Intelligent Distributed Computing 

Abstract: The Internet of Things is a revolutionary concept, within cyberphysical systems, rich in potential as well as in multifacet requirements and development issues. To properly address them and to fully support IoT systems development, Agent-Based Computing represents a suitable and effective modeling, programming, simulation paradigm. As matter of facts, agent metaphors, concepts, techniques, methods and tools have been widely exploited to develop IoT systems. Main contemporary contributions in this direction are surveyed and reported in this work.

Abstract: Nowadays, many e-Commerce tools support customers with automatic recommendations. Many of them are centralized and lack in efficiency and scalability, while other ones are distributed and require a computational overhead excessive for many devices. Moreover, all the past proposals are not “open” and do not allow new personalized terms to be introduced into the domain ontology. In this paper, we present a distributed recommender, based on a multi-tiered agent system, trying to face the issues outlined above. The proposed system is able to generate very effective suggestions without a too onerous computational task. We show that our system introduces significant advantages in terms of openess, privacy and security.

Abstract: Adversarial Machine Learning (AML) refers to the study of the robustness of classification models when processing data samples that have been intelligently manipulated to confuse them Procedures aimed at furnishing such confusing samples exploit concrete vulnerabilities of the learning algorithm of the model at hand, by which perturbations can make a given data instance to be misclassified In this context, the literature has so far gravitated on different AML strategies to modify data instances for diverse learning algorithms, in most cases for image classification This work builds upon this background literature to address AML for distance based time series classifiers (eg, nearest neighbors), in which attacks (ie modifications of the samples to be classified by the model) must be intelligently devised by taking into account the measure of similarity used to compare time series In particular, we propose different attack strategies relying on guided perturbations of the input time series based on gradient information provided by a smoothed version of the distance based model to be attacked Furthermore, we formulate the AML sample crafting process as an optimization problem driven by the Pareto trade-off between (1) a measure of distortion of the input sample with respect to its original version; and (2) the probability of the crafted sample to confuse the model In this case, this formulated problem is efficiently tackled by using multi-objective heuristic solvers Several experiments are discussed so as to assess whether the crafted adversarial time series succeed when confusing the distance based model under target

Abstract: This paper presents an ant-inspired method for clustering semantic Web services. The method considers the degree of semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services we propose a matching method and a set of metrics. The proposed metrics evaluate the degree of match between the ontology concepts describing two services. We have tested the ant-inspired clustering method on the SAWSDL-TC benchmark and we have evaluated its performance using the Dunn Index, the Intra-Cluster Variance metric and an original metric we introduce in this paper.