A Systematic Review of User Mental Models on Applications Sustainability

In Human-Computer Interaction (HCI), a user’s mental model affects application sustainability. This study's goal is to find and assess previous work in the area of user mental models and how it relates to the sustainability of application. Thus, a systematic review process was used to identify 641 initial articles, which were then screened based on inclusion and exclusion criteria. According to the review, it has been observed that the mental model of a user has an impact on the creation of applications not only within the domain of Human-Computer Interaction (HCI), but also in other domains such as Enterprise Innovation Ecology, Explainable Artificial Intelligence (XAI), Information Systems (IS), and various others. The examined articles discussed company managers' difficulties in prioritising innovation and ecology, and the necessity to understand users' mental models to build and evaluate intelligent systems. The reviewed articles mostly used experimental, questionnaire, observation, and interviews, by applying either qualitative, quantitative, or mixed-method methodologies. This study highlights the importance of user mental models in application sustainability, where developers may create apps that suit user demands, fit with cognitive psychology principles, and improve human-AI collaboration by understanding user mental models. This study also emphasises the importance of user mental models in the long-term viability and sustainability of applications, and provides significant insights for application developers and researchers in building more user-centric and sustainable applications

Dynamical models and machine learning for supervised segmentation

This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials

Development of a Procedure for Automating Thermal Zoning for Building Energy Simulation

Although several of today’s Building Information Modeling (BIM) tools automatically produce default building thermal zoning in the required Building Energy Simulation (BES) electronic formats, these same models do not provide detailed documentation about how their algorithm(s) work or any guidance about how to create and evaluate the building thermal zones in the BES during the early stages of the design of buildings, relying instead on the user to select the thermal zones. Therefore, there is a need to develop a well-documented, accurate thermal zoning method that can assist designers with their building energy simulation. The purpose of this study is to develop a method to automatically or semi-automatically divide a commercial building into HVAC thermal zones for a building energy simulation that provides feedback to the user regarding how the resultant zones provide comfortable indoor conditions. This study accomplishes a number of objectives, which include: 1) development of a new thermal zoning method to automatically, or semi-automatically create a building thermal zone in simulation models; 2) development of a simplified, commercial base-case model based on the information from the NREL commercial building model, “Run 3A” DOE-2 simulation model, and ASHRAE Standard 90.1-2013; 3) parametric studies of different configurations of thermal zones to evaluate several influential parameters on the developed new thermal zoning method

Inferring User Personality from Twitter

Personality affects how a person behaves when interacting with people and computer systems. It can determine one’s needs and preferences in different contexts, which is especially useful for adaptive and recommender systems. Personality questionnaires are widely used to acquire information about user personality. However, filling in them can be tedious. Analyzing the user interactions can be another way of obtaining that information. Since personality has an impact on the way a person interacts with others, and social networks are widely used for this purpose, information about user interactions through social networks can give a clue about their personality. In this paper, we present a system able to obtain data about user interactions in Twitter and analyze them in order to infer user personality. The system has been used not only to infer personality but also to compare and evaluate different user models, classifiers and personality dimensionsThis work has been funded by the BLIND, project BLIND (BLIND) and by BLIND, project BLIND (BLIND). We also thank all the people that participated in this research fulfilling the personality test

Interpreting infrastructure: Defining user value for digital financial intermediaries.

The 3DaRoC project is exploring digital connectivity and peer-to-peer relationships in financial services. In the light of the near collapse of the UK and world financial sector, understanding and innovating new and more sustainable approaches to financial services is now a critical topic. At the same time, the increasing penetration and take-up of robust high-speed networks, dependable peerto- peer architectures and mobile multimedia technologies offer novel platforms for offering financial services over the Internet. These new forms of digital connectivity give rise to opportunities in doing financial transactions in different ways and with radically different business models that offer the possibility of transforming the marketplace. One area in the digital economy that has had such an effect is in the ways that users access and use digital banking and payment services. The impact of the new economic models presented by these digital financial services is yet to be fully determined, but they have huge potential as disruptive innovations, with a potentially transformative effect on the way that services are offered to users. Little is understood about how technical infrastructures impact on the ways that people make sense of the financial services that they use, or on how these might be designed more effectively. 3DaRoC is exploring this space working with our partners and end users to prototype and evaluate new online, mobile, ubiquitous and tangible technologies, exploring how these services might be extended.Executive Summary: Drawing from Studies of Use - the value, use and interpretation of infrastructure in digital intermediaries to their users. The UK economy has a huge dependence on financial services, and this is increasingly based on digital platforms. Innovating new economic models around consumer financial services through the use of digital technologies is seen as increasingly important in developed economies. There are a number of drivers for this, ranging from national economic factors to the prosaic nature of enabling cheap, speedy and timely interactions for users. The potential for these new digital solutions is that they will allay an over-reliance on the traditional banking sector, which has proved itself to be unstable and risky, and we have seen a number of national policy moves to encourage growth in this sector. Partly as a result of the 2008 banking crisis, there has been an explosion in peer-to-peer financial services for non-professional consumers. These organisations act as intermediaries between users looking to trade goods or credit. However, building self-sustaining or profitable financial services within this novel space is itself fraught with commercial, regulatory, technical and social problems. This document reports on the value, use and interpretation of infrastructure in digital intermediaries to their users, describing analysis of contextual field studies carried out in two retail digital financial intermediary organisations: Zopa Limited and the Bristol Pound. It forms the second milestone document in the 3DaRoC project, developing patterns of use that have arisen on the back of the technical infrastructures in the two organisations that form cases for examination. Its purpose is to examine how the two different technical infrastructures that underpin the transactions that they support–composed of the back-office hardware and software, data structures, the networking and communications technologies used, supported consumer devices, and the user interfaces and interaction design–have provided opportunities for users to realise their financial and other needs. While we orient towards the issues of service use (and its problems), we also examine the activities and expectations of their various users. Our research has involved teams from Lancaster University examining Zopa and Brunel University focusing on the Bristol Pound over approximately a one-year period from October 2013 to October 2014. Extensive interviews, document analysis, observation of user interactions, and other methods have been employed to develop the process analyses of the firms presented here. This report comprises of three key sections: descriptions of the user demographics for Zopa and the Bristol Pound, a discussion about the user experience and its role in community, and an examination of the role of usage data in the development of these a products. We conclude with final analytical section drawing preliminary conclusions from the research presented.The 3DaRoC project is funded by the RCUK Digital Economy ‘Research in the Wild’ theme (grant no. EP/K012304/1)

A knowledge-light approach to personalised and open-ended human activity recognition.

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part depend on their ability to adapt to individual variations in human movement and to facilitate a range of human activity classes. Research in personalised HAR aims to learn models that are sensitive to the subtle nuances in human movement whilst Open-ended HAR learns models that can recognise activity classes out of the pre-defined set available at training. Current approaches to personalised HAR impose a data collection burden on the end user; whilst Open-ended HAR algorithms are heavily reliant on intermediary-level class descriptions. Instead of these 'knowledge-intensive' HAR algorithms; in this article, we propose a 'knowledge-light' method. Specifically, we show how by using a few seconds of raw sensor data, obtained through micro-interactions with the end-user, we can effectively personalise HAR models and transfer recognition functionality to new activities with zero re-training of the model after deployment. We introduce a Personalised Open-ended HAR algorithm, MNZ, a user context aware Matching Network architecture and evaluate on 3 HAR data sources. Performance results show up to 48.9% improvement with personalisation and up to 18.3% improvement compared to the most common 'knowledge-intensive' Open-ended HAR algorithms