructional means for teaching strategic reading at tertiary level. In order to help students acquire the strategic reading abilities deemed necessary for a successful academic experience, the Department of English at Potchefstroom University implemented a content-based strategic reading module. This module was offered to students via Varsite (technology-enhanced aspect of the module); a learning content management system developed at Potchefstroom University. This system provides an integrated environment for developing, managing and delivering learning content. The purpose of this study was to: discuss the structure and format of the content-based strategic reading module as developed and implemented for delivery within a distributed learning environment; determine what the reading comprehension and reading strategy use profile of first-year students at Potchefstroom University looks like; determine whether the students in the experimental group, who completed the strategic reading component of the English for Professional Purposes course in a technology-enhanced environment, attained statistically as well as practically significantly higher mean scores on their end-of-semester English, Communication Studies, and TOEFL reading comprehension tests, than did the students in the control group, who were not exposed to the technology-enhanced environment; determine whether the students in the experimental group differed statistically as well as practically significantly from the students in the control group in terms of their reading strategy use; determine the scope of the reading problem among the first-year students participating in this study; identify the strengths and weaknesses in the reading assessment profiles of one efficient and one inefficient student; make recommendations in terms of the reading support needed by these students; identify the factors that can affect first-year English Second Language (ESL) students' acceptance and use of the technology-enhanced component of a strategic reading module offered via mixed mode delivery; determine which factors can be considered as statistically significant predictors of technology acceptance and use by first-year ESL students; and discuss the implications of the above-mentioned results for the designing of technology-enhanced courses as well as the support that should be given to ESL learners who must use the technology. In this study a combined qualitative and quantitative research method was used. A Dominant-Less Dominant design was used. The qualitative research approach was consistent with naturalistic case study methodology. For the quantitative research component a quasi-experimental non-randomised pre-test post-test control group design was used. The participants in this study included the entire population of one hundred and thirty-one students taking the English for Professional Purposes module. The students included speakers of Afrikaans and Setswana. These students majored in Communication Studies and Psychology. Ten paper-and-pencil instruments were used in this study. In addition to the paper-and- pencil instruments, various qualitative data collection methods were also used, namely semi-structured interviews, e-mail messages, informal conversations and the researcher's field notes. The data were analysed by means of descriptive (i.e., means, standard deviations) as well as multivariate statistics (i.e., Pearson product moment correlations; t-tests; factor analyses; and stepwise multiple regression). The results of the study can be summarised as follows: The strategic reading module of the English for Professional Purposes course was designed for mixed mode delivery. The structure and format of the strategic reading module consisted of an interactive study guide, contact sessions, and Varsite (i.e., a learning content management system). The results indicated that the students who received strategic reading instruction in the technology-enhanced environment received both statistically and practically significantly higher marks on three reading comprehension measures than did the students in the control group. This was true for successful students, as well as for those considered to be at-risk. The post-test results indicated that the students in the experimental group used certain strategies statistically (p<0.05), as well as practically significantly (small to large effect sizes), more often than the students in the control group. An analysis of the reading assessment profiles of the students participating in this study indicated that they experienced problems across all aspects of the reading components assessed (vocabulary, fluency, and reading comprehension and reading strategies). An analysis of the successful student's reading assessment profile indicated that his/her profile was far flatter than that of the at-risk student; the successful student had far fewer ups and downs in his/her profile than the at-risk student (i.e., the majority of the successful student's mean reading assessment scores were scattered around or above the norm/guidelines for first-year students). The results of an exploratory factor analysis indicated that computer self-efficacy, ease of use, enjoyment, outcome expectations, usefulness, and quality of resources were major factors affecting ESL students' acceptance and use of the technology-enhanced component of a strategic reading module. In addition, the results of the multiple regression analysis indicated that approximately 71% of the total variance of Varsite acceptance and use was explained by computer self-efficacy, ease of use, enjoyment, and outcome expectations. Usefulness and the quality of the resources also contributed to the total variance, but the contribution was not statistically significant. / Thesis (Ph.D. (English))--North-West University, Potchefstroom Campus, 2004.

The focus of this research is on a tolerance space-based approach to image analysis and correspondence. The problem considered in this thesis is one of extracting perceptually relevant information from groups of objects based on their descriptions. Object descriptions are represented by feature vectors containing probe function values in a manner similar to feature extraction in pattern classification theory. The motivation behind this work is the synthesizing of human perception of nearness for improvement of image processing systems. In these systems, the desired output is similar to the output of a human performing the same task. Thus, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way that humans perceive the similarity of objects. In this thesis, near set theory is presented and advanced, and work is presented toward a near set approach to performing content-based image retrieval. Furthermore, results are given based on these new techniques and future work is presented. The contributions of this thesis are: the introduction of a nearness measure to determine the degree that near sets resemble each other; a systematic approach to finding tolerance classes, together with proofs demonstrating that the proposed approach will find all tolerance classes on a set of objects; an approach to applying near set theory to images; the application of near set theory to the problem of content-based image retrieval; demonstration that near set theory is well suited to solving problems in which the outcome is similar to that of human perception; two other near set measures, one based on Hausdorff distance, the other based on Hamming distance.

