Publications

Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • JOURNAL ARTICLE
    Gomez-Romero J, Molina-Solana M, Oehmichen A, Guo Yet al., 2018,

    Visualizing large knowledge graphs: A performance analysis

    , FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 89, Pages: 224-238, ISSN: 0167-739X
  • JOURNAL ARTICLE
    Molina-Solana M, Kennedy M, Amador Diaz Lopez J, 2018,

    foo.castr: visualising the future AI workforce

    , Big Data Analytics, Vol: 3, ISSN: 2058-6345

    Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.
  • JOURNAL ARTICLE
    Dolan D, Jensen HJ, Mediano PAM, Molina-Solana M, Rajpal H, Rosas F, Sloboda JAet al., 2018,

    The Improvisational State of Mind: A Multidisciplinary Study of an Improvisatory Approach to Classical Music Repertoire Performance

    , FRONTIERS IN PSYCHOLOGY, Vol: 9, ISSN: 1664-1078
  • JOURNAL ARTICLE
    Gómez-Romero J, Molina-Solana M, Ros M, Ruiz MD, Martin-Bautista MJet al., 2018,

    Comfort as a service: a new paradigm for residential environmental quality control

    , Sustainability, Vol: 10, ISSN: 1937-0709

    This paper introduces the concept of Comfort as a Service (CaaS), a new energy supply paradigm for providing comfort to residential customers. CaaS takes into account the available passive and active elements, the external factors that affect energy consumption and associated costs, and occupants' behaviors to generate optimal control strategies for the domestic equipment automatically. As a consequence, it releases building occupants from operating the equipment, which gives rise to a disruption of the traditional model of paying per consumed energy in favor of a model of paying per provided comfort. In the paper, we envision a realization of CaaS based on several technologies such as ambient intelligence, big data, cloud computing and predictive computing. We discuss the opportunities and the barriers of CaaS-centered business and exemplify the potential of CaaS deployments by quantifying the expected energy savings achieved after limiting occupants' control over the air conditioning system in a test scenario.
  • JOURNAL ARTICLE
    Creswell A, Bharath AA, 2018,

    Denoising Adversarial Autoencoders.

    , IEEE Trans Neural Netw Learn Syst

    Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabeled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabeled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean input samples from corrupted ones. Representations may be further improved by introducing regularization during training to shape the distribution of the encoded data in the latent space. We suggest denoising adversarial autoencoders (AAEs), which combine denoising and regularization, shaping the distribution of latent space using adversarial training. We introduce a novel analysis that shows how denoising may be incorporated into the training and sampling of AAEs. Experiments are performed to assess the contributions that denoising makes to the learning of representations for classification and sample synthesis. Our results suggest that autoencoders trained using a denoising criterion achieve higher classification performance and can synthesize samples that are more consistent with the input data than those trained without a corruption process.
  • JOURNAL ARTICLE
    Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018,

    Natural ventilation in cities: the implications of fluid mechanics

    , BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218
  • JOURNAL ARTICLE
    Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, de Montjoye Y-Aet al., 2017,

    Improving official statistics in emerging markets using machine learning and mobile phone data

    , EPJ Data Science, Vol: 6
  • JOURNAL ARTICLE
    Molina-Solana M, Birch D, Guo Y-K, 2017,

    Improving data exploration in graphs with fuzzy logic and large-scale visualisation

    , APPLIED SOFT COMPUTING, Vol: 53, Pages: 227-235, ISSN: 1568-4946
  • JOURNAL ARTICLE
    Molina-Solana M, Ros M, Dolores Ruiz M, Gomez-Romero J, Martin-Bautista MJet al., 2017,

    Data science for building energy management: A review

    , RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 70, Pages: 598-609, ISSN: 1364-0321
  • JOURNAL ARTICLE
    Steele JE, Sundsoy PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Engo-Monsen K, de Montjoye Y-A, Iqbal AM, Hadiuzzaman KN, Lu X, Wetter E, Tatem AJ, Bengtsson Let al., 2017,

    Mapping poverty using mobile phone and satellite data

    , JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 14, ISSN: 1742-5689
  • JOURNAL ARTICLE
    de Montjoye YKJV, Rocher L, Pentland AS, 2016,

    bandicoot: an open-source Python toolbox to analyze mobile phone metadata

    , Journal of Machine Learning Research, Vol: 17, ISSN: 1532-4435

    bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license
  • JOURNAL ARTICLE
    Ma Z-B, Yang Y, Liu Y-X, Bharath AAet al., 2016,

    Recurrently Decomposable 2-D Convolvers for FPGA-Based Digital Image Processing

    , IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 63, Pages: 979-983, ISSN: 1549-7747
  • JOURNAL ARTICLE
    Taquet M, Quoidbach J, de Montjoye Y-A, Desseilles M, Gross JJet al., 2016,

    Hedonism and the choice of everyday activities

    , Proceedings of the National Academy of Sciences, Vol: 113, Pages: 9769-9773, ISSN: 0027-8424
  • JOURNAL ARTICLE
    McGinn D, Birch D, Akroyd D, Molina-Solana M, Guo Y, Knottenbelt WJet al., 2016,

    Visualizing Dynamic Bitcoin Transaction Patterns

    , BIG DATA, Vol: 4, Pages: 109-119, ISSN: 2167-6461
  • JOURNAL ARTICLE
    Bertone G, Calore F, Caron S, Ruiz R, Kim JS, Trotta R, Weniger Cet al., 2016,

    Global analysis of the pMSSM in light of the Fermi GeV excess: prospects for the LHC Run-II and astroparticle experiments

    , JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, ISSN: 1475-7516

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