The design principle of composability among Web services is one of the most crucial reasons for the success and popularity of Web services. However, achieving error-free automatic Web service composition is still a challenge. In this paper, we propose a recursive composition based modeling and verification technique for Web service interaction. The application of recursive composition over a Web service with respect to a given set of Web services yields a recursive composition interaction graph (RCIG). In order to capture the requirement specifications of a Web service interaction scenario, we propose recursive composition specification language (RCSL) as a requirement specification language. Further, we employ the proposed RCIG as an interpretation model to interpret the semantics of a RCSL formula. Our verification technique is based on the generation and analysis of all possible interaction patterns. Performance evaluation results, provided in this paper, show that our proposition is implementable for the real world applications. The key advantages of the proposed approach are: (i) it does not require explicit system modeling as in model checking based approaches, (ii) it captures primitive characteristics of Web service interaction patterns, such as recursive composition, sequential and parallel flow, etc, and (iii) it supports automatic composition of services.

Matrix factorization (MF) techniques are one of the most succesful realisations of recommender systems based on collaborative filtering/prediction (CF). For instance, in a movie recommender system based on CF, the inputs to the system are user ratings on movies (items) the users have already seen. To predict user preferences on movies they have not yet watched one needs to understand the patterns in the partially observed rating matrix. It is possible to visualize this setting as a matrix completion problem, i.e., completing entries in a partially observed data matrix. Then the objective is to compute user latent factor and item latent factor such that the rating matrix is completed. The factorization is usually accomplished by minimizing an objective function using gradient descent or its variants such as conjugate gradient or stochastic gradient descent. In this paper we make use of a particular MF technique called Maximum Margin Matrix Factorization (MMMF) and show that it is suitable for multi-level discrete rating matrix. The factorization is accomplished by minimizing the hinge loss objective function. We propose to improve the gradient search by combining a component of particle Swarm Optimisation (PSO) search. Though earlier attempts of improving PSO search by adding gradient information exist, the main objective of the present work is to improvise gradient/stochastic-gradient search. Our proposed algorithm finds better minimizing points early (fewer number of iterations) not only for the loss function but also for other performance metrics of collaborative filtering such as RMSE and MAE. There has not been any earlier attempt to combine particle swarm optimisation with maximum margin matrix factorisation for collaborative filtering.

In this paper we outline a novel method for classifying ballistic as well as quasi-ballistic missiles using real-time neural network. Fast classification time plays a stellar role for early and prompt action in air-defense scenario. In-order to get the trajectory information of the missile we initially use simulated radar measurements and for final validation real-world radar track is used. Trajectories are segmented to allow small as well as large trajectories to be trained and classified by the same architecture of the neural network. This is needed because ballistic missiles can follow nominal, lofted or depressed trajectory to reach to its target points even when launched from the same point.