Learning from Ambiguity

There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. Each example is a bag, consisting of any number of instances. A bag is labeled negative if all instances in it are negative. A bag is labeled positive if at least one instance in it is positive. Because the instances themselves are not labeled, each positive bag is an ambiguous example. We would like to learn a concept which will correctly classify unseen bags. We have developed a measure called Diverse Density and algorithms for learning from multiple-instance examples. We have applied these techniques to problems in drug design, stock prediction, and image database retrieval. These serve as examples of how to translate the ambiguity in the application domain into bags, as well as successful examples of applying Diverse Density techniques.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Development Of Climate Change Information Database And Its Use In Civic Consciousness Enlightenment

GCM outputs such as CMIP3 are available via network access to PCMDI web site. Meteorological researchers are familiar with the usage of the GCM data, but the most of researchers other than meteorology such as agriculture, civil engineering, etc., and general people are not familiar with the GCM. There are some difficulties to use GCM; 1) to download the enormous quantity of data, 2) to understand the GCM methodology, parameters and grids. In order to provide a quick access way to GCM, Climate Change Information Database has been developed. The purpose of the database is to bridge the users and meteorological specialists and to facilitate the understanding the climate changes. The resolution of the data is unified, and climate change amount or factors for each meteorological element are provided from the database. All data in the database are interpolated on the same 80km mesh. Available data are the present-future projections of 27 GCMs, 16 meteorological elements (precipitation, temperature, etc.), 3 emission scenarios (A1B, A2, B1). We showed the summary of this database to residents in Toyama prefecture and measured the effect of showing and grasped the image for the climate change by using the Internet questionary survey. The persons who feel a climate change at the present tend to feel the additional changes in the future. It is important to show the monitoring results of climate change for a citizen and promote the understanding for the climate change that had already occurred. It has been shown that general images for the climate change promote to understand the need of the mitigation, and that it is important to explain about the climate change that might occur in the future even if it did not occur at the present in order to have people recognize widely the need of the adaptation.

Performance Evaluation Of SMOS Soil Moisture Retrieval Parameters For Hydrological Application

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarisation is implemented with modifications, which includes the effective temperatures simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R2 =0.359 for approach 4 (WRF-NOAH LSM based LST with optimized roughness parameters) followed by the approach 2 (optimized roughness parameters and MODIS based LST) (R2 =0.293), approach 3 (WRF-NOAH LSM based LST with no optimization) (R2 =0.267) and approach 1(MODIS based LST with no optimization) (R2 =0.163). Similarly, during the validation a highest performance is reported by approach 4. The other approaches are also following a similar trend as calibration. All the performances are depicted through Taylor diagram which indicates that the H polarisation using ECMWF based LST is giving a better performance for SMD estimation than the original SMOS L2 products at a catchment scale.

Caracterização da área de Ensino de Biologia e Ciências no mestrado profissional da UFRN a partir das dissertações

The deployment of master professionals in Mathematics and Science Teaching by CAPES provided an interesting perspective, involving institutional incentive to upgrade teacher training in this modality. In this sense, the objective was to map the profile of the candidates in the Graduate School of Natural Sciences and Mathematics UFRN (PPGECNM), develop a database with specific descriptors for dissertations and research the group creation biology research and dissertations produced by graduate students in the Master course. This thesis has three threads, where the first is entitled: "The teaching of science as part of research: emergency, improvement and current reflections", giving a brief description of the creation of graduate studies and the emergence of Brazil in Science education. The second topic: "Professional Masters in Teaching of Natural Sciences and Mathematics-UFRN: candidate profile the biological ', shows the general characteristics of PPGECNM-UFRN and who are the candidates seeking the biological area of the Graduate Program. The third topic. "The construction and consolidation of the research group in biology education in PPGECNM-UFRN", a survey was made of data on the creation of the research group in biology education from the analysis of academic productions of teachers and masters graduates this Masters course, for those involved, some additional interviews about the experience in training in biology education. Data used in these investigations related to these students correspond to the technical product to be used in other studies profile teachers /dissertations from the PPGECNM / UFRN. Research shows that: the existence of a larger number of postgraduate courses in the field of Science Education provides greater productivity in academic research on the subject, the emergence of a research group on education in biology, tied to a post degree in education, is an attraction to strengthen lines of research in science education by teachers researchers in other disciplines, the candidates seeking permanent area for research in biology education signals the importance of this investment because if shows the need to search for the continuing education of practicing teachers, demands with interests in investigations from real problems from school

