In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4', 10', 20' and 40' time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.

Engineered nanomaterials (ENMs) are increasingly infiltrating our lives as a result of their applications across multiple fields. However, ENM formulations may result in the modulation of pathways and mechanisms of toxic action that endanger human health and the environment. Alternative testing methods such as in silico approaches are becoming increasingly popular for assessing the safety of ENMs, as they are cost- and time-effective. Additionally, computational approaches support the industrial safer-by-design challenge and the REACH legislation objective of reducing animal testing. Because of the novelty of the field, there is also an evident need for harmonization in terms of databases, ontology, and modeling infrastructures. To this end, we present Jaqpot Quattro, a comprehensive open-source web application for ENM modeling with emphasis on predicting adverse effects of ENMs. We describe the system architecture and outline the functionalities, which include nanoQSAR modeling, validation services, read-across predictions, optimal experimental design, and interlaboratory testing.

Abstract. With the emergence of Smart TV and related interconnected devices, second screen solutions have rapidly appeared to provide more content for end-users and enrich their TV experience. Given the various data and sources involved -videos, actors, social media and online databases-the aforementioned market poses great challenges concerning user context management and sophisticated recommendations that can be addressed to the end-users. This paper presents an innovative Context Management model and a related first and second screen recommendation service, based on a user-item graph analysis as well as collaborative filtering techniques in the context of a Dynamic Social & Media Content Syndication (SAM) platform. The model evaluation provided is based on datasets collected online, presenting a comparative analysis concerning efficiency and effectiveness of the current approach, and illustrating its added value.

No abstract

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.