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.

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.

Microblogs play an important role for Online Reputation Management. Companies and organizations in general have an increasing interest in obtaining the last minute information about which are the emerging topics that concern their reputation. In this paper, we present a new technique to cluster a collection of tweets emitted within a short time span about a specific entity. Our approach relies on transfer learning by contextualizing a target collection of tweets with a large set of unlabeled "background" tweets that help improving the clustering of the target collection. We include background tweets together with target tweets in a TwitterLDA process, and we set the total number of clusters. In practice, this means that the system can adapt to find the right number of clusters for the target data, overcoming one of the limitations of using LDA-based approaches (the need of establishing a priori the number of clusters). Our experiments using RepLab 2012 data show that using the background collection gives a 20% improvement over a direct application of TwitterLDA using only the target collection. Our data also confirms that the approach can effectively predict the right number of target clusters in a way that is robust with respect to the total number of clusters established a priori.

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