Representing and Inferring Visual Perceptual Skills in Dermatological Image Understanding

Experts have a remarkable capability of locating, perceptually organizing, identifying, and categorizing objects in images specific to their domains of expertise. Eliciting and representing their visual strategies and some aspects of domain knowledge will benefit a wide range of studies and applications. For example, image understanding may be improved through active learning frameworks by transferring human domain knowledge into image-based computational procedures, intelligent user interfaces enhanced by inferring dynamic informational needs in real time, and cognitive processing analyzed via unveiling the engaged underlying cognitive processes. An eye tracking experiment was conducted to collect both eye movement and verbal narrative data from three groups of subjects with different medical training levels or no medical training in order to study perceptual skill. Each subject examined and described 50 photographical dermatological images. One group comprised 11 board-certified dermatologists (attendings), another group was 4 dermatologists in training (residents), and the third group 13 novices (undergraduate students with no medical training). We develop a novel hierarchical probabilistic framework to discover the stereotypical and idiosyncratic viewing behaviors exhibited by the three expertise-specific groups. A hidden Markov model is used to describe each subject\u27s eye movement sequence combined with hierarchical stochastic processes to capture and differentiate the discovered eye movement patterns shared by multiple subjects\u27 eye movement sequences within and among the three expertise-specific groups. Through these patterned eye movement behaviors we are able to elicit some aspects of the domain-specific knowledge and perceptual skill from the subjects whose eye movements are recorded during diagnostic reasoning processes on medical images. Analyzing experts\u27 eye movement patterns provides us insight into cognitive strategies exploited to solve complex perceptual reasoning tasks. Independent experts\u27 annotations of diagnostic conceptual units of thought in the transcribed verbal narratives are time-aligned with discovered eye movement patterns to help interpret the patterns\u27 meanings. By mapping eye movement patterns to thought units, we uncover the relationships between visual and linguistic elements of their reasoning and perceptual processes, and show the manner in which these subjects varied their behaviors while parsing the images

The 1989 Goddard Conference on Space Applications of Artificial Intelligence

The following topics are addressed: mission operations support; planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; and modeling and simulation

Dynamical interpolation of surface pCO2 between lines of observation in the North Atlantic Ocean

