A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Enabling Privacy and Trust in Edge AI Systems

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing and control pipelines spanning end devices, edge computing, and cloud environments. However, continuous collaboration between cloud, edge and device is susceptible to information leakage and loss, leading to insecure and unreliable operation. This raises an important question: how can we design, develop, and evaluate high-performing ML systems that are trustworthy and privacy-preserving in resource-constrained edge environments? In this thesis, I address this question by designing and implementing privacy-preserving and trustworthy ML systems for distributed applications. I first introduce a system that establishes trust in the explanations generated from a popular visualization technique, saliency maps, using counterfactual reasoning. Through the proposed evaluation system, I assess the degree to which hypothesized explanations correspond to the semantics of edge-based reinforcement learning environments. Second, I examine the privacy implications of personalized models in distributed mobile services by proposing time-series based model inversion attacks. To thwart such attacks, I present a distributed framework, Pelican, that learns and deploys transfer learning-based personalized ML models in a privacy preserving manner on resource-constrained mobile devices. Third, I investigate ML models that are deployed on local devices for inference and highlight the ease with which proprietary information embedded in these models can be exposed. For mitigating such attacks, I present a secure on-device application framework, SODA, which is supported by real-time adversarial detection. Finally, I present an end-to-end privacy-aware system for a real-world application to model group interaction behavior via mobility sensing. The proposed system, W4-Groups, distributes computation across device, edge, and cloud resources to strengthen its privacy and trustworthiness guarantees

Authentic alignment : toward an Interpretative Phenomenological Analysis (IPA) informed model of the learning environment in health professions education

It is well established that the goals of education can only be achieved through the constructive alignment of instruction, learning and assessment. There is a gap in research interpreting the lived experiences of stakeholders within the UK learning environment toward understanding the real impact – authenticity – of curricular alignment. This investigation uses a critical realist framework to explore the emergent quality of authenticity as a function of alignment.This project deals broadly with alignment of anatomy pedagogy within UK undergraduate medical education. The thread of alignment is woven through four aims: 1) to understand the alignment of anatomy within the medical curriculum via the relationships of its stakeholders; 2) to explore the apparent complexity of the learning environment (LE); 3) to generate a critical evaluation of the methodology, Interpretative Phenomenological Analysis as an approach appropriate for realist research in the complex fields of medical and health professions education; 4) to propose a functional, authentic model of the learning environment.Findings indicate that the complexity and uncertainty inherent in the LE can be reflected in spatiotemporal models. Findings meet the thesis aims, suggesting: 1) the alignment of anatomy within the medical curriculum is complex and forms a multiplicity of perspectives; 2) this complexity is ripe for phenomenological exploration; 3) IPA is particularly suitable for realist research exploring complexity in HPE; 4) Authentic Alignment theory offers a spatiotemporal model of the complex HPE learning environment:the T-icosa

Putting time into space: the temporal coherence of spatial applications in the housing market

International audienceRelationships between past events, future expectations and present decisions, typically examined through a temporal prism within applied economics, have been lately moving to the spatial dimension through spatial econometrics. However, violations of the “arrow of time”, and thus causality, have been identified in spatial econometric techniques applied to spatio-temporal data consisting of observations each at a specific location and distinct moment in time. A comprehensive review classifies for the first time several redresses to this issue in a currently fragmented literature. This paper puts back the temporal dimension into spatial Hedonic Pricing models through a unique specification of a spatio-temporal model that successfully isolates three distinct effects. First, past sale prices affecting current prices, which exemplifies the “sales comparison” approach. Second, a contemporaneous peer effect that is occurring within the narrow time frame of interaction between market participants prior to a sale. Third, the signals of sellers' expectations are captured in the asking prices of other houses not yet sold, while the buyer is active in the market. This affects the final sale price, due to strategic behavior and anchoring. This is the first instance that effects other than the own asking price can be handled in such models. In “boom” market conditions, this mechanism introduces the expectation of increasing prices to the hedonic price function, potentially contributing to housing market “bubble” propagation

Felt_space infrastructure: Hyper vigilant spatiality to valence the visceral dimension

Felt_space infrastructure: Hypervigilant spatiality to valence the visceral dimension. This thesis evolves perception as a hypothesis to reframe architectural praxis negotiated through agent-situation interaction. The research questions the geometric principles of architectural ordination to originate the ‘felt_space infrastructure’, a relational system of measurement concerned with the role of perception in mediating sensory space and the cognised environment. The methodological model for this research fuses perception and environmental stimuli, into a consistent generative process that penetrates the inner essence of space, to reveal the visceral parameter. These concepts are applied to develop a ‘coefficient of affordance’ typology, ‘hypervigilant’ tool set, and ‘cognitive_tope’ design methodology. Thus, by extending the architectural platform to consider perception as a design parameter, the thesis interprets the ‘inference schema’ as an instructional model to coordinate the acquisition of spatial reality through tensional and counter-tensional feedback dynamics. Three site-responsive case studies are used to advance the thesis. The first case study is descriptive and develops a typology of situated cognition to extend the ‘granularity’ of perceptual sensitisation (i.e. a fine-grained means of perceiving space). The second project is relational and questions how mapping can coordinate perceptual, cognitive and associative attention, as a ‘multi-webbed vector field’ comprised of attractors and deformations within a viewer-centred gravitational space. The third case study is causal, and demonstrates how a transactional-biased schema can generate, amplify and attenuate perceptual misalignment, thus triggering a visceral niche. The significance of the research is that it progresses generative perception as an additional variable for spatial practice, and promotes transactional methodologies to gain enhanced modes of spatial acuity to extend the repertoire of architectural practice