Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Experimental Results and Evaluation of SmartBox Stimulation Device in a P2P E-learning System

In this paper, we present the experimental results and evaluation of the SmartBox stimulation device in P2P e-learning system which is based on JXTA-Overlay. We also show the design and implementation of the SmartBox environment that is used for stimulating the learners motivation to increase the learning efficiency. The SmartBox is integrated with our P2P system as a useful tool for monitoring and controlling learners¿ activity. We found by experimental results that the SmartBox is an effective way to increase the learner¿s concentration. We also investigated the relation between learner¿s body movement, concentration, and amount of study. From the experimental results, we conclude that the use of SmartBox is an effective way to stimulate the learners in order to continue studying while maintaining the concentration.

Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

Drosophila rely on learning while foraging under semi-natural conditions

Learning is predicted to affect manifold ecological and evolutionary processes, but the extent to which animals rely on learning in nature remains poorly known, especially for short-lived non-social invertebrates. This is in particular the case for Drosophila, a favourite laboratory system to study molecular mechanisms of learning. Here we tested whether Drosophila melanogaster use learned information to choose food while free-flying in a large greenhouse emulating the natural environment. In a series of experiments flies were first given an opportunity to learn which of two food odours was associated with good versus unpalatable taste; subsequently, their preference for the two odours was assessed with olfactory traps set up in the greenhouse. Flies that had experienced palatable apple-flavoured food and unpalatable orange-flavoured food were more likely to be attracted to the odour of apple than flies with the opposite experience. This was true both when the flies first learned in the laboratory and were then released and recaptured in the greenhouse, and when the learning occurred under free-flying conditions in the greenhouse. Furthermore, flies retained the memory of their experience while exploring the greenhouse overnight in the absence of focal odours, pointing to the involvement of consolidated memory. These results support the notion that even small, short lived insects which are not central-place foragers make use of learned cues in their natural environments.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Learning in Product Development Projects - Responding to challenges in global industrial company

Verkostoitunut kansainvälinen tuotekehitys on tärkeä osa menestystä nykypäivän muuttuvassa yritysmaailmassa. Toimintojen tehostamiseksi myös projektitoiminnot on sopeutettava kansainväliseen toimintaympäristöön. Kilpailukyvyn säilyttämiseksi projektitoimintoja on lisäksi jatkuvasti tehostettava. Yhtenäkeinona nähdään projektioppiminen, jota voidaan edistää monin eri tavoin. Tässätyössä keskitytään projektitiedonhallinnan kehittämisen tuomiin oppimismahdollisuuksiin. Kirjallisuudessa kerrotaan, että projektitiedon jakaminen ja sen hyödyntäminen seuraavissa projekteissa on eräs projektioppimisen edellytyksistä. Tämäon otettu keskeiseksi näkökulmaksi tässä tutkimuksessa. Lisäksi tutkimusalueen rajaamiseksi työ tarkastelee erityisesti projektioppimista kansainvälisten tuotekehitysprojektien välillä. Työn tavoitteena on esitellä keskeisiä projektioppimisen haasteita ja etsiä konkreettinen ratkaisu vastaamaan näihin haasteisiin. Tuotekehitystoiminnot ja kansainvälinen hajautettu projektiorganisaatio kohtaavat lisäksi erityisiä haasteita, kuten tiedon hajautuneisuus, projektihenkilöstön vaihtuvuus, tiedon luottamuksellisuus ja maantieteelliset haasteet (esim. aikavyöhykkeet ja toimipisteen sijainti). Nämä erityishaasteet on otettu huomioon ratkaisua etsittäessä. Haasteisiin päädyttiin vastaamaan tietotekniikkapohjaisella ratkaisulla, joka suunniteltiin erityisesti huomioiden esimerkkiorganisaation tarpeet ja haasteet. Työssä tarkastellaan suunnitellun ratkaisun vaikutusta projektioppimiseen ja kuinka se vastaa havaittuihin haasteisiin. Tuloksissa huomattiin, että projektioppimista tapahtui, vaikka oppimista oli vaikea suoranaisesti huomata tutkimusorganisaation jäsenten keskuudessa. Projektioppimista voidaan kuitenkin sanoa tapahtuvan, jos projektitieto on helposti koko projektiryhmän saatavilla ja se on hyvin järjesteltyä. Muun muassa nämä ehdot täyttyivät. Projektioppiminen nähdään yleisesti haastavana kehitysalueena esimerkkiorganisaatiossa. Suuri osa tietämyksestä on niin sanottua hiljaistatietoa, jota on hankala tai mahdoton saattaa kirjalliseen muotoon. Näin olleen tiedon siirtäminen jää suurelta osin henkilökohtaisen vuorovaikutuksen varaan. Siitä huolimatta projektioppimista on mahdollista kehittää erilaisin toimintamallein ja menetelmin. Kehitys vaatii kuitenkin resursseja, pitkäjänteisyyttä ja aikaa. Monet muutokset voivat vaatia myös organisaatiokulttuurin muutoksen ja vaikuttamista organisaation jäseniin. Motivaatio, positiiviset mielikuvat ja selkeät strategiset tavoitteet luovat vakaan pohjan projektioppimisen kehittämiselle.

Developing Processes and Learning in a Project-based Firm

Tämän diplomityön tavoitteena on kuvata tiedonkulkua projektiliiketoimintaa harjoittavassa yrityksessä sekä analysoida kuvausta määrittäen mahdolliset kehityskohdat. Työssätuotetut kuvaukset ja kehityskohtien määrittäminen toimivat pohjana yrityksen kehittäessä projektien hallintaansa tulevaisuudessa. Työssä valitaan tietojohtamisen näkökulma sopivaksi lähestymistavaksi yrityksen toiminnananalysointiin. Haastatteluin kerätyn tutkimusmateriaalin perusteella luodaan prosessikuvaukset jotka mallintavat tietovirtoja yrityksen projektien aikana tapahtuvien prosessien välillä. Kuvausta peilataan tietämyksen luomisen sekä projektien tietojohtamisen teoriaan ja määritetään kehityskohteita. Kehityskohteiden määrittämisen lisäksi ehdotetaan mahdollisia toimenpiteitä tiedon ja tietämyksen hallinnan kehittämiseksi. Kokemusten ja opittujen asioiden sekäpalautteen kerääminen projektien aikana sekä niiden jälkeen havaittiin tärkeimmäksi kehityskohdaksi. Näiden keräämisen voidaan todeta vaativan järjestelmällisyyttä jotta projektien onnistumiset sekä niissä saavutetut parannukset voidaan toistaa jatkossa ja virheet sekä epäonnistumiset sitä vastoin välttää.

Comparison of three separate antiadhesive barriers for intraperitoneal onlay mesh hernia repair in an experimental model (Br J Surg 2011; 98: 442-449).

The Editors welcome topical correspondence from readers relating to articles published in the Journal. Responses should be sent electronically via the BJS website (www.bjs.co.uk). All letters will be reviewed and, if approved, appear on the website. A selection of these will be edited and published in the Journal. Letters must be no more than 250 words in length.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.