Building Next Generation Consortium Services – part 2: Next Generation IT-services – Why, What, When?

Kristiina Hormia-Poutasen esitys CBUC-konferenssissa Barcelonassa 12.4.2013.

Building Next Generation Consortium Services – Introduction to the seminar

Kristiina Hormia-Poutasen esitys CBUC-konferenssissa 12.4.2013 Barcelonassa.

Creating generation Y sources of motivation for enhanced performance

New challenges have been created in the modern work environment as the diversity of the workforce is greater than ever in terms of generations. There will become a large demand of generation Y employees as the baby boomer generation employees retire at an accelerated rate. The purpose of this study is to investigate Y generation specific characteristics and to identify motivational systems to enhance performance. The research questions are: 1. What are Y generation characteristics? 2. What motivational systems organizations can form to motivate Y generation employees and in turn, create better performance? The Y generation specific characteristics identified from the literature include; achievement oriented; confident; educated; multitasking; having a need for feedback; needing management support; sociable and tech savvy. The proposed motivational systems can be found in four areas of the organization; HRM, training and development, communication and decision making policies. Three focus groups were held to investigate what would motivate generation Y employees to achieve better performance. Two of these focus groups were Finnish natives and the third consisted of international students. The HRM systems included flexibility and a culture of fun. It was concluded that flexibility within the workplace and role was a great source of motivation. Culture of fun was not responded to as favorably although most focus group participants rated enjoyableness as one of their top motivating factors. Training and development systems include training programs and mentoring as sources of potential motivation. Training programs were viewed as a mode to gain a better position and were not necessarily seen as motivational systems. Mentoring programs were not concluded to have a significant effect on motivation. Communication systems included keeping up with technology, clarity and goals as well as feedback. Keeping up with technology was seen as an ineffective tool to motivate. Clarity and goal setting was seen as very important to be able to perform but not necessarily motivating. Feedback had a highly motivating effect on these focus groups. Decision making policies included collaboration and teamwork as well as ownership. Teams were familiar and meet the social needs of Y generation employees and are motivating. Ownership was equated with trust and responsibility and was highly valued as well as motivating to these focus group participants.

Reaching influential early adopters effectively in Finland : Rock Science – new generation board game

Innovations diffuse at different speed among the members of a social system through various communication channels. The group of early adopters can be seen as the most influential reference group for majority of people to base their innovation adoption decisions on. Thus, the early adopters can often accelerate the diffusion of innovations. The purpose of this research is to discover means of diffusion for an innovative product in Finnish market through the influential early adopters in respect to the characteristics of the case product. The purpose of the research can be achieved through the following sub objectives:  Who are the potential early adopters for the case product and why?  How the potential early adopters of the case product should be communicated with?  What would be the expectations, preferences, and experiences of the early adopters of the case product? The case product examined in this research is a new board game called Rock Science which is considered to be incremental innovation bringing board gaming and hard rock music together in a new way. The research was conducted in two different parts using both qualitative and quantitative research methods. This mixed method research began with expert interviews of six music industry experts. The information gathered from the interviews enabled researcher to compose the questionnaire for the quantitative part of the study. Internet survey that was sent out resulted with a sample of 97 responses from the targeted population. The key findings of the study suggest that (1) the potential early adopters for the case product are more likely to be young adults from the capital city area with great interest in rock music, (2) the early adopters can be reached effectively through credible online sources of information, and (3) the respondents overall product feedback is highly positive, except in the case of quality-price ratio of the product. This research indicates that more effective diffusion of Rock Science board game in Finland can be reached through (1) strategic alliances with music industry and media partnerships, (2) pricing adjustments, (3) use of supporting game formats, and (4) innovative use of various social media channels.

