Analysis and verification of ECA rules in intelligent environments

Intelligent Environments (IEs) are physical spaces where Information Technology (IT) and other pervasive computing technologies are combined in order to achieve specific goals for the users and the environment. IEs have the goal of enriching user experience, increasing awareness of the environment. A number of applications are currently being deployed in domains ranging from smart homes to e-health and autonomous vehicles. Quite often IE support human activities, thus essential requirements to be ensured are correctness, reliability, safety and security. In this paper we present how a set of techniques and tools that have been developed for the verification of software can be employed in the verification of IE described by means of event-condition-action rules. More precisely, we reduce the problem of verifying key properties of these rules to satisfiability and termination problems that can be addressed using state-of-the-art Satisfiability Modulo Theory (SMT) solvers and program analysers. Our approach has been implemented in a tool called vIRONy. Our approach has been validated on a number of case studies from the literature

Symbolic verification of event–condition–action rules in intelligent environments

In this paper we show how state-of-the art SMT-based techniques for software verification can be employed in the verification of event–condition–action rules in intelligent environments. Moreover, we exploit the specific features of intelligent environments to optimise the verification process. We compare our approach with previous work in a detailed evaluation section, showing how it improves both performance and expressivity of the language for event–condition–action rules

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

A reactive constrained programming language on supervisioned data

The modern object-oriented programming languages are based on the abstract data types that join together data model and its dynamic behavior. If this synthesis has many advantages in terms of description of individuals, some problems arise in the analysis of non-trivial global behavior generated by a set of heterogeneous data. As an example, we can think to adding a new module to a real time system with many variables — operation that can produce unexpected behaviors of the global system. In this work we propose a minimum set (core) of instructions required to specify robust programs and support the analysis of components for which the static data model is separated from the dynamic part‘ This separation allows to write and test programs in an easy and incremental way. The data model makes use of the first order logic, while the dynamic component is expressed as a set of ECA Rules. This novel approach on the one hand allows the programming of real time systems characterized by small data models and for which the termination and the consistency of all possible configurations are ensured. On the other it supports the use of data models that might be partially or dynamically defined

IRON: Reliable domain specific language for programming IoT devices

A domain-specific language (DSL) is a programming language that is specialized to a particular application domain. IRON is a DSL for the IoT domain which allows not only to program in an easy way using the Event-Condition-Action (ECA) rules but also to prevent incorrect actions. In this paper, we formally describe the semantics of IRON. The anomalies that IRON prevents are: (i) the presence of cycles that determine the non-termination, (ii) the ambiguous actions that do not allow the definition of a final configuration, (iii) the breaking of invariances. In addition to the formal description of IRON, an interpreter was created in a host language (LUA) that captures and manages the three anomalies. This provides a general scheme for the implementation of languages based on ECA rules

MR-imaging: a new approach for glioma characterization

Gliomas are the most common primary brain tumors. The diffuse infiltration of white matter tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding anatomical structures are crucial in determining prognosis and treatment planning. Conventional Magnetic Resonance (MR) sequences (e.g. T1- or T2—weighted images) have limited sensitivity and specificity in diagnosing brain tumors,[1] because they do not always allow precise delineation of tumor margins, or tumor differentiation from edema and /or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radiotherapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white—matter abnormalities, by detecting the presence of small areas with tumor—cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies. In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by 3D statistical analysis. In order to investigate the pathological tissues, greyscale digital FLAIR images have been processed. Hence, several well—known statistical quantities have been used to gather meaningful information from the available dataset. The most commonly used indexes of location are mean, mode, median and quartiles. The dispersion (or variability) is given by the variance s2, which is related with its second order moment of the distribution, and its square root, the standard deviation s; dividing the latter by the absolute value of the mean one obtains the coefficient of variation CV, i.e. a non-dimensional measure of spread. Another feature of interest is the heterogeneity, usually characterized by the Gini concentration index and entropy, scaling range from 0 (minimum concentration) up to 1 (maximum concentration). Skewness and kurtosis represent the 3rd and 4th order moments of the distribution, and locate the asymmetry and the “distance” from a perfectly normally distributed variable. Finally, an estimation of the fractal dimension is performed using by box counting. Box counting is a method of gathering data for analyzing complex patterns by breaking a dataset, object, image, etc. into smaller and smaller pieces, typically "box"—shaped, and analyzing the pieces at each smaller scale[2]. This arsenal of instruments allowed us to determine the statistical differences among different gliomas