107 research outputs found
Human Factors As A Parameter For Improving Interface Usability And User Satisfaction
The endeavour to optimize HCI should integrate a wide array of user characteristics that have an effect throughout users’ interactions with a system. Human factors such as cognitive traits and current state, from a psychological point of view, are undoubtedly significant in the shaping of the perceived and objective quality of interactions with a system. The research that is presented in this paper focuses on identifying human factors that relate to users’ performance in Web applications that involve information processing, and a framework of personalization rules that are expected to increase users’ performance is depicted. The empirical results that are presented are derived from environments both learning and commercial; in the case of e-learning personalization was beneficial, while the interaction with a commercial site needs to be further investigated due to the implicit character of information processing in the Web
RFID-Integrated Retail Supply Chain Services: Lessons Learnt From The Smart Project
This paper proposes a service-oriented architecture that utilizes the automatic, unique identification capabilities of RFID technology, data stream management systems and web services, to support RFID-integrated supply chain services. In the lifespan of SMART project (IST-2005, FP6) two services have been deployed supporting dynamic-pricing of fresh products and management of promotion events. The two services have been field-tested in three retail stores in Greece, Ireland, and Cyprus. The valuable lessons learnt, concerning RFID readability challenges, consumer privacy, customers and store staff health concerns, investment cost, and so on, are reported to provide guidance to future developers of RFID-integrated supply chain services as well as to set an agenda for academic research
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
ADAPTING mLEARNING ENVIRONMENTS ON LEARNERS’ COGNITIVE STYLES AND VISUAL WORKING MEMORY SPAN
The research that is described in this paper focuses on incorporating theories of individual differences in information processing within the context of mobile hypertext and hypermedia interactive environments. Based on previous findings of the authors in the field of adaptive eLearning, the main purpose was to enhance the quality of information presentation and users’ interactions in the Web by matching their specific needs and preferences. Our more recent experiments, explore how to improve learning process by adapting course content presentation to student cognitive styles and capabilities in mobile environments such as PDA phones. A framework has been developed to comprehensively model student’s cognitive styles and visual working memory span and present the appropriate subject matter, including the content, format, guidance, etc. to suit an individual student by increasing efficiency during interaction. Main aim is to overcome constraints like small screen size and processing/memory capabilities for navigation enhancements that limit the presentation and guidance of the material. An increase on users’ satisfaction as well as more efficient information processing (both in terms of accuracy and task completion time), has been observed in the personalized condition than the original one. Consequently, it is supported that human factors may be used in order to enhance the design of mobile hypertext (or hypermedia) environments in a measurable and meaningful way
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures
Cloud controllers support the operation and quality management of dynamic cloud architectures by automatically scaling the compute resources to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of architecture adaptation rules. However, for a cloud provider, deployed application architectures are black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. We propose the dynamic learning of adaptation rules for deployed application architectures in the cloud. We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime. The benefit is that we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to configure cloud controllers by simply adjusting weights representing priorities for architecture quality instead of defining complex rules. FQL4KE has been experimentally validated using the cloud application framework ElasticBench in Azure and OpenStack. The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scalin
07061 Abstracts Collection -- Autonomous and Adaptive Web Services
From 4.2.2007 to 9.2.2007, the Dagstuhl Seminar 07061 ``Autonomous and Adaptive Web Services\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Ethics of personalized information filtering
Online search engines, social media, news sites and retailers are all investing heavily in the development of ever more refined information filtering to optimally tune their services to the specific demands of their individual users and customers. In this position paper we examine the privacy consequences of user profile models that are used to achieve this information personalization, the lack of transparency concerning the filtering choices and the ways in which personalized services impact the user experience. Based on these considerations we argue that the Internet research community has a responsibility to increase its efforts to investigate the means and consequences of personalized information filtering
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