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

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

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%

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

Techno-economic assessment of building energy efficiency systems using behavioral change: A case study of an edge-based micro-moments solution

Energy efficiency based on behavioral change has attracted increasing interest in recent years, although, solutions in this area lack much needed techno-economic analysis. That is due to the absence of both prospective studies and consumer awareness. To close such gap, this paper proposes the first techno-economic assessment of a behavioral change-based building energy efficiency solution, to the best of the authors' knowledge. From the one hand, the technical assessment is conducted through (i) introducing a novel edge-based energy efficiency solution; (ii) analyzing energy data using machine learning tools and micro-moments, and producing intelligent, personalized, and explainable action recommendations; and (iii) proceeding with a technical evaluation of four application scenarios, i.e., data collection, data analysis and anomaly detection, recommendation generation, and data visualization. On the other hand, economic assessment is performed by examining the marketability potential of the proposed solution via a market and research analysis of behavioral change-based systems for energy efficiency applications. Also, various factors impacting the commercialization of the final product are investigated before providing recommended actions to ensure its potential marketability via conducting a Go/No-Go evaluation. In conclusion, the proposed solution is designed at a low cost and can save up to 28%-68% of the consumed energy, which results in a Go decision to commercialize the technology. 2021 Elsevier LtdThis paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Blockchain-based recommender systems: Applications, challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Use of virtual communities for the welfare of groups with particular needs

Abstract The growth of scientific, technological and technical knowledge created the need for fast and precise communication of information and effective training of physicians, nurses and patients. Our aim is to support groups with particular needs by employing methods used by "communities of practice" and "learning communities" of the internet. The idea is to build bring together patients, their families, doctors and nurses into self-supportive communities, where doctors will disseminate their scientific knowledge, nurses will provide practical advices and family members will exchange empirical knowledge. The approach exploits internet merits and extends group-therapy in two axis: a) universal (distant) membership, b) asynchronous consults and support. We describe the structure, roles and services of self-supportive "web communities of patients"