Expressing the Behavior of Three Very Different Concurrent Systems by Using Natural Extensions of Separation Logic

Separation Logic is a non-classical logic used to verify pointer-intensive code. In this paper, however, we show that Separation Logic, along with its natural extensions, can also be used as a specification language for concurrent-system design. To do so, we express the behavior of three very different concurrent systems: a Subway, a Stopwatch, and a 2x2 Switch. The Subway is originally implemented in LUSTRE, the Stopwatch in Esterel, and the 2x2 Switch in Bluespec

Semantics Through Pictures: towards a diagrammatic semantics for object-oriented modelling notations

An object-oriented (OO) model has a static component, the set of allowable snapshots or system states, and a dynamic component, the set of filmstrips or sequences of snapshots. Diagrammatic notations, such as those in UML, each places constraints on the static and/or dynamic models. A formal semantics of OO modeling notations can be constructed by providing a formal description of (i) sets of snapshots and filmstrips, (ii) constraints on those sets, and (iii) the derivation of those constraints from diagrammatic notations. In addition, since constraints are contributed by many diagrams for the same model, a way of doing this compositionally is desirable. One approach to the semantics is to use first-order logic for (i) and (ii), and theory inclusion with renaming, as in Larch, to characterize composition. A common approach to (iii) is to bootstrap: provide a semantics for a kernel of the notation and then use the kernel to give a semantics to the other notations. This only works if a kernel which is sufficiently expressive can be identified, and this is not the case for UML. However, we have developed a diagrammatic notation, dubbed constraint diagrams, which seems capable of expressing most if not all static and dynamic constraints, and it is proposed that this be used to give a diagrammatic semantics to OO models

Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary Semantic Descriptors

We describe a novel approach to image based localisation in urban environments using semantic matching between images and a 2-D map. It contrasts with the vast majority of existing approaches which use image to image database matching. We use highly compact binary descriptors to represent semantic features at locations, significantly increasing scalability compared with existing methods and having the potential for greater invariance to variable imaging conditions. The approach is also more akin to human map reading, making it more suited to human-system interaction. The binary descriptors indicate the presence or not of semantic features relating to buildings and road junctions in discrete viewing directions. We use CNN classifiers to detect the features in images and match descriptor estimates with a database of location tagged descriptors derived from the 2-D map. In isolation, the descriptors are not sufficiently discriminative, but when concatenated sequentially along a route, their combination becomes highly distinctive and allows localisation even when using non-perfect classifiers. Performance is further improved by taking into account left or right turns over a route. Experimental results obtained using Google StreetView and OpenStreetMap data show that the approach has considerable potential, achieving localisation accuracy of around 85% using routes corresponding to approximately 200 meters.Comment: 8 pages, submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 201