Iterative Unsupervised GMM Training for Speaker Indexing

The paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn\'t require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that the number of the speakers has to be known in advance. The results of initial experiments show that the proposed training method enables to create precise GMM speaker models from only a small amount of training data

KP-LAB Knowledge Practices Laboratory -- Specification of end-user applications

deliverablesThe present deliverable provides a high-level view on the new specifications of end user applications defined in the WPII during the M37-M46 period of the KP-Lab project. This is the last in the series of four deliverables that cover all the tools developed in the project, the previous ones being D6.1, D6.4 and D6.6. This deliverable presents specifications for the new functionalities for supporting the dedicated research studies defined in the latest revision of the KP-Lab research strategy. The tools addressed are: the analytic tools (Data export, Time-line-based analyser, Visual analyser), Clipboard, Search, Versioning of uploadable content items, Visual Model Editor (VME) and Visual Modeling Language Editor (VMLE). The main part of the deliverable provides the summary of tool specifications and the description of the Knowledge Practices Environment architecture, as well as an overview of the revised technical design process, of the tools’ relationship with the research studies, and of the driving objectives and the high-level requirements relevant for the present specifications. The full specifications of tools are provided in the annexes 1-9

COST292 experimental framework for TRECVID 2008

In this paper, we give an overview of the four tasks submitted to TRECVID 2008 by COST292. The high-level feature extraction framework comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a multi-modal classifier based on SVMs and several descriptors. The third system uses three image classifiers based on ant colony optimisation, particle swarm optimisation and a multi-objective learning algorithm. The fourth system uses a Gaussian model for singing detection and a person detection algorithm. The search task is based on an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. The rushes task submission is based on a spectral clustering approach for removing similar scenes based on eigenvalues of frame similarity matrix and and a redundancy removal strategy which depends on semantic features extraction such as camera motion and faces. Finally, the submission to the copy detection task is conducted by two different systems. The first system consists of a video module and an audio module. The second system is based on mid-level features that are related to the temporal structure of videos

Knowledge Management In A Distributed Organisation

this paper we introduce the research in scope of KnowWeb (EC funded project). We focus our attention on two important issues -- (i) how to capture tacit, contextual knowledge that is connected to the documents and (ii) how to support knowledge management in geographically distributed organisations through up-to-date communication and AI technologie

Food Traceability Chain Supported by the Ebbits IoT Middleware

The paper presents the food traceability prototype, which was implemented as a pilot application of the FP7 EU project ebbits. The platform architecture, built upon the principles of the Internet of Things (IoT), People, and Services, is described in aspects of the supported interoperability and semantic orchestration of services involved in the food production chain. The platform represents physical objects as digital objects that go through different phases in the production chain. The information produced in each phase is stored by involved actors and could be retrieved back by the consumers through orchestrating services provided by the actors in the production chain. These services are resolved by a product service orchestration, which is supported by a semantic backend

Scheduling in a Multi-Agent Environment

. A new scheduling agent for existing CIM multi-agent system is being currently developed at the Technical University of Kosice. The basic idea is to create an agent based on Scheduling Program developed at the Technical University of Kosice. The Scheduling Program was developed in the ECL i PS e programming language using its constraint logic programming capabilities. It is used for solving JobShop problems. This paper gives the reader some basic information about prepared software. 1 Introduction The basic idea of CIM (Computer Integrated Manufacturing) is to integrate all company activities into a unified management structure exploring a large scale hierarchy of computers. Considering all possible activities (planning, scheduling, product design, manufacturing, etc.) and the fact that some of these tasks are running in parallel on geographically distributed sites, the integration is a nontrivial problem. DISCIM - geographically DIStributed decision making system for CIM [1]. DI..