An empirical study of inter-concept similarities in multimedia ontologies

Generic concept detection has been a widely studied topic in recent research on multimedia analysis and retrieval, but the issue of how to exploit the structure of a multimedia ontology as well as different inter-concept relations, has not received similar attention. In this paper, we present results from our empirical analysis of different types of similarity among semantic concepts in two multimedia ontologies, LSCOM-Lite and CDVP-206. The results show promise that the proposed methods may be helpful in providing insight into the existing inter-concept relations within an ontology and selecting the most facilitating set of concepts and hierarchical relations. Such an analysis as this can be utilized in various tasks such as building more reliable concept detectors and designing large-scale ontologies

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices

Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a methodology aimed at high-scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, large-scale graphs with up to one million nodes and millions of edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains; our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.Comment: To appear: 8 pages, paper to be published at the Fifth IEEE ICDM Workshop on Data Mining in Networks, 2015 as Hugo Gualdron, Robson Cordeiro, Jose Rodrigues (2015) StructMatrix: Large-scale visualization of graphs by means of structure detection and dense matrices In: The Fifth IEEE ICDM Workshop on Data Mining in Networks 1--8, IEE

Procedural Generation of Complex Roundabouts for Autonomous Vehicle Testing

High-definition roads are an essential component of realistic driving scenario simulation for autonomous vehicle testing. Roundabouts are one of the key road segments that have not been thoroughly investigated. Based on the geometric constraints of the nearby road structure, this work presents a novel method for procedurally building roundabouts. The suggested method can result in roundabout lanes that are not perfectly circular and resemble real-world roundabouts by allowing approaching roadways to be connected to a roundabout at any angle. One can easily incorporate the roundabout in their HD road generation process or use the standalone roundabouts in scenario-based testing of autonomous driving

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio