Learning to Understand the Retailers - The New Approach of a Paperboard Manufacturer

The aim of the study is to expand networking between a packaging material manufacturer and retailers in order to develop products which appeal to brand owners and their customers. The in-built targets are to understand the retailer’s role in the value chain, clarify who makes packaging decision of private label products, and canvass the importance of sustainability. The present value chain of the packaging material manufacturer is reviewed first. It is assumed that sustainability could be a common interest, and The Consumer Goods Forum’s “A Global Language for Packaging and Sustainability” report is shortly discussed. The presentation of the most common packaging materials is based on a guide called “Packaging in the Sustainability Agenda: A Guide for Corporate Decision Makers”. The terms manufacturer’s brand and private label are defined. A retail value chain with emphasis on the role of customers as partners is introduced. The study area is the Nordic countries, and the information about Nordic retailers was provided first by desk research. The interviews were made in Finland, Sweden, Norway and Denmark. The study method is qualitative: the intention was to get initial insights, ideas and understandings. The results are compiled under the headings: sustainability, private labels, cooperation and packaging development. Also the reasons for good profitability of private labels are explained. Sustainability or responsibility is a key driver for innovation in the retail sector. Private labels have become brands. The ways of cooperation between a packaging material manufacturer and a retailer could be education and training. Packaging development is of great interest to retailers and they are willing to contribute.

Informal learning and competence development during a management trainee program

The purpose of this research was to study how management trainee program participants experienced the program with respect to their learning and competence development. Additionally, the purpose was also to examine what the trainees learned and how the learning occurred. Furthermore, factors affecting learning in the workplace were examined. The theoretical framework of this research was formed utilizing individual competence and informal learning frameworks. Research was conducted as a single case study and data was gathered by thematic interviews. The results of this research indicate that the trainees experienced the program as a good method for learning the overall picture of the organization and its business. Regarding competence development, especially knowledge- and cognitive competence categories were developed during the program. The best learning outcomes were achieved through learning by doing, in co-operation with others, and learning from others. The results indicate that the planning of the program and its structure have a significant effect on learning. Furthermore, a sufficient level of challenge was experienced as being important for the quality of the learning as well.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Improving and User Interface to E-Procurement Software Used in Asset Accounting and Investigating Effects to Efficiency

This diploma thesis has been done to international organization which takes care from the accounting actions of two major companies. In this organization are used three different purchasing tools which are used when new asset master data is wanted to input to SAP R/3- system. The aim of this thesis is to find out how much changing the user interface of one of these three e-procurement programs will affect to overall efficiency in asset accounting. As an addition will be introduced project framework which can be used in future projects and which help to avoid certain steps in the development process. At the moment data needs to be inputted manually with many useless mouse clicks and data needs to be searched from many various resources which slow down the process. Other organization has better tools at the moment than the myOrders system which is under investigation Research was started by exploring the main improvement areas. After this possible defects were traced. Suggested improvements were thought by exploring literature which has been written from usability design and research. Meanwhile also directional calculations from the benefits of the project were done alongside with the analysis of the possible risks and threats. After this NSN IT approved the changes which they thought was acceptable. The next step was to program them into tool and test them before releasing to production environment. The calculations were made also from implemented improvements and compared them to planned ones From whole project was made a framework which can be utilized also to other similar projects. The complete calculation was not possible because of time schedule of the project. Important observation in the project was that efficiency is not improved not only by changing the GUI but also improving processes without any programming. Feedback from end user should be also listened more in development process. End-user is after all the one who knows the best how the program should look like.

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.

Finnish Preclinical and Clinical Medical Students as Strategic L2 English Learners: Language Learning Strategies and Communicating with Patients

This thesis focused on medical students’ language learning strategies for patient encounters. The research questions concerned the types of learning strategies that medical students use and the differences between the preclinical students and the clinical students, two groups who have had varying amounts of experience with patients. Additionally, strategy use was examined through activity systems to gain information on the context of language learning strategy use in order to learn language for patient encounters. In total, 130 first-year medical students (preclinical) and 39 fifth-year medical students (clinical) participated in the study by filling in a questionnaire on language learning strategies. In addition, two students were interviewed in order to create activity systems for the medical students at different stages of their studies. The study utilised both quantitative and qualitative research methods; the analysis of the results relies on Oxford’s Strategic Self-Regulation Model in the quantitative part and on activity theory in the qualitative part. The theoretical sections of the study introduced earlier research and theories regarding English for specific purposes, language learning strategies and activity theory. The results indicated that the medical students use affective, sociocultural-interactive and metasociocultural-interactive strategies often and avoid using negative strategies, which hinder language learning or cease communication altogether. Slight differences between the preclinical and clinical students were found, as clinical students appear to use affective and metasociocultural-interactive strategies more frequently compared to the preclinical students. The activity systems of the two students interviewed were rather similar. The students were at different stages of their studies, but their opinions were very similar. Both reported the object of learning to be mutual understanding between the patient and the doctor, which in part explains the preference for strategies that support communication and interaction. The results indicate that the nature of patient encounters affects the strategy use of the medical students at least to some extent.

