POWKist: visualising cultural heritage linked datasets.

A collaboration between dot.rural at the University of Aberdeen and the iSchool at Northumbria University, POWkist is a pilot-study exploring potential usages of currently available linked datasets within the cultural heritage domain. Many privately-held family history collections (shoebox archives) remain vulnerable unless a sustainable, affordable and accessible model of citizen-archivist digital preservation can be offered. Citizen-historians have used the web as a platform to preserve cultural heritage, however with no accessible or sustainable model these digital footprints have been ad hoc and rarely connected to broader historical research. Similarly, current approaches to connecting material on the web by exploiting linked datasets do not take into account the data characteristics of the cultural heritage domain. Funded by Semantic Media, the POWKist project is investigating how best to capture, curate, connect and present the contents of citizen-historians’ shoebox archives in an accessible and sustainable online collection. Using the Curios platform - an open-source digital archive - we have digitised a collection relating to a prisoner of war during WWII (1939-1945). Following a series of user group workshops, POWkist is now connecting these ‘made digital’ items with the broader web using a semantic technology model and identifying appropriate linked datasets of relevant content such as DBPedia (an archived linked dataset of Wikipedia) and Ordnance Survey Open Data. We are analysing the characteristics of cultural heritage linked datasets, so that these materials are better visualised, contextualised and presented in an attractive and comprehensive user interface. Our paper will consider the issues we have identified, the solutions we are developing and include a demonstration of our work-in-progress.

Front end development for building automation systems using JavaScript Frameworks

Automation technologies are widely acclaimed to have the potential to significantly reduce energy consumption and energy-related costs in buildings. However, despite the abundance of commercially available technologies, automation in domestic environments keep on meeting commercial failures. The main reason for this is the development process that is used to build the automation applications, which tend to focus more on technical aspects rather than on the needs and limitations of the users. An instance of this problem is the complex and poorly designed home automation front-ends that deter customers from investing in a home automation product. On the other hand, developing a usable and interactive interface is a complicated task for developers due to the multidisciplinary challenges that need to be identified and solved. In this context, the current research work investigates the different design problems associated with developing a home automation interface as well as the existing design solutions that are applied to these problems. The Qualitative Data Analysis approach was used for collecting data from research papers and the open coding process was used to cluster the findings. From the analysis of the data collected, requirements for designing the interface were derived. A home energy management functionality for a Web-based home automation front-end was developed as a proof-of-concept and a user evaluation was used to assess the usability of the interface. The results of the evaluation showed that this holistic approach to designing interfaces improved its usability which increases the chances of its commercial success.

Introducing the National Library for Health Skin Conditions Specialist Library

Background: This paper introduces the new National Library for Health Skin Conditions Specialist Library http://www.library.nhs.uk/skin. Description: The aims, scope and audience of the new NLH Skin Conditions Specialist Library, and the composition and functions of its core Project Team, Editorial Team and Stakeholders Group are described. The Library's collection building strategy, resource and information types, editorial policies, quality checklist, taxonomy for content indexing, organisation and navigation, and user interface are all presented in detail. The paper also explores the expected impact and utility of the new Library, as well as some possible future directions for further development. Conclusion: The Skin Conditions Specialist Library is not just another new Web site that dermatologists might want to add to their Internet favourites then forget about it. It is intended to be a practical, "one-stop shop" dermatology information service for everyday practical use, offering high quality, up-to-date resources, and adopting robust evidence-based and knowledge management approaches.

Embedding sustainability into the new computer science curriculum for English schools

The primary goals of this study are to: embed sustainable concepts of energy consumption into certain part of existing Computer Science curriculum for English schools; investigate how to motivate 7-to-11 years old kids to learn these concepts; promote responsive ICT (Information and Communications Technology) use by these kids in their daily life; raise their awareness of today’s ecological challenges. Sustainability-related ICT lessons developed aim to provoke computational thinking and creativity to foster understanding of environmental impact of ICT and positive environmental impact of small changes in user energy consumption behaviour. The importance of including sustainability into the Computer Science curriculum is due to the fact that ICT is both a solution and one of the causes of current world ecological problems. This research follows Agile software development methodology. In order to achieve the aforementioned goals, sustainability requirements, curriculum requirements and technical requirements are firstly analysed. Secondly, the web-based user interface is designed. In parallel, a set of three online lessons (video, slideshow and game) is created for the website GreenICTKids.com taking into account several green design patterns. Finally, the evaluation phase involves the collection of adults’ and kids’ feedback on the following: user interface; contents; user interaction; impacts on the kids’ sustainability awareness and on the kids’ behaviour with technologies. In conclusion, a list of research outcomes is as follows: 92% of the adults learnt more about energy consumption; 80% of the kids are motivated to learn about energy consumption and found the website easy to use; 100% of the kids understood the contents and liked website’s visual aspect; 100% of the kids will try to apply in their daily life what they learnt through the online lessons.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

A HUMAN-CENTERED APPROACH TO IMPROVING THE USER EXPERIENCE OF SOFTWARE UPDATES

Software updates are critical to the security of software systems and devices. Yet users often do not install them in a timely manner, leaving their devices open to security exploits. This research explored a re-design of automatic software updates on desktop and mobile devices to improve the uptake of updates through three studies. First using interviews, we studied users’ updating patterns and behaviors on desktop machines in a formative study. Second, we distilled these findings into the design of a low-fi prototype for desktops, and evaluated its efficacy for automating updates by means of a think-aloud study. Third, we investigated individual differences in update automation on Android devices using a large scale survey, and interviews. In this thesis, I present the findings of all three studies and provide evidence for how automatic updates can be better appropriated to fit users on both desktops and mobile devices. Additionally, I provide user interface design suggestions for software updates and outline recommendations for future work to improve the user experience of software updates.

Baran: an interaction-centred user monitoring framework

User Quality of Experience (QoE) is a subjective entity and difficult to measure. One important aspect of it, User Experience (UX), corresponds to the sensory and emotional state of a user. For a user interacting through a User Interface (UI), precise information on how they are using the UI can contribute to understanding their UX, and thereby understanding their QoE. As well as a user’s use of the UI such as clicking, scrolling, touching, or selecting, other real-time digital information about the user such as from smart phone sensors (e.g. accelerometer, light level) and physiological sensors (e.g. heart rate, ECG, EEG) could contribute to understanding UX. Baran is a framework that is designed to capture, record, manage and analyse the User Digital Imprint (UDI) which, is the data structure containing all user context information. Baran simplifies the process of collecting experimental information in Human and Computer Interaction (HCI) studies, by recording comprehensive real-time data for any UI experiment, and making the data available as a standard UDI data structure. This paper presents an overview of the Baran framework, and provides an example of its use to record user interaction and perform some basic analysis of the interaction.

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.

A next application prediction service using the BaranC framework

Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

A reflective characterization of occasional user

This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU). Three user categories, novice, intermediate and expert, have dominated the work of user interface (UI) designers, researchers and educators for decades. These categories were created to conceptualise user's needs, strategies and goals around the 80s. Since then, UI paradigm shifts, such as direct manipulation and touch, along with other advances in technology, gave new access to people with little computer knowledge. This fact produced a diversification of the existing user categories not observed in the literature review of traditional classification of users. The findings of this work include a new characterisation of the occasional user, distinguished by user's uncertainty of repetitive use of an interface and little knowledge about its functioning. In addition, the specification of the OU, together with principles and recommendations will help UI community to informatively design for users without requiring a prospective use and previous knowledge of the UI. The OU is an essential type of user to apply user-centred design approach to understand the interaction with technology as universal, accessible and transparent for the user, independently of accumulated experience and technological era that users live in.

Interface virtual com sensor 3D para interação de pessoas acamadas

A interação homem-máquina tem evoluído significativamente nos últimos anos, a ponto de permitir desenvolver soluções adequadas para apoio a pessoas que possuem um certo tipo de limitação física ou cognitiva. O desenvolvimento de técnicas naturais e intuitivas de interação, as chamadas Natural User Interface (NUI), permitem, hoje, que pessoas que estejam acamadas e/ou com incapacidade motora possam executar um conjunto de ações por intermédio de gestos, aumentando assim a sua qualidade de vida. A solução implementada neste projecto é baseada em processamento de imagem e visão por computador através do sensor 3D Kinect e consiste numa interface natural para o desenvolvimento de uma aplicação que reconheça gestos efetuados por uma mão humana. Os gestos identificados pela aplicação acionam um conjunto de ações adequados a uma pessoa acamada, como, por exemplo, acionar a emergência, ligar ou desligar a TV ou controlar a inclinação da cama. O processo de desenvolvimento deste projeto implicou várias etapas. Inicialmente houve um trabalho intenso de investigação sobre as técnicas e tecnologias consideradas importantes para a realização do trabalho - a etapa de investigação, a qual acompanhou praticamente todo o processo. A segunda etapa consistiu na configuração do sistema ao nível do hardware e do software. Após a configuração do sistema, obtiveram-se os primeiros dados do sensor 3D Kinect, os quais foram convertidos num formato mais apropriado ao seu posterior tratamento. A segmentação da mão permitiu posteriormente o reconhecimento de gestos através da técnica de matching para os seis gestos implementados. Os resultados obtidos são satisfatórios, tendo-se contabilizado cerca de 96% de resultados válidos. A área da saúde e bem-estar tem necessidade de aplicações que melhorem a qualidade de vida de pessoas acamadas, nesse sentido, o protótipo desenvolvido faz todo o sentido na sociedade actual, onde se verifica o envelhecimento da população.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.

An Image Recognition Software for Identification of Vehicle Homologation Tags

In recent years, we have witnessed great changes in the industrial environment as a result of the innovations introduced by Industry 4.0, especially in the integration of Internet of Things, Automation and Robotics in the manufacturing field. The project presented in this thesis lies within this innovation context and describes the implementation of an Image Recognition application focused on the automotive field. The project aims at helping the supply chain operator to perform an effective and efficient check of the homologation tags present on vehicles. The user contribution consists in taking a picture of the tag and the application will automatically, exploiting Amazon Web Services, return the result of the control about the correctness of the tag, the correct positioning within the vehicle and the presence of faults or defects on the tag. To implement this application we ombined two IoT platforms widely used in industrial field: Amazon Web Services(AWS) and ThingWorx. AWS exploits Convolutional Neural Networks to perform Text Detection and Image Recognition, while PTC ThingWorx manages the user interface and the data manipulation.

Building a Open Source Framework for Virtual Medical Training

This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools. Using the instantiation tool, the process of building applications is fast and easy. Therefore, computer programmers can obtain an initial application and adapt it to their needs. This tool allows the user to include, delete, and edit parameters in the functionalities chosen as well as storing these parameters for future use. In order to verify the efficiency of the framework, some case studies are presented.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.