Design and evaluation of a personal digital assistant- based alerting service for clinicians.

PURPOSE: This study describes the system architecture and user acceptance of a suite of programs that deliver information about newly updated library resources to clinicians' personal digital assistants (PDAs). DESCRIPTION: Participants received headlines delivered to their PDAs alerting them to new books, National Guideline Clearinghouse guidelines, Cochrane Reviews, and National Institutes of Health (NIH) Clinical Alerts, as well as updated content in UpToDate, Harrison's Online, Scientific American Medicine, and Clinical Evidence. Participants could request additional information for any of the headlines, and the information was delivered via e-mail during their next synchronization. Participants completed a survey at the conclusion of the study to gauge their opinions about the service. RESULTS/OUTCOME: Of the 816 headlines delivered to the 16 study participants' PDAs during the project, Scientific American Medicine generated the highest proportion of headline requests at 35%. Most users of the PDA Alerts software reported that they learned about new medical developments sooner than they otherwise would have, and half reported that they learned about developments that they would not have heard about at all. While some users liked the PDA platform for receiving headlines, it seemed that a Web database that allowed tailored searches and alerts could be configured to satisfy both PDA-oriented and e-mail-oriented users.     

Web usage mining at an academic health sciences library: an exploratory study.

OBJECTIVES: This paper explores the potential of multinomial logistic regression analysis to perform Web usage mining for an academic health sciences library Website. METHODS: Usage of database-driven resource gateway pages was logged for a six-month period, including information about users' network addresses, referring uniform resource locators (URLs), and types of resource accessed. RESULTS: It was found that referring URL did vary significantly by two factors: whether a user was on-campus and what type of resource was accessed. CONCLUSIONS: Although the data available for analysis are limited by the nature of the Web and concerns for privacy, this method demonstrates the potential for gaining insight into Web usage that supplements Web log analysis. It can be used to improve the design of static and dynamic Websites today and could be used in the design of more advanced Web systems in the future.     

Character N-Gram Tokenization for European Language Text Retrieval

The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n = 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval.     

Scholarly communication in the digital environment: what do authors want?

This article reports on a large‐scale international survey of authors' perception and experience of the journals system conducted by ciber in association with National Opinion Polls (NOP). It explores the factors that inform authors' decisions where to publish and, in particular, which groups of readers they perceive to be most important. It probes readership behaviour and the values that underlie authors' attitudes towards copyright and emerging business models, notably open access. It is concluded that many aspects of author behaviour are highly conservative and that a significant shift towards open access is, in the short to medium term, highly unlikely.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.