Galois lattice theory for probabilistic visual landmarks

This paper presents an original application of the Galois lattice theory, the visual landmark selection for topological localization of an autonomous mobile robot, equipped with a color camera. First, visual landmarks have to be selected in order to characterize a structural environment. Second, such landmarks have to be detected and updated for localization. These landmarks are combinations of attributes, and the selection process is done through a Galois lattice. This paper exposes the landmark selection process and focuses on probabilistic landmarks, which give the robot thorough information on how to locate itself. As a result, landmarks are no longer binary, but probabilistic. The full process of using such landmarks is described in this paper and validated through a robotics experiment

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Structurally Tractable Uncertain Data

Many data management applications must deal with data which is uncertain, incomplete, or noisy. However, on existing uncertain data representations, we cannot tractably perform the important query evaluation tasks of determining query possibility, certainty, or probability: these problems are hard on arbitrary uncertain input instances. We thus ask whether we could restrict the structure of uncertain data so as to guarantee the tractability of exact query evaluation. We present our tractability results for tree and tree-like uncertain data, and a vision for probabilistic rule reasoning. We also study uncertainty about order, proposing a suitable representation, and study uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium 201

Hybridization of Bayesian networks and belief functions to assess risk. Application to aircraft deconstruction

This paper aims to present a study on knowledge management for the disassembly of end-of-life aircraft. We propose a model using Bayesian networks to assess risk and present three approaches to integrate the belief functions standing for the representation of fuzzy and uncertain knowledge

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Development of a model-based algorithm for the assessment of the Obsessive-Compulsive Disorder

Questa tesi presentata AAS-PD (Sistema di Assessment Adattivo per i disturbi psicologici), un sistema computerizzato di assessment psicologico adattivo per il Disturbo Ossessivo-Compulsivo (DOC). Tale sistema software è basato su una rappresentazione forma del DOC, chiamata Formal Psychological Assessment (FPA), e rappresenta una novità nel campo della psicologia clinica. AAS-PD prende una struttura di conoscenza (struttura clinica), ed esegue l'assessment facendo inferenze probabilistiche su tale struttura, usando come criterio di stop la misura dell'entropia della struttura. I risultati mostrano che AAS-PD assegna correttamente pattern di risposta a stati clinici, evidenziando inoltre alcuni miglioramenti del modello formale da fare. Sviluppi futuri comportano lo sviluppo di un vero e proprio software capace di supportare il clinico nell'assessment dei principali disturbi psicologici / This thesis presents AAS-PD (Adaptive Assessment System for psychological disorders), a computerized adaptive psychological assessment system for the Obsessive-Compulsive Disorder (OCD). This software system is based on a formal representation of the OCD called Formal Psychological Assessment (FPA), and represents an innovation in the field of clinical psychology. AAS-PD requires a knowledge structure (clinical structure), and performs the assessment by making probabilistic inferences of such a structure, using as stop criterion the measure of entropy of the structure. The results show that PD-AAS properly assigns response patterns to clinical states, and note some improvements of the formal model to do. Future developments will involve the development of a real software that supports the clinician in the assessment of the major psychological disordersope