214 research outputs found
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
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
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
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning
Social dilemmas have been widely studied to explain how humans are able to
cooperate in society. Considerable effort has been invested in designing
artificial agents for social dilemmas that incorporate explicit agent
motivations that are chosen to favor coordinated or cooperative responses. The
prevalence of this general approach points towards the importance of achieving
an understanding of both an agent's internal design and external environment
dynamics that facilitate cooperative behavior. In this paper, we investigate
how partner selection can promote cooperative behavior between agents who are
trained to maximize a purely selfish objective function. Our experiments reveal
that agents trained with this dynamic learn a strategy that retaliates against
defectors while promoting cooperation with other agents resulting in a
prosocial society.Comment:
A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data
Epidemic outbreaks are an important healthcare challenge, especially in
developing countries where they represent one of the major causes of mortality.
Approaches that can rapidly target subpopulations for surveillance and control
are critical for enhancing containment processes during epidemics.
Using a real-world dataset from Ivory Coast, this work presents an attempt to
unveil the socio-geographical heterogeneity of disease transmission dynamics.
By employing a spatially explicit meta-population epidemic model derived from
mobile phone Call Detail Records (CDRs), we investigate how the differences in
mobility patterns may affect the course of a realistic infectious disease
outbreak. We consider different existing measures of the spatial dimension of
human mobility and interactions, and we analyse their relevance in identifying
the highest risk sub-population of individuals, as the best candidates for
isolation countermeasures. The approaches presented in this paper provide
further evidence that mobile phone data can be effectively exploited to
facilitate our understanding of individuals' spatial behaviour and its
relationship with the risk of infectious diseases' contagion. In particular, we
show that CDRs-based indicators of individuals' spatial activities and
interactions hold promise for gaining insight of contagion heterogeneity and
thus for developing containment strategies to support decision-making during
country-level pandemics
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