An object based algebra for specifying a fault tolerant software architecture

AbstractIn this paper we present an algebra of actors extended with mechanisms to model crash failures and their detection. We show how this extended algebra of actors can be successfully used to specify distributed software architectures. The main components of a software architecture can be specified following an object-oriented style and then they can be composed using asynchronous message passing or more complex interaction patterns. This formal specification can be used to show that several requirements of a software system are satisfied at the architectural level despite failures. We illustrate this process by means of a case study: the specification of a software architecture for intelligent agents which supports a fault tolerant anonymous interaction protocol

Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks

AbstractEnergy Harvesting - Wireless Sensor Networks (EH-WSNs) constitute systems of networked sensing nodes that are capable of extracting energy from the environment and that use the harvested energy to operate in a sustainable state. Sustainability, seen as design goal, has a significant impact on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key reinforcement scheme specifically designed for EH-WSNs. The proposed scheme allows each node to take into consideration and adapt to the amount of energy available in the system. In particular, we present two approaches, one static and one fully dynamic, and we discuss some experimental results

neurosent pdi at semeval 2018 task 3 understanding irony in social networks through a multi domain sentiment model

n/

neurosent pdi at semeval 2018 task 7 discovering textual relations with a neural network model

n/

Process Extraction from Text: state of the art and challenges for the future

Automatic Process Discovery aims at developing algorithmic methodologies for the extraction and elicitation of process models as described in data. While Process Discovery from event-log data is a well established area, that has already moved from research to concrete adoption in a mature manner, Process Discovery from text is still a research area at an early stage of development, which rarely scales to real world documents. In this paper we analyze, in a comparative manner, reference state-of-the-art literature, especially for what concerns the techniques used, the process elements extracted and the evaluations performed. As a result of the analysis we discuss important limitations that hamper the exploitation of recent Natural Language Processing techniques in this field and we discuss fundamental limitations and challenges for the future concerning the datasets, the techniques, the experimental evaluations, and the pipelines currently adopted and to be developed in the future

Persuasive Explanation of Reasoning Inferences on Dietary Data

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behaviour. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: i) the natural language generation of messages that explain the reasoner inconsistency; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users’ behaviours

A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies

International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach