WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications

The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation. Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability. All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area

Intrinsic Dimension Estimation for non-Euclidean manifolds: from metagenomics to unweighted networks

Within the field of unsupervised manifold learning, Intrinsic Dimension estimators are among the most important analysis tools. The Intrinsic Dimension provides a measure of the dimensionality of the hidden manifold from which data are sampled, even if the manifold is embedded in a space with a much higher number of features. The present Thesis tackles the still unanswered problem of computing the Intrinsic Dimension (ID) of spaces characterised by non-Euclidean metrics. In particular, we focus on datasets where the distances between points are measured by means of Manhattan, Hamming or shortest-path metrics and, thus, can only assume discrete values. This peculiarity has deep consequences on the way datapoints populate the neighbourhoods and on the structure on the manifold. For this reason we develop a general purpose, nearest-neighbours-based ID estimator that has two peculiar features: the capability of selecting explicitly the scale at which the Intrinsic Dimension is computed and a validation procedure to check the reliability of the provided estimate. We thus specialise the estimator to lattice spaces, where the volume is measured by means of the Ehrhart polynomials. After testing the reliability of the estimator on artificial datasets, we apply it to genomics sequences and discover an unexpectedly low ID, suggesting that the evolutive pressure exerts strong restraints on the way the nucleotide basis are allowed to mutate. This same framework is then employed to profile the scaling of the ID of unweighted networks. The diversity of the obtained ID signatures prompted us into using it as a signature to characterise the networks. Concretely, we employ the ID as a summary statistics within an Approximate Bayesian Computation framework in order to pinpoint the parameters of network mechanistic generative models of increasing complexity. We discover that, by targeting the ID of a given network, other typical network properties are also fairly retrieved. As a last methodological development, we improved the ID estimator by adaptively selecting, for each datapoint, the largest neighbourhoods with an approximately constant density. This offers a quantitative criterion to automatically select a meaningful scale at which the ID is computed and, at the same time, allows to enforce the hypothesis of the method, implying more reliable estimates

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

Imageability and Intelligibility in 3D Game Environments Examining Experiential and Cultural Influence on the Design Process

The games industry has developed online multiplayer three-dimensional game worlds that allow players from different geographical locations to engage in competitive and cooperative gameplay together. This has enabled players from different cultures to inhabit the same virtual game world, bypassing any geographical or cultural boundaries found in the real world. These 3D game worlds ask the player to use the basic principles of spatial awareness and movement from the real world, and are often virtual representations of real world environments. These spaces are designed for players from all nationalities to inhabit concurrently. There is now a need to determine design considerations for these multicultural multiplayer game worlds but any investigation must consider the historical evidence from the games industry of cultural differences in gameplay preferences. This thesis discusses the effect of cultural knowledge on the spatial design and interpretation of three-dimensional game environments that are based on real world affordances. A new methodology for the comparative analysis of the design of three-dimensional game environments is established using Space Syntax metrics. This facilitates the discussion of cultural models applied to design thinking for the implementation and interpretation of game environments. Through spatial metrics the analysis of the intelligibility underlying three-dimensional game environments is correlated to the imageability of the projected two-dimensional screen image. The application of this methodology to internationally popular, and culturally specific, game environments establishes new knowledge on tacit cultural influences within game design processes. The analysed intelligibility of the environments indicates cognitive differences between Eastern and Western cultures, already recognised in the interpretation of two-dimensional imagery, also exist within the design and interpretation of three-dimensional game spaces. This study establishes a new methodology through the analysis of intelligibility for design research into game environments. The resulting evaluation of tacit cultural influences within the design of the environments establishes new cultural differences and commonalities. These design characteristics can inform future game design methodologies within industry for the design and implementation of multicultural game environments

Indicators and Scenarios for Sustainable Development at the Local Level

Cities around the world have faced the impact of the COVID-19 pandemic with unprecedented speed, due to our hyper-connected society. As history teaches us, epidemics plague society because of the vulnerabilities generated by humans through their relationships with the environment, with other species and with each other. The recent pandemic is a stark reminder that urbanization has changed the way people and communities live, work, and interact, and it is even more necessary than in the past to adopt a multidisciplinary approach to the development of systemic operational skills that can address complex issues within cities. This work showed how many measures adopted during the emergency have now become part of daily life. The lesson of the pandemic is that people’s health is connected to and dependent on the health of the planet, and cities are at the center of this relationship. The objective of the research starts from the need to identify a selection of post-COVID indicators providing an analysis methodology suitable for the creation of its own final set with the identification of specific key performance indicators (KPIs) of the project, replicable in other urban contexts, on which to base the analysis of the level of local sustainability, especially at the neighborhood scale. The proposed methodological framework is developed in two phases: (1) indicator selection and baseline scenario, set out to investigate the existing correlations between the urban environment and the neighborhood level of cities. On the basis of the assessment of the KPIs, selected on the basis of numerous comparisons with the project’s internal and external stakeholders, thanks to the creation of an interactive dashboard with Tableau software, it was possible to analyze the basic scenario of proximity at the neighborhood scale for the City of Turin, highlighting weak points and priority areas on which to act, experimenting with the theme “Inhabiting proximity” as an urban response to the pandemic