Kun pelko astuu lavalle : eli kuinka opin elämään esiintymisjännityksen kanssa

This thesis focuses on stage fright; what it is and how to find the ideal solution in connection with stage fright. To find the way toward free expression; to find the way from lack of control to con-trol. It also describes how to learn to live with stage fright. Different experiences of stage fright have been used as source material, along with relevant theoretical information. The thesis highlights the importance of handling stage fright and studies stage fright in-depth, with examples. It presents studies and collected theories according to Päivi Arjas. It discusses presence and contact in the work of an actor with reference to the Gestalt method. It also presents methods of reducing stage fright. Ideal presence on stage is addressed, and how to turn stage fright into a positive thing. Finally, it considers the good aspects of stage fright in more deepth and goes through learning to live with stage fright. The thesis concludes that it is possible to heal stage fright. Gestalt methodology was of assistance with the healing process, with becoming conscious of symptoms, and thus in finding one's own way of turning anxious suspense into a positive way forward.

Alkuaineanalysaattorin keskusyksikön suunnittelu ja toteutus

Tässä diplomityössä esitellään jatkuvatoimisen alkuaineanalysaattorin kehitykseen liittyvän projektin alkuosa. Tässä osuudessa on tarkoituksena löytää analysaattorin vanhalle keskusyksikölle uusi korvaava kaupallinen prosessorikortti sekä suunnitella ja toteuttaa uudelle keskusyksikölle analysaattorin toiminnan vaatima ohjelma. Hihna-analysaattori on sulautettu reaaliaikajärjestelmä. Työssä esitellään sulautetun järjestelmän suunnittelun ja toteutuksen yleisiä toimintatapoja ja ratkaisuja. Erilaisista toteutusvaihtoehdoista esitellään niiden etuja ja haittoja. Työn toteutuksessa käytetään PC/104-standardin mukaisia valmiita kaupallisia yksiköitä. Tämä ISA-standardin laajennus soveltuu hyvin käytettäväksi sulautetussa järjestelmissä. Uudella keskusyksiköllä on mahdollista liittyä analysaattorin jäljelle jääviin yksiköihin erillisen sovitinkortin välityksellä. Työn lopputuloksena valittu toteutusratkaisu mahdollistaa analysaattorijärjestelmän vapaan jatkokehityksen, mikä ei ollut mahdollista vanhalla toteutuksella. Analysaattoriin on nyt mahdollista kehittää uusia ominaisuuksia, ja lisäksi sen nykyinen toiminta on hallitaan paremmin.

Simulation Supported Robot Programming using XML

The main objective of this master's thesis is to study robot programming using simulation software, and also how to embed the simulation software into company's own robot controlling software. The further goal is to study a new communication interface to the assembly line's components -more precisely how to connect the robot cell into this new communication system. Conveyor lines are already available where the conveyors use the new communication standard. The robot cell is not yet capable of communicating with to other devices using the new communication protocols. The main problem among robot manufacturers is that they all have their own communication systems and programming languages. There has not been any common programming language to program all the different robot manufacturers robots, until the RRS (Realistic Robot Simulation) standards were developed. The RRS - II makes it possible to create the robot programs in the simulation software and it gives a common user interface for different robot manufacturers robots. This thesis will present the RRS - II standard and the robot manufacturers situation for the RRS - II support. Thesis presents how the simulation software can be embedded into company's own robot controlling software and also how the robot cell can be connected to the CAMX (Computer Aided Manufacturing using XML) communication system.

