Model Checking Timed and Stochastic Properties with CSL^{TA}

Markov chains are a well-known stochastic process that provide a balance between being able to adequately model the system's behavior and being able to afford the cost of the model solution. The definition of stochastic temporal logics like continuous stochastic logic (CSL) and its variant asCSL, and of their model-checking algorithms, allows a unified approach to the verification of systems, allowing the mix of performance evaluation and probabilistic verification. In this paper we present the stochastic logic CSLTA, which is more expressive than CSL and asCSL, and in which properties can be specified using automata (more precisely, timed automata with a single clock). The extension with respect to expressiveness allows the specification of properties referring to the probability of a finite sequence of timed events. A typical example is the responsiveness property "with probability at least 0.75, a message sent at time 0 by a system A will be received before time 5 by system B and the acknowledgment will be back at A before time 7", a property that cannot be expressed in either CSL or asCSL. We also present a model-checking algorithm for CSLTA.     

Facilitating the Rapid Development and Scalable Orchestration of Composite Web Services

The development of new Web services through the composition of existing ones has gained a considerable momentum as a means to realise business-to-business collaborations. Unfortunately, given that services are often developed in an ad hoc fashion using manifold technologies and standards, connecting and coordinating them in order to build composite services is a delicate and time-consuming task. In this paper, we describe the design and implementation of a system in which services are composed using a model-driven approach, and the resulting composite services are orchestrated following a peer-to-peer paradigm. The system provides tools for specifying composite services through statecharts, data conversion rules, and multi-attribute provider selection policies. These specifications are interpreted by software components that interact in a peer-to-peer way to coordinate the execution of the composite service. We report results of an experimental evaluation showing the relative advantages of this peer-to-peer approach with respect to a centralised one.     

A statistical framework for few-shot action recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition remains crucial. This work addresses the challenge of classifying short video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action. The method is based on Gunner Farneback’s dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.

     

Theoretical Migration Model for Micellar Capillary Electrophoresis and Its Application to the Separation of Anionic Metal Complexes of HEDTC and CDTA

A mathematical model relating the effective mobility of an analyte in micellar capillary electrophoresis (MCE) to the concentration of surfactant and organic modifier in the background electrolyte (BGE) was derived. Effective mobility is expressed in terms of the electrophoretic mobility of the analyte, the partition coefficient of the analyte into the micelle, and the influence of organic modifier on these two factors. The performance of the model was evaluated using Cd(II), Pb(II), Co(II), Ni(II), Bi(III), Cu(II), and Hg(II) complexes of bis(2-hydroxyethyl)dithiocarbamate, all of which carry a partial negative charge, and Cd(II), Pb(II), Co(II), Ni(II), Bi(III), Cu(II), Hg(II), Fe(III), Ag(I), Tl(I), and Mn(II) complexes of trans-1,2-diaminocyclohexane-N,N,N',N'-tetraacetic acid, all of which are anionic having charges in the range -1 to -3. These analytes were separated in borate BGEs containing 10-50 mM sodium dodecyl sulfate and 0-20% (v/v) methanol. Nonlinear regression was used to derive parameters for the model from experimental data and these parameters were used to predict effective mobilities of the analytes. Predicted values of effective mobilties agreed with experimental values to within 3.1%. Values of parameters from the model equation are used to explain changes in separation selectivity observed at different BGE compositions and the model equation is shown to be applicable to computer-assisted optimization of the BGE composition, in MCE using a limited number of experiments.     

Robustness study of emerged communities from exchanges in peer-to-peer networks

The study of emerged community structure is an important challenge in networks analysis. In fact, several methods have been proposed in the literature to statistically determine the significance of discovered structures. Nevertheless, most of existing analysis models consider only the structural aspect of emerged communities. We are interested in studying the robustness of emerged communities in peer-to-peer (P2P) networks. More precisely, we consider the emerged communities in the induced graph by all the exchanges in these networks. Hence, rather than examining the robustness only on the structural properties of the graph, we will focus on the parameters that allow the emergence of community structures. In fact, perturbing these parameters might destroy most of the obtained properties at the emerged level. To the best of our knowledge, robustness of networks has never been considered from this angle before. In this paper, we study the impact of perturbing the content and the profile of nodes on the emerged communities in P2P networks. We show how these alterations affect both structure and information supported by the emerged structures.     

