Search results for " Program"

<title>Fast motion estimation based on spatio-temporal Gabor filters: parallel implementation on multi-DSP</title>

2000

The aim of our work is to implement a system of motion estimation in image sequences processing on DSP's: fast motion estimation based on Gabor spatio-temporal filters. Our approach consists to calculate optical flow using an energy-based method, named combined filtering which associates the energetic responses of Gabor spatio- temporal filters organized in triads. For this purpose, we applicate a technique developed by the Laboratory LIS in France, inspired from the architecture of Heeger. To reduce the computation time, we present also a parallel implementation of the algorithm on a multi-DSP architecture using SynDEx tool which is a programming environment to generate optimized distribut…

Breedbot: An Edutainment Robotics System to Link Digital and Real World

2007

The paper describes Breedbot an edutainment software and hardware system that could be used to evolve autonomous agents in digital (software) world and to transfer the evolved minds in physical agents (robots). The system is based on a wide variety of Artificial Life techniques (Artificial Neural Networks, Genetic Algorithms, User Guided Evolutionary Design and Evolutionary Robotics). An user without any computer programming skill can determine the robot behaviour. Breedbot was used as a didactic tool in teaching Evolutionary Biology and as a futuristic toy by several Science Centers. The digital side of Breedbot is downloadable from www.isl.unina.it/breedbot.

An Agents and Artifacts Approach to Distributed Data Mining

2013

This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision trees built with J48, but the approach is flexible enough to allow different kinds of learning models. The twofold contribution of this work includes: i) JaCA-DDM: an extensible tool implemented in the agent oriented programming language Jason [2] and CArtAgO [10,9] to experiment DDM agent-based approaches on different, well known training sets. A…

An adaptive probabilistic graphical model for representing skills in PbD settings

2010

A new inter-cloud service-level guarantee protocol applied to space missions

2017

Nowadays, the term cloud computing often falsely assumes the availability of an unlimited pool of resources. On the contrary, if a cloud provider reaches its limits, it may pose the risk of breaking their service level agreement (SLA). Space agencies could start using the cloud computing model within their IT infrastructure with multiple ground control points around the world to reduce the cost. An inter-cloud communication protocol with a guarantee of the service level will significantly reduce the cost if each ground control segment is considered as a cloud provider. This paper outlines a new protocol that was developed to take into consideration the end-to-end service-level guarantee. Th…

Chapter 3. Prosodic versatility, hierarchical rank and pragmatic function in conversational markers

2019

Generating App Product Lines in a Model-Driven Cross-Platform Development Approach

2016

Within software product lines (SPL) similar software products are created based on common features. We applied this versatile approach to cross-platform app development by extending the domain-specific language (DSL) of an established model-driven development framework. The goal was to support the formulation of coherent building blocks of business use cases, referred to as workflow elements. While the former implementation already abstracted from technical details and provided the possibility to reuse low level features, it now enables to build business apps by combining coherent, self-contained workflow elements. Providing this support on the language level facilitates reusable component-…

On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming

2021

Grammar-guided Genetic Programming is a common approach for program synthesis where the user’s intent is given by a set of input/output examples. For use in real-world software development, the generated programs must work on previously unseen test cases too. Therefore, we study in this work the generalizability of programs synthesized by grammar-guided GP with lexicase selection. As benchmark, we analyze proportionate and tournament selection too. We find that especially for program synthesis problems with a low output cardinality (e.g., a Boolean output) lexicase selection overfits the training cases and does not generalize well to unseen test cases. An analysis using common software metr…

Methods for optimal shape design of electrical devices

1996

Often the primary problem facing designers of structural systems is determining the shape of the structure. In spite of graphical work stations and modern software for analyzing the structure, finding the best geometry for the structure by “trial and error” is still a very tedious and timeconsuming task. The goal in optimal shape design (structural optimization, or redesign) is to computerize the design process and therefore shorten the time it takes to design new products or improve the existing design. Structural optimization is already used in many applications in industry. In general, however, structural optimization is just beginning to penetrate the industrial community. Integrating F…

Learning Bayesian Metanetworks from Data with Multilevel Uncertainty

2006

Managing knowledge by maintaining it according to dynamic context is among the basic abilities of a knowledge-based system. The two main challenges in managing context in Bayesian networks are the introduction of contextual (in)dependence and Bayesian multinets. We are presenting one possible implementation of a context sensitive Bayesian multinet-the Bayesian Metanetwork, which implies that interoperability between component Bayesian networks (valid in different contexts) can be also modelled by another Bayesian network. The general concepts and two kinds of such Metanetwork models are considered. The main focus of this paper is learning procedure for Bayesian Metanetworks.