Context-Based Vision System for Place and Object Recognition

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., table, chair, car, computer). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user.

RamboNodes for the Metropolitan Ad Hoc Network

We present an algorithm to store data robustly in a large, geographically distributed network by means of localized regions of data storage that move in response to changing conditions. For example, data might migrate away from failures or toward regions of high demand. The PersistentNode algorithm provides this service robustly, but with limited safety guarantees. We use the RAMBO framework to transform PersistentNode into RamboNode, an algorithm that guarantees atomic consistency in exchange for increased cost and decreased liveness. In addition, a half-life analysis of RamboNode shows that it is robust against continuous low-rate failures. Finally, we provide experimental simulations for the algorithm on 2000 nodes, demonstrating how it services requests and examining how it responds to failures.

Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

Contextual models for object detection using boosted random fields

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.

Dynamically Stable Legged Locomotion (September 1985-Septembers1989)

This report documents our work in exploring active balance for dynamic legged systems for the period from September 1985 through September 1989. The purpose of this research is to build a foundation of knowledge that can lead both to the construction of useful legged vehicles and to a better understanding of animal locomotion. In this report we focus on the control of biped locomotion, the use of terrain footholds, running at high speed, biped gymnastics, symmetry in running, and the mechanical design of articulated legs.

Atelier ORIXAS: Projeto Tripartite África/Brasil/França: luta contra a desertificação: promoção da segurança alimentar e redução da pobreza.

No âmbito do Programa de Cooperação Científica Tripartite entre a Agence Inter-établissements de Recherche pourle Développement (AIRD), Agence Panafricaine de la Grande Muraille Verte (APGMV) e o Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), o projeto ORIXAS "Observatórios Regionais Integrados de Regiões Árida, Semiáridas e Sub-úmidas secas" concebido em uma visão transversal, foca principalmente em metodologias e ferramentas para apoiar dispositivos de monitoramento ambiental para ser aplicado nos países inseridos na iniciativa africana Grande Muralha Verde - GMV (Burkina-Faso, Djibouti, Érythrée, Éthiopie, Mali, Mauritanie, Niger, Nigeria, Sénégal, Soudan, Tchad) e tem como objetivo desenvolver abordagens metodológicas e produtos compartilhados para melhorar a avaliação e monitoramento da desertificação e os impactos diretos ou indiretos de iniciativas para lutar contra o desmatamento e desertificação no âmbito da GMV. Esta publicação contempla aspectos metodológicos utilizados pelo projeto "ORIXAS" durante a primeira oficina de trabalho coletivo África-Brasil-França - Atelier (MAISON DE LA TÉLÉDÉTECTION), realizada de 10 a 19 de junho de 2014, em Montpellier França, objetivando informar a forma de execução dos estudos que vêm sendo realizados no escopo do projeto, visando principalmente a luta contra a desertificação, promoção da segurança alimentar e redução da pobreza nos países inseridos na iniciativa africana Grande Muralha Verde - GMV.

Evolution feature oriented model driven product line engineering approach for synergistic and dynamic service evolution in Clouds: four kinds of schema.

The proposed research will focus on developing a novel approach to solve Software Service Evolution problems in Computing Clouds. The approach will support dynamic evolution of the software service in clouds via a set of discovered evolution patterns. An initial survey informed us that such an approach does not exist yet and is in urgent need. Evolution Requirement can be classified into evolution features; researchers can describe the whole requirement by using evolution feature typology, the typology will define the relation and dependency between each features. After the evolution feature typology has been constructed, evolution model will be created to make the evolution more specific. Aspect oriented approach can be used for enhance evolution feature-model modularity. Aspect template code generation technique will be used for model transformation in the end. Product Line Engineering contains all the essential components for driving the whole evolution process.

Determinants of port centrality in maritime container transportation.

This paper adapts Freeman’s measures of degree, closeness and betweenness centrality and applies them to assessing: port centrality in relation to direct connectivity; accessibility to all ports in the network (direct and indirect routes) and; as an intermediary between other ports. An additional parameter added to the formulae ensures that the relative importance of available shipping capacity and foreland market coverage are also accounted for. Validation of this adapted measure is provided by the results obtained from an empirical application. These reveal that foreland market coverage exerts a particularly strong influence on a port’s demand and closeness centrality

Roll Project Bin Packing Benchmark Problems.

This document describes two sets of Benchmark Problem Instances for the One Dimensional Bin Packing Problem. The problem instances are supplied as compressed (zipped) SQLITE database files.

Roll Project Job Shop scheduling benchmark problems.

This document describes two sets of benchmark problem instances for the job shop scheduling problem. Each set of instances is supplied as a compressed (zipped) archive containing a single CSV file for each problem instance using the format described in http://rollproject.org/jssp/jsspGen.pdf

Roll Project Rich Vehicle Routing benchmark problems.

This document describes a large set of Benchmark Problem Instances for the Rich Vehicle Routing Problem. All files are supplied as a single compressed (zipped) archive containing the instances, in XML format, an Object-Oriented Model supplied in XSD format, documentation and an XML parser written in Java to ease use.

A hyper-heuristic ensemble method for static job-shop scheduling.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

Synthetic biology: a utilitarian perspective

I examine the positive and negative features of synthetic biology (‘SynBio’) from a utilitarian ethical perspective. The potential beneficial outcomes from SynBio in the context of medicine are substantial; however it is not presently possible to predict precise outcomes due to the nascent state of the field. Potential negative outcomes from SynBio also exist, including iatrogenesis and bioterrorism; however it is not yet possible to quantify these risks. I argue that the application of a ‘precautionary’ approach to SynBio is ethically fraught, as is the notion that SynBio-associated knowledge ought to be restricted. I conclude that utilitarians ought to support a broadly laissez-faire stance in respect of SynBio.

‘Complementary & alternative medicine’ (CAM): ethical and policy issues

Editorial for Bioethics 2016. 30:(2)

Farmacocinética do paracetamol

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas