Measuring quality of perception in distributed multimedia: Verbalizers vs. imagers

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2008 ElsevierThis paper presents the results of a study which investigated the impact of cognitive styles on perceptual multimedia quality. More specifically, we examine the different preferences demonstrated by verbalizers and imagers when viewing multimedia content presented with different quality of service (QoS) levels pertaining to frame rates and color depth. Recognizing multimedia’s infotainment duality, we used the quality of perception (QoP) metric to characterize perceived quality. Results showed that in terms of low and high dynamisms clips, the frame rate at which multimedia content is displayed influences the levels of information assimilated by Imagers. Whilst black and white presentations are shown to be beneficial for both Biomodals and Imagers in order to experience enhanced levels of information assimilation, Imagers were shown to enjoy presentations in full 24-bit colour

Development Of Information Visualization Methods For Use In Multimedia Applications

The aim of the article is development of a technique for visualizing information for use in multimedia applications. In this study, to visualize information, it is proposed first to compile a list of key terms of the subject area and create data tables. Based on the structuring of fragments of the subject area, a visual display of key terms in the form of pictograms, a visual display of key terms in the form of images, and a visual display of data tables are performed. The types of visual structures that should be used to visualize information for further use in multimedia applications are considered. The analysis of existing visual structures in desktop publishing systems and word processors is performed.To build a mechanism for visualizing information about the task as a presentation, a multimedia application is developed using Microsoft Visual Studio software, the C# programming language by using the Windows Forms application programming interface. An algorithm is proposed for separating pieces of information text that have key terms. Tabular data was visualized using the “parametric ruler” metaphorical visualization method, based on the metaphor of a slide rule.The use of the parametric ruler method on the example of data visualization for the font design of children's publications is proposed. Interaction of using the method is ensured due to the fact that the user will enter the size of the size that interests for it and will see the ratio of the values of other parameters. The practical result of the work is the creation of a multimedia application “Visualization of Publishing Standards” for the visualization of information for the font design of publications for children. The result of the software implementation is the finished multimedia applications, which, according to the standardization visualization technique in terms of prepress preparation of publications, is the final product of the third stage of the presentation of the visual for

Bibliometric cartography of information retrieval research by using co-word analysis

The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987-1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987-1991 and 1992-1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes

Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.The following article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning to support lifelong learners. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners. Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. It further suggests an evaluation approach for recommender systems in technology-enhanced learning.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones