In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D2FAS) algorithm for mobile sensors to actively explore the road network to gather and assimilate the most informative data for predicting the traffic phenomenon. We analyze the time and communication complexity of D2FAS and demonstrate that it can scale well with a large number of observations and sensors. We provide a theoretical guarantee on its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the Gaussian process (GP) model: The computation of such a sparse approximate GP model can thus be parallelized and distributed among the mobile sensors (in a Google-like MapReduce paradigm), thereby achieving efficient and scalable prediction. We also theoretically guarantee its active sensing performance that improves under various practical environmental conditions. Empirical evaluation on real-world urban road network data shows that our D2FAS algorithm is significantly more time-efficient and scalable than state-of-the-art centralized algorithms while achieving comparable predictive performance.

In this paper, we present some high level information fusion approaches for numeric and symbolic data. We study the interest of such method particularly for classifier fusion. A comparative study is made in a context of sea bed characterization from sonar images. The classi- fication of kind of sediment is a difficult problem because of the data complexity. We compare high level information fusion and give the obtained performance.

Security operators are increasingly interested in solutions that can provide an automatic understanding of potentially crowded public environments. In this paper, an on-going research is presented, on building a complex system consists of three main components: human security operators carrying sensors, mobile robotic platforms carrying sensors and network of fixed sensors (i.e. cameras) installed in the environment. The main objectives of this research are: 1) to develop models and solutions for an intelligent integration of sensorial information coming from different sources, 2) to develop effective human-robot interaction methods in the paradigm multi-human vs. multi-robot, 3) to integrate all these components in a system that allows for robust and efficient coordination among robots, vision sensors and human guards, in order to enhance surveillance in crowded public environments.

A data integration system provides transparent access to different data sources by suitably combining their data, and providing the user with a unified view of them, called global schema. However, source data are generally not under the control of the data integration process, thus integrated data may violate global integrity constraints even in presence of locally-consistent data sources. In this scenario, it may be anyway interesting to retrieve as much consistent information as possible. The process of answering user queries under global constraint violations is called consistent query answering (CQA). Several notions of CQA have been proposed, e.g., depending on whether integrated information is assumed to be sound, complete, exact or a variant of them. This paper provides a contribution in this setting: it uniforms solutions coming from different perspectives under a common ASP-based core, and provides query-driven optimizations designed for isolating and eliminating inefficiencies of the general approach for computing consistent answers. Moreover, the paper introduces some new theoretical results enriching existing knowledge on decidability and complexity of the considered problems. The effectiveness of the approach is evidenced by experimental results. To appear in Theory and Practice of Logic Programming (TPLP).

In this paper we provide insight into the BodyMedia FIT® armband system — a wearable multi-sensor technology that achieves the goals of continuous physiological monitoring (especially energy expenditure estimation) and weight management using machine learning and data modeling methods. This system has been commercially available since 2001 and more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s succ

Researchers Inconsistency and partial information is the norm both in AI and databases, as well as in information retrieval, in knowledge bases used in many real world applications have been working on the problems that arise with the integration that support, among other things, human of heterogeneous knowledge bases for decades [Baral et decision making processes. In this work we argue al., 1991; Benferhat et al., 1997; Besnard and Schaub, 1998; that the management of this kind of data needs to be Arenas et al., 1999; Bohannon et al., 2005]. However, almost context-sensitive, creating a synergy with the user all past approaches proceeded under the assumption that to build useful, flexible data management systems.

This work develops an approach to efficient reasoning in first-order knowledge bases with incomplete information. We build on Levesque's proper knowledge bases approach, which supports limited incomplete knowledge in the form of a possibly infinite set of positive or negative ground facts. We propose a generalization which allows these facts to involve unknown individuals, as in the work on labeled null values in databases. Dealing with such unknown individuals has been shown to be a key feature in the database literature on data integration and data exchange. In this way, we obtain one of the most expressive first-order open-world settings for which reasoning can still be done efficiently by evaluation, as in relational databases. We show the soundness of the reasoning procedure and its completeness for queries in a certain normal form.

Statutory records of underground utility apparatus (such as pipes andcables) are notoriously inaccurate, so street surveys are usually undertakenbefore road excavation takes place to minimize the extent and duration ofexcavation and for health and safety reasons. This involves the use ofsensors such as Ground Penetrating Radar (GPR). The GPR scans are thenmanually interpreted and combined with the expectations from the utilityrecords and other data such as surveyed manholes. The task is complex owingto the difficulty in interpreting the sensor data, and the spatialcomplexity and extent of under street assets. We explore the application ofAI techniques, in particular Bayesian data fusion (BDF), to automaticallygenerate maps of buried apparatus. Hypotheses about the spatial location anddirection of buried assets are extracted by identifying hyperbolae in theGPR scans. The spatial location of surveyed manholes provides further inputto the algorithm, as well as the prior expectations from the statutoryrecords. These three data sources are used to produce the most probable mapof the buried assets. Experimental results on real and simulated data setsare presented.

Methods for fusing document lists that were retrieved in response to a query often utilize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevance-status propagation between documents. The propagation is governed by inter-document-similarities and by retrieval scores of documents in the lists. Empirical evaluation demonstrates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only retrieval scores or ranks.