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
    Molina-Solana MJ, Guo Y, Birch D, 2017,

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

    , Applied Soft Computing, Vol: 53, Pages: 227-235, ISSN: 1872-9681

    This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.

  • Journal article
    Molina-Solana M, Ros M, Ruiz MD, Gómez-Romero J, Martin-Bautista MJet al., 2016,

    Data science for building energy management: A review

    , Renewable and Sustainable Energy Reviews, Vol: 70, Pages: 598-609, ISSN: 1364-0321

    The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

  • Journal article
    Lefaudeux D, De Meulder B, Loza MJ, Peffer N, Rowe A, Baribaud F, Bansal AT, Lutter R, Sousa AR, Corfield J, Pandis I, Bakke PS, Caruso M, Chanez P, Dahlen S-E, Fleming LJ, Fowler SJ, Horvath I, Krug N, Montuschi P, Sanak M, Sandstrom T, Shaw DE, Singer F, Sterk PJ, Roberts G, Adcock IM, Djukanovic R, Auffray C, Chung KF, U-BIOPRED Study Groupet al., 2016,

    U-BIOPRED clinical adult asthma clusters linked to a subset of sputum -omics

    , Journal of Allergy and Clinical Immunology, Vol: 139, Pages: 1797-1807, ISSN: 1097-6825
  • Journal article
    Wilson SJ, Ward JA, Sousa AR, Corfield J, Bansal AT, De Meulder B, Lefaudeux D, Auffray C, Loza MJ, Baribaud F, Fitch N, Sterk PJ, Chung KF, Gibeon D, Sun K, Guo YK, Adcock I, Djukanovic R, Dahlen B, Chanez P, Shaw D, Krug N, Hohlfeld J, Sandström T, Howarth PH, U-BIOPRED Study Groupet al., 2016,

    Severe asthma exists despite suppressed tissue inflammation: findings of the U-BIOPRED study.

    , European Respiratory Journal, Vol: 48, Pages: 1307-1319, ISSN: 1399-3003

    The U-BIOPRED study is a multicentre European study aimed at a better understanding of severe asthma. It included three steroid-treated adult asthma groups (severe nonsmokers (SAn group), severe current/ex-smokers (SAs/ex group) and those with mild-moderate disease (MMA group)) and healthy controls (HC group). The aim of this cross-sectional, bronchoscopy substudy was to compare bronchial immunopathology between these groups.In 158 participants, bronchial biopsies and bronchial epithelial brushings were collected for immunopathologic and transcriptomic analysis. Immunohistochemical analysis of glycol methacrylate resin-embedded biopsies showed there were more mast cells in submucosa of the HC group (33.6 mm(-2)) compared with both severe asthma groups (SAn: 17.4 mm(-2), p<0.001; SAs/ex: 22.2 mm(-2), p=0.01) and with the MMA group (21.2 mm(-2), p=0.01). The number of CD4(+) lymphocytes was decreased in the SAs/ex group (4.7 mm(-2)) compared with the SAn (11.6 mm(-2), p=0.002), MMA (10.1 mm(-2), p=0.008) and HC (10.6 mm(-2), p<0.001) groups. No other differences were observed.Affymetrix microarray analysis identified seven probe sets in the bronchial brushing samples that had a positive relationship with submucosal eosinophils. These mapped to COX-2 (cyclo-oxygenase-2), ADAM-7 (disintegrin and metalloproteinase domain-containing protein 7), SLCO1A2 (solute carrier organic anion transporter family member 1A2), TMEFF2 (transmembrane protein with epidermal growth factor like and two follistatin like domains 2) and TRPM-1 (transient receptor potential cation channel subfamily M member 1); the remaining two are unnamed.We conclude that in nonsmoking and smoking patients on currently recommended therapy, severe asthma exists despite suppressed tissue inflammation within the proximal airway wall.

  • 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
    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

    Most theories of motivation have highlighted that human behavior is guided by the hedonic principle, according to which our choices of daily activities aim to minimize negative affect and maximize positive affect. However, it is not clear how to reconcile this idea with the fact that people routinely engage in unpleasant yet necessary activities. To address this issue, we monitored in real time the activities and moods of over 28,000 people across an average of 27 d using a multiplatform smartphone application. We found that people’s choices of activities followed a hedonic flexibility principle. Specifically, people were more likely to engage in mood-increasing activities (e.g., play sports) when they felt bad, and to engage in useful but mood-decreasing activities (e.g., housework) when they felt good. These findings clarify how hedonic considerations shape human behavior. They may explain how humans overcome the allure of short-term gains in happiness to maximize long-term welfare.

  • Journal article
    McGinn D, Birch DA, Akroyd D, Molina-Solana M, Guo Y, Knottenbelt Wet al., 2016,

    Visualizing Dynamic Bitcoin Transaction Patterns

    , Big Data, Vol: 4, Pages: 109-119, ISSN: 2167-647X

    This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.

  • Journal article
    Bertone G, Calore F, Caron S, Austri RRD, 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, Vol: 2016, ISSN: 1475-7516
  • Journal article
    Ma ZB, Yang Y, Liu YX, Bharath AAet al., 2016,

    Recurrently decomposable 2-D convolvers for FPGA-based digital image processing

    , IEEE Transactions on Circuits and Systems, Vol: 63, Pages: 979-983, ISSN: 1549-7747

    Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.

  • Journal article
    Creswell A, Bharath AA, 2016,

    Task Specific Adversarial Cost Function

    The cost function used to train a generative model should fit the purpose ofthe model. If the model is intended for tasks such as generating perceptuallycorrect samples, it is beneficial to maximise the likelihood of a sample drawnfrom the model, Q, coming from the same distribution as the training data, P.This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P].However, if the model is intended for tasks such as retrieval or classificationit is beneficial to maximise the likelihood that a sample drawn from thetraining data is captured by the model, equivalent to minimising KL[P||Q]. Thecost function used in adversarial training optimises the Jensen-Shannon entropywhich can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here,we propose an alternative adversarial cost function which allows easy tuning ofthe model for either task. Our task specific cost function is evaluated on adataset of hand-written characters in the following tasks: Generation,retrieval and one-shot learning.

  • Journal article
    de Montjoye YKJV,

    Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics

    , arXiv

    The extensive collection and processing of personal information in big data analytics has given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling, and disclosure of private data. To reap the benefits of analytics without invading the individuals' private sphere, it is essential to draw the limits of big data processing and integrate data protection safeguards in the analytics value chain. ENISA, with the current report, supports this approach and the position that the challenges of ...

  • Book chapter
    de Montjoye YKJV, 2015,

    Modeling and UnderstandingIntrinsic Characteristics of Human Mobility

    , Social Phenomena From Data Analysis to Models, Publisher: Springer, ISBN: 9783319140117

    Humans are intrinsically social creatures and our mobility is central to understanding how our societies grow and function. Movement allows us to congregate with our peers, access things we need, and exchange information. Human mobility has huge impacts on topics like urban and transportation planning, social and biologic spreading, and economic outcomes. So far, modeling these processes has been hindered by a lack of data. This is radically changing with the rise of ubiquitous devices. In this chapter, we discuss recent progress deriving insights from the massive, high resolution data sets collected from mobile phone and other devices. We begin with individual mobility, where empirical evidence and statistical models have shown important intrinsic and universal characteristics about our movement: we, as human, are fundamentally slow to explore new places, relatively predictable, and mostly unique. We then explore methods of modeling aggregate movement of people from place to place and discuss how these estimates can be used to understand and optimize transportation infrastructure. Finally, we highlight applications of these findings to the dynamics of disease spread, social networks, and economic outcomes.

  • Journal article
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: a comparison of patch descriptor performance

    , Pattern Recognition Letters, Vol: 66, Pages: 109-117, ISSN: 1872-7344

    Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization—both in terms of knowing which route a user is on—and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user’s position. The techniques include single-frame descriptors, those using sequences of frames, and both color and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.

  • Conference paper
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743
  • Conference paper
    Tauheed F, Heinis T, Ailamaki A, 2015,

    THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads

    , Pages: 939-950

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