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In this article we present the state of the art in the field of autonomous surface ship navigation using machine learning. We discuss the main challenges towards the development of fully autonomous navigation systems with the International Regulations for Preventing Collisions at Sea (COLREGs). Finally, we propose two alternative approaches that are based on machine learning. Existing COLREGs-based navigation and collision avoidance algorithms are based on traditional search-based planning and optimization algorithms. We consider that these approaches are suitable when the problem space is defined completely and rigorously. However, experts believe that is not the case for COLREGs since it leaves many aspects open to the interpretation of the captain. For example, COLREGs expects that any collision avoidance action shall be taken with due regard to the observance of good seamanship, a concept not defined in the convention. Furthermore, many rules are defined using undefined concepts like safe distance, or keywords like early, or substantial, without giving any definition. COLREGs even allow for the rules to be broken to avoid an accident. Due to this, traditional planning approaches may not be able to handle complex scenarios that are underspecified according to COLREGs. An alternative is the use of machine learning (ML), reinforcement learning (RL) and imitation learning (IL) at the core of autonomous navigation systems. Machine learning is known to succeed and outperform traditional approaches specially in vaguely defined problem domains, where it is difficult, if not impossible, to create a full formal specification of the phenomenon under study. We consider this to be the case for COLREGs-based navigation and we conjecture that a ML-based navigation approach can outperform existing search-based and optimization algorithms.

El reconocimiento de actividades humanas puede ser herramienta clave para conseguir una mejor asistencia a personas con necesidades especiales, mayores que viven independientemente, así como en asistencia sanitaria, o en monitorización o rehabilitaci ón remota, y en control de procesos o protocolos industriales, entre otros casos. En esta tesis se muestran ejemplos de uso en algunas de estas áreas. Trabajos futuros deben centrarse en tratar de reducir el trabajo manual requerido para extraer y representar conocimiento experto, el cual es considerable actualmente, con el fin de representar las reglas de dominio de manera suficientemente coherente, específica, general y reproducible., Tesis Univ. Granada. Programa Oficial de Doctorado en: Tecnologías de la Información y la Comunicación

Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with large-scale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra.

Identification of behavioural contradictions is an important aspect of software engineering, in particular for checking the consistency between a business process model used as system specification and a corresponding workflow model used as implementation. In this paper, we propose causal behavioural profiles as the basis for a consistency notion, which capture essential behavioural information, such as order, exclusiveness, and causality between pairs of activities. Existing notions of behavioural equivalence, such as bisimulation and trace equivalence, might also be applied as consistency notions. Still, they are exponential in computation. Our novel concept of causal behavioural profiles provides a weaker behavioural consistency notion that can be computed efficiently using structural decomposition techniques for sound free-choice workflow systems if unstructured net fragments are acyclic or can be traced back to S- or T-nets.

Modelling complex concurrent systems is often difficult and error-prone, in particular when new concepts coming from advanced practical applications are considered. These new application domains include dynamicity, mobility, security, and localization dependent computing. In order to fully model and prototype such systems we propose to use several concepts introduced in our specification language CO-OPN, like context, dynamicity, mobility, subtyping and inheritance. CO-OPN (Concurrent Object Oriented Petri Net) is a formal specification language for modelling distributed systems; it is based on coordinated algebraic Petri nets. This paper focuses on the use of several basic mechanisms of CO-OPN for modelling mobile systems and the generation of corresponding Java code. A significant example of distributors accessible through mobile devices (for example, PDA with Bluetooth) is fully modelled and implemented with our technique.

The MUI project looks at flexible ways of creating user-initiated connections between services in wireless networks. A central idea is to migrate user interfaces from controlled devices to devices with better input/output capabilities. The paper shows the different parts of the MUI architecture, and motivates design choices. An initial implementation and a framework for building MUI services are described.