735 research outputs found

    A study of existing Ontologies in the IoT-domain

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    Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

    Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation

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    A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs ("eXpert AUGmented" variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40\%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.Comment: 38 pages, 30 figure

    4W1H in IoT semantics

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    International audienceIoT systems are now being deployed worldwide to sense phenomena of interest. The existing IoT systems are often independent which limits the use of sensor data to only one application. Semantic solutions have been proposed to support reuse of sensor data across IoT systems and applications. This allows integration of IoT systems for increased productivity by solving challenges associated with their interoperability and heterogeneity. Several ontologies have been proposed to handle different aspects of sensor data collection in IoT systems, ranging from sensor discovery to applying reasoning on collected sensor data for drawing inferences. In this paper, we study and categorise the existing ontologies based on the fundamental ontological concepts (e.g., sensors, context, location, and more) required for annotating different aspects of data collection and data access in an IoT application. We identify these fundamental concepts by answering the 4Ws (What, When, Who, Where) and 1H (How) identified using the 4W1H methodology

    Towards Building Real-Time, Convenient Route Recommendation System for Public Transit

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    International audiencePublic transportation is essential for sustainable and economical development of cities. Several transport organizations aim to provide service information to commuters through web and mobile apps. This information includes possible routes between two stations, estimated travel and arrival times, and real-time updates about traffic conditions. However, this information is currently not personalized according to commuter preferences. In this work, we emphasize the need for personalized transit service information to commuters and present a vision of our work in this direction. Our final goal is to develop a fully-functional personalized route recommendation system for public transit commuters. This involves identifying commuter preferences and suitable recommendation techniques, and developing a platform to communicate this information to the commuters. We identify the requirements for the development of this platform, and propose an architecture for our system. As a proof of concept, we present an Android participatory sensing application - MetroCognition, which acquires feedback on convenience experienced by commuters in public transit

    Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

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    Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (Ό̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ÂŻ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ÂŻ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),Ό̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

    Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV