Coevolution of adaptive technology, maladaptive culture and population size in a producer-scrounger game.

Technology (i.e. tools, methods of cultivation and domestication, systems of construction and appropriation, machines) has increased the vital rates of humans, and is one of the defining features of the transition from Malthusian ecological stagnation to a potentially perpetual rising population growth. Maladaptations, on the other hand, encompass behaviours, customs and practices that decrease the vital rates of individuals. Technology and maladaptations are part of the total stock of culture carried by the individuals in a population. Here, we develop a quantitative model for the coevolution of cumulative adaptive technology and maladaptive culture in a 'producer-scrounger' game, which can also usefully be interpreted as an 'individual-social' learner interaction. Producers (individual learners) are assumed to invent new adaptations and maladaptations by trial-and-error learning, insight or deduction, and they pay the cost of innovation. Scroungers (social learners) are assumed to copy or imitate (cultural transmission) both the adaptations and maladaptations generated by producers. We show that the coevolutionary dynamics of producers and scroungers in the presence of cultural transmission can have a variety of effects on population carrying capacity. From stable polymorphism, where scroungers bring an advantage to the population (increase in carrying capacity), to periodic cycling, where scroungers decrease carrying capacity, we find that selection-driven cultural innovation and transmission may send a population on the path of indefinite growth or to extinction.

Action Research : the first steps to start up a pilot experiment in heritage education

Peer-reviewed

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Oppimisen tukeminen tieto- ja viestintätekniikan avulla II-asteella

Tutkimuksessa tarkastellaan tieto- ja viestintätekniikan tuomaa lisäarvoa kon-taktiopetukseen sekä Moodle verkko-oppimisalustan hyödyntämistapoja opet-tajan, oppijanja työelämäyhteistyön näkökulmista. Tutkimuksessa on kaksi päätavoitetta: selvittää yleiset verkko-opetusmallit ja verkko-opetusmalleja tukevat työkalut sekä selvittää Etelä-Kymenlaakson am-mattiopistossa käytössä olevan Moodle verkko-oppimisalustan nykyistä laaja-alaisempaan käyttöönottoon liittyvät haasteet ja kehitystarpeet. Kokemusten pohjalta jälkimmäisen tavoitteen suurimmaksi yksittäiseksi haasteeksi nousee verkko-opintojakson aloitus. Opettaja joutuu verkko-opintojakson käynnistämi-sen yhteydessä tekemään liikaa monimutkaisiamäärittelyjä. Kehitystarpeissa puolestaan haetaan ratkaisuja kysymykseen; mitenverkko-oppimisalusta mahdollistaa työelämäyhteyksien parantamisen ja miten se palvelee paremmin oppijoita verkko-opintojaksojen löytämisen suhteen. Tutkimuksen tuloksena rakentui uusi tapa verkko-opintojaksojen luomiseenja kategorisointiin. Kehitetyn toimintatavan ansiosta opettajan ei tarvitse enää luoda opintojaksoa verkko-oppimisalustaan, vaan niiden aihiot luodaan ohjel-mallisesti oppilashallintojärjestelmästä saatavista tiedoista muunnosohjelman välityksellä. Opettajan tehtäväksi jää vain materiaalin tuottaminen ja kyseisellä opintojaksolla tarvitsemiensa työkalujen alustaminen. Uudella kategorisointita-valla ja mielikuvitusrikkaammilla työtilojen hyödyntämistavoilla saavutetaan seuraavat edut: järjestelmä palvelee paremmin työelämäyhteistyötä, oppijoille selkeytyy oma opintokokonaisuus paremmin ja oppijoiden sekä opettajien on helpompi löytää verkko-oppimisalustassa olevat opintojaksot.

Action Research : the first steps to start up a pilot experiment in heritage education

Peer-reviewed

POP UP & LEAN START UP - yrittäjyyden opetusmallin kehittämishanke toisen asteen koulutuksessa