Interoperability specifications and an automatic validation tool as a key factor for the support of a repository ecosystem

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

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Dual light barrier method for six degrees of freedom tool center point calibration of an industrial robot

Tool center point calibration is a known problem in industrial robotics. The major focus of academic research is to enhance the accuracy and repeatability of next generation robots. However, operators of currently available robots are working within the limits of the robot´s repeatability and require calibration methods suitable for these basic applications. This study was conducted in association with Stresstech Oy, which provides solutions for manufacturing quality control. Their sensor, based on the Barkhausen noise effect, requires accurate positioning. The accuracy requirement admits a tool center point calibration problem if measurements are executed with an industrial robot. Multiple possibilities are available in the market for automatic tool center point calibration. Manufacturers provide customized calibrators to most robot types and tools. With the handmade sensors and multiple robot types that Stresstech uses, this would require great deal of labor. This thesis introduces a calibration method that is suitable for all robots which have two digital input ports free. It functions with the traditional method of using a light barrier to detect the tool in the robot coordinate system. However, this method utilizes two parallel light barriers to simultaneously measure and detect the center axis of the tool. Rotations about two axes are defined with the center axis. The last rotation about the Z-axis is calculated for tools that have different width of X- and Y-axes. The results indicate that this method is suitable for calibrating the geometric tool center point of a Barkhausen noise sensor. In the repeatability tests, a standard deviation inside robot repeatability was acquired. The Barkhausen noise signal was also evaluated after recalibration and the results indicate correct calibration. However, future studies should be conducted using a more accurate manipulator, since the method employs the robot itself as a measuring device.

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Sentence Compression with Supervised Machine Learning

The subject of the thesis is automatic sentence compression with machine learning, so that the compressed sentences remain both grammatical and retain their essential meaning. There are multiple possible uses for the compression of natural language sentences. In this thesis the focus is generation of television program subtitles, which often are compressed version of the original script of the program. The main part of the thesis consists of machine learning experiments for automatic sentence compression using different approaches to the problem. The machine learning methods used for this work are linear-chain conditional random fields and support vector machines. Also we take a look which automatic text analysis methods provide useful features for the task. The data used for machine learning is supplied by Lingsoft Inc. and consists of subtitles in both compressed an uncompressed form. The models are compared to a baseline system and comparisons are made both automatically and also using human evaluation, because of the potentially subjective nature of the output. The best result is achieved using a CRF - sequence classification using a rich feature set. All text analysis methods help classification and most useful method is morphological analysis. Tutkielman aihe on suomenkielisten lauseiden automaattinen tiivistäminen koneellisesti, niin että lyhennetyt lauseet säilyttävät olennaisen informaationsa ja pysyvät kieliopillisina. Luonnollisen kielen lauseiden tiivistämiselle on monta käyttötarkoitusta, mutta tässä tutkielmassa aihetta lähestytään television ohjelmien tekstittämisen kautta, johon käytännössä kuuluu alkuperäisen tekstin lyhentäminen televisioruudulle paremmin sopivaksi. Tutkielmassa kokeillaan erilaisia koneoppimismenetelmiä tekstin automaatiseen lyhentämiseen ja tarkastellaan miten hyvin erilaiset luonnollisen kielen analyysimenetelmät tuottavat informaatiota, joka auttaa näitä menetelmiä lyhentämään lauseita. Lisäksi tarkastellaan minkälainen lähestymistapa tuottaa parhaan lopputuloksen. Käytetyt koneoppimismenetelmät ovat tukivektorikone ja lineaarisen sekvenssin mallinen CRF. Koneoppimisen tukena käytetään tekstityksiä niiden eri käsittelyvaiheissa, jotka on saatu Lingsoft OY:ltä. Luotuja malleja vertaillaan Lopulta mallien lopputuloksia evaluoidaan automaattisesti ja koska teksti lopputuksena on jossain määrin subjektiivinen myös ihmisarviointiin perustuen. Vertailukohtana toimii kirjallisuudesta poimittu menetelmä. Tutkielman tuloksena paras lopputulos saadaan aikaan käyttäen CRF sekvenssi-luokittelijaa laajalla piirrejoukolla. Kaikki kokeillut teksin analyysimenetelmät auttavat luokittelussa, joista tärkeimmän panoksen antaa morfologinen analyysi.