Fostering computational thinking skills with tangible user interfaces

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonGiven how technology surrounds our whole life, learning to code is becoming more and more crucial for the general public: think for example of the amount of software involved in managing a flight, or when you just turn on the engine of your car. People want to play an increasingly active role in their life and there is already evidence in an overall heightened interest in coding from the many successful public initiatives aiming at introducing coding skills to a wide audience. Nonetheless, coding skills are not just about programming but require an ability of problem-solving, abstraction, pattern recognition to name but a few; in a word, the so-called Computational Thinking (CT) skills, namely a set of thinking skills, habits, and approaches that are integral to solving complex problems using a computer and widely applicable in today’s information society. Due to this sudden global interest in promoting CT skills to many broad and diverse audiences, several tools and methods have been designed with the aim of supporting the introduction of programming concepts in more effective and less daunting ways than the past. A popular theory of learning that can come to the aid on this matter is Piaget’s constructivism, which argues that people produce knowledge and form new meanings based upon their experiences in the real world and social interactions. Thus, exploiting human’s natural ability for objects manipulation in the physical world and its afforded interactions could be an effective way of supporting users in learning abstract concepts such as the ones underpinning CT. Tangible User Interfaces are an interaction paradigm that was devised to foster collaborative learning and exploit humans’ natural dexterity for physical objects manipulation to provide an easy to use interface that can be used even by inexperienced people. They exploit the physical world to offer a concrete representation of the abstract concepts learners usually struggle with and thus employing them to teach those concepts underpinning CT might represent an effective and engaging way of supporting the learning of such skills. This thesis investigates this claim through the development of a software platform combining its digital and physical features to promote CT skills in different domains. The platform design is informed by a review of related work, a workshop with domain experts, and was validated through a series of studies in different application scenarios which reported promising results in terms of CT support

MEASURING RESILIENT COMMUNITIES : An analytical and predictive tool

© 2022 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong. This is the accepted manuscript version of an article which has been published in final form at https://caadria2022.org/measuring_resilient_communities_an_analytical_and_predictive_tool/This work presents the initial results of an analytical tool designed to quantitatively assess the level of resilience of urban areas. We use Deep Neural Networks to extract features of resilience from a trained model that classifies urban areas using a pre-assigned value range of resilience. The model returns the resilience value for any urban area, indicating the distance between the centre of the selected area and relevant typologies, including green areas, buildings, natural elements and infrastructures. Our tool also indicates the urban morphological characteristics that have a larger impact on the resilience score. In this way we can learn why a neighbourhood is successful (or not) and how to improve its level of resilience. The model employs Convolutional Neural Networks (CNNs) with Keras on Tensorflow for the computation. The outputs are loaded onto a Node.JS environment and bootstrapped with React.js to generate the online demo.Final Accepted Versio

Resilient Communities: A Novel Workflow

© 2021 Carta, Pintacuda, Owen and Turchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/This study presents a novel workflow to define how resilient communities can be analysed and improved through the optimisation of sustainable design principles through quantitative methods. Our model analyses successful sustainable communities extracting information about daily routines (commuting, working, use of buildings etc.). From these routines, we infer a set of key successful aspects based on location, density and proximity. We then model a resilient community and analyse it using a combination of clustering techniques to find patterns and correlations in the success of existing communities. The proposed workflow is applied to the city of Copenhagen as a case study. The aim of the proposed model is to suggest to designers and city-level policy makers improvements (with manipulation of variables like density, proximity and location of urban typologies) to help them to achieve different levels of sustainable goals as set out by the United Nations Global Challenges including integration inclusiveness and resilience. By using a clustering technique, patterns of proximity have been identified along with density and initial correlations in the observed urban typologies. Some of these correlations were used to illustrate the potential of this novel workflow.Peer reviewedFinal Published versio

An ant-colony based approach for real-time implicit collaborative information seeking

This document is an Accepted Manuscript of the following article: Alessio Malizia, Kai A. Olsen, Tommaso Turchi, and Pierluigi Crescenzi, ‘An ant-colony based approach for real-time implicit collaborative information seeking’, Information Processing & Management, Vol. 53 (3): 608-623, May 2017. Under embargo until 31 July 2018. The final, definitive version of this paper is available online at doi: https://doi.org/10.1016/j.ipm.2016.12.005, published by Elsevier Ltd.We propose an approach based on Swarm Intelligence — more specifically on Ant Colony Optimization (ACO) — to improve search engines’ performance and reduce information overload by exploiting collective users’ behavior. We designed and developed three different algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms — NaïveRank, RandomRank, and SessionRank — leverage on different principles of ACO in order to exploit users’ interactions and provide them with more relevant results. We designed an evaluation experiment employing two widely used standard datasets of query-click logs issued to two major Web search engines. The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users’ intent.Peer reviewedFinal Accepted Versio

A data-driven agent based simulation platform for early health economics device evaluation