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

The application of distributed virtual machines for enterprise computer management: A two-tier network file system for image provisioning and management

In order to simplify computer management, several system administrators are adopting advanced techniques to manage software configuration of enterprise computer networks, but the tight coupling between hardware and software makes every PC an individual managed entity, lowering the scalability and increasing the costs to manage hundreds or thousands of PCs. Virtualization is an established technology, however its use is been more focused on server consolidation and virtual desktop infrastructure, not for managing distributed computers over a network. This paper discusses the feasibility of the Distributed Virtual Machine Environment, a new approach for enterprise computer management that combines virtualization and distributed system architecture as the basis of the management architecture. © 2008 IEEE.

Application of ontologies in peer-to-peer database with routing queries based on ant colony algorithm

A significant set of information stored in different databases around the world, can be shared through peer-topeer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-topeer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. This paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. The main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results. © 2011 IEEE.

Report on the retrieval of data for small areas of microcomputers ( REDATAM) National Training Workshop

Through this workshop, database experts from the various ministries and the Central Statistical Office (CSO) were introduced to the CREATE and PROCESS modules of the REDATAM software, which could be used for database creation and analysis of data. This workshop was the second in a series of workshops aimed at promoting human-resource and capacity-building at the national and regional levels in the use of the REDATAM software. It also served as a qualifier for a follow-up workshop on the use of the web-publishing application of the software to be held in 2010.

Swarm-based descriptor combination and its application for image classification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

CART application to image primitives tracking

[EN] [EN] In this paper we present a new method for image primitives tracking based on a CART (Classification and Regression Tree). Primitives tracking procedure uses lines and circles as primitives. We have applied the proposed method to sport event scenarios, specifically, soccer matches. We estimate CART parameters using a learning procedure based on RGB image channels. In order to illustrate its performance, it has been applied to real HD (High Definition) video sequences and some numerical experiments are shown. The quality of the primitives tracking with the decision tree is validated by the percentage error rates obtained and the comparison with other techniques as a morphological method. We also present applications of the proposed method to camera calibration and graphic object insertion in real video sequences.

Sviluppo di funzionalità di Image Targeting con apps basate su realtà aumentata

Dopo una breve introduzione sulla realtà aumentata (definizione, storia e stato dell’arte) viene effettuata un'analisi delle librerie esistenti per l’implementazione su dispositivi mobile. Considerando compatibilità con i recenti SO, frequenza degli aggiornamenti, costi per le licenze e funzionalità offerte viene scelta la libreria Vuforia, originariamente sviluppata da Qualcomm e poi acquistata da PTC inc. Si conviene poi che le apps basate su realtà aumentata creano il contenuto “aumentato” in due maniere: o tramite riconoscimento di una specifica immagine oppure tramite localizzazione GPS. Di questi due metodi descritti, il primo risulta molto più affidabile e per questo viene sviluppata una app che crea un contenuto in 3D (aumentato) riconoscendo una immagine: funzionalità di Image Targeting. Il progetto considera le seguenti varianti: l’immagine da riconoscere, chiamata “target”, può essere in un database locale oppure cloud mentre il contenuto in 3D aumentato può essere sia statico che animato. Durante la fase di implementazione vengono fornite anche alcuni nozioni di base di Computer Graphic per il rendering del modello 3D. La tesi si conclude con una panoramica di apps presenti sullo store che funzionano secondo questo principio di Image Targeting, i possibili utilizzi in ambito educativo/ludico ed i costi di realizzazione.