The main contribution of this thesis, is to define similarity measures between two images with the main focus on content-based image retrieval (CBIR). Each image is considered as a set of visual elements that can be described with a set of visual descriptions (features). The similarity between images is then defined as the nearness between sets of elements based on a tolerance and a fuzzy tolerance relation. A tolerance relation is used to describe the approximate nature of the visual perception. A fuzzy tolerance relation is adopted to eliminate the need for a sharp threshold and hence model the gradual changes in perception of similarities. Three real valued similarity measures as well as a fuzzy valued similarity measure are proposed. All of the methods are then used in two CBIR experiments and the results are compared with classical measures of distance (namely, Kantorovich, Hausdorff and Mahalanobis). The results are compared with other published research papers. An important advantage of the proposed methods is shown to be their effectiveness in an unsupervised setting with no prior information. Eighteen different features (based on color, texture and edge) are used in all the experiments. A feature selection algorithm is also used to train the system in choosing a suboptimal set of visual features.

Research conducted in South Africa indicates that many South African students who register for undergraduate study each year are under-prepared for university education and that many of these English Second Language students also have low levels of reading ability. This has an adverse effect on their chances of academic success. These students very often become part of the "revolving door syndrome". In order to meet the reading needs of students in the 21" century, educators are pressed to develop effective instructional means for teaching strategic reading at tertiary level. In order to help students acquire the strategic reading abilities deemed necessary for a successful academic experience, the Department of English at Potchefstroom University implemented a content-based strategic reading module. This module was offered to students via Varsite (technology-enhanced aspect of the module); a learning content management system developed at Potchefstroom University. This system provides an integrated environment for developing, managing and delivering learning content. The purpose of this study was to: discuss the structure and format of the content-based strategic reading module as developed and implemented for delivery within a distributed learning environment; determine what the reading comprehension and reading strategy use profile of first-year students at Potchefstroom University looks like; determine whether the students in the experimental group, who completed the strategic reading component of the English for Professional Purposes course in a technology-enhanced environment, attained statistically as well as practically significantly higher mean scores on their end-of-semester English, Communication Studies, and TOEFL reading comprehension tests, than did the students in the control group, who were not exposed to the technology-enhanced environment; determine whether the students in the experimental group differed statistically as well as practically significantly from the students in the control group in terms of their reading strategy use; determine the scope of the reading problem among the first-year students participating in this study; identify the strengths and weaknesses in the reading assessment profiles of one efficient and one inefficient student; make recommendations in terms of the reading support needed by these students; identify the factors that can affect first-year English Second Language (ESL) students' acceptance and use of the technology-enhanced component of a strategic reading module offered via mixed mode delivery; determine which factors can be considered as statistically significant predictors of technology acceptance and use by first-year ESL students; and discuss the implications of the above-mentioned results for the designing of technology-enhanced courses as well as the support that should be given to ESL learners who must use the technology. In this study a combined qualitative and quantitative research method was used. A Dominant-Less Dominant design was used. The qualitative research approach was consistent with naturalistic case study methodology. For the quantitative research component a quasi-experimental non-randomised pre-test post-test control group design was used. The participants in this study included the entire population of one hundred and thirty-one students taking the English for Professional Purposes module. The students included speakers of Afrikaans and Setswana. These students majored in Communication Studies and Psychology. Ten paper-and-pencil instruments were used in this study. In addition to the paper-and- pencil instruments, various qualitative data collection methods were also used, namely semi-structured interviews, e-mail messages, informal conversations and the researcher's field notes. The data were analysed by means of descriptive (i.e., means, standard deviations) as well as multivariate statistics (i.e., Pearson product moment correlations; t-tests; factor analyses; and stepwise multiple regression). The results of the study can be summarised as follows: The strategic reading module of the English for Professional Purposes course was designed for mixed mode delivery. The structure and format of the strategic reading module consisted of an interactive study guide, contact sessions, and Varsite (i.e., a learning content management system). The results indicated that the students who received strategic reading instruction in the technology-enhanced environment received both statistically and practically significantly higher marks on three reading comprehension measures than did the students in the control group. This was true for successful students, as well as for those considered to be at-risk. The post-test results indicated that the students in the experimental group used certain strategies statistically (p<0.05), as well as practically significantly (small to large effect sizes), more often than the students in the control group. An analysis of the reading assessment profiles of the students participating in this study indicated that they experienced problems across all aspects of the reading components assessed (vocabulary, fluency, and reading comprehension and reading strategies). An analysis of the successful student's reading assessment profile indicated that his/her profile was far flatter than that of the at-risk student; the successful student had far fewer ups and downs in his/her profile than the at-risk student (i.e., the majority of the successful student's mean reading assessment scores were scattered around or above the norm/guidelines for first-year students). The results of an exploratory factor analysis indicated that computer self-efficacy, ease of use, enjoyment, outcome expectations, usefulness, and quality of resources were major factors affecting ESL students' acceptance and use of the technology-enhanced component of a strategic reading module. In addition, the results of the multiple regression analysis indicated that approximately 71% of the total variance of Varsite acceptance and use was explained by computer self-efficacy, ease of use, enjoyment, and outcome expectations. Usefulness and the quality of the resources also contributed to the total variance, but the contribution was not statistically significant. / Thesis (Ph.D. (English))--North-West University, Potchefstroom Campus, 2004.