Tutkimuksen tavoitteena oli tarkastella ammatillisen koulutuksen yrittäjyyskasvatuksen käytännön opetuksen toteutusmahdollisuuksia. Tarkasteluun vaikuttaa vuonna 2015 voimaan astuva ammatillisen perusopetuksen opetussuunnitelmauudistus. Käytännön opetuksen toteutuksen viitekehykseksi valittiin synteesi pop up ja lean startup liiketoimintamallien viitekehyksistä. Tutkimuksen kohteena olivat viralliset opetussuunnitelmauudistuksen asiakirjat ja liiketoimintamalleja käsittelevä kirjallisuus. Tutkimusmenetelmänä käytettiin integoivaa narratiivista kirjallisuusanalyysiä. Aineiston analyysissä käytettiin Atlas-ti ohjelmistoa. Tutkimuksen tuloksena muodostui kuva työelämälähtöisistä uudistuvista opetussuunnitelmista, missä yrittäjyyskasvatuksen rakenteet eivät todellisuudessa muutu entisestä merkittävästi. Sen sijaan, uutta tulevat olemaan opetuksen toteutukselle asetetut haasteet: yksilöllistetyt opintopolut ja opiskelumallien monipuolistamisen merkittävä kasvu. Yrittäjyyskasvatukselle asetetaan Suomessa yhteiskunnallisesti korkeita tavoitteita. Se, miten niihin päästään ja millaisella pedagogiikalla, jää eri ammatillisten oppilaitosten ratkaistavaksi paikallisesti. Pop up ja lean startup liiketoimintamallit tarjoavat tulevaisuuden kannalta merkittäviä mahdollisuuksia yrittäjyyden käytännön opetuksen viitekehyksiksi. Lisäksi ne sopivat nopeatempoiseen ja ajallisesti tiukkarajaiseen opetuksen viitekehykseen hyvin uudenaikaisina asiakaslähtöisinä innovaatio- ja liiketoimintamalleina.

Apprentissage de représentations musicales à l'aide d'architectures profondes et multiéchelles

L'apprentissage machine (AM) est un outil important dans le domaine de la recherche d'information musicale (Music Information Retrieval ou MIR). De nombreuses tâches de MIR peuvent être résolues en entraînant un classifieur sur un ensemble de caractéristiques. Pour les tâches de MIR se basant sur l'audio musical, il est possible d'extraire de l'audio les caractéristiques pertinentes à l'aide de méthodes traitement de signal. Toutefois, certains aspects musicaux sont difficiles à extraire à l'aide de simples heuristiques. Afin d'obtenir des caractéristiques plus riches, il est possible d'utiliser l'AM pour apprendre une représentation musicale à partir de l'audio. Ces caractéristiques apprises permettent souvent d'améliorer la performance sur une tâche de MIR donnée. Afin d'apprendre des représentations musicales intéressantes, il est important de considérer les aspects particuliers à l'audio musical dans la conception des modèles d'apprentissage. Vu la structure temporelle et spectrale de l'audio musical, les représentations profondes et multiéchelles sont particulièrement bien conçues pour représenter la musique. Cette thèse porte sur l'apprentissage de représentations de l'audio musical. Des modèles profonds et multiéchelles améliorant l'état de l'art pour des tâches telles que la reconnaissance d'instrument, la reconnaissance de genre et l'étiquetage automatique y sont présentés.

A criança cega no ensino básico num processo inclusivo de aquisição de competências pessoais e sociais : estruturar e potenciar um ambiente pessoal de aprendizagem e promover as inerentes relação interpessoal e a cooperação comunicativa de espaços virtuais com e para a criança cega

A presente dissertação de mestrado, centra-se no desenvolvimento teórico de uma ideia de projecto de investigação, relacionada com o conceito de Ambiente Pessoal de Aprendizagem (APA) que em conexão com as tecnologias digitais, aplicadas nos contextos educativos actuais, providencia maiores possibilidades integração comunicacional e social, em crianças com deficiência visual. Grande ênfase de parte conceptual do estudo, assenta na ideia de Promoção de Espaços Inclusivos de Cooperação Comunicativa em Cegueira Infantil (PEICC-CI). Um dos objectivos do projecto, será o desenvolvimento de um espaço inclusivo de comunicação em rede que possa promover uma maior integração entre ensino formal e informal. Esta ideia de projecto, integra uma metodologia qualitativa e descritiva, a aplicar nas interacções sociais de um pequeno grupo de alunos participantes, que serão estudados de acordo com as suas capacidades comunicativas durante o plano de estudo. A ideia de projecto apresentada, tentará defender a importância de criação de sistemas tecnológicos mais inclusivos e dinâmicos, com base na aprendizagem informal - que possam proporcionar a crianças cegas, uma igual participação democrática de comunicação e convivência social em meios escolares, dentro das novas sociedades de conhecimento e informação.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Analisi biomeccanica del MAWASHI GERI JODAN in cinture nere di Karate: modelli tecnici a confronto