Viihteen laatu nykytietokonepeleissä

Tässä pro gradu tutkielmassa tarkastellaan viihteen laadun tekijöitä nykyaikaisissa tietokonepeleissä ja niiden vaikutusta pelin viihdytysarvoon. Tutkielmassa on analysoitukeskeisiä pelialan artikkeleita, jotka käsittelevät pelaajien vaatimuksia hyvältä peliltä. Artikkeleita analysoimalla on löydetty 14 erilaista tietokonepelin viihdearvon osatekijää: osallistuminen, palaute, säännöt, pelaamaan oppiminen, mielekäs tekeminen, kilpailu, immersio, yhtenäinen ja ymmärrettävä pelimaailma, tarinan luominen, turvalliset kokemukset, sosiaalinen pääoma, ennakko-odotukset, tekniset vaatimukset ja markkinoiden odotukset. Löydettyjä viihteen laadun osatekijöitä etsitään pelityypin mukaan valittuista 12 nykypelistä, ja tulokset paljastavat, että tietyt tekijät ovat yhtenäisiä hyviksi koetuilla peleillä. Hyvät pelit näyttäisivät omaavan toimivat kontrollit, selkeät palautteet, tasapainoiset säännöt, mielenkiintoista tekemistä, palkintoja etenemisestä ja mahdollisuuksia vaikuttaa pelin tapahtumiin koko pelaamisen ajan. Hyväksi peliksi on määritelty peli, joka sai keskiarvokseen 90% tai enemmän peliarvosteluissa jaetuista pisteistä. Lisäksi hyvän pelin ei näyttäisi tarvitsevan toteuttaa täydellisesti kaikkia viihteellisen laadun osatekijöitä.

Lasikuituputkien kelauslinjan automatisointi

Lasikuituputkien kelaamiseen tarkoitettu automaatio voidaan toteuttaa suorakulmaisen kolmion trigonometrian perusteella. Kelaaminen tapahtuu pyörittämällä muottia ja liikuttamalla samalla muottia sivuttain. Näin tehdessä saadaan kelattavat lasikuitulangat leviämään koko muotin leveydelle. Jos langat halutaan saada kelattua tiiviisti toistensa viereen, on muotin liikuttava tietty matka yhtä muotin pyörähdystä kohden. Matkaa kutsutaan nousuksi. Nousu riippuu lankojen muodostaman kimpun leveydestä, kelauskulmasta ja muotin säteestä. Kelauskulmalla voidaan vaikuttaa kelattavan tuotteen aksiaaliseen ja pitkittäiseen lujuuteen. Kelauskoneen automaatiojärjestelmä koostuu sähkökäytöistä, ohjaavasta yksiköstä, valvomosta ja erilaisista antureista. Eri osat yhdistetään toisiinsa väylätekniikalla. Sähkökäytöt ovat yleensä servokäyttöjä, koska niiltä vaaditaan tarkkuutta ja nopeutta. Ohjaavana yksikkönä toimii yleensä teollisuus logiikka. Anturointia tarvitaan esimerkiksi hartsin lämpötilan säätöön ja lankojen ratakireyden säätöön. Anturoinnilla ja säädöillä pyritään parantamaan kelattavan tuotteen laatua. Koneen ohjelmallinen toteutus vaatii kelauskoneen mekaniikan ja kelausprosessin ymmärtämistä. Lisäksi on huomioitava koneen käyttäjät ja heidän turvallisuus.

Uuden robottisolun ohjelmointitavan mahdollisuudet pienille ja keskisuurille yrityksille

Robotin ohjelmointi on aikaa vievää ja tarvitsee robotin ohjelmoinnin tuntevan operaattorin toimimaan robotin opettajana. Saadakseen robottisolun kustannustehokkaaksi operaattorilla olisi hyvä olla useampi solu hoidettavanaan samaan aikaan. Tämä ei ole suuri ongelma suurille yrityksille, joissa voi olla kymmeniä robottisoluja. Jos kyseessä on pieni tai keskisuuri yritys, automatisointi-investointi voi jäädä tekemättä ohjelmoinnin vaikeuden aiheuttaman ongelman vuoksi. Diplomityössä keskityttiin tutkimaan robotisointia pienten ja keskisuurten yritysten kannalta. Teoriaosassa on keskitytty robottisolun suunnittelun kannalta tarvittaviin perustietoihin robotin rakenteesta, ohjausjärjestelmästä, ohjelmoinnista sekä turvallisuudesta. Näiden perustietojen lisäksi on huomioitu hitsauksen automatisointia sekä taluttamalla ohjelmoitavan robottisolun tekninen konsepti. Taluttamalla ohjelmoitavan robottisolun konseptin käsittelyosassa on myös perehdytty taluttamalla ohjelmoinnin vaatimiin komponentteihin kuten voima/vääntö-anturi. Robottisolun suunnittelu on tehtävä koneasetuksen vaatimusten mukaan. Turvallisuus osiossa on käsitelty koneasetuksen vaatimuksia koneensuunnittelulle ja käytännön osassa on käsitelty Winnovan taluttamalla ohjelmoitavan robottisolun suunnittelua koneasetuksen ohjeiden mukaan. Käytännön osassa on tutkittu taluttamalla ohjelmoinnin tuomia etuja muihin ohjelmointimenetelmiin nähden sekä suoritettu investointilaskelmat taluttamalla ohjelmoitavasta ja opettamalla ohjelmoitavasta robottisolusta. Koetuloksista nähdään taluttamalla ohjelmoinnin olevan nopeampi ja yksinkertaisempi tapa ohjelmoida robottia kuin opettamalla ohjelmointi. Investointilaskelmien vertailusta nähdään taluttamalla ohjelmoinnin tulevan opettamalla ohjelmointia edullisemmaksi vaihtoehdoksi käyttökustannusten edullisuuden ansiosta.

Teaching mathematics and programming : new approaches with empirical evaluation

Programming and mathematics are core areas of computer science (CS) and consequently also important parts of CS education. Introductory instruction in these two topics is, however, not without problems. Studies show that CS students find programming difficult to learn and that teaching mathematical topics to CS novices is challenging. One reason for the latter is the disconnection between mathematics and programming found in many CS curricula, which results in students not seeing the relevance of the subject for their studies. In addition, reports indicate that students' mathematical capability and maturity levels are dropping. The challenges faced when teaching mathematics and programming at CS departments can also be traced back to gaps in students' prior education. In Finland the high school curriculum does not include CS as a subject; instead, focus is on learning to use the computer and its applications as tools. Similarly, many of the mathematics courses emphasize application of formulas, while logic, formalisms and proofs, which are important in CS, are avoided. Consequently, high school graduates are not well prepared for studies in CS. Motivated by these challenges, the goal of the present work is to describe new approaches to teaching mathematics and programming aimed at addressing these issues: Structured derivations is a logic-based approach to teaching mathematics, where formalisms and justifications are made explicit. The aim is to help students become better at communicating their reasoning using mathematical language and logical notation at the same time as they become more confident with formalisms. The Python programming language was originally designed with education in mind, and has a simple syntax compared to many other popular languages. The aim of using it in instruction is to address algorithms and their implementation in a way that allows focus to be put on learning algorithmic thinking and programming instead of on learning a complex syntax. Invariant based programming is a diagrammatic approach to developing programs that are correct by construction. The approach is based on elementary propositional and predicate logic, and makes explicit the underlying mathematical foundations of programming. The aim is also to show how mathematics in general, and logic in particular, can be used to create better programs.

Hållpunkter för lärande : småbarns möten

Developed from human activities, mathematical knowledge is bound to the world and cultures that men and women experience. One can say that mathematics is rooted in humans’ everyday life, an environment where people reach agreement regarding certain “laws” and principles in mathematics. Through interaction with worldly phenomena and people, children will always gain experience that they can then in turn use to understand future situations. Consequently, the environment in which a child grows up plays an important role in what that child experiences and what possibilities for learning that child has. Variation theory, a branch of phenomenographical research, defines human learning as changes in understanding and acting towards a specific phenomenon. Variation theory implies a focus on that which it is possible to learn in a specific learning situation, since only a limited number of critical aspects of a phenomenon can be simultaneously discerned and focused on. The aim of this study is to discern how toddlers experience and learn mathematics in a daycare environment. The study focuses on what toddlers experience, how their learning experience is formed, and how toddlers use their understanding to master their environment. Twenty-three children were observed videographically during everyday activities. The videographic methodology aims to describe and interpret human actions in natural settings. The children are aged from 1 year, 1 month to 3 years, 9 months. Descriptions of the toddlers’ actions and communication with other children and adults are analyzed phenomenographically in order to discover how the children come to understand the different aspects of mathematics they encounter. The study’s analysis reveals that toddlers encounter various mathematical concepts, similarities and differences, and the relationship between parts and whole. Children form their understanding of such aspects in interaction with other children and adults in their everyday life. The results also show that for a certain type of learning to occur, some critical conditions must exist. Variation, simultaneity, reasonableness and fixed points are critical conditions of learning that appear to be important for toddlers’ learning. These four critical conditions are integral parts of the learning process. How children understand mathematics influences how they use mathematics as a tool to master their surrounding world. The results of the study’s analysis of how children use their understanding of mathematics shows that children use mathematics to uphold societal rules, to describe their surrounding world, and as a tool for problem solving. Accordingly, mathematics can be considered a very important phenomenon that children should come into contact with in different ways and which needs to be recognized as a necessary part of children’s everyday life. Adults working with young children play an important role in setting perimeters for children’s experiences and possibilities to explore mathematical concepts and phenomena. Therefore, this study is significant as regards understanding how children learn mathematics through everyday activities.