Quantifying Privacy Loss of Human Mobility Graph Topology

Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy

Wearables, smartphones, and artificial intelligence for digital phenotyping and health

Ubiquitous progress in wearable sensing and mobile computing technologies, alongside growing diversity in sensor modalities, has created new pathways for the collection of health and well-being data outside of laboratory settings, in a longitudinal fashion. Wearable and mobile devices have the potential to provide low-cost, objective measures of physical activity, clinically relevant data for patient assessment, and scalable behavior monitoring in large populations. These data can be used in both interventional and observational studies to derive insights regarding the links between behavior, health. and disease, as well as to advance the personalization and effectiveness of commercial wellness applications. Today, over 400,000 participants have had their behavior tracked prospectively using accelerometers for epidemiological studies across the globe. Traditionally, epidemiologists and clinicians have relied upon self-report measures of physical activity and sleep which, while valuable in the absence of alternatives, are subject to bias and often provide partial, incomplete information Physical behavior data extracted from wearable devices are being used to derive sensor-assessed, objective measures of physical behaviors, overcoming the limitations of self-report with the aim of relating these to clinical endpoints and eventually applying the findings to preventive and predictive medicine. Moreover, the application of artificial intelligence (AI), sensor fusion, and signal processing to wearable sensor data has led to improved human activity recognition and behavioral phenotyping. Here, we review the state of the art in wearable and mobile sensing technology in epidemiology and clinical medicine and discuss how AI is changing the field

Measuring urban social diversity using interconnected geo-social networks

Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.This work was supported by the Project LASAGNE, Contract No. 318132 (STREP), funded by the European Commission and EPSRC through Grant GALE (EP/K019392).This is the author accepted manuscript. The final version is available from the Association for Computing Machinery via http://dx.doi.org/10.1145/2872427.288306

Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges.

As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetcab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional drivers users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high

Autoimmune mucocutaneous blistering diseases after SARS-Cov-2 vaccination: A Case report of Pemphigus Vulgaris and a literature review

Background: Cases of severe autoimmune blistering diseases (AIBDs) have recently been reported in association with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination. Aims: To describe a report of oropharyngeal Pemphigus Vulgaris (OPV) triggered by the mRNABNT162b2 vaccine (Comirnaty®/ Pfizer/ BioNTech) and to analyze the clinical and immunological characteristics of the AIBDs cases reported following the SARS-CoV-2 vaccination. Methods: The clinical and immunological features of our case of OPV were documented. A review of the literature was conducted and only cases of AIBDs arising after the SARS-CoV-2 vaccination were included. Case report: A 60-year old female patients developed oropharyngeal and nasal bullous lesions seven days after the administration of a second dose of the mRNABNT162b2 vaccine (Comirnaty®/ Pfizer/BioNtech). According to the histology and direct immunofluorescence findings showing the presence of supra-basal blister and intercellular staining of IgG antibodies and the presence of a high level of anti-Dsg-3 antibodies (80 U/ml; normal < 7 U/ml) in the serum of the patients, a diagnosis of oropharyngeal Pemphigus Vulgaris was made. Review: A total of 35 AIBDs cases triggered by the SARS-CoV-2 vaccination were found (including our report). 26 (74.3%) were diagnosed as Bullous Pemphigoid, 2 (5.7%) as Linear IgA Bullous Dermatosis, 6 (17.1%) as Pemphigus Vulgaris and 1 (2.9%) as Pemphigus Foliaceus. The mean age of the sample was 72.8 years and there was a predominance of males over females (F:M=1:1.7). In 22 (62.9%) cases, the disease developed after Pfizer vaccine administration, 6 (17.1%) after Moderna, 3 (8.6%) after AstraZeneca, 3 (8.6%) after CoronaVac (one was not specified). All patients were treated with topical and/or systemic corticosteroids, with or without the addition of immunosuppressive drugs, with a good clinical response in every case. Conclusion: Clinicians should be aware of the potential, though rare, occurrence of AIBDs as a possible adverse event after the SARS-CoV-2 vaccination. However, notwithstanding, they should encourage their patients to obtain the vaccination in order to assist the public health systems to overcome the COVID-19 pandemic

Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers. Our experiments using a real-world dataset of 33, 000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood –valence and arousal– with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.This work was supported by the Embiricos Trust Scholarship of Jesus College Cambridge, EPSRC through Grants DTP (EP/N509620/1) and UBHAVE (EP/I032673/1), and Nokia Bell Labs through the Centre of Mobile, Wearable Systems and Augmented Intelligence

LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms

Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. This is an important feature when context, actions, and users change. However, enabling CL on resource-constrained embedded systems is challenging due to the limited labeled data, memory, and computing capacity. In this paper, we propose LifeLearner, a hardware-aware meta continual learning system that drastically optimizes system resources (lower memory, latency, energy consumption) while ensuring high accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies to explicitly cope with data scarcity issues and ensure high accuracy, (2) effectively combine lossless and lossy compression to significantly reduce the resource requirements of CL and rehearsal samples, and (3) developed hardware-aware system on embedded and IoT platforms considering the hardware characteristics. As a result, LifeLearner achieves near-optimal CL performance, falling short by only 2.8% on accuracy compared to an Oracle baseline. With respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically reduces the memory footprint (by 178.7x), end-to-end latency by 80.8-94.2%, and energy consumption by 80.9-94.2%. In addition, we successfully deployed LifeLearner on two edge devices and a microcontroller unit, thereby enabling efficient CL on resource-constrained platforms where it would be impractical to run SOTA methods and the far-reaching deployment of adaptable CL in a ubiquitous manner. Code is available at https://github.com/theyoungkwon/LifeLearner.Comment: Accepted for publication at SenSys 202

Monitoring a large construction site using wireless sensor networks

Despite the significant advances made by wireless sensor network research, deployments of such networks in real application environments are fraught with significant difficulties and challenges that include robust topology design, network diagnostics and maintenance. Based on our experience of a six-month-long wireless sensor network deployment in a large construction site, we highlight these challenges and argue the need for new tools and enhancements to current protocols to address these challenges.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314/1). We would like to thank Costain-Skanska Joint Venture (CSJV) and our industrial partner Crossrail for allowing access and instrumentation of the Paddington site. We would also like to thank Dr Munenori Shibata from Japan Railway Technical Research Institute for his assistance with network deployment.This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2820990.2820997 Data supporting this paper is available from https://www.repository.cam.ac.uk/handle/1810/250538