The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate federated learning, a paradigm that enables mobile devices to produce a machine-learning model without sharing their data. However, the majority of the existing federated frameworks follow a centralized approach. In this work, we introduce IPLS, a fully decentralized federated learning framework that is partially based on the interplanetary file system (IPFS). By using IPLS and connecting into the corresponding private IPFS network, any party can initiate the training process of a machine-learning model or join an ongoing training process that has been started by another party. IPLS scales with the number of participants, is robust against intermittent connectivity and dynamic participant departures/arrivals, requires minimal resources and guarantees that the accuracy of the trained model quickly converges to that of a centralized federated learning framework with a negligible accuracy drop of less than 10/00.

The aim of this work is to provide an up-to-date comprehensive review of the peer-reviewed articles, the research projects and the entrepreneurial efforts that consider the utilization of blockchain technology in the energy sector in general and the power grid in particular. Through our review study we systematically classify existing applications of blockchain technology in the energy sector according to their field of activity. The comprehensive and holistic picture provided aims to contribute to the body of knowledge of the applicability of blockchain technology within the energy sector and pave the way for further research in this field.

Adaptive Information Filtering seeks a solution to the problem of information overload through a tailored representation of the user's interests, called user profile, which constantly adapts to changes in them. Evolutionary Algorithms have been proposed as a solution to the problem of profile adaptation, but the relevant attempts have not produced successful real world applications. In this paper, we argue that Adaptive Information Filtering is a complex and dynamic problem not easily addressed with Genetic Algorithms and Memetic Algorithms that adopt weighted keyword vector for profile representation. We discuss the theoretical issues and provide experimental evidence showing that such an approach suffers due to the large number of dimensions in the underlying vector space. The genetic operators cannot randomly produce the right combinations of keyword weights, given the very large number of possible combinations. With the current work, we wish to reanimate the interest in Evolutionary Information Filtering. Profile adaptation is a challenging problem with no established solution and biologically inspired solutions have still an important role to play in solving it. This however requires experimental methodologies that reflect the complexity and dynamics of the problem. This paper is part of ongoing research work in this direction.

The Vector Space Model has been and to a great extent still is the de facto choice for profile representation in content-based Information Filtering. However, user profiles represented as weighted keyword vectors have inherent dimensionality problems. As the number of profile keywords increases, the vector representation becomes ambiguous, due to the exponential increase in the volume of the vector space and in the number of possible keyword combinations. We argue that the complexity and dynamics of Information Filtering require user profile representations which are resilient and resistant to this "curse of dimensionality". A user profile has to be able to incorporate many features and to adapt to a variety of interest changes. We propose an alternative, network-based profile representation that meets these challenging requirements. Experiments show that the network profile representation can more effectively capture additional information about a user's interests and thus achieve significant performance improvements over a vector-based representation comprising the same weighted keywords.

In recent years, 3D media have become more and more widespread and have been made available in numerous online repositories. A systematic and formal approach for representing and organizing shape-related information is needed to share 3D media, to communicate the knowledge associated to shape modeling processes and to facilitate its reuse in useful cross-domain usage scenarios. In this paper we present an initial attempt to formalize an ontology for digital shapes, called the Common Shape Ontology (CSO). We discuss about the rationale, the requirements and the scope of this ontology, we present in detail its structure and describe the most relevant choices related to its development. Finally, we show how the CSO conceptualization is used in domain-specific application scenarios.

In this paper, we present an integrated system architecture for metacomputing on top of distributed scientific resources via semantic-driven information management. Our approach is to consider both data and programs as objects of an application-specific ontology, describing them via corresponding metadata schemata that can be exploited to search for and to generate information in an efficient and automated way. Metacomputations are expressed in the form of workflows thereby enabling a flexible combination of data and program objects that can reside on different servers or grid resources. Workflow descriptions are associated with ontology concepts and can be activated as a side-effect of searching for information to produce new data on demand. We also report on the current status of an implementation that provides support for several aspects of this architecture.