Health economics is a relatively new but growing field within the discipline of economics and is concerned with making the best use of scarce resources. Early health economic estimates of new medical devices, in particular, can assist producers of health technology in making appropriate product design and investment decisions. It allows companies to understand their likely market and possible reimbursement more thoroughly. Despite the many advantages of point-of-care testing the key problem facing decision makers at the moment is the poor understanding of the potential value gained from new or alternative product or service offerings. Understanding medical device features in the wider market place can be addressed used agent based modelling and simulation (ABMS). In this paper we examine the use of ABMS underpinned by a novel data-driven approach to model generation. A sepsis use case is presented where pathway and device characteristics are defined using the ‘headroom’ method and a semantic evidence capture application. Types and sub-types are automatically extracted into agent models and subsequently executed in our own data-driven agent based simulation platform (TEASIM). A highly typed data-driven approach is evaluated in a manner that clearly presents the technical aspects of TEASIM platform and its practical usage. Initial evaluation of a data-driven approach (and the TEASIM platform) is positive. The approach offers a viable guide to product development in a cost-effective manner, especially in the earlier stages when deciding between potential product configurations or features.Innovate UK provided the funding for the Tea-PoCT project

TAPAS : A tangible End-User Development tool supporting the repurposing of Pervasive Displays

This document is the Accepted Manuscript version. Under embargo until 8 June 2018. The final, definitive version is available online at doi: https://doi.org/10.1016/j.jvlc.2016.11.002, published by Elsevier Ltd.These days we are witnessing a spread of many new digital systems in public spaces featuring easy to use and engaging interaction modalities, such as multi-touch, gestures, tangible, and voice. This new user-centered paradigm — known as the NUI — aims to provide a more natural and rich experience to end users; this supports its adoption in many ubiquitous domains, as it naturally holds for Pervasive Displays: these systems are composed of variously-sized displays and support many-to-many interactions with the same public screens at the same time. Due to their public and moderated nature, users need an easy way of adapting them to heterogeneous usage contexts in order to support their long-term adoption. In this paper, we propose an End-User Development approach to this problem introducing TAPAS, a system that combines a tangible interaction with a puzzle metaphor, allowing users to create workflows on a Pervasive Display to satisfy their needs; its design and visual syntax stem from a study we carried out with designers, whose findings are also part of this work. We then carried out a preliminary evaluation of our system with second year university students and interaction designers, gathering useful feedback to improve TAPAS and employ it in many other domains.Peer reviewedFinal Accepted Versio

Human-AI co-creation: evaluating the impact of large-scale text-to-image generative models on the creative process

Large-scale Text-to-image Generative Models (LTGMs) are a cutting-edge class of Artificial Intelligence (AI) algorithms specifically designed to generate images from natural language descriptions (prompts). These models have demonstrated impressive capabilities in creating high-quality images from a wide range of inputs, making them powerful tools for non-technical users to tap into their creativity. The field is advancing rapidly and we are witnessing the emergence of an increasing number of tools, such as DALL-E, MidJourney and StableDiffusion, that are leveraging LTGMs to support creative work across various domains. However, there is a lack of research on how the interaction with these tools might affect the users’ creativity and their ability to control the generated outputs. In this paper, we investigate how the interaction with LTGMs-based tools might impact creativity by analyzing the feedback provided by groups of design students developing an architectural project with the help of LTGMs tools

RECOMM. Measuring Resilient Communities: an analytical and predictive tool

We present initial findings of our project RECOMM: an analytical tool that evaluates the resilience of urban areas. The tool utilises Deep Neural Networks to identify characteristics of resilience and assigns a resilience score to different urban areas based on the proximity to certain features such as green spaces, buildings, natural elements, and infrastructure. The tool also identifies which urban morphological factors have the greatest impact on resilience. The method uses Convolutional Neural Networks (CNNs) with the Keras library on Tensorflow for calculations and the results are displayed in an online demo built with Node.js and React.js. This work contributes to the analysis and design of sustainable cities and communities by offering a tool to assess resilience through urban form

CINT City Net-Zero tool: a method to quantitatively assess carbon data in urban areas

We present CINT (City Net-zero Tool): an analytical and predictive model de-signed to quantitatively assess carbon data in urban areas. We developed a work-flow to collect existing data from city councils on carbon footprint, consumption and production, and tested the inter-operability between urban public data and GIS data. We implemented the model using Kernel Density Estimation (KDE) to infer the carbon emissions related to individual buildings based on a station-based dataset. We present initial testing on the integration of data from OpenStreetMap with a vector model and initial testing on modelling data into graph networks to generate enquiries and inference on carbon data and urban scenarios. This method shows how we can integrate more datasets into our base model (graph-based geo-referenced map) to infer unknown information (for example the estimated NO2 emission per each building)

Rich digital collaborations in a small rural community

In this chapter we describe experience in the design and installation of a low-cost multi-touch table in a rural island community. We will discuss the creation of the table including: pragmatic challenges of installation, and then re-installation as the physical fabric of the multi-purpose building (café, cinema, meeting area and cattle market) altered; technical challenges of using off-the-shelf components to create state-of-the art multi-touch interactions and tactile BYOD (bring your own device) end-user programming; design challenges of creating high-production value bespoke mountings and furniture using digital fabrication in an environment that could include sewing needles, ketchup laden sandwiches and cow manure. The resulting installation has been used in semi-in-the-wild studies of bespoke applications, leading to understandings of the way small communities could use advanced interactions. More broadly this sits within a context of related studies of information technology in rural developments and a desire to understand how communities can become users of the rich streams of open data now available, and, perhaps more important, offer ways in which small communities can become empowered through the creation and control of their own data