Two Cultures of Arts Education, Finland and Canada? An integrated View

Taidekasvatuksen kaksi kulttuuria, Suomi ja Kanada? Integroitu näkemys Tutkimuksessa kuvataan kanadalaisen Learning Through The Arts –pedagogiikan mukainen suomalainen kokeiluhanke, jonka aikana taiteilija–opettaja-parit opettivat yhdessä eri oppiaineita koululuokille: esim. matematiikkaa tanssien, biologiaa maalaten tai yhdistäen eri taiteenlajeja projektimuotoiseen oppimiseen. Hanketta arvioitaessa nousee esille, ei niinkään yksittäisten taiteilijoiden ja opettajien toiminta, vaan pikemminkin Kanadan ja Suomen rakenteelliset sekä kulttuuriset eroavuudet. Tutkimus sivuaa myös Suomessa käytävää keskustelua taiteen hyödyllisyydestä ja pohtii samalla taito- ja taideaineiden asemaa koulussa. Työn teoreettisessa osassa integroidaan opetussuunnitelmateoriaa, kasvatuksen historiaa ja filosofiaa, tähdentäen taidekasvatuksen merkitystä osana koko ihmisen kasvatusta. Opetussuunnitelmateorian osalta tarkastellaan romanttista ja klassista opetussuunnitelmaa, jotka eroavat toisistaan menetelmiensä, sisältöjensä, tavoitteidensa sekä arvioinnin osalta. Ns. kovat ja pehmeät aineet tai matemaattis-luonnontieteelliset aineet vastakohtanaan humanismi, voidaan ymmärtää sekä historiallisia että epistemologisia taustojaan vasten. Pepperin maailmanhypoteesien mukaisesti on kasvatuksen ongelmien ratkaisemiseksi hahmotettavissa neljä selvästi toisistaan eroavaa lähestymistapaa: formismi; organisismi; mekanisismi; sekä kontekstualismi. Kantin filosofiaan viitaten tutkimus puolustaa käsitystä taiteesta rationaalisena ja propositionaalisena kokonaisuutena, joka ei ole vain kommunikaation väline, vaan yksi todellisuuden kohtaamisen lajeista, tiedon ja etiikan rinnalla. Näin ajateltuna taito- ja taidekasvatuksen tulisi olla luonteeltaan aina myös kulttuurikasvatusta. Tutkimuksen tulosten perusteella voidaan väittää, että moniammatillinen yhteistyö monipuolistaa koulun opetusta. Mikäli huolehditaan siitä, että taiteilijat saavat riittävästi koulutusta opettamiseen liittyvissä asioissa, on mahdollista käyttää taiteilijoita opettajien rinnalla koulutyössä.

Measuring irregularities and surface defects from printed patterns

Quality inspection and assurance is a veryimportant step when today's products are sold to markets. As products are produced in vast quantities, the interest to automate quality inspection tasks has increased correspondingly. Quality inspection tasks usuallyrequire the detection of deficiencies, defined as irregularities in this thesis. Objects containing regular patterns appear quite frequently on certain industries and science, e.g. half-tone raster patterns in the printing industry, crystal lattice structures in solid state physics and solder joints and components in the electronics industry. In this thesis, the problem of regular patterns and irregularities is described in analytical form and three different detection methods are proposed. All the methods are based on characteristics of Fourier transform to represent regular information compactly. Fourier transform enables the separation of regular and irregular parts of an image but the three methods presented are shown to differ in generality and computational complexity. Need to detect fine and sparse details is common in quality inspection tasks, e.g., locating smallfractures in components in the electronics industry or detecting tearing from paper samples in the printing industry. In this thesis, a general definition of such details is given by defining sufficient statistical properties in the histogram domain. The analytical definition allowsa quantitative comparison of methods designed for detail detection. Based on the definition, the utilisation of existing thresholding methodsis shown to be well motivated. Comparison of thresholding methods shows that minimum error thresholding outperforms other standard methods. The results are successfully applied to a paper printability and runnability inspection setup. Missing dots from a repeating raster pattern are detected from Heliotest strips and small surface defects from IGT picking papers.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Reflexive model of research-based theatre – processing innovation at the crossroads of theatre, reflection and practice-based innovation activities

Contemporary organisations have to embrace the notion of doing ‘more with less’. This challenges knowledge production within companies and public organisations, forcing them to reorganise their structures and rethink what knowledge production actually means in the context of innovation and how knowledge is actually produced among various professional groups within the organisation in their everyday actions. Innovations are vital for organisational survival, and ‘ordinary’ employees and customers are central but too-often ignored producers of knowledge for contemporary organisations. Broader levels of participation and reflexive practices are needed. This dissertation discusses the missing links between innovation research conducted in the context of industrial management, arts, and culture; applied drama and theatre practices (specifically post-Boalian approaches); and learning – especially organising reflection – in organisational settings. This dissertation (1) explores and extends the role of research-based theatre to organising reflection and reflexive practices in the context of practice-based innovation, (2) develops a reflexive model of RBT for investigating and developing practice-based organisational process innovations in order to contribute to the development of a tool for innovation management and analysis, and (3) operationalises this model within private- and publicsector organisations. The proposed novel reflexive model of research-based theatre for investigating and developing practice-based organisational process innovations extends existing methods and offers a different way of organising reflection and reflexive practices in the context of general innovation management. The model was developed through five participatory action research processes conducted in four different organisations. The results provide learning steps – a reflection path – for understanding complex organisational life, people, and relations amid renewal and change actions. The proposed model provides a new approach to organising and cultivating reflexivity in practice-based innovation activities via research-based theatre. The results can be utilised as a guideline when processing practice-based innovation within private or public organisations. The model helps innovation managers to construct, together with their employees, temporary communities where they can learn together through reflecting on their own and each others’ experiences and to break down assumptions related to their own perspectives. The results include recommendations for practical development steps applicable in various organisations with regard to (i) application of research-based theatre and (ii) related general innovation management. The dissertation thus contributes to the development of novel learning approaches in knowledge production. Keywords: practice-based innovation, research-based theatre, learning, reflection, mode 2b knowledge production

Visual Arts Data Skills for Researchers (VADS4R)

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Data Management [in eprints] for arts research: the experience at University of the Arts London

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

From KAPTUR to VADS4R: Exploring Research Data Management in the Visual Arts

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014