From software extensions to product lines of dataflow programs

Abstract Dataflow programs are widely used. Each program is a directed graph where nodes are computations and edges indicate the flow of data. In prior work, we reverse-engineered legacy dataflow programs by