A framework to support qualitative reasoning about COAs in a dynamic spatial environment

We propose a framework to reason qualitatively about courses of action (COAs) which need to be executed in a realistic geographic space that may change. Particularly, the framework aims to support human mental ‘What-If’ analysis by simulating the execution of COAs in a virtual geographic environment, which can change during the simulation, and by allowing the user to explore various scenarios (different COAs) and to analyse their outcomes using causal reasoning techniques. In this article, we first present a framework which is based on a conceptual model of spatio-temporal situations, a multi-agent geosimulation platform and qualitative spatio-temporal causal reasoning techniques. Then, we illustrate the framework using a case study.     

A trust negotiation system for digital library Web services

A scalable approach to trust negotiation is required in digital library (DL) environments that have large and dynamic user populations. In this paper we introduce Trust-Serv, a model-driven trust negotiation framework for Web services, and show how it can be used to effectively handle trust negotiation in DLs. The framework employs a model for trust negotiation based on state machines, extended with security abstractions. High-level specifications expressed with the state-machine-based model are then translated into formats suitable for automating the trust negotiation process. The proposed framework also supports negotiation policy lifecycle management, an important trait in the dynamic environments that characterize DLs. In particular, we present a set of policy change operations that enable the dynamic evolution of negotiation policies without disrupting ongoing negotiations. The proposed approach has been implemented as a container-centric mechanism that is transparent to the DL and to the developers of DL Web services, simplifying DL development and management as well as enabling scalable deployments.     

Robust non-fragile LQ controllers: The static state feedback case

This paper describes the synthesis of non-fragile or resilient regulators for linear systems. A general framework for fragility is described using state-space methodologies, and the LQ/H₂ static state-feedback problem is examined in detail. We discuss the multiplicative structured uncertainties case, and propose remedies of the fragility problem using an optimization programming framework via matrix inequalities. A special case that leads to a convex optimization framework via linear matrix inequalities (LMIs) will be considered. The benchmark problem is taken as an example to show how special controller gain variations can affect the performance of the closed-loop system.     

Non-linear impulsive dynamical systems. Part II: Stability of feedback interconnections and optimality

In a companion paper (Nonlinear Impulsive Dynamical Systems. Part I: Stability and Dissipativity) Lyapunov and invariant set stability theorems and dissipativity theory were developed for non-linear impulsive dynamical systems. In this paper we build on these results to develop general stability criteria for feedback interconnections of non-linear impulsive systems. In addition, a unified framework for hybrid feedback optimal and inverse optimal control involving a hybrid non-linear-non-quadratic performance functional is developed. It is shown that the hybrid cost functional can be evaluated in closed-form as long as the cost functional considered is related in a specific way to an underlying Lyapunov function that guarantees asymptotic stability of the non-linear closed-loop impulsive system. Furthermore, the Lyapunov function is shown to be a solution of a steady-state, hybrid Hamilton‐Jacobi‐Bellman equation.     

A novel augmented reality visualization in jaw surgery: enhanced ICP based modified rotation invariant and modified correntropy

Image registration, accuracy, processing time and occlusions are the main limitations of augmented reality (AR) based jaw surgery. Therefore, the main aim of this paper is to reduce the registration error, which will help in improving the accuracy and reducing the processing time. Also, it aims to remove outliers and remove the registration outcomes trapped in local minima to improve the alignment problems and remove the occlusion caused by surgery instrument. The enhanced Iterative Closest Point (ICP) algorithm with rotation invariant and correntropy was used for the proposed system. Markerless image registration technique was used for AR-based jaw surgery. The problem of occlusion caused by surgical tools and blood is solved by using stereo based tracing with occlusion handling techniques. This research reduced alignment error 0.59 mm?~?0.62 mm against 0.69?~?0.72 mm of state-of-the-art solution. The processing time of video frames was enhanced to 11.9?~?12.8 fps against 8?~?9.15 fps in state-of-the-art solution. This paper is focused on providing fast and accurate AR-based system for jaw surgery. The proposed system helps in improving the AR visualization during jaw surgery. The combination of methods and technology helped in improving AR visualization for jaw surgery and to overcome the failure caused by a large rotation angle and provides an initial parameter for better image registration. It also enhances performance by removing outliers and noises. The pose refinement stage provides a better result in terms of processing time and accuracy.