Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms.

A framework for adaptive e-learning for continuum mechanics and structural analysis

This paper presents a project for providing the students of Structural Engineering with the flexibility to learn outside classroom schedules. The goal is a framework for adaptive E-learning based on a repository of open educational courseware with a set of basic Structural Engineering concepts and fundamentals. These are paramount for students to expand their technical knowledge and skills in structural analysis and design of tall buildings, arch-type structures as well as bridges. Thus, concepts related to structural behaviour such as linearity, compatibility, stiffness and influence lines have traditionally been elusive for students. The objective is to facilitate the student a teachinglearning process to acquire the necessary intuitive knowledge, cognitive skills and the basis for further technological modules and professional development in this area. As a side effect, the system is expected to help the students improve their preparation for exams on the subject. In this project, a web-based open-source system for studying influence lines on continuous beams is presented. It encompasses a collection of interactive user-friendly applications accessible via Web, written in JavaScript under JQuery and Dygraph Libraries, taking advantage of their efficiency and graphic capabilities. It is performed in both Spanish and English languages. The student is enabled to set the geometric, topologic, boundary and mechanic layout of a continuous beam. While changing the loading and the support conditions, the changes in the beam response prompt on the screen, so that the effects of the several issues involved in structural analysis become apparent. This open interaction with the user allows the student to simulate and virtually infer the structural response. Different levels of complexity can be handled, whereas an ongoing help is at hand for any of them. Students can freely boost their experiential learning on this subject at their own pace, in order to further share, process, generalize and apply the relevant essential concepts of Structural Engineering analysis. Besides, this collection is being added to the "Virtual Lab of Continuum Mechanics" of the UPM, launched in 2013 (http://serviciosgate.upm.es/laboratoriosvirtuales/laboratorios/medios-continuos-en-construcci%C3%B3n)

Técnicas de machine learning para la detección de la negación en textos clínicos en español

En los últimos años han surgido nuevos campos de las tecnologías de la información que exploran el tratamiento de la gran cantidad de datos digitales existentes y cómo transformarlos en conocimiento explícito. Las técnicas de Procesamiento del Lenguaje Natural (NLP) son capaces de extraer información de los textos digitales presentados en forma narrativa. Además, las técnicas de machine learning clasifican instancias o ejemplos en función de sus atributos, en distintas categorías, aprendiendo de otros previamente clasificados. Los textos clínicos son una gran fuente de información no estructurada; en consecuencia, información no explotada en su totalidad. Algunos términos usados en textos clínicos se encuentran en una situación de afirmación, negación, hipótesis o histórica. La detección de esta situación es necesaria para la estructuración de información, pero a su vez tiene una gran complejidad. Extrayendo características lingüísticas de los elementos, o tokens, de los textos mediante NLP; transformando estos tokens en instancias y las características en atributos, podemos mediante técnicas de machine learning clasificarlos con el objetivo de detectar si se encuentran afirmados, negados, hipotéticos o históricos. La selección de los atributos que cada token debe tener para su clasificación, así como la selección del algoritmo de machine learning utilizado son elementos cruciales para la clasificación. Son, de hecho, los elementos que componen el modelo de clasificación. Consecuentemente, este trabajo aborda el proceso de extracción de características, selección de atributos y selección del algoritmo de machine learning para la detección de la negación en textos clínicos en español. Se expone un modelo para la clasificación que, mediante el algoritmo J48 y 35 atributos obtenidos de características lingüísticas (morfológicas y sintácticas) y disparadores de negación, detecta si un token está negado en 465 frases provenientes de textos clínicos con un F-Score del 73%, una exhaustividad del 66% y una precisión del 81% con una validación cruzada de 10 iteraciones. ---ABSTRACT--- New information technologies have emerged in the recent years which explore the processing of the huge amount of existing digital data and its transformation into knowledge. Natural Language Processing (NLP) techniques are able to extract certain features from digital texts. Additionally, through machine learning techniques it is feasible to classify instances according to different categories, learning from others previously classified. Clinical texts contain great amount of unstructured data, therefore information not fully exploited. Some terms (tokens) in clinical texts appear in different situations such as affirmed, negated, hypothetic or historic. Detecting this situation is necessary for the structuring of this data, however not simple. It is possible to detect whether if a token is negated, affirmed, hypothetic or historic by extracting its linguistic features by NLP; transforming these tokens into instances, the features into attributes, and classifying these instances through machine learning techniques. Selecting the attributes each instance must have, and choosing the machine learning algorithm are crucial issues for the classification. In fact, these elements set the classification model. Consequently, this work approaches the features retrieval as well as the attributes and algorithm selection process used by machine learning techniques for the detection of negation in clinical texts in Spanish. We present a classification model which, through J48 algorithm and 35 attributes from linguistic features (morphologic and syntactic) and negation triggers, detects whether if a token is negated in 465 sentences from historical records, with a result of 73% FScore, 66% recall and 81% precision using a 10-fold cross-validation.

EmotiBlog: a finer-grained and more precise learning of subjectivity expression models

The exponential growth of the subjective information in the framework of the Web 2.0 has led to the need to create Natural Language Processing tools able to analyse and process such data for multiple practical applications. They require training on specifically annotated corpora, whose level of detail must be fine enough to capture the phenomena involved. This paper presents EmotiBlog – a fine-grained annotation scheme for subjectivity. We show the manner in which it is built and demonstrate the benefits it brings to the systems using it for training, through the experiments we carried out on opinion mining and emotion detection. We employ corpora of different textual genres –a set of annotated reported speech extracted from news articles, the set of news titles annotated with polarity and emotion from the SemEval 2007 (Task 14) and ISEAR, a corpus of real-life self-expressed emotion. We also show how the model built from the EmotiBlog annotations can be enhanced with external resources. The results demonstrate that EmotiBlog, through its structure and annotation paradigm, offers high quality training data for systems dealing both with opinion mining, as well as emotion detection.

Machine learning techniques for automatic opinion detection in non-traditional textual genres

This paper presents a preliminary study in which Machine Learning experiments applied to Opinion Mining in blogs have been carried out. We created and annotated a blog corpus in Spanish using EmotiBlog. We evaluated the utility of the features labelled firstly carrying out experiments with combinations of them and secondly using the feature selection techniques, we also deal with several problems, such as the noisy character of the input texts, the small size of the training set, the granularity of the annotation scheme and the language object of our study, Spanish, with less resource than English. We obtained promising results considering that it is a preliminary study.

Constellations and the unsupervised learning of graphs

In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of fast matching with graph transformations. Our experiments, both with random graphs and in realistic situations (visual localization), show that our prototypes improve the set median graphs and also the prototypes derived from our previous incremental method. We also discuss how the method scales with a growing number of images.

Los MOOC. Un nuevo modelo de e-learning en el panorama educativo actual

La educación está enmarcada por las características de una sociedad actual en la que internet es el medio donde se están implementando nuevos enfoques dirigidos a la formación. Los MOOC, así, se están configurando como una nueva forma de e-learning en el contexto actual, especialmente en la enseñanza superior. En este trabajo abordamos este nuevo término para analizar, por un lado, su significado, características y principales plataformas virtuales que los ofrecen y, por otro lado, las cuestiones que deben resolverse con el fin de configurar un nuevo modelo de e-learning. Concluimos que este nuevo modelo debe ser acorde con una planificación de política educativa y análisis curricular.

Sustainable Development and Collective Learning: Theory and a European Case Study. Bruges European Economic Policy (BEEP) Briefing 7/2004

Network governance of collective learning processes is an essential approach to sustainable development. The first section of the article briefly refers to recent theories about both market and government failures that express scepticism about the way framework conditions for market actors are set. For this reason, the development of networks for collective learning processes seems advantageous if new solutions are to be developed in policy areas concerned with long-term changes and a stepwise internalisation of externalities. With regard to corporate actors’ interests, the article shows recent insights from theories about the knowledge-based firm, where the creation of new knowledge is based on the absorption of societal views. This concept shifts the focus towards knowledge generation as an essential element in the evolution of sustainable markets. This involves at the same time the development of new policies. In this context innovation-inducing regulation is suggested and discussed. The evolution of the Swedish, German and Dutch wind turbine industries are analysed based on the approach of governance put forward in this article. We conclude that these coevolutionary mechanisms may take for granted some of the stabilising and orientating functions previously exercised by basic regulatory activities of the state. In this context, the main function of the governments is to facilitate learning processes that depart from the government functions suggested by welfare economics.

JAABA: interactive machine learning for automatic annotation of animal behavior

We present a machine learning-based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

Dynamic, convex, and robust optimization with Bayesian learning for response-guided dosing

Thesis (Ph.D.)--University of Washington, 2016-08

Productive leaders and productive leadership: Schools as learning organisations

This paper draws on a three-year study of 24 schools involving classroom observations and interviews with teachers and principals. Through an examination of three cases, sets of leadership practices that focus on the learning of both students and teachers are described. This set of practices is called productive leadership and how these practices are dispersed among productive leaders in three schools is described. This form of leadership supports the achievement of both academic and social outcomes through a focus on pedagogy, a culture of care and related organizational processes. The concepts of learning organisations and teacher professional learning communities as ways of framing relationships in schools, in which ongoing teacher learning is complementary to student learning, are espoused.

Constraints to organizational learning during major change at a mental health services facility

This paper explains what happened during a three years long qualitative study at a mental health services organization. The study focuses on differences between espoused theory and theory in use during the implementation of a new service delivery model. This major organizational change occurred in a National policy environment of major health budget cutbacks. Primarily as a result of poor resourcing provided to bring about policy change and poor implementation of a series of termination plans, a number of constraints to learning contributed to the difficulties in implementing the new service delivery model. The study explores what occurred during the change process. Rather than blame participants of change for the poor outcomes, the study is set in a broader context of a policy environment—that of major health cutbacks.

Intelligent agent supported personalization for virtual learning environments

Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically.

Ten Australian elementary teachers' discourse and reported pedagogical practices during cooperative learning

This article describes the types of discourse 10 Australian grade 4-6 teachers used after they had been trained to embed cooperative learning in their curriculum and to use communication skills to promote students' thinking and to scaffold their learning. One audiotaped classroom social science lesson involving cooperative learning was analyzed for each teacher. We provide vignettes from 2 teachers as they worked with groups and from 2 student groups. The data from the audiotapes showed that the teachers used a range of mediated-learning behaviors in their interactions with the children that included challenging their perspectives, asking more cognitive and metacognitive questions, and scaffolding their learning. In turn, in their interactions with each other, the children modelled many of the types of discourse they heard their teachers use. Follow-up interviews with the teachers revealed that they believed it was important to set expectations for children's group behaviors, teach the social skills students needed to deal with disagreement in groups, and establish group structures so children understood what was required both from each other and the task. The teachers reported that mixed ability and gender groups worked best and that groups should be no larger than 5 students. All teachers' programs were based on a child-centered philosophy that recognized the importance of constructivist approaches to learning and the key role interaction plays in promoting social reasoning and learning.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD