Assistive technology design and development for acceptable robotics companions for ageing years

© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe

Corporate social and environmental disclosure: evidence from Saudi Arabia

The aim of this study is to elevate the understanding of corporate social and environmental disclosure (CSED) by examining the nature and level of CSED by the listed companies in Saudi Arabia. It analyses CSED determinant’s which includes: firm characteristics and corporate governance aspects. Four theoretical perspectives, namely stakeholder, legitimacy, institutional, and Agency theory, used to assist in better understanding and analysing the findings on the CSED in Saudi Arabia. This study adopts a quantitative approach; the selected sample consists of 164 corporate reports of Saudi companies listed on the Saudi Stock Exchange, in 2012. Content analysis is used to measure the extent of social and environmental information that are reported. An information index was devised. The data were examined using descriptive and statistical tests multivariate analyses and negative binomial regression. The results show more than 70% of the companies report social and environmental information, most of the disclosures are related to human recourses, community involvement and economics. Human recourses category rate is 41.5 %, community involvement at 24.5%, and economic disclosure is 20%. Less attention is given to environmental, customers and products reporting. The Saudi government encourages companies to follow the Saudisation regulations and the Ministry of Labour regulations. Hence companies tend to report considerably more on information issues addressed by the government. This study examines the factors affecting the level of CSED which are firm characteristics and corporate governance. CSED level is positively associated with firm characteristics (firm size, age, profitability, and leverage), and corporate governance mechanism (government ownership and audit firm size). There were no significant results for managerial ownership, foreign ownership, CEO duality, board size and independency. The determinants of CSED categories indicate that firm age is the most influential factor affecting the five categories and human resource is the category that is related with most of the factors

Predicting and Improving Performance on Introductory Programming Courses (CS1)

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This thesis makes five fundamental contributions. The first is a revalidation of a prediction model named PreSS. The second contribution is the development of a web-based, real time implementation of the PreSS model, named PreSS#. The third contribution is a large longitudinal, multi-variate, multi-institutional study identifying predictors of performance and analysing machine learning techniques (including deep learning and convolutional neural networks) to further develop the PreSS model. This resulted in a prediction model with approximately 71% accuracy, and over 80% sensitivity, using data from 11 institutions with a sample size of 692 students. The fourth contribution is a study on insights on gender differences in CS1; identifying psychological, background, and performance differences between male and female students to better inform the prediction model and the interventions. The final, fifth contribution, is the development of two interventions that can be implemented early in CS1, once identified by PreSS# to potentially improve student outcomes. The work described in this thesis builds substantially on earlier work, providing valid and reliable insights on gender differences, potential interventions to improve performance and an unsurpassed, generalizable prediction model, developed into a real time web-based system

2008-2009, University of Memphis bulletin

University of Memphis bulletin containing the undergraduate catalog for 2008-2009.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1449/thumbnail.jp

2011-2012, University of Memphis bulletin

University of Memphis bulletin containing the undergraduate catalog for 2011-2012.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1452/thumbnail.jp

2009-2010, University of Memphis bulletin

University of Memphis bulletin containing the undergraduate catalog for 2009-2010.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1450/thumbnail.jp

2010-2011, University of Memphis bulletin

University of Memphis bulletin containing the undergraduate catalog for 2010-2011.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1451/thumbnail.jp

2013-2014, University of Memphis bulletin

University of Memphis bulletin containing the undergraduate catalog for 2013-2014.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1454/thumbnail.jp