WNODES: USER GUIDE

WNoDeS permits usage of grid resources through a Cloud (IaaS) service, providing virtualized Worker Node

WNODES: CLIENT INSTALLATION AND CONFIGURATION

WNoDeS permits usage of grid resources through a Cloud (IaaS) service, providing virtualized Worker Node

Decentralized Control of a Swarm of Unmanned Aerial Vehicles

Molti operatori controllano un solo Unmanned Aerial Vehicle -- Veicolo Aereo Non Equipaggiato -- rendendo il sistema di controllo non scalabile. Attualmente, nell'ambito del controllo di questo tipo di veicoli, la tendenza \'e quella di gestire un gruppo di Unmanned Aerial Vehicle tramite un solo operatore in modo da avere un sistema in grado di operare con migliaia di Unmanned Aerial Vehicle che volano sopra una nazione. Swarm Intelligence, basata sui cosiddetti insetti sociali, fornisce le linee guida per progettare sistemi decentralizzati. In particolare, gli insetti sociali sono in grado di perseguire diversi obiettivi, dalla costruzione e difesa del nido, alla ricerca del cibo, al prendersi cura del nido, all'assegnazione di squadre di operai, alla costruzione di ponti. Questa tesi presenta un framework per il controllo decentralizzato di uno sciame di Unmanned Aerial Vehicle basato su funzioni di potentiale artificiale caratterizzate da propriet\'a attrattive e repulsive, che sono usate rispettivamente per raggiungere l'obiettivo e per evitare le eventuali collisioni. Ciascun veicolo dello sciame utilizza un numero limitato di informazioni degli altri veicoli, ed inoltre \'e caratterizzato come un agente con dinamica molto semplice. In questo schema, pi\'u agenti di uno sciame sono in grado di raggiungere una configurazione e di mantenerla, mentre migrano come gruppo ed evitano collisioni tra di loro. Pertanto, i comportamenti del sistema a sciame proposto in questa tesi sono la configurazione e la migrazione del gruppo, e includono la elusione di collisioni. In particolare, questa tesi analizza diverse espressioni di potenziale per determinare in quanto tempo lo sciame converge alla direzione e velocit\'a desiderata, e quanto \'e capace lo sciame ad evitare le collisioni tra gli agenti. Inoltre, sono state determinate due metriche che forniscono la stima del migliore potenziale in un determinato scenario. Una metrica quantifica quanto velocemente lo sciame converge ad una data velocit\'a, e la seconda analizza quanto robusto \'e il potenziale per evitare le collisioni. Le simulazioni mostrano che la soluzione proposta permette di costruire un sistema a sciame in grado di gestire la migrazione e la configurazione del gruppo in presenza di ostacoli utilizzando un numero limitato di informazioni

The Impact of Grid on Health Care Digital Repositories

Grid computing has attracted worldwide attention in a variety of applications like Health Care. In this paper we identified the Grid services that could facilitate the integration and interoperation of Health Care data and frameworks world-wide. While many of the current Health Care Grid projects address issues such as data location and description on the Grid and the security aspects, the problems connected to data storage, integrity, preservation and distribution have been neglected. We describe the currently available Grid storage services and protocols that can come in handy when dealing with those problems. We further describe a Grid infrastructure to build a cooperative Health Care environment based on currently available Grid services and a service able to validate it

Geant4 Maintainability Assessed with Respect to Software Engineering References

We report a methodology developed to quantitatively assess the maintainability of Geant4 with respect to software engineering references. The level of maintainability is determined by combining a set of metrics values whose references are documented in literature.Comment: 5 pages, 2 figures, 4 tables, IEEE NSS/MIC 201

Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics

This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the COVID-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, non-government organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal uses the sentiment analysis of Twitter data relevant to COVID-19 and classification of tweets with machine learnin

Comparing Methods for Mitigating Gender Bias in Word Embedding

Word embedding captures the semantic and syntactic meaning of words into dense vectors. It contains biases learning from data that include constructs, cultural stereotypes, and inequalities of the society. Many methods for removing bias in traditional word embedding have been proposed. In this study we use the original GloVe word embedding and perform a comparison among debiasing methods built on top of GloVe in order to determine which methods perform the best removing bias. We have defined half-sibling regression, repulsion attraction neutralization GloVe method and compared it with gender-preserving, gender-neutral GloVe method and other debiased methods. According to our results, no methods outperform in all the analyses and completely remove gender information from gender neutral words. Furthermore, all the debiasing methods perform better than the original GloVe

Creating Intelligent Computational Edge through Semantic Mediation

This research proposes semantic mediation based on reasoning and the first order logic for mediating the best possible configuration of Computational Edge, relevant for software applications which may benefit for running computations with proximity to their data sources. The mediation considers the context in which these applications exist and exploits the semantic of that context for decision making on where computational elements should reside and which data they should use. The application of semantic mediation could address the initiative to accommodate algorithms from predictive and learning technologies, push AI towards computational edges and potentially contribute towards creating a computing continuum

A Preliminary Mapping Study of Software Metrics Thresholds

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