The present PhD thesis aims to elucidate driving mechanisms of oceanic surface pCO2 variability and to develop and analyze techniques for mapping pCO2 on a basinscale in the North Atlantic. First of all, a number of sensitivity tests are carried out in a coarse resolution coupled ecosystem-circulation model simulating the period 1948-2002. The individual contributions by wind stress and surface heat fluxes to naturally driven interannual-to-decadal variability of air-sea fluxes of CO2 and O2 are examined using different atmospheric forcing fields. The model results reveal a pronounced dominance of wind stress in driving interannual-to-decadal variability of CO2 fluxes in the entire model domain. Although the simulated mean carbon uptake takes place in the subpolar basin, interannual fluctuations are of the same magnitude in the subpolar region, the subtropics and the equatorial Atlantic. For O2, mechanisms causing temporal variations can be separated into a wind-stress driven equatorial and a heat-flux driven subtropical and subpolar basin. Subsequently, the potential of monitoring North Atlantic ocean-surface pCO2 on a basin scale by combining Voluntary Observing Ship (VOS) observations with ARGO float and remote sensing data respectively is explored in the context of an eddy-resolving model. Here, model output is sampled according to realistic VOS-line, ARGO float and satellite coverage of the reference year 2005. The synthetic VOS-line observations form a training data set for a self-organizing neural network which is, in the first case, applied to simulated satellite data of SST and surface chlorophyll in order to derive basinwide monthly maps of surface pCO2. In the second case the trained neural network is used to derive punctual pCO2 estimates from ARGO float SST and salinity data which are extrapolated by objective mapping. For a remote-sensing based mapping the basinwide mean RMS-error amounts to 19.0 ppm when missing data in the satellite coverage due to clouds and low solar irradiation at high latitudes in winter is neglected and 21.1 ppm if this missing data is replaced by climatological SST and chlorophyll values. Extrapolated float-based estimates cover 70% of the considered area (15°N to 65°N) with a basinwide mean RMS-error of 15.9 ppm and provide a better accuracy in the reproduction of annual cycles of pCO2 and CO2 fluxes due to their independence of satellite coverage.Ziel der vorliegenden Dissertation ist es, einerseits die Steuerungsmechanismen des ozeanischen pCO2 zu untersuchen und Methoden zu entwickeln und zu analysieren, um pCO2 im Nordatlantik auf beckenweiter Skala zu bestimmen. Zunächst werden Sensitivitätsuntersuchungen mit einem grob auflösenden biogeochemischen Modell des Nordatlantiks durchgeführt, wobei hier die Zeitspanne 1948-2002 simuliert wird. Die individuellen Anteile von Windstress und Wärmeflüssen an der gesamten zwischenjährlich-dekadischen Variabilität der CO2- und O2-Flüsse werden untersucht, indem mehrere Modellläufe mit unterschiedlichem atmosphärischen Antrieb integriert werden. Die Ergebnisse zeigen, dass die Variabilität der CO2-Flüsse auf diesen Zeitskalen im gesamten Modellgebiet Windstress-getrieben ist. Die Hauptaufnahme von Kohlenstoff durch den Ozean findet in der Simulation im subpolaren Nordatlantik statt. Die zwischenjährlichen Schwankungen in der Aufnahme haben in der subpolaren, subtropischen und äquatorialen Region jedoch dieselbe Größenordnung. Für O2 ist bezüglich der interannualen Variabilität eine klare Aufteilung in ein Windstress-dominiertes äquatoriales Becken und eine Wärmefluss-getriebene Region nördlich davon zu erkennen. Darüber hinaus wird die Möglichkeit untersucht, pCO2-Messungen sogenannter Voluntary Observing Ships (VOS) mit Satellitenbeobachtungen und ARGO Float Daten zu kombinieren. Hierbei werden mit Hilfe eines Wirbel-auflösenden biogeochemischen Modells die jeweiligen Beobachtungsdaten gemäß ihrer tatsächlichen Abdeckung im Referenzjahr 2005 simuliert. Die synthetischen Messungen der VOS-Linien bilden den Trainingsdatensatz für ein künstliches Neuronales Netz, das anschließend entweder auf die simulierten Satellitenbeobachtungen von SST und Chlorophyll oder ARGO Float SST- und Salzghaltsdaten angewandt wird. Im ersteren Fall ergeben sich direkt beckenweite pCO2-Schätzungen, im letzteren punktuelle Approximationen, die durch Gaußsches Gewichten extrapoliert werden. Für die Satelliten-gestützten pCO2-Karten ergibt sich ein beckenweiter RMS-Fehler von 19.0 ppm, wenn das Fehlen von Daten durch Wolken und zu geringe winterliche Einstrahlung in hohen Breiten vernachlässigt wird. Werden diese Datenlücken durch klimatologische Werte von SST und Chlorophyll ersetzt, steigt der RMS-Fehler auf 21.5 ppm an. Die extrapolierten Float-gestützten pCO2 Schätzungen umfassen 70% der betrachteten Region (15°N bis 65°N) und besitzen einen RMS-Fehler von 15.9 ppm. Letztere ermöglichen eine genauere Wiedergabe der Jahresgänge von pCO2 und den CO2-Flüssen, da sie unabhängig von der Satellitenabdeckung sind

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

Advanced Information Systems and Technologies

This book comprises the proceedings of the VI International Scientific Conference “Advanced Information Systems and Technologies, AIST-2018”. The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing, computer networking and telecomunications, modern methods and information technologies of sustainable development. They will be useful for students, graduate students, researchers who interested in computer science

Bayesilaisten menetelmien ja klusterointimenetelmien testaus kunnossapidon vianetsinnässä

