The purpose of this study was to investigate the use of electronic databases by masters students in the Faculty of Humanities, Development and Social Sciences at the University of KwaZulu-Natal, Pietermaritzburg campus. The study tried to establish which electronic databases masters students used and how frequently they were used. In addition, the study investigated what the students used the electronic databases for and what problems they encountered while using these databases. A set of recommendations based on the findings were identified. The study population consisted of Humanities, Development and Social Sciences (HDSS) masters students. A total of 139 masters students responded, which was a response rate of 68%. The approach undertaken by the researcher was triangulation where both qualitative and quantitative data was collected. The instrument that the researcher employed as the quantitative method of data collection was the questionnaire and a focus group was used as the qualitative method of data collection. The quantitative data was analysed using SPSS and the qualitative data was analysed using thematic content analysis. A pre-test of the questionnaire for the study was done on six registered masters students in the Faculty of Science and Agriculture at the University of KwaZulu-Natal, Pietermaritzburg campus (UKZNP) in order to identify any unclear issues in the questionnaire. The outcome of the study revealed that a majority of HDSS masters students at UKZNP did use the electronic databases. A number of problems were experienced when using the databases. The top three databases used by the masters students were EbscoHost , followed by SABINET and then ProQuest. Masters students mentioned some benefits of using the electronic databases. Students revealed that they became aware of the library databases from a variety of sources such as lecturers, friends and orientation programmes. There were other non-library databases that masters students used besides the library electronic databases. A majority of 75.2% of the students were satisfied with the library service. / Thesis (M.Info.Studs.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

Objectives: The EZSCAN is a non-invasive device that, by evaluating sweat gland function, may detect subjects with type 2 diabetes mellitus (T2DM). The aim of the study was to conduct a systematic review and meta-analysis including studies assessing the performance of the EZSCAN for detecting cases of undiagnosed T2DM. Methodology/Principal findings: We searched for observational studies including diagnostic accuracy and performance results assessing EZSCAN for detecting cases of undiagnosed T2DM. OVID (Medline, Embase, Global Health), CINAHL and SCOPUS databases, plus secondary resources, were searched until March 29, 2017. The following keywords were utilized for the systematic searching: type 2 diabetes mellitus, hyperglycemia, EZSCAN, SUDOSCAN, and sudomotor function. Two investigators extracted the information for meta-analysis and assessed the quality of the data using the Revised Version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Pooled estimates were obtained by fitting the logistic-normal random-effects model without covariates but random intercepts and using the Freeman-Tukey Arcsine Transformation to stabilize variances. Heterogeneity was also assessed using the I2 measure. Four studies (n = 7,720) were included, three of them used oral glucose tolerance test as the gold standard. Using Hierarchical Summary Receiver Operating Characteristic model, summary sensitivity was 72.0% (95%CI: 60.0%– 83.0%), whereas specificity was 56.0% (95%CI: 38.0%– 74.0%). Studies were very heterogeneous (I2 for sensitivity: 79.2% and for specificity: 99.1%) regarding the inclusion criteria and bias was present mainly due to participants selection. Conclusions: The sensitivity of EZSCAN for detecting cases of undiagnosed T2DM seems to be acceptable, but evidence of high heterogeneity and participant selection bias was detected in most of the studies included. More studies are needed to evaluate the performance of the EZSCAN for undiagnosed T2DM screening, especially at the population level.

The purpose of this thesis is to identify and quantify the information clues found in the document representations in the World Wide Web environment. This study uses three topics to find document representations: custom publishing, distance education, and tactile graphics. Four Web search engines are used: AltaVista, InfoSeek Ultra, Lycos, and Open Text Index. The findings of the random sample show that the search engines produce little duplication in their display of the results. Just over half of the cases reveal information clues about the document's authorship, origin, format or subject. The summary field shows the highest number of information clues. The title and Uniform Resource Locator fields do not contain many information clues. Few of the fields contain clues about the authorship of the documents. Topical relevance is questionable in many of the cases. The study recommends further research on the comparison of search engines, on the study of searches on the Web for commercial, academic and personal topics, and on information seeking behaviors relating to Web searching. Recommendations are made for Web training and Web page design to assist users in finding relevant information more quickly. / Arts, Faculty of / Library, Archival and Information Studies (SLAIS), School of / Graduate

Natural language interfaces to databases (NLIDB) are systems that aim to bridge the gap between the languages used by humans and computers, and automatically translate natural language sentences to database queries. This thesis proposes a novel approach to NLIDB, using graph-based models. The system starts by collecting as much information as possible from existing databases and sentences, and transforms this information into a knowledge base for the system. Given a new question, the system will use this knowledge to analyze and translate the sentence into its corresponding database query statement. The graph-based NLIDB system uses English as the natural language, a relational database model, and SQL as the formal query language. In experiments performed with natural language questions ran against a large database containing information about U.S. geography, the system showed good performance compared to the state-of-the-art in the field.

The purpose of this study was to compare four different methods of bibliographic instruction in order to determine which method would have the most effect on student learning.

Thesis (MMus (Music))--University of Stellenbosch, 2009. / In the absence of a state-sponsored South African archive that focuses on collecting, ordering, cataloguing and preserving special music collections for research, the Documentation Centre for Music (DOMUS) was established in 2005 as a research project at the University of Stellenbosch. Music research in South Africa is often impeded by inaccessibility of materials, staff shortages at archives and libraries, financial constraints and time-consuming ordering and cataloguing processes. Additionally there is, locally, restricted knowledge of the existence, location and status of relevant primary sources. Accessibility clearly depends on knowing of the existence of materials, as well as the extent to which collections have been ordered and catalogued. An overview of repositories such as the Nasionale Afrikaanse Letterkundige Museum and Navorsingsentrum (NALN), the now defunct National Documentation Centre for Music and the International Library of African Music (ILAM) paints a troubling picture of archival neglect and disintegration. Apart from ILAM, which has a very specific collecting and research focus, this trend was one that ostensibly started in the 1980s and is still continuing. It could be ascribed to a lack of planning and forward thinking under the previous political dispensation, aggravated by policies of transformation and restructuring in the current one. Existing sources supporting research on primary materials are dated and not discipline-specific. Thus this study aims to address issues of inaccessibility of primary music materials by creating a comprehensive and ongoing national electronic database of special music collections in South Africa. It is hoped that this will help to alert researchers to the existence and status of special music collections housed at various levels of South African academic and civil society.

Effective decision support plays vital roles in people's daily life, as well as for professional practitioners such as health care providers. Without correct information and timely derived knowledge, a decision is often suboptimal and may result in signi cant nancial loss or compromises of the performance. In this dissertation, we study text mining and topic modeling and propose to use text mining methods, in combination with topic models, to discover knowledge from texts popularly available from a wide variety of sources, such as research publications, news, medical diagnose notes, and further employ discovered knowledge to assist social and medical decision support. Examples of such decisions include hospital patient readmission prediction, which is a national initiative for health care cost reduction, academic research topics discovery and trend modeling, and social preference modeling for friend recommendation in social networks etc. To carry out text mining, our research, in Chapter 3, first emphasizes on single document analyzing to investigate textual stylometric features for user pro ling and recognition. Our research confirms that by using properly designed features, it is possible to identify the authors who wrote the article, using a number of sample articles written by the author as the training data. This study serves as the base to assert that text mining is a powerful tool for capturing knowledge in texts for better decision making. In the Chapter 4, we advance our research from single documents to documents with interdependency relationships, and propose to model and predict citation relationship between documents. Given a collection of documents with known linkage relationships, our research will discover e ective features to train prediction models, and predict the likelihood of two documents involving a citation relationships. This study will help accurately model social network linkage relationships, and can be used to assist e ective decision making for friend recommendation in social networking, and reference recommendation in scienti c writing etc. In the Chapter 5, we advance a topic discovery and trend prediction principle to discover meaningful topics from a set of data collection, and further model the evolution trend of the topic. By proposing techniques to discover topics from text, and using temporal correlation between trend for prediction, our techniques can be used to summarize a large collection of documents as meaningful topics, and further forecast the popularity of the topic in a near future. This study can help design systems to discover popular topics in social media, and further assist resource planning and scheduling based on the discovered topics and the their evolution trend. In the Chapter 6, we employ both text mining and topic modeling to the medical domain for effective decision making. The goal is to discover knowledge from medical notes to predict the risk of a patient being re-admitted in a near future. Our research emphasizes on the challenge that re-admitted patients are only a small portion of the patient population, although they bring signficant financial loss. As a result, the datasets are highly imbalanced which often result in poor accuracy for decision making. Our research will propose to use latent topic modeling to carryout localized sampling, and combine models trained from multiple copies of sampled data for accurate prediction. This study can be directly used to assist hospital re-admission assessment for early warning and decision support. The text mining and topic modeling techniques investigated in the dissertation can be applied to many other domains, involving texts and social relationships, towards pattern and knowledge based e ective decision making. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Location based services (LBS) are gaining widespread user acceptance and increased daily usage. GPS based mobile navigation systems (Garmin), location-related social network updates and "check-ins" (Facebook), location-based games (Nokia), friend queries (Foursquare) and ads (Google) are some of the popular LBSs available to mobile users today. Despite these successes, current user services fall short of a vision where mobile users could ask for continuous location-based services with always-up-to-date information around them, such as the list of friends or favorite restaurants within 15 minutes of driving. Providing such a location based service in real time faces a number of technical challenges. In this dissertation research, we propose a suite of novel techniques and system architectures to address some known technical challenges of continuous location queries and updates. Our solution approaches enable the creation of new, practical and scalable location based services with better energy efficiency on mobile clients and higher throughput at the location servers. Our first contribution is the development of RoadTrack, a road network aware and query-aware location update framework and a suite of algorithms. A unique characteristic of RoadTrack is the innovative design of encounter points and system-defined precincts to manage the desired spatial resolution of location updates for different mobile clients while reducing the complexity and energy consumption of location update strategies. The second novelty of this dissertation research is the technical development of Dandelion data structures and algorithms that can deliver superior performance for the periodic re-evaluation of continuous road-network distance based location queries, when compared with the alternative of repeatedly performing a network expansion along a mobile user's trajectory. The third contribution of this dissertation research is the FastExpand algorithm that can speed up the computation of single-issue shortest-distance road network queries. Finally, we have developed the open source GT MobiSim mobility simulator, a discrete event simulation platform to generate realistic driving trajectories for real road maps. It has been downloaded and utilized by many to evaluate the efficiency and effectiveness of the location query and location update algorithms, including the research efforts in this dissertation.

Accepted for publication in Physics World (January 2007) / With the vast majority of scientific papers now available online, this paper (accepted for publication in Physics World) describes how the Web is allowing physicists and information providers to measure more accurately the impact of these papers and their authors. Provides a historical background of citation analysis, impact factor, new citation data sources (e.g., Google Scholar, Scopus, NASA's Astrophysics Data System Abstract Service, MathSciNet, ScienceDirect, SciFinder Scholar, Scitation/SPIN, and SPIRES-HEP), as well as h-index, g-index, and a-index.

Large-scale data management systems rely more and more on cloud storage, where the need for efficient search capabilities clashes with the need for data confidentiality. Encryption and efficient accessibility are naturally at odds, as for instance strong encryption necessitates that ciphertexts reveal nothing about underlying data. Searchable encryption is an active field in cryptography studying encryption schemes that provide varying levels of efficiency, functionality, and security, and efficient searchable encryption focuses on schemes enabling sub-linear (in the size of the database) search time. I present the first cryptographic study of efficient searchable symmetric encryption schemes supporting two types of search queries, range queries and error-tolerant queries. The natural solution to accommodate efficient range queries on ciphertexts is to use order-preserving encryption (OPE). I propose a security definition for OPE schemes, construct the first OPE scheme with provable security, and further analyze security by characterizing one-wayness of the scheme. Efficient error-tolerant queries are enabled by efficient fuzzy-searchable encryption (EFSE). For EFSE, I introduce relevant primitives, an optimal security definition and a (somewhat space-inefficient, but in a sense efficient as possible) scheme achieving it, and more efficient schemes that achieve a weaker, but practical, security notion. In all cases, I introduce new appropriate security definitions, construct novel schemes, and prove those schemes secure under standard assumptions. The goal of this line of research is to provide constructions and provable security analysis that should help practitioners decide whether OPE or FSE provides a suitable efficiency-security-functionality tradeoff for a given application.