An overwhelming and growing amount of data is available online. The problem of untrustworthy online information is augmented by its high economic potential and its dynamic nature, e.g. transient domain names, dynamic content, etc. In this paper, we address the problem of assessing the credibility of web pages by a decentralized social recommender system. Specifically, we concurrently employ i) item-based collaborative filtering (CF) based on specific web page features, ii) user-based CF based on friend ratings and iii) the ranking of the page in search results. These factors are appropriately combined into a single assessment based on adaptive weights that depend on their effectiveness for different topics and different fractions of malicious ratings. Simulation experiments with real traces of web page credibility evaluations suggest that our hybrid approach outperforms both its constituent components and classical content-based classification approaches.
Diabetes Type 1 is a metabolic disease which results in a lack of insulin production, causing high glucose levels in the blood. It is crucial for diabetic patients to balance this glucose level, and they depend on external substances to do so. In order to keep this level under control, they usually need to resort to invasive glucose control methods, such as taking a sample drop of blood from their finger and have it analyzed. Recently, other directions emerged to offer alternative ways to estimate glucose level, using indirect sensor measurements including ECG monitoring and other physiological parameters. This paper showcases a framework for inferring semantically annotated glycemic events on the patient, which leverages data from mobile wearable sensors deployed on a sport-belt. This work is part of the D1namo project for non-invasive diabetes monitoring, and focuses on the representation and query processing of the data produced by the wearable sensors, using semantic technologies and vocabularies that extend existing Web standards. Furthermore, this work shows how different layers of data, from raw measurements to complex events can be represented and linked in this framework, and experimental evidence is provided of how these layers can be efficiently exploited using an RDF Stream Processing engine.
Modern smartphones are powerful platforms that have become part of the everyday life for most people. Thanks to their sensing and computing capabilities, smartphones can unobtrusively identify simple user states (e.g., location, performed activity, etc.), enabling a plethora of applications that provide insights on the lifestyle of the users. In this paper, we introduce routineSense: a system for the automatic reconstruction of complex daily routines from simple user states, implemented as an incremental processing framework. Such framework combines opportunistic sensing and user feedback to discover frequent and exceptional routines that can be used to segment and aggregate multiple user activities in a timeline. We use a comprehensive dataset containing rich geographic information to assess the feasibility and performance of routineSense, showing a near threefold improvement on the current state-of-the-art.
Abstract-The richness of user-centric information gathered by modern devices can be used to keep track of memorable events, therefore acting as a prosthesis of the prone-to-forget human memory. We propose to combine virtual and physical sensors from mobile devices to infer digital memories of user activities in a semi-supervised fashion. In MemorySense, sensor data is processed by a space and energy efficient algorithm to recognize basic activities. We then use semantic reasoning to aggregate these activities into the digital equivalent of a human episodic memory.
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