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The Mobile Data Challenge: Big Data for Mobile Computing Research
TLDR
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. Expand
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IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT
TLDR
We present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. Expand
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ConXsense: automated context classification for context-aware access control
TLDR
We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Expand
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Context-Based Zero-Interaction Pairing and Key Evolution for Advanced Personal Devices
TLDR
We present a novel robust and inexpensive approach for secure zero-interaction pairing suitable for IoT and wearable devices. Expand
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Human 17 beta-hydroxysteroid dehydrogenase type 1 and type 2 isoenzymes have opposite activities in cultured cells and characteristic cell- and tissue-specific expression.
17 beta-Hydroxysteroid dehydrogenase (17HSD) isoenzymes catalyse the interconversion between highly active 17 beta-hydroxy- and low-activity 17-keto-steroids and thereby regulate the biologicalExpand
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From big smartphone data to worldwide research: The Mobile Data Challenge
TLDR
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. Expand
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Peek-a-boo: i see your smart home activities, even encrypted!
TLDR
A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles through the sensors already embedded in these devices. Expand
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DÏoT: A Federated Self-learning Anomaly Detection System for IoT
TLDR
We present DÏoT, an autonomous self-learning distributed system for detecting compromised IoT devices. Expand
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A probabilistic kernel method for human mobility prediction with smartphones
TLDR
This paper presents a study on location prediction using smartphone data, in which we address modeling and application aspects. Expand
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IoT Sentinel Demo: Automated Device-Type Identification for Security Enforcement in IoT
TLDR
We present our implementation of IoT Sentinel, which is a system aimed at protecting the user's network from vulnerable IoT devices. Expand
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