COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content

MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user

Grids and the Virtual Observatory

We consider several projects from astronomy that benefit from the Grid paradigm and associated technology, many of which involve either massive datasets or the federation of multiple datasets. We cover image computation (mosaicking, multi-wavelength images, and synoptic surveys); database computation (representation through XML, data mining, and visualization); and semantic interoperability (publishing, ontologies, directories, and service descriptions)

Do functional traits improve prediction of predation rates for a disparate group of aphid predators?

Aphid predators are a systematically disparate group of arthropods united on the basis that they consume aphids as part of their diet. In Europe, this group includes Araneae, Opiliones, Heteroptera, chrysopids, Forficulina, syrphid larvae, carabids, staphylinids, cantharids and coccinellids. This functional group has no phylogenetic meaning but was created by ecologists as a way of understanding predation, particularly for conservation biological control. We investigated whether trait-based approaches could bring some cohesion and structure to this predator group. A taxonomic hierarchy-based null model was created from taxonomic distances in which a simple multiplicative relationship described the Linnaean hierarchies (species, genera, etc.) of fifty common aphid predators. Using the same fifty species, a functional groups model was developed using ten behavioural traits (e.g. polyphagy, dispersal, activity, etc.) to describe the way in which aphids were predated in the field. The interrelationships between species were then expressed as dissimilarities within each model and separately analysed using PROXSCAL, a multidimensional scaling (MDS) program. When ordinated using PROXSCAL and then statistically compared using Procrustes analysis, we found that only 17% of information was shared between the two configurations. Polyphagy across kingdoms (i.e. predatory behaviour across animal, plant and fungi kingdoms) and the ability to withstand starvation over days, weeks and months were particularly divisive within the functional groups model. Confirmatory MDS indicated poor prediction of aphid predation rates by the configurations derived from either model. The counterintuitive conclusion was that the inclusion of functional traits, pertinent to the way in which predators fed on aphids, did not lead to a large improvement in the prediction of predation rate when compared to the standard taxonomic approach

A NOVEL PROFILE’S SELECTION ALGORITHM USING AI

In order to better understand the job requirements, recruitment processes, and hiring processes it is needed to know the people skills. For a recruiter this entails analyzing and comparing the curricula of each available candidate and determining the most appropriate candidate that the activities that are required by the position. This process must be carried in the shortest length of time possible. In this paper, an algorithm is proposed to identify those candidates, either workers or college graduates

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Dealing with uncertain entities in ontology alignment using rough sets

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

3D environment mapping using the Kinect V2 and path planning based on RRT algorithms

This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.Peer ReviewedPostprint (published version