Abstract
The design and development of the Swordfish Autonomous Surface Vehicle (ASV) system is discussed. Swordfish is an ocean capable 4.5m long catamaran designed for network centric operations (with ocean and air going vehicles and human operators). In the basic configuration, Swordfish is both a survey vehicle and a communications node with gateways for broadband, Wi-Fi and GSM transports and underwater acoustic modems. In another configuration, Swordfish mounts a docking station for the autonomous underwater vehicle Isurus from Porto University. Swordfish has an advanced control architecture for multi-vehicle operations with mixed initiative interactions (human operators are allowed to interact with the control loops).

Abstract
We consider the problem of securing routing information in Mobile Ad-hoc Networks (MANETs). Focusing on the Optimized Link State Routing protocol, we devise a feedback reputation mechanism which assesses the integrity of routing control traffic by correlating local routing data with feedback messages sent by the receivers of control traffic. Based on this assessment, misbehaving nodes are shown to be reliably detected and can be adequately punished in terms of their ability to communicate through the network. To the best of our knowledge, this is the first practical implementation of a reputation mechanism in a standardized proactive routing protocol for MANETs.

Abstract
The increasing requirement for ubiquitous access of the users, enable the seamless support of different networks, with different technologies, and also with different types, such as moving networks and ad-hoc networks. This paper describes the Ad-hoc network integration architecture being developed inside the IST project Daidalos II. The main purpose of this architecture is to seamlessly support the movement of nodes between ad-hoc and infrastructure networks, maintaining in the ad-hoc networks all the features being supported in the infrastructure, such as, efficient routing for unicast and multicast flows, distributed QoS mechanisms' security, and seamless mobility, including multihoming support.

Abstract
The amount of data collected and stored in databases is growing considerably for almost all areas of human activity. Processing this amount of data is very expensive, both humanly and computationally. This justifies the increased interest both on the automatic discovery of useful knowledge from databases, and on using parallel processing for this task. Multi Relational Data Mining (MRDM) techniques, such as Inductive Logic Programming (ILP), can learn rules from relational databases consisting of multiple tables. However current ILP systems are designed to run in main memory and can have long running times. We propose a pipelined data-parallel algorithm for ILP. The algorithm was implemented and evaluated on a commodity PC cluster with 8 processors. The results show that our algorithm yields excellent speedups, while preserving the quality of learning.

Abstract
Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail. In this paper we present a novel ILP system called April, capable of exploring several parallel strategies in distributed and shared memory machines.

Abstract