146 research outputs found
Increased Content Accessibility For Wikis And Blogs
This paper aims to introduce a useful approach on the combined use of template based publishing tools (i.e. for blogs and wikis) and content personalization services. The approach considers that the original developers of web content have limited awareness on accessibility issues, and they are facilitated and guided by the editing interface. The publishing mechanism is responsible for storing web content in a flexible representation, where structured content is separated from the formatting information. Intermediate brokering services (i.e aggregators, mediators or simply the portal software) produce multiple versions of the same content in order to increase content accessibility. Finally, end-users are able to set their preferences on how the content will be presented and get a homogeneous representation of the community content. The different versions may comprise multiple languages, audio and text representations etc and be based on a single version of the original content. The structured nature of content produced by template based tools allows intermediate services to intervene and reproduce the original content in various formats and client tools to filter and present information according to user needs and capabilities. The paper presents the focal points of the suggested approach, details on the underlying architecture and presents the required supporting infrastructure
Mining Frequent Generalized Patterns for Web Personalization in the presence of Taxonomies
The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern (FGP) algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories. The results can be used to generate association rules and subsequently recommendations for the users. The algorithm can be applied to the log files of a typical Web site; however, it can be more helpful in a Web 2.0 application, such as a feed aggregator or a digital library mediator, where content is semantically annotated and the taxonomic nature is more complex, requiring us to extend FGP in a version called FGP+. The authors experimentally evaluate both algorithms using Web log data collected from a newspaper Web site
Real-time recommendations for energy-efficient appliance usage in households
According to several studies, the most influencing factor in a household\u27s energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house\u27s energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%
A Framework for the Quality Assurance of Blended E-Learning Communities
Abstract. E-learning enables learners to decide what to learn, when, how and how fast. In the blended e-learning paradigm, knowledge is delivered using a combination of online and traditional distant education practices. The purpose of this paper is to propose a set of criteria for the evaluation of the educational process in blended e-learning communities. The systematic surveying and evaluation of the various parameters that affect the educational outcome is the primary aim of the quality assurance process. Existing evaluation methods provide general guidelines, which fail to cover the traditional distant education procedures (e.g. educational material, sporadic face-to-face meetings) that accompany e-learning activities. The key reason for the success of a blended e-learning approach is the balance between computer based and face-to-face interactions and the harmonic merge of the two. First, we review the current quality evaluation models for education and focus on the criteria that apply to blended e-learning approaches. Then, we discuss the issues arising from the combination of the two alternatives and propose solutions for improving the quality of the whole process
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions
The current study focuses on systematically analyzing the recent advances in
the field of Multimodal eXplainable Artificial Intelligence (MXAI). In
particular, the relevant primary prediction tasks and publicly available
datasets are initially described. Subsequently, a structured presentation of
the MXAI methods of the literature is provided, taking into account the
following criteria: a) The number of the involved modalities, b) The stage at
which explanations are produced, and c) The type of the adopted methodology
(i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are
discussed. Finally, a comprehensive analysis of current challenges and future
research directions is provided.Comment: 26 pages, 11 figure
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations
Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. a large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our aI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. a supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine. 2013 IEEE.The statements made herein are solely the responsibility of the authors. This work was supported in part by the National Priorities Research Program (NPRP) from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 10-0130-170288.Scopu
- …