Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

A Transparency Index Framework for Machine Learning powered AI in Education

The increase in the use of AI systems in our daily lives, brings calls for more ethical AI development from different sectors including, finance, the judiciary and to an increasing extent education. A number of AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI, such as fairness, explainability and safety. However, the abstract nature of these existing ethical AI guidelines often makes them difficult to operationalise in real-world contexts. The inadequacy of the existing situation with respect to ethical guidance is further complicated by the paucity of work to develop transparent machine learning powered AI systems for real-world. This is particularly true for AI applied in education and training. In this thesis, a Transparency Index Framework is presented as a tool to forefront the importance of transparency and aid the contextualisation of ethical guidance for the education and training sector. The transparency index framework presented here has been developed in three iterative phases. In phase one, an extensive literature review of the real-world AI development pipelines was conducted. In phase two, an AI-powered tool for use in an educational and training setting was developed. The initial version of the Transparency Index Framework was prepared after phase two. And in phase three, a revised version of the Transparency Index Framework was co- designed that integrates learning from phases one and two. The co-design process engaged a range of different AI in education stakeholders, including educators, ed-tech experts and AI practitioners. The Transparency Index Framework presented in this thesis maps the requirements of transparency for different categories of AI in education stakeholders, and shows how transparency considerations can be ingrained throughout the AI development process, from initial data collection to deployment in the world, including continuing iterative improvements. Transparency is shown to enable the implementation of other ethical AI dimensions, such as interpretability, accountability and safety. The 3 optimisation of transparency from the perspective of end-users and ed-tech companies who are developing AI systems is discussed and the importance of conceptualising transparency in developing AI powered ed-tech products is highlighted. In particular, the potential for transparency to bridge the gap between the machine learning and learning science communities is noted. For example, through the use of datasheets, model cards and factsheets adapted and contextualised for education through a range of stakeholder perspectives, including educators, ed-tech experts and AI practitioners

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

Virtual Coaching, Self-Directed Learning, and the Implementation of Evidence-Based Practices: A Single Qualitative Case Study

The purpose of this single instrumental case study was to understand how a virtual coaching program provides opportunities for self-directed learning during the implementation of evidence-based practices for adults at Navigator Coaching. The theory guiding this study was Deci and Ryan’s self-determination theory as conceptualizations of self-directed learning described in the literature mirror descriptions of self-determination. The central research question was: How does a virtual coaching program provide opportunities for self-directed learning during the implementation of evidence-based practices? As a single instrumental case, the setting for this study was one virtual life-coaching program in North America. The sample of participants included 12 adults who were currently enrolled in the program for a minimum of 6 months and participated in weekly program activities. Multiple data collection methods were employed to describe and understand the case: observations, audiovisual materials, and individual interviews. Interpretational analysis and a multistep data analysis process including direct interpretation, categorical aggregation, correspondence tables, and interpretive commentaries were utilized to develop the themes and overall synthesis of the case. Opportunities for self-directed learning were provided in weekly live sessions, modules in the program library, and in the Facebook group. Program members utilized instructional opportunities to satisfy their need for autonomy, thus becoming students of self. Participation in a purposeful community that was focused on solutions provided opportunities for program members to satisfy competence and relatedness needs. Program members implemented evidence-based practices and developed skills to create weekly learning plans, which assisted them in becoming agents of their highest selves

An investigation into the academic writing: Difficulties of Saudi Postgraduate Students

This interpretive study aims to investigate the difficulties in English academic writing as perceived by Saudi postgraduate students and their English supervisors in an English-speaking country. In accordance with the exploratory nature of the methodological approach adopted in this study, the research design of the current study employs a sequential mixed-methods design. The quantitative phase is represented by the questionnaire whereas semi-structured interviews and document analysis constitute the qualitative phase. From the sample, 275 students were asked to fill in the prepared questionnaire whilst 15 students, of both genders, and 9 supervisors were asked to participate in an interview. Ten samples of students’ written feedback from their supervisors were provided. Data were analysed quantitatively using SPSS descriptive statistics and qualitatively using MAXQDA software. The findings of the current study revealed that Saudi postgraduate students face the following difficulties in their English academic writing: not having sufficient academic vocabulary, avoiding plagiarism, using cohesive devices properly, constructing logical arguments, making coherent links between ideas, and demonstrating critical thinking in their academic writing. Furthermore, the current study highlighted that the difficulties could be attributed to a number of factors, including those related to learners, context, and instruction. Several strategies were proposed that could assist Saudi students in improving their academic writing. Additionally, the lack of academic preparation in the KSA had a negative influence on the proficiency of Saudi postgraduate students in their English academic writing, resulting in disparities between the expectations placed on students in their postgraduate studies in the UK and the actual results achieved by Saudi students. The findings also revealed that EAP courses in the UK often aided students in learning writing techniques; however, these courses have certain limitations. According to the findings of the current study, a theoretical model is suggested to help Saudi postgraduate students in their English academic writing. Based on the study findings, implications are drawn for policy makers and for practice in the education system in Saudi Arabia. Finally, suggestions for further research are provided

A governance framework for algorithmic accountability and transparency

Algorithmic systems are increasingly being used as part of decision-making processes in both the public and private sectors, with potentially significant consequences for individuals, organisations and societies as a whole. Algorithmic systems in this context refer to the combination of algorithms, data and the interface process that together determine the outcomes that affect end users. Many types of decisions can be made faster and more efficiently using algorithms. A significant factor in the adoption of algorithmic systems for decision-making is their capacity to process large amounts of varied data sets (i.e. big data), which can be paired with machine learning methods in order to infer statistical models directly from the data. The same properties of scale, complexity and autonomous model inference however are linked to increasing concerns that many of these systems are opaque to the people affected by their use and lack clear explanations for the decisions they make. This lack of transparency risks undermining meaningful scrutiny and accountability, which is a significant concern when these systems are applied as part of decision-making processes that can have a considerable impact on people's human rights (e.g. critical safety decisions in autonomous vehicles; allocation of health and social service resources, etc.). This study develops policy options for the governance of algorithmic transparency and accountability, based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems. Based on a review and analysis of existing proposals for governance of algorithmic systems, a set of four policy options are proposed, each of which addresses a different aspect of algorithmic transparency and accountability: 1. awareness raising: education, watchdogs and whistleblowers; 2. accountability in public-sector use of algorithmic decision-making; 3. regulatory oversight and legal liability; and 4. global coordination for algorithmic governance

VR-Based Safety Training Program for High-Rise Building Construction

The rates of fatal and non-fatal accidents within the construction industry across the globe are surging despite the massive efforts that are being exerted toward maintaining a safe working environment. Past research has proved that the provision of effective safety training programs is a primary course of action that should be taken to minimize construction accidents, fatalities, and both fatal and nonfatal injuries. However, in acknowledging the limitations of traditional safety training programs within the construction industry, several researchers have addressed the urge to incorporate novel training practices that are based on the modern virtual reality (VR) technology to promote “learning by doing” and “experiential learning” in their educational approaches. Nevertheless, there is a lack of incorporating major learning theories as a solid foundation for the design and development of VR-based training programs. Also, there is a lack of comprehensive VR-based safety training programs that specifically address the safety of high-rise building construction. This research aims to develop a comprehensive, fully immersive, and interactive VR-based safety training program that addresses the hazards and risks pertaining to the construction of high-rise buildings based on major learning theories in an attempt to enhance the learning outcomes of construction workers and safety officers. To conclude, the framework developed proved its efficiency and effectiveness in achieving the desired learning outcomes using VR-based training programs

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions