700 research outputs found

    Connecting qualitative spatial and temporal representations by propositional closure

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    This paper establishes new relationships between existing qualitative spatial and temporal representations. Qualitative spatial and temporal representation (QSTR) is concerned with abstractions of infinite spatial and temporal domains, which represent configurations of objects using a finite vocabulary of relations, also called a qualitative calculus. Classically, reasoning in QSTR is based on constraints. An important task is to identify decision procedures that are able to handle constraints from a single calculus or from several calculi. In particular the latter aspect is a longstanding challenge due to the multitude of calculi proposed. In this paper we consider propositional closures of qualitative constraints which enable progress with respect to the longstanding challenge. Propositional closure allows one to establish several translations between distinct calculi. This enables joint reasoning and provides new insights into computational complexity of individual calculi. We conclude that the study of propositional languages instead of previously considered purely relational languages is a viable research direction for QSTR leading to expressive formalisms and practical algorithms

    Qualitative and quantitative spatio-temporal relations in daily living activity recognition

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    For the effective operation of intelligent assistive systems working in real-world human environments, it is important to be able to recognise human activities and their intentions. In this paper we propose a novel approach to activity recognition from visual data. Our approach is based on qualitative and quantitative spatio-temporal features which encode the interactions between human subjects and objects in an efficient manner. Unlike the state of the art, our approach uses significantly fewer assumptions and does not require knowledge about object types, their affordances, or the sub-level activities that high-level activities consist of. We perform an automatic feature selection process which provides the most representative descriptions of the learnt activities. We validated the method using these descriptions on the CAD-120 benchmark dataset, consisting of video sequences showing humans performing daily real-world activities. The method is shown to outperform state of the art benchmarks

    Relation algebras and their application in temporal and spatial reasoning

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    Abstract Qualitative temporal and spatial reasoning is in many cases based on binary relations such as before, after, starts, contains, contact, part of, and others derived from these by relational operators. The calculus of relation algebras is an equational formalism; it tells us which relations must exist, given several basic operations, such as Boolean operations on relations, relational composition and converse. Each equation in the calculus corresponds to a theorem, and, for a situation where there are only nitely many relations, one can construct a composition table which can serve as a look up table for the relations involved. Since the calculus handles relations, no knowledge about the concrete geometrical objects is necessary. In this sense, relational calculus is pointless. Relation algebras were introduced into temporal reasoning by Allen [1] and into spatial reasoning by Egenhofer and Sharm

    Using a Game Engine to Integrate Experimental, Field, and Simulation Data for Science Education: You Are the Scientist!

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    The purpose of this project is to use a game engine tointegrate geo-referenced research data, whether experimental orsimulated, to present it interactively to the user. Geo-referencedmeans that every image, video, or sound file, every pressuremap, and every simulated temperature chart is attached to aspecific point on a map or body. These data may also be timereferenced,so that different data sets may be available at thesame location for different times of the day or seasons of the year.Target users for the interactive applications are high-school andcollege students who can then conduct their own “experiments”or “explorations” as a way to get exposed to the problems andmethodologies of science and research. We use two examples ofprojects to illustrate the approach

    Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation

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    The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the pedestrians and other vulnerable road users and potentially to external auditors in case of accidents. However, nowadays, most explainable methods still rely on quantitative analysis of the AD scene representations captured by multiple sensors. This paper proposes a novel representation of AD scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative spatiotemporal reasoning of long-term scenes. The construction of this graph exploits the recent Qualitative Constraint Acquisition paradigm. Our experimental results on NuScenes, an open real-world multi-modal dataset, show that the qualitative eXplainable graph of an AD scene composed of 40 frames can be computed in real-time and light in space storage which makes it a potentially interesting tool for improved and more trustworthy perception and control processes in AD

    Dealing with variability in ecological modelling: An analysis of a random non-autonomous logistic population model

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    This paper presents a methodology to deal with the randomness associated toecological modelling. Data variability makes it necessary to analyse the impactof random perturbations on the fitted model parameters. We conduct suchanalysis for the logistic growth model with a certain sigmoid functional formof the carrying capacity, which was proposed in the literature for the study ofparasite growth during infection. We show how the probability distributions ofthe parameters are set via the maximum entropy principle. Then the randomvariable transformation method allows for computing the density function ofthe population
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