Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.

Competitive flexibility in systems supplier companies in mechanical engineering industry

Systems suppliers are focal actors in mechanical engineering supply chains, in between general contractors and component suppliers. This research concentrates on the systems suppliers’ competitive flexibility, as a competitive advantage that the systems supplier gains from independence from the competitive forces of the market. The aim is to study the roles that power, dependence relations, social capital, and interorganizational learning have on the competitive flexibility. Research on this particular theme is scarce thus far. The research method applied here is the inductive multiple case study. Interviews from four case companies were used as main source of the qualitative data. The literature review presents previous literature on subcontracting, supply chain flexibility, supply chain relationships, social capital and interorganizational learning. The result of this study are seven propositions and consequently a model on the effects that the dominance of sales of few customers, power of competitors, significance of the manufactured system in the end product, professionalism in procurement and the significance of brand products in the business have on the competitive flexibility. These relationships are moderated by either social capital or interorganizational learning. The main results obtained from this study revolve around social capital and interorganizational learning, which have beneficial effects on systems suppliers’ competitive flexibility, by moderating the effects of other constructs of the model. Further research on this topic should include quantitative research to provide the extent to which the results can be reliably generalized. Also each construct of the model gives possible focus for more thorough research.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Use Of Systems Approach with Total Quality Management in Industrial Organizations

The objective of the work has been to study why systems thinking should be used in combination with TQM, what are the main benefits of the integration and how it could best be done. The work analyzes the development of systems thinking and TQM with time and the main differences between them. The work defines prerequisites for adopting a systems approach and the organizational factors which embody the development of an efficient learning organization. The work proposes a model based on combination of an interactive management model and redesign to be used for application of systems approach with TQM in practice. The results of the work indicate that there are clear differences between systems thinking and TQM which justify their combination. Systems approach provides an additional complementary perspective to quality management. TQM is focused on optimizing operations at the operational level while interactive management and redesign of organization are focused on optimization operations at the conceptual level providing a holistic system for value generation. The empirical study demonstrates the applicability of the proposed model in one case study company but its application is tenable and possible also beyond this particular company. System dynamic modeling and other systems based techniques like cognitive mapping are useful methods for increasing understanding and learning about the behavior of systems. The empirical study emphasizes the importance of using a proper early warning system.

Problems and potentialities of e-Learning for regular undergraduate courses in emergency medicine

BACKGROUND: E-learning techniques are spreading at great speed in medicine, raising concerns about the impact of adopting them. Websites especially designed to host courses are becoming more common. There is a lack of evidence that these systems could enhance student knowledge acquisition. GOAL: To evaluate the impact of using dedicated-website tools over cognition of medical students exposed to a first-aid course. METHODS: Prospective study of 184 medical students exposed to a twenty-hour first-aid course. We generated a dedicated-website with several sections (lectures, additional reading material, video and multiple choice exercises). We constructed variables expressing the student's access to each section. The evaluation was composed of fifty multiple-choice tests, based on clinical problems. We used multiple linear regression to adjust for potential confounders. RESULTS: There was no association of website intensity of exposure and the outcome - beta-coeficient 0.27 (95%CI - 0.454 - 1.004). These findings were not altered after adjustment for potential confounders - 0.165 (95%CI -0.628 - 0.960). CONCLUSION: A dedicated website with passive and active capabilities for aiding in person learning had not shown association with a better outcome.

Solving the puzzle of mobile learning adoption

The dissertation seeks to explore how to improve users‘ adoption of mobile learning in current education systems. Considering the difference between basic and tertiary education in China, the research consists of two separate but interrelated parts, which focus on the use of mobile learning in basic and tertiary education contexts, respectively. In the dissertation, two adoption frameworks are developed based on previous studies. The frameworks are then evaluated using different technologies. Concerning mobile learning use in basic education settings, case study methodology is utilized. A leading provider of mobile learning services and products in China, Noah Ltd., is investigated. Multiple sources of evidence are collected to test the framework. Regarding mobile learning adoption in tertiary education contexts, survey research methodology is utilized. Based on 209 useful responses, the framework is evaluated using structural equation modelling technology. Four proposed determinants of intention to use are evaluated, which are perceived ease of use, perceived near-term usefulness, perceived ong-term usefulness and personal innovativeness. The dissertation provides a number of new insights for both researchers and practitioners. In particular, the dissertation specifies a practical solution to deal with the disruptive effects of mobile learning in basic education, which keeps the use of mobile learning away from the schools across such as European countries. A list of new and innovative mobile learning technologies is systematically introduced as well. Further, the research identifies several key factors driving mobile learning adoption in tertiary education settings. In theory, the dissertation suggests that since the technology acceptance model is initiated in work-oriented innovations by testing employees, it is not necessarily the best model for studying educational innovations. The results also suggest that perceived longterm usefulness for educational systems should be as important as perceived usefulness for utilitarian systems, and perceived enjoyment for hedonic systems. A classification based on the nature of systems purpose (utilitarian, hedonic or educational) would contribute to a better understanding of the essence of IT innovation adoption.

Learning Robot Environment through Simulation

Traditionally simulators have been used extensively in robotics to develop robotic systems without the need to build expensive hardware. However, simulators can be also be used as a “memory”for a robot. This allows the robot to try out actions in simulation before executing them for real. The key obstacle to this approach is an uncertainty of knowledge about the environment. The goal of the Master’s Thesis work was to develop a method, which allows updating the simulation model based on actual measurements to achieve a success of the planned task. OpenRAVE was chosen as an experimental simulation environment on planning,trial and update stages. Steepest Descent algorithm in conjunction with Golden Section search procedure form the principle part of optimization process. During experiments, the properties of the proposed method, such as sensitivity to different parameters, including gradient and error function, were examined. The limitations of the approach were established, based on analyzing the regions of convergence.

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.

Emancipating and developing learning activity: Systemic intervention and re-instrumentation in higher education.

The central theme of this thesis is the emancipation and further development of learning activity in higher education in the context of the ongoing digital transformation of our societies. It was developed in response to the highly problematic mainstream approach to digital re-instrumentation of teaching and studying practises in contemporary higher education. The mainstream approach is largely based on centralisation, standardisation, commoditisation, and commercialisation, while re-producing the general patterns of control, responsibility, and dependence that are characteristic for activity systems of schooling. Whereas much of educational research and development focuses on the optimisation and fine-tuning of schooling, the overall inquiry that is underlying this thesis has been carried out from an explicitly critical position and within a framework of action science. It thus conceptualises learning activity in higher education not only as an object of inquiry but also as an object to engage with and to intervene into from a perspective of intentional change. The knowledge-constituting interest of this type of inquiry can be tentatively described as a combination of heuristic-instrumental (guidelines for contextualised action and intervention), practical-phronetic (deliberation of value-rational aspects of means and ends), and developmental-emancipatory (deliberation of issues of power, self-determination, and growth) aspects. Its goal is the production of orientation knowledge for educational practise. The thesis provides an analysis, argumentation, and normative claim on why the development of learning activity should be turned into an object of individual|collective inquiry and intentional change in higher education, and why the current state of affairs in higher education actually impedes such a development. It argues for a decisive shift of attention to the intentional emancipation and further development of learning activity as an important cultural instrument for human (self-)production within the digital transformation. The thesis also attempts an in-depth exploration of what type of methodological rationale can actually be applied to an object of inquiry (developing learning activity) that is at the same time conceptualised as an object of intentional change within the ongoing digital transformation. The result of this retrospective reflection is the formulation of “optimally incomplete” guidelines for educational R&D practise that shares the practicalphronetic (value related) and developmental-emancipatory (power related) orientations that had been driving the overall inquiry. In addition, the thesis formulates the instrumental-heuristic knowledge claim that the conceptual instruments that were adapted and validated in the context of a series of intervention studies provide means to effectively intervene into existing practise in higher education to support the necessary development of (increasingly emancipated) networked learning activity. It suggests that digital networked instruments (tools and services) generally should be considered and treated as transient elements within critical systemic intervention research in higher education. It further argues for the predominant use of loosely-coupled, digital networked instruments that allow for individual|collective ownership, control, (co-)production, and re-use in other contexts and for other purposes. Since the range of digital instrumentation options is continuously expanding and currently shows no signs of an imminent slow-down or consolidation, individual and collective exploration and experimentation of this realm needs to be systematically incorporated into higher education practise.

Enhancing human security through crisis management : opportunities and challenges for learning

Inhimilliseen turvallisuuteen kriisinhallinnan kautta – oppimisen mahdollisuuksia ja haasteita Kylmän sodan jälkeen aseelliset konfliktit ovat yleensä alkaneet niin sanotuissa hauraissa valtioissa ja köyhissä maissa, ne ovat olleet valtioiden sisäisiä ja niihin on osallistunut ei-valtiollisia aseellisia ryhmittymiä. Usein ne johtavat konfliktikierteeseen, jossa sota ja vakaammat olot vaihtelevat. Koska kuolleisuus konflikteissa voi jäädä alle kansainvälisen määritelmän (1000 kuollutta vuodessa), kutsun tällaisia konflikteja ”uusiksi konflikteiksi”. Kansainvälinen yhteisö on pyrkinyt kehittämään kriisinhallinnan ja rauhanrakentamisen malleja, jotta pysyvä rauhantila saataisiin aikaiseksi. Inhimillinen turvallisuus perustuu näkemykseen, jossa kunnioitetaan jokaisen yksilön ihmisoikeuksia ja jolla on vaikutusta myös kriisinhallinnan ja rauhanrakentamisen toteuttamiseen. Tutkimukseen kuuluu kaksi empiiristä osaa: Delfoi tulevaisuuspaneeliprosessin sekä kriisinhallintahenkilöstön haastattelut. Viisitoista eri alojen kriisinhallinta-asiantuntijaa osallistui paneeliin, joka toteutettiin vuonna 2008. Paneelin tulosten mukaan tulevat konfliktit usein ovat uusien konfliktien kaltaisia. Lisäksi kriisinhallintahenkilöstöltä edellytetään vuorovaikutus- ja kommunikaatiokykyä ja luonnollisesti myös varsinaisia ammatillisia valmiuksia. Tulevaisuuspaneeli korosti vuorovaikutus- ja kommunikaatiotaitoja erityisesti siviilikriisinhallintahenkilöstön kompetensseissa, mutta samat taidot painottuivat sotilaallisen kriisinhallinnan henkilöstön kompetensseissakin. Kriisinhallinnassa tarvitaan myös selvää työnjakoa eri toimijoiden kesken. Kosovossa työskennelleen henkilöstön haastatteluaineisto koostui yhteensä 27 teemahaastattelusta. Haastateltavista 9 oli ammattiupseeria, 10 reservistä rekrytoitua rauhanturvaajaa ja 8 siviilikriisinhallinnassa työskennellyttä henkilöä. Haastattelut toteutettiin helmi- ja kesäkuun välisenä aikana vuonna 2008. Haastattelutuloksissa korostui vuorovaikutus- ja kommunikaatiotaitojen merkitys, sillä monissa käytännön tilanteissa haastateltavat olivat ratkoneet ongelmia yhteistyössä muun kriisinhallintahenkilöstön tai paikallisten asukkaiden kanssa. Kriisinhallinnassa toteutui oppimisprosesseja, jotka usein olivat luonteeltaan myönteisiä ja informaalisia. Tällaisten onnistumisten vaikutus yksilön minäkuvaan oli myönteinen. Tällaisia prosesseja voidaan kuvata ”itseä koskeviksi oivalluksiksi”. Kriisinhallintatehtävissä oppimisella on erityinen merkitys, jos halutaan kehittää toimintoja inhimillisen turvallisuuden edistämiseksi. Siksi on tärkeää, että kriisinhallintakoulutusta ja kriisinhallintatyössä oppimista kehitetään ottamaan huomioon oppimisen eri tasot ja ulottuvuudet sekä niiden merkitys. Informaaliset oppimisen muodot olisi otettava paremmin huomioon kriisinhallintakoulutusta ja kriisinhallintatehtävissä oppimista kehitettäessä. Palautejärjestelmää olisi kehitettävä eri tavoin. Koko kriisinhallintaoperaation on saatava tarvittaessa myös kriittistä palautetta onnistumisista ja epäonnistumisista. Monet kriisinhallinnassa työskennelleet kaipaavat kunnollista palautetta työrupeamastaan. Liian rutiininomaiseksi koettu palaute ei edistä yksilön oppimista. Spontaanisti monet haastatellut pitivät tärkeänä, että kriisinhallinnassa työskennelleillä olisi mahdollisuus debriefing- tyyppiseen kotiinpaluukeskusteluun. Pelkkä tällainen mahdollisuus ilmeisesti voisi olla monelle myönteinen uutinen, vaikka tilaisuutta ei hyödynnettäisikään. Paluu kriisinhallintatehtävistä Suomeen on monelle haasteellisempaa kuin näissä tehtävissä työskentelyn aloittaminen ulkomailla. Tutkimuksen tulokset kannustavat tutkimaan kriisinhallintaa oppimisen näkökulmasta. On myös olennaista, että kriisinhallinnan palautejärjestelmiä kehitetään mahdollisimman hyvin edistämään sekä yksilöllistä että organisatorista oppimista kriisinhallinnassa. Kriisinhallintaoperaatio on oppimisympäristö. Kriisinhallintahenkilöstön kommunikaatio- ja vuorovaikutustaitojen kehittäminen on olennaista tavoiteltaessa kestävää rauhanprosessia, jossa konfliktialueen asukkaatkin ovat mukana.

Summaries of lecture recordings used as learning material in blended learning

Julkaisumaa: 530 AN ANT Alankomaiden Antillit

Learning Grasp Affordances from Vision

Monimutkaisissa ja muuttuvissa ympäristöissä työskentelevät robotit tarvitsevat kykyä manipuloida ja tarttua esineisiin. Tämä työ tutkii robottitarttumisen ja robottitartuntapis-teiden koneoppimisen aiempaa tutkimusta ja nykytilaa. Nykyaikaiset menetelmät käydään läpi, ja Le:n koneoppimiseen pohjautuva luokitin toteutetaan, koska se tarjoaa parhaan onnistumisprosentin tutkituista menetelmistä ja on muokattavissa sopivaksi käytettävissä olevalle robotille. Toteutettu menetelmä käyttää intensititeettikuvaan ja syvyyskuvaan po-hjautuvia ominaisuuksi luokitellakseen potentiaaliset tartuntapisteet. Tämän toteutuksen tulokset esitellään.

Adaptive Routing Approaches for Networked Many-Core Systems

Through advances in technology, System-on-Chip design is moving towards integrating tens to hundreds of intellectual property blocks into a single chip. In such a many-core system, on-chip communication becomes a performance bottleneck for high performance designs. Network-on-Chip (NoC) has emerged as a viable solution for the communication challenges in highly complex chips. The NoC architecture paradigm, based on a modular packet-switched mechanism, can address many of the on-chip communication challenges such as wiring complexity, communication latency, and bandwidth. Furthermore, the combined benefits of 3D IC and NoC schemes provide the possibility of designing a high performance system in a limited chip area. The major advantages of 3D NoCs are the considerable reductions in average latency and power consumption. There are several factors degrading the performance of NoCs. In this thesis, we investigate three main performance-limiting factors: network congestion, faults, and the lack of efficient multicast support. We address these issues by the means of routing algorithms. Congestion of data packets may lead to increased network latency and power consumption. Thus, we propose three different approaches for alleviating such congestion in the network. The first approach is based on measuring the congestion information in different regions of the network, distributing the information over the network, and utilizing this information when making a routing decision. The second approach employs a learning method to dynamically find the less congested routes according to the underlying traffic. The third approach is based on a fuzzy-logic technique to perform better routing decisions when traffic information of different routes is available. Faults affect performance significantly, as then packets should take longer paths in order to be routed around the faults, which in turn increases congestion around the faulty regions. We propose four methods to tolerate faults at the link and switch level by using only the shortest paths as long as such path exists. The unique characteristic among these methods is the toleration of faults while also maintaining the performance of NoCs. To the best of our knowledge, these algorithms are the first approaches to bypassing faults prior to reaching them while avoiding unnecessary misrouting of packets. Current implementations of multicast communication result in a significant performance loss for unicast traffic. This is due to the fact that the routing rules of multicast packets limit the adaptivity of unicast packets. We present an approach in which both unicast and multicast packets can be efficiently routed within the network. While suggesting a more efficient multicast support, the proposed approach does not affect the performance of unicast routing at all. In addition, in order to reduce the overall path length of multicast packets, we present several partitioning methods along with their analytical models for latency measurement. This approach is discussed in the context of 3D mesh networks.

Adult education policy in the European Union during the Lisbon decade : the shift to vocational adult learning and work skills matching

This doctoral dissertation investigates the adult education policy of the European Union (EU) in the framework of the Lisbon agenda 2000–2010, with a particular focus on the changes of policy orientation that occurred during this reference decade. The year 2006 can be considered, in fact, a turning point for the EU policy-making in the adult learning sector: a radical shift from a wide--ranging and comprehensive conception of educating adults towards a vocationally oriented understanding of this field and policy area has been observed, in particular in the second half of the so--called ‘Lisbon decade’. In this light, one of the principal objectives of the mainstream policy set by the Lisbon Strategy, that of fostering all forms of participation of adults in lifelong learning paths, appears to have muted its political background and vision in a very short period of time, reflecting an underlying polarisation and progressive transformation of European policy orientations. Hence, by means of content analysis and process tracing, it is shown that the new target of the EU adult education policy, in this framework, has shifted from citizens to workers, and the competence development model, borrowed from the corporate sector, has been established as the reference for the new policy road maps. This study draws on the theory of governance architectures and applies a post-ontological perspective to discuss whether the above trends are intrinsically due to the nature of the Lisbon Strategy, which encompasses education policies, and to what extent supranational actors and phenomena such as globalisation influence the European governance and decision--making. Moreover, it is shown that the way in which the EU is shaping the upgrading of skills and competences of adult learners is modeled around the needs of the ‘knowledge economy’, thus according a great deal of importance to the ‘new skills for new jobs’ and perhaps not enough to life skills in its broader sense which include, for example, social and civic competences: these are actually often promoted but rarely implemented in depth in the EU policy documents. In this framework, it is conveyed how different EU policy areas are intertwined and interrelated with global phenomena, and it is emphasised how far the building of the EU education systems should play a crucial role in the formation of critical thinking, civic competences and skills for a sustainable democratic citizenship, from which a truly cohesive and inclusive society fundamentally depend, and a model of environmental and cosmopolitan adult education is proposed in order to address the challenges of the new millennium. In conclusion, an appraisal of the EU’s public policy, along with some personal thoughts on how progress might be pursued and actualised, is outlined.

Building the ‘Digital’ Library: Routes to Managing our Institutional Digital Learning, Teaching and Research Assets Together

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