Personalized And Situation-Aware Recommendations For Runners

The project uService investigates the transformation of a mobile user into a service super prosumer, i.e., a producer, provider and consumer of services at the same time. The goal is to develop a platform which enables a user to create, discover and consume mobile services anywhere and at any time on the mobile device. uRun is an application scenario of the project in the field of mobile health and fitness. The uRun framework provides a mobile assistance system particularly for runners, which combines Web 2.0 and Web 3.0 technologies and personalized and situation-aware recommendation mechanisms. The ability to create individual and mobile health and fitness services as well as a personalized and situation-aware assistance system based on a semantic knowledge base are considered to provide an edge over existing consumer-centric health care systems. In this article, we describe the recommendation mechanism and the incorporation of semantic knowledge for the uService platform and the uRun framework

Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also found five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. In addition, we investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavior has been developed

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be ``holistic'', i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: rst, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best t the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of ``holistic'' RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles

CeTEAL News, September/October 2017

https://digitalcommons.coastal.edu/ceteal-news/1004/thumbnail.jp

Product configuration of photovoltaic systems in developing countries – Case Ghana

This study aims at introducing product configuration of photovoltaic systems in developing countries with focus on Ghana. The current problem of electricity production and delivery in Ghana forms the background of the objective of the study. Solving this, particular attention is focused on solar photovoltaic technologies. The objective was to look at how various configurations of photovoltaic systems would help either households or businesses in developing countries to improve their day-to-day life and activities. Focusing on photovoltaic system configurations offers households and businesses the options of standalone, backup or hybrid systems although, the study limits its options to backup systems as a result of the rationing of electricity in Ghana. The theoretical part provided comprehensive background for the study with an insight into the current energy situation and the renewable energy policies in Ghana. Furthermore, an in-depth understanding of the different components (i.e. panels, charge controllers, inverters, battery and load) of a photovoltaic system is achieved with a look at their basic technical parameters. The empirical research is conducted via a focus group study and a survey. The focus group study was conducted in Finland among African students while the survey was done in Ghana through questionnaire sent to 102 respondents via an online survey portal: Google Form. From the result, the most common areas of use for solar photovoltaic are for lighting, household and office appliances for which varied configuration can be established. The research established the electrification problem in Ghana and one key recommendation in solving this is the use of renewable energy such as solar photovoltaic systems.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A knowledge-based approach towards human activity recognition in smart environments

For many years it is known that the population of older persons is on the rise. A recent report estimates that globally, the share of the population aged 65 years or over is expected to increase from 9.3 percent in 2020 to around 16.0 percent in 2050 [1]. This point has been one of the main sources of motivation for active research in the domain of human activity recognition in smart-homes. The ability to perform ADL without assistance from other people can be considered as a reference for the estimation of the independent living level of the older person. Conventionally, this has been assessed by health-care domain experts via a qualitative evaluation of the ADL. Since this evaluation is qualitative, it can vary based on the person being monitored and the caregiver\u2019s experience. A significant amount of research work is implicitly or explicitly aimed at augmenting the health-care domain expert\u2019s qualitative evaluation with quantitative data or knowledge obtained from HAR. From a medical perspective, there is a lack of evidence about the technology readiness level of smart home architectures supporting older persons by recognizing ADL [2]. We hypothesize that this may be due to a lack of effective collaboration between smart-home researchers/developers and health-care domain experts, especially when considering HAR. We foresee an increase in HAR systems being developed in close collaboration with caregivers and geriatricians to support their qualitative evaluation of ADL with explainable quantitative outcomes of the HAR systems. This has been a motivation for the work in this thesis. The recognition of human activities \u2013 in particular ADL \u2013 may not only be limited to support the health and well-being of older people. It can be relevant to home users in general. For instance, HAR could support digital assistants or companion robots to provide contextually relevant and proactive support to the home users, whether young adults or old. This has also been a motivation for the work in this thesis. Given our motivations, namely, (i) facilitation of iterative development and ease in collaboration between HAR system researchers/developers and health-care domain experts in ADL, and (ii) robust HAR that can support digital assistants or companion robots. There is a need for the development of a HAR framework that at its core is modular and flexible to facilitate an iterative development process [3], which is an integral part of collaborative work that involves develop-test-improve phases. At the same time, the framework should be intelligible for the sake of enriched collaboration with health-care domain experts. Furthermore, it should be scalable, online, and accurate for having robust HAR, which can enable many smart-home applications. The goal of this thesis is to design and evaluate such a framework. This thesis contributes to the domain of HAR in smart-homes. Particularly the contribution can be divided into three parts. The first contribution is Arianna+, a framework to develop networks of ontologies - for knowledge representation and reasoning - that enables smart homes to perform human activity recognition online. The second contribution is OWLOOP, an API that supports the development of HAR system architectures based on Arianna+. It enables the usage of Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). The third contribution is the evaluation and exploitation of Arianna+ using OWLOOP API. The exploitation of Arianna+ using OWLOOP API has resulted in four HAR system implementations. The evaluations and results of these HAR systems emphasize the novelty of Arianna+