Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

Earth as Interface: Exploring chemical senses with Multisensory HCI Design for Environmental Health Communication

As environmental problems intensify, the chemical senses -that is smell and taste, are the most relevantsenses to evidence them.As such, environmental exposure vectors that can reach human beings comprise air,food, soil and water[1].Within this context, understanding the link between environmental exposures andhealth[2]is crucial to make informed choices, protect the environment and adapt to new environmentalconditions[3].Smell and taste lead therefore to multi-sensorial experiences which convey multi-layered information aboutlocal and global events[4]. However, these senses are usually absent when those problems are represented indigital systems. The multisensory HCIdesign framework investigateschemical sense inclusion withdigital systems[5]. Ongoing efforts tackledigitalization of smell and taste for digital delivery, transmission or substitution [6]. Despite experimentsproved technological feasibility, its dissemination depends on relevant applicationdevelopment[7].This thesis aims to fillthose gaps by demonstratinghow chemical senses provide the means to link environment and health based on scientific andgeolocation narratives [8], [9],[10]. We present a Multisensory HCI design process which accomplished symbolicdisplaying smell and taste and led us to a new multi-sensorial interaction system presented herein. We describe the conceptualization, design and evaluation of Earthsensum, an exploratory case study project.Earthsensumoffered to 16 participants in the study, environmental smell and taste experiences about real geolocations to participants of the study. These experiences were represented digitally using mobilevirtual reality (MVR) and mobile augmented reality (MAR). Its technologies bridge the real and digital Worlds through digital representations where we can reproduce the multi-sensorial experiences. Our study findings showed that the purposed interaction system is intuitive and can lead not only to a betterunderstanding of smell and taste perception as also of environmental problems. Participants comprehensionabout the link between environmental exposures and health was successful and they would recommend thissystem as education tools. Our conceptual design approach was validated and further developments wereencouraged.In this thesis,we demonstratehow to applyMultisensory HCI methodology to design with chemical senses. Weconclude that the presented symbolic representation model of smell and taste allows communicatingtheseexperiences on digital platforms. Due to its context-dependency, MVR and MAR platforms are adequatetechnologies to be applied for this purpose.Future developments intend to explore further the conceptual approach. These developments are centredon the use of the system to induce hopefully behaviourchange. Thisthesisopens up new application possibilities of digital chemical sense communication,Multisensory HCI Design and environmental health communication.À medida que os problemas ambientais se intensificam, os sentidos químicos -isto é, o cheiroe sabor, são os sentidos mais relevantes para evidenciá-los. Como tais, os vetores de exposição ambiental que podem atingir os seres humanos compreendem o ar, alimentos, solo e água [1]. Neste contexto, compreender a ligação entre as exposições ambientais e a saúde [2] é crucial para exercerescolhas informadas, proteger o meio ambiente e adaptar a novas condições ambientais [3]. O cheiroe o saborconduzemassima experiências multissensoriais que transmitem informações de múltiplas camadas sobre eventos locais e globais [4]. No entanto, esses sentidos geralmente estão ausentes quando esses problemas são representados em sistemas digitais. A disciplina do design de Interação Humano-Computador(HCI)multissensorial investiga a inclusão dossentidos químicos em sistemas digitais [9]. O seu foco atual residena digitalização de cheirose sabores para o envio, transmissão ou substituiçãode sentidos[10]. Apesar dasexperimentaçõescomprovarem a viabilidade tecnológica, a sua disseminação está dependentedo desenvolvimento de aplicações relevantes [11]. Estatese pretendepreencher estas lacunas ao demonstrar como os sentidos químicos explicitama interconexãoentre o meio ambiente e a saúde, recorrendo a narrativas científicas econtextualizadasgeograficamente[12], [13], [14]. Apresentamos uma metodologiade design HCImultissensorial que concretizouum sistema de representação simbólica de cheiro e sabor e nos conduziu a um novo sistema de interação multissensorial, que aqui apresentamos. Descrevemos o nosso estudo exploratório Earthsensum, que integra aconceptualização, design e avaliação. Earthsensumofereceu a 16participantes do estudo experiências ambientais de cheiro e sabor relacionadas com localizações geográficasreais. Essas experiências foram representadas digitalmente através derealidade virtual(VR)e realidade aumentada(AR).Estas tecnologias conectamo mundo real e digital através de representações digitais onde podemos reproduzir as experiências multissensoriais. Os resultados do nosso estudo provaramque o sistema interativo proposto é intuitivo e pode levar não apenas a uma melhor compreensão da perceção do cheiroe sabor, como também dos problemas ambientais. O entendimentosobre a interdependência entre exposições ambientais e saúde teve êxitoe os participantes recomendariam este sistema como ferramenta para aeducação. A nossa abordagem conceptual foi positivamentevalidadae novos desenvolvimentos foram incentivados. Nesta tese, demonstramos como aplicar metodologiasde design HCImultissensorialpara projetar com ossentidos químicos. Comprovamosque o modelo apresentado de representação simbólica do cheiroe do saborpermite comunicar essas experiênciasem plataformas digitais. Por serem dependentesdocontexto, as plataformas de aplicações emVR e AR são tecnologias adequadaspara este fim.Desenvolvimentos futuros pretendem aprofundar a nossa abordagemconceptual. Em particular, aspiramos desenvolvera aplicaçãodo sistema para promover mudanças de comportamento. Esta tese propõenovas possibilidades de aplicação da comunicação dos sentidos químicos em plataformas digitais, dedesign multissensorial HCI e de comunicação de saúde ambiental

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin