This study evaluates the data sources and research methods used in earlier studies to rank the research productivity of Library and Information Science (LIS) faculty and schools. In doing so, the study identifies both tools and methods that generate more accurate publication count rankings as well as databases that should be taken into consideration when conducting comprehensive searches in the literature for research and curricular needs. With a list of 2,625 items published between 1982 and 2002 by 68 faculty members of 18 American Library Associationâ (ALA-) accredited LIS schools, hundreds of databases were searched. Results show that there are only 10 databases that provide significant coverage of the LIS indexed literature. Results also show that restricting the data sources to one, two, or even three databases leads to inaccurate rankings and erroneous conclusions. Because no database provides comprehensive coverage of the LIS literature, researchers must rely on a wide range of disciplinary and multidisciplinary databases for ranking and other research purposes. The study answers such questions as the following: Is the Association of Library and Information Science Educationâ s (ALISEâ s) directory of members a reliable tool to identify a complete list of faculty members at LIS schools? How many and which databases are needed in a multifile search to arrive at accurate publication count rankings? What coverage will be achieved using a certain number of databases? Which research areas are well covered by which databases? What alternative methods and tools are available to supplement gaps among databases? Did coverage performance of databases change over time? What counting method should be used when determining what and how many items each LIS faculty and school has published? The authors recommend advanced analysis of research productivity to provide a more detailed assessment of research productivity of authors and programs.

Nowadays, weblogs or blogs are important tools for personal or workgroup websites publication. These tools give the necessary performances to create, edit, evaluate, publish and file digital contents, in the framework of a standarized workflow, and for managing the digital information life cycle. Nevertheless, these tools must be complemented with existence of technical funcionalities necessary to get a correct implantation and use. The aim of the work is to assess the way in which weblogs implement the technical solutions necessary to utilize correctly classification tools. The evaluation took into account let to extract a collection of conclusions of great interest to analize the state of art of the content classification tools integration and the weblogs management systems. As a general conclusion, it can be assured that the current generation of weblogs management systems do not offer all the desired performances for the classical classification tools, offering also a very heterogeneous scene.

==Background== In most academic disciplines journals play an important role in disseminating findings of research among the disciplinary community members. Understanding a discipline's body of journals is therefore of grave importance when looking for previous research, compiling an overview of previous research and and in order to make a decision regarding the best place for publishing research results. Furthermore, based on Bradford's Law of scattering, one can assume that in order to be able to compile a satisfying overview of previous research a wide range of journals has to be scanned, but also that there are some 'core' journals which are of more importance to specific disciplines than others. ==Aim== This thesis aims to compile a comprehensive master list of journals which publish articles of relevance to Library and Information Science (LIS). A method to rank journals by their importance is introduced and some key characteristics of the disciplines body of journals are discussed. Databases indexing the disciplines journals are also compared. ==Method== The master list of LIS journals was created by combining the journal listings of secondary sources indexing the field's literature. These sources were six databases focusing on LIS literature: INFODATA, Current Contents, Library and Information Science Abstracts, Library Information Science Technology Abstracts, Information Science and Technology Abstracts, and Library Literature and Information Science, the LIS subsection in three databases with a general focus: Social Science Citation Index, Academic Search Premier, and Expanded Academic ASAP, and the listing of LIS journals from the Elektronische Zeitschriften Bibliothek. Problems related to editorial policies and technical shortcomings are discussed, before comparing: predominant publication languages, places of publication, open access, peer review, and the ISI Journal Impact Factors (JIF). Journals were also ranked by the number of occurrences in multiple databases in order to identify 'core' publications. The number of journals overlapping between databases are estimated and a matrix giving the overlap is visualized using multi dimensional scaling. Lastly, the degree of journals overlapping with other disciplines is measured. ==Results== A comprehensive master list of 1,205 journals publishing articles of relevance to LIS was compiled. The 968 active journals are mostly published in English, with one third of the journals coming from the US and another third from the UK and Germany. Nearly 16% of all journals are open access, 11% have a ISIJIF, and 42% are peer reviewed. Fifteen core journal could be identified and a list of the top fourteen journals published in Germany is introduced. Databases have between five to 318 journals in common and the journal collection shows an substantial overlap with a wide range of subjects, with the biggest journal overlap with Computing Studies, and Business and Economics. ==Conclusion== The aim of compiling a comprehensive list of LIS journal was achieved. The list will contribute to our understanding of scholarly communication within the LIS discipline and provide academics and practitioners with a better understanding of journals within the discipline. The ranking approach proved to be sufficient, showing good similarity with other studies over the last 40 years. The master list of LIS journals has also potential use to further research.

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Many manual biomedical annotation tasks can be categorized as instances of the typical multi-label classification problem where several categories or labels from a fixed set need to assigned to an input instance. MeSH term assignment to biomedical articles and diagnosis code extraction from medical records are two such tasks. To address this problem automatically, in this thesis, we present a way to utilize latent associations between labels based on output label sets. We used random indexing as a method to determine latent associations and use the associations as a novel feature in a learning-to-rank algorithm that reranks candidate labels selected based on either k-NN or binary relevance approach. Using this new feature as part of other features, for MeSH term assignment, we train our ranking model on a set of 200 documents, test it on two public datasets, and obtain new state-of-the-art results in precision, recall, and mean average precision. In diagnosis code extraction, we reach an average micro F-score of 0.478 based on a large EMR dataset from the University of Kentucky Medical Center, the first study of its kind to our knowledge. Our study shows the advantages and potential of random indexing method in determining and utilizing implicit relationships between labels in multi-label classification problems.

Ces dernières années, le contenu disponible sur le Web a augmenté de manière considérable dans ce qu’on appelle communément le Web social. Pour l’utilisateur moyen, il devient de plus en plus difficile de recevoir du contenu de qualité sans se voir rapidement submergé par le flot incessant de publications. Pour les fournisseurs de service, le passage à l’échelle reste problématique. L’objectif de cette thèse est d’aboutir à une meilleure expérience utilisateur à travers la mise en place de systèmes de filtrage et de recommandation. Le filtrage consiste à offrir la possibilité à un utilisateur de ne recevoir qu’un sous ensemble des publications des comptes auxquels il est abonné. Tandis que la recommandation permet la découverte d’information à travers la suggestion de comptes à suivre sur des sujets donnés. Nous avons élaboré MicroFilter un système de filtrage passant à l’échelle capable de gérer des flux issus du Web ainsi que RecLand, un système de recommandation qui tire parti de la topologie du réseau ainsi que du contenu afin de générer des recommandations pertinentes. / In the last years, the amount of available data on the social Web has exploded. For the average user, it became hard to find quality content without being overwhelmed with publications. For service providers, the scalability of such services became a challenging task. The aim of this thesis is to achieve a better user experience by offering the filtering and recommendation features. Filtering consists to provide for a given user, the ability of receiving only a subset of the publications from the direct network. Where recommendation allows content discovery by suggesting relevant content producers on given topics. We developed MicroFilter, a scalable filtering system able to handle Web-like data flows and RecLand, a recommender system that takes advantage of the network topology as well as the content in order to provide relevant recommendations.

The scale of multimedia collections has grown very fast over the last few years. Facebook stores more than 100 billion images, 200 million are added every day. In order to cope with this growth, methods for content-based image retrieval must adapt gracefully. The work presented in this thesis goes in this direction. Two observations drove the design of the high-dimensional indexing technique presented here. Firstly, the collections are so huge, typically several terabytes, that they must be kept on secondary storage. Addressing disk related issues is thus central to our work. Secondly, all CPUs are now multi-core and clusters of machines are a commonplace. Parallelism and distribution are both key for fast indexing and high-throughput batch-oriented searching. We describe in this manuscript a high-dimensional indexing technique called eCP. Its design includes the constraints associated to using disks, parallelism and distribution. At its core is an non-iterative unstructured vectorial quantization scheme. eCP builds on an existing indexing scheme that is main memory oriented. Our first contribution is a set of extensions for processing very large data collections, reducing indexing costs and best using disks. The second contribution proposes multi-threaded algorithms for both building and searching, harnessing the power of multi-core processors. Datasets for evaluation contain about 25 million images or over 8 billion SIFT descriptors. The third contribution addresses distributed computing. We adapt eCP to the MapReduce programming model and use the Hadoop framework and HDFS for our experiments. This time we evaluate eCP's ability to scale-up with a collection of 100 million images, more than 30 billion SIFT descriptors, and its ability to scale-out by running experiments on more than 100 machines.

The results of photoluminescence (PL) spectra of TlGaS2 single crystal were reported in the 500-1400 nm wavelength and in the 15-115 K temperature range. Three broad PL bands with an asymmetric Gaussian lineshapes were observed to be centered at 568 nm (A-band), 718 nm (B-band) and 1102 nm (C-band). The shift of the emission band peak energy as well as the change of the half-width of the emission band with temperature and excitation laser intensity were also studied. We analyzed the observed results using the configurational coordinate (CC) model. The powder diffraction patterns of TlInS2 and TlGaS2 crystals were obtained and the diffraction data were indexed using CRYSFIRE computer program packet. TlInS2 has hexagonal system with parameters a = 3.83 and c = 14.88 Ao. TlGaS2 has monoclinic system with parameters a = 9.62, b = 4.01 and c = 7.52 Ao with &amp / #946 / = 96.30o. Our diffraction studies at low temperatures did not reveal any phase transition for TlInS2 as reported in the literature. The specific heat capacities of both TlInS2 and TlGaS2 crystals calculated from Differential Scanning Calorimetry (DSC) measurements at low temperatures are reported in the thesis.

Searching relevant information based on content features in video databases is an interesting and challenging research topic that has drawn lots of attention recently. Video similarity search has many practical applications such as TV broadcast monitoring, copyright compliance enforcement and search result clustering, etc. However, existing studies are limited to provide fast and accurate solutions due to the diverse variations among the videos in large collections. In this thesis, we introduce the database support for effective and efficient video similarity search from various sources, even if there exists some transformation distortion, partial content re-ordering, insertion, deletion or replacement. Specifically, we focus on processing two different types of content-based queries: video clip retrieval in a large collection of segmented short videos, and video subsequence identification from a long unsegmented stream. The first part of the thesis investigates the problem of how to process a number of individual kNN searches on the same database simultaneously to reduce the computational overhead of current content-based video search systems. We propose a Dynamic Query Ordering (DQO) algorithm for efficiently processing Batch Nearest Neighbor (BNN) search in high-dimensional space, with advanced optimizations of both I/O cost and CPU cost. The second part of the thesis challenges an unstudied problem of temporal localization of similar content from a long unsegmented video sequence, with extension to identify the occurrence of potentially different ordering or length with respect to query due to video content editing. A graph transformation and matching approach supported by the above BNN search is proposed, as a filter-and-refine query processing strategy to effectively but still efficiently identify the most similar subsequence. The third part of the thesis extends the method of Bounded Coordinate System (BCS) we introduced earlier for video clip retrieval. A novel collective perspective of exploiting the distributional discrepancy of samples for assessing the similarity between two video clips is presented. Several ideas of non-parametric hypothesis tests in statistics are utilized to check the hypothesis whether two ensembles of points are from a same distribution. The proposed similarity measures can provide a more comprehensive analysis that captures the essence of invariant distribution information for retrieving video clips. For each part, we demonstrate comprehensive experimental evaluations, which show improved performance compared with state-of-the-art methods. In the end, some scheduled extensions of this work are highlighted as future research objectives.

Thesis (Ph.D.)--University of Melbourne, Dept. of Information Systems and Faculty of Architecture, Building and Planning 2006. / Typescript. Includes bibliographical references (leaves 261-275).