Data-driven condition monitoring of cut-to-length forest harvesters has developed to a state where substantial amounts of high quality data are available from the harvesting process and especially from the harvester head, which is the main functional part of the harvester. However, the methods that are capable of extracting the essential information from the data are relatively immature. Methods from the field of industrial process monitoring have been applied to the forest harvesting process, but so far with little success. The problem with these methods is that the variation in environmental conditions and the contribution of the human operator have a great influence on both process performance and efficiency. To date, the development of means for measuring these factors has not reached a desired level. This thesis introduces three previously unapplied methods for data-driven condition monitoring on the forest harvester head performance index data. These methods have been used in the process industry earlier. One of the introduced methods is a density based clustering method and the other two are probabilistic methods called the Gaussian mixture and the Bayesian network modeis. The starting point of the analysis involves determining the distribution of the data, finding patterns in the data and identifying dependencies between the index variables. Further based on these observations the process in-control and out-of-control states, including the fault states and the related variabies, are explored. The theoretical part of this thesis introduces forest harvester operation and the collected data, basic concepts of data-driven condition monitoring as well as the data-driven condition monitoring methods and related multivariate statistics. The experimental part applies the introduced condition monitoring modeis to the index data followed by an analysis of the models' suitability. The final conclusions present the findings that contain qualitative observations and recommendations about the models and the data. The main result is that the data is not sufficient to he used with the condition monitoring methods examined in this thesis. Finally, the main findings are Iisted and recommendations for overcoming the shortcomings are proposed. These results can he utilized in the future research of maintenance fault detection of forest harvesters.Tavaralajimenetelmän metsäkoneen datapohjainen kunnonvalvonta on kehittynyt tasolle, jossa huomattava määrä korkealaatuista tietoa on saatavilla harvesterin puunkäsittelyprosessista ja erityisesti harvesteripäältä, joka on harvesterin tärkein toiminnallinen osa. Menetelmät, joilla olennainen informaatio pyritään löytämään datasta, eivät kuitenkaan ole kehittyneet samalla tavalla. Prosessiteollisuudessa käytettyjä menetelmiä on yritetty soveltaa myös metsäkoneisiin, mutta toistaiseksi menestys on ollut heikkoa. Ongelmana on ollut, että ympäristömuuttujien sekä harvesterin kuljettajan vaikutukset puunkorjuuprosessin suorituskykyyn ja tehokkuuteen ovat erittäin suuria. Lisäksi näiden vaikutusten luotettava mittaaminen ei ole vielä ollut riittävällä tasolla. Tässä diplomityössä esitellään kolme harvesteripään datapohjaisen kunnonvalvonnan menetelmää, joita ei ennen ole käytetty metsäkoneissa. Menetelmiä on käytetty prosessiteollisuuden puolella aiemmin. Yksi käytetyistä menetelmistä on tiheyspohjainen klusterointimenetelmä ja kaksi muuta ovat todennäköisyyspohjaisia malleja nimeltään Gaussilainen sekamalli ja Bayesilainen verkko. Analyysin lähtökohtana on datan jakautuneisuuden tutkiminen, säännönmukaisuuksien etsiminen havainnoista sekä riippuvuuksien etsiminen havaittujen muuttujien väliltä. Edelleen näiden havaintojen pohjalta prosessin tilat, mukaan lukien vikatilat ja niihin liittyvät muuttujat pyritään tunnistamaan. Työn teoriaosassa esitellään metsäkoneen toiminnan ja työvaiheiden perusteet, data-pohjaisen kunnonvalvonnan peruskäsitteet sekä datapohjaisen kunnonvalvonnan menetelmiä sekä näihin liittyvät tilastollisten monimuuttujamenetelmien perusteet. Kokeellisessa osassa esiteltyjä menetelmiä sovelletaan dataan ja näiden sopivuutta analysoidaan. Yhteenveto-osioissa esitellään tulokset, jotka sisältävät kvalitatiivisia havaintoja sekä suosituksia koskien malleja ja dataa. Keskeisimpänä tuloksena on, että käytetty data ei ole riittävää tässä työssä käytettyjen kunnonvalvontamenetelmien tarpeisiin. Pääasialliset ongelmakohdat sekä ehdotuksia näiden ongelmien poistamiseksi on esitetty. Näitä tuloksia voidaan käyttää tulevissa tutkimuksissa