In martial arts there are several ways to perform the turning kick . Following the martial arts or different learning models many types of kicks take shape. Mawashi geri is the karate turning kick. At the moment there are two models of mawashi geri, one comes from the traditional karate (OLD), and the other newer (NEW), who agrees to the change of the rules of W.K.F. (World Karate Federation) happened in 2000 (Macan J. et all 2006) . In this study we are focus on the differences about two models the mawashi geri jodan of karate. The purpose of this study is to analyse cinematic and kinetic parameters of mawashi geri jodan. Timing of the striking and supporting leg actions were also evaluated A Vicon system 460 IR with 6 cameras at sample frequency of 200 Hz was used. 37 reflective markers have been set on the skin of the subjects following the “PlugInGait-total body model”. The participants performed five repetitions of mawashi geri jodan at maximum rapidity with their dominant leg against a ball suspended in front of them placed at ear height. Fourteen skilled subjects (mean level black belt 1,7 dan; age 20,9±4,8 yrs; height 171,4±7,3 cm; weight 60,9±10,2 Kg) practicing karate have been split in two group through the hierarchical cluster analysis following their technical characteristics. By means of the Mann Whitney-U test (Spss-package) the differences between the two groups were verified in preparatory and execution phase. Kicking knee at start, kicking hip and knee at take-off were different between the two groups (p < 0,05). Striking hip flexion during the spin of the supporting foot was different between the two groups (p < 0,05). Peak angular velocity of hip flexion were different between the two groups (p < 0,05). Groups showed differences also in timing of the supporting spin movement. While Old group spin the supporting foot at 30% of the trial, instead New start spinning at 44% of the trial. Old group showed a greater supporting foot spin than New (Old 110° Vs New 82°). Abduction values didn’t show any differences between the two groups. At the hit has been evaluated a 120° of double hips abduction, for the entire sample. Striking knee extension happened for everybody after the kicking hip flexion and confirm the proximal-distal action of the striking leg (Sorensen H. 1996). In contrast with Pearson J.N. 1997 and Landeo R 2007, peak velocity of the striking foot is not useful to describe kick performance because affected by the stature. Two groups are different either in preparatory phase or in execution phase. The body is set in difference manner already before the take-off of the kicking foot. The groups differ for the timing of the supporting foot action Trainer should pay attention to starting posture and on abduction capacities of the athletes.

Leveraging Informality Within eLearning

The next generation of learners expect more informality in learning. Formal learning systems such as traditional LMS systems no longer meet the needs of a generation of learners used to Twitter and Facebook, social networking and user-generated content. Regardless of this, however, formal content and learning models are still important and play a major role in educating learners, particularly in enterprise. The eLite project at DERI addressed this emerging dichotomy of learning styles, reconciling the traditional with the avant garde by using innovative technology to add informal learning capabilities to formal learning architectures.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

La evaluación de los aprendizajes en los sMOOC. Estudio de caso en el Proyecto Europeo ECO

El siguiente artículo hace una reflexión crítica sobre los MOOC, prestando especial atención al análisis de los nuevos sistemas de evaluación; en concreto, el método peer to peer, y cómo esto afecta al rol de docentes y estudiantes. El estudio se ha llevado a cabo tomando como referencia dos sMOOC liderados por el Proyecto Europeo ECO (Elearning, Communication and Open-data: Massive Mobile, Ubiquitous and Open Learning). Los resultados que se presentan han sido analizados desde una perspectiva cuantitativa, utilizando como muestra a los miembros de la comunidad de aprendizaje que han participado en ambos cursos. A través de la utilización de un cuestionario se ha podido conocer cómo han valorado su experiencia formativa y su grado de satisfacción. La mitad de los sujetos encuestados ha considerado adecuado y justo el nuevo sistema evaluativo, sin embargo existe otra mitad que lo considera injusto y que tiene lagunas. Se ha abordado la evaluación como una parte intrínseca del proceso educativo y por ello se ha enfatizado en aspectos como el empoderamiento del alumnado, la cultura de la participación y la interacción social, conceptos que nos acercan a nuevos modelos de aprendizaje que potencian el intelecto colectivo y dejan atrás sistemas transmisivos de conocimiento.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.