Maritime Data Transfer Protocol (MDTP): A Proposal for a Data Transmission Protocol in Resource-Constrained Underwater Environments Involving Cyber-Physical Systems

The utilization of autonomous maritime vehicles is becoming widespread in operations that are deemed too hazardous for humans to be directly involved in them. One of the ways to increase the productivity of the tools used during missions is the deployment of several vehicles with the same objective regarding data collection and transfer, both for the benefit of human staff and policy makers. However, the interchange of data in such an environment poses major challenges, such as a low bandwidth and the unreliability of the environment where transmissions take place. Furthermore, the relevant information that must be sent, as well as the exact size that will allow understanding it, is usually not clearly established, as standardization works are scarce in this domain. Under these conditions, establishing a way to interchange information at the data level among autonomous maritime vehicles becomes of critical importance since the needed information, along with the size of the transferred data, will have to be defined. This manuscript puts forward the Maritime Data Transfer Protocol, (MDTP) a way to interchange standardized pieces of information at the data level for maritime autonomous maritime vehicles, as well as the procedures that are required for information interchange.SWARMs (Smart and Networking Underwater Robots in Cooperation Meshes) 1034 European research project. It is under Grant Agreement 1035 n.662107-SWARMs-ECSEL-2014-1 and is being partially supported by the Spanish Ministry of Economy and Competitiveness (Ref: PCIN-2014-022-C02-02) and the ECSEL JU

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.Eurpean Commission, H2020, 66210

Una aproximación no supervisada para la desambiguación del sentido de las palabras basada en agrupamiento

Los métodos de agrupamiento han sido ampliamente usados en muchas tareas de Procesamiento de la Información con el fin de capturar categorías de objetos desconocidos. Sin embargo, el agrupamiento ha sido poco utilizado como método para etiquetar sentidos en la Desambiguación del Sentido de las Palabras (WSD), es decir, como una forma de identificar grupos formados por sentidos de palabras semánticamente relacionados que pueden ser utilizados con éxito en el proceso de desambiguación. En este artículo presentamos un método de desambiguación no supervisado basado en el agrupamiento de sentidos de palabras que además es capaz de encontrar relaciones implícitas (no presentes en WordNet) entre los sentidos de las palabras de la oración. Investigamos en profundidad el rol del agrupamiento y su contribución al WSD. En los resultados experimentales se demuestra la utilidad del agrupamiento para la desambiguación no supervisada.Clustering methods have been extensively used in many Information Processing tasks in order to capture unknown object categories. However, clustering has been scarcely used as a sense labeling method for Word Sense Disambiguation (WSD), that is, as a way to identify groups of semantically related word senses that can be successfully used in a disambiguation process. In this paper, we present an unsupervised disambiguation method relying on word sense clustering that also reveals the implicit relationships (not asserted in WordNet) existing among these word senses. We also investigate in depth the role of clustering and its contribution to WSD. Experimental results demonstrate the usefulness of clustering for unsupervised WSD