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Development and demonstration of intelligent technology for semiconductor plasma processing equipment
No full textResearch of Extreme Scaling Problems in Science and Engineering through Ultra Massive Computing Technology
No full textApplication of Homomorphic Encryption Technology to Privacy Preserving Remote Computation for Medical Big Data Analysis
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Signaling Smartness: Smart Cities and Digital Art in Public Spaces
Get PDFInformational urbanism is a new research area in information science. In this study, art history joins informational urbanism: Are digital artworks in public urban spaces recognized as essential assets of a smart city? We employed case study research, working with the example of the huge digital media façade of the Arthouse Graz as an artwork in a public space. In a mixed-methods approach, we asked passers-by and interviewed experts on Graz as a smart city and on the Arthouse’s role concerning the image of Graz as a smart city. The research found strong hints that indeed digital artworks with large screens or media façades at public spaces are parts of a city’s weak location factors as well as of the city’s urban structure and may symbolize the city’s smartness. A practical implication of this finding is that artists, computer and information scientists, city planners, and architects should include interactive contemporary digital art into city spaces in order to demonstrate the city’s way towards knowledge society
Altmetrics: Factor Analysis for Assessing the Popularity of Research Articles on Twitter
Get PDFAltmetrics measure the frequency of references about an article on social media platforms, like Twitter. This paper studies a variety of factors that affect the popularity of articles (i.e., the number of article mentions) in the field of psychology on Twitter. Firstly, in this study, we classify Twitter users mentioning research articles as academic versus non-academic users and experts versus non-experts, using a machine learning approach. Then we build a negative binomial regression model with the number of Twitter mentions of an article as a dependant variable, and nine Twitter related factors (the number of followers, number of friends, number of status, number of lists, number of favourites, number of retweets, number of likes, ratio of academic users, and ratio of expert users) and seven article related factors (the number of authors, title length, abstract length, abstract readability, number of institutions, citation count, and availability of research funding) as independent variables. From our findings, if a research article is mentioned by Twitter users with a greater number of friends, status, favourites, and lists, by tweets with a large number of retweets and likes, and largely by Twitter users with academic and expertise knowledge on the field of psychology, the article gains more Twitter mentions. In addition, articles with a greater number of authors, title length, abstract length, and citation count, and articles with research funding get more attention from Twitter users
A Conceptual Framework for an Information Behavior Model Based on the Collaboration Perspective between User and System for Information Retrieval
Get PDFThis research aimed (1) to study and analyze the ability of current information retrieval (IR) systems based on views of information behavior (IB), and (2) to propose a conceptual framework for an IB model based on the collaboration between the system and user, with the intent of developing an IR system that can apply intelligent techniques to enhance system efficiency. The methods in this study consisted of (1) document analysis which included studying the characteristics and efficiencies of the current IR systems and studying the IB models in the digital environment, and (2) implementation of the Delphi technique through an in-depth interview method with experts. The research results were presented in three main parts. First, the IB model was categorized into eight stages, different from traditional IB, in the digital environment, which can correspond to all behaviors and be applied to with an IR system. Second, insufficient functions and log file storage hinder the system from effectively understanding and accommodating user behavior in the digital environment. Last, the proposed conceptual framework illustrated that there are stages that can add intelligent techniques to the IR system based on the collaboration perspective between the user and system to boost the users’ cognitive ability and make the IR system more user-friendly. Importantly, the conceptual framework for the IB model based on the collaboration perspective between the user and system for IR assisted the ability of information systems to learn, recognize, and comprehend human IB according to individual characteristics, leading to enhancement of interaction between the system and users