Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy

Carbon Abatement Leaders and Laggards Non Parametric Analyses of Policy Oriented Kuznets Curves

We study the eventual structural differences of climate change leading ‘actors’ such as Northern EU countries, and ‘lagging actors’ - southern EU countries and the ‘Umbrella group’ - with regard to long run (1960-2001) carbon-income relationships. Parametric and semi parametric panel models show that the groups of countries that were in the Kyoto arena less in favour of stringent climate policy, have yet to experience a turning point, though they at least show relative delinking in their monotonic carbon-income relationship. Northern EU instead robustly shows bell shapes across models, which seem to depend on time related (policy) events. Time related effects are more relevant than income effects in explaining the occurrence of robust Kuznets curves. The reaction of northern EU to exogenous policy events such as the 1992 climate change convention that gave earth to the Kyoto era, and even the second oil shock that preceded it in the 80’s are among the causes of the observed structural differences.Carbon Kuznets Curves, Kyoto, Long Run Dynamics, Policy Events, Heterogeneous Panels, Cross-Section Correlation, Semi Parametric Models, Common Time Trends

Water Consumption and Long-Run Urban Development: The Case of Milan

Analyses of long run consumption series are rare in literature. We study the evolution of water consumption in Milan in the twentieth century. The objective is twofold: on one side, the univariate analysis tries both to assess the impact of relevant socio-economic and environmental changes on water consumption in Milan and verify if consumers have deeply rooted consumption habits. On the other side, the multivariate analysis is used to identify the socio-economic factors that are relevant in explaining consumption evolution. Results indicate both that water users have well entrenched consumption habits and that population, climate and economic structure behave more similarly, in Euclidean terms, to water consumption than to other economic and social variables.Urban consumption, Long-run, Development, Environmental changes

You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information

Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still catergorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message. In this paper, we use Twitter as a case study to quantify the uniqueness of the association between metadata and user identity and to understand the effectiveness of potential obfuscation strategies. More specifically, we analyze atomic fields in the metadata and systematically combine them in an effort to classify new tweets as belonging to an account using different machine learning algorithms of increasing complexity. We demonstrate that through the application of a supervised learning algorithm, we are able to identify any user in a group of 10,000 with approximately 96.7% accuracy. Moreover, if we broaden the scope of our search and consider the 10 most likely candidates we increase the accuracy of the model to 99.22%. We also found that data obfuscation is hard and ineffective for this type of data: even after perturbing 60% of the training data, it is still possible to classify users with an accuracy higher than 95%. These results have strong implications in terms of the design of metadata obfuscation strategies, for example for data set release, not only for Twitter, but, more generally, for most social media platforms.Comment: 11 pages, 13 figures. Published in the Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, US

Probabilistic Matching: Causal Inference under Measurement Errors

The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be inferred from other observed attributes. In such cases, inaccuracies of the applied inference methods will result in noisy outputs. In this study, we propose a novel approach for causal inference when one or more key variables are noisy. Our method utilizes the knowledge about the uncertainty of the real values of key variables in order to reduce the bias induced by noisy measurements. We evaluate our approach in comparison with existing methods both on simulated and real scenarios and we demonstrate that our method reduces the bias and avoids false causal inference conclusions in most cases.Comment: In Proceedings of International Joint Conference Of Neural Networks (IJCNN) 201