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Session Overview
Session
Paper Session 01: Information and Technology [SDGs 4,12]
Time:
Sunday, 25/Oct/2020:
11:00am - 12:30pm

Session Chair: Dietmar Wolfram, University of Wisconsin-Milwaukee, United States of America

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Presentations
11:00am - 11:15am
ID: 145 / PS-01: 1
Long Papers
Topics: Library and Information Science
Keywords: virtual reality, library programming, informal learning, social engagement, digital media

Librarian Perspectives on the Role of Virtual Reality in Public Libraries

Kung Jin Lee1, W.E. King1, Negin Dahya2, Jin Ha Lee1

1University of Washington, USA; 2University of Toronto, Canada

This paper explores librarians’ perception of virtual reality as well as opportunities and challenges associated with implementing VR in public libraries. We interviewed 36 librarians who developed and offered VR programs as part of the research project, before and after the programming. The findings show how VR is used in the library in the focus as a learning tool and to be used as a social tool. Across the interviews, librarians talk about how the immersion and interactivity of what VR has to offer is different from other digital media. However, the unique qualities of VR, discussed throughout the paper, present possible challenges associated with offeringVR in the library. Librarians observed that while VR has a lot of potential as a technology for learning and social engagement it also depends on the different content being provided. The librarians felt that this criteria has yet been achieved. In addition, each librarian had a different understanding of what constitutes learning.



11:15am - 11:30am
ID: 221 / PS-01: 2
Long Papers
Topics: Library and Information Science
Keywords: Digital library, user-centred design, user experience, interface, instrumental and non-instrumental quality, mood

Evaluating the User Experience in a Digital Library

Maram Barifah1, Monica Landoni1, Ayman Eddakrouri2

1USI, Switzerland; 2Effat University, Saudi Arabia

Digital libraries (DL) have been evaluated through two main meth- ods: implicitly by analyzing the query behavior extracted from the log files (LF), and explicitly by conducting user studies and usability tests. Unfortunately, such techniques fail to provide a full understanding of the quality of the user experience (UX). Re- cently, researchers called for developing measurements that are more meaningful by considering UX which is gaining momentum as a critical success factor across all sectors. UX is considered as the ultimate criterion of the user-centered design. This paper tries to fill this gap by adapting Mahlke’s interaction user experience frame- work to DL. A laboratory user study with 65 undergraduate students was conducted. Three data collection tools were considered: ques- tionnaires, screen recording, and pick-a-mode scale. The findings of the study indicate that considering the UX when evaluating the DL provides more insights of different aspects of the user interactions, perceptions and affective variables. The need to improve the DL system and simplify its interface still persists. Revisiting interfaces by visualizing the functions is a possible solution.



11:30am - 11:40am
ID: 143 / PS-01: 3
Short Papers
Topics: Data Science; Analytics; and Visualization
Keywords: cross-lingual plagiarism detection, cross-lingual word embedding, word mover's distance

Employing Word Mover’s Distance for Cross-Lingual Plagiarized Text Detection

Chia-Ming Chang, Chia-Hsuan Chang, San-Yih Hwang

National Sun Yat-sen University, Taiwan

While the blossom of globalization has achieved many successes, cross-lingual plagiarism, however, has become popular. Detecting plagiarism across languages requires the capability of comparing semantical similarities between texts of different languages. Previous works rely on massive bilingual resources such as comparable corpus, parallel corpus, and even commercial machine translation as references. However, towards domain-specific applications, collecting such resources is labor-intensive and impractical. Also, the absence of interpretability of existing methods lead to the difficulty of investigating retrieval results by humans. Hence, it is imperative to have a resource-light and interpretable method for cross-lingual plagiarism detection. In this study, we propose a new detection method, called CL-WMD, which is built upon word embedding techniques. CL-WMD requires only a small set of translation pairs to constitute a bilingual reference and calculates semantical distances between texts by word mover’s distance, which can provide explicable word alignment information between two compared text spans. Our experiments are conducted under a bilingual scientific publication corpus composed of two typologically diverse languages: English and Chinese. The results demonstrate that CL-WMD has higher accuracy than most existing methods and achieves better or comparable performance when compared to the translation-based method in paragraph-level and sentence-level plagiarism detection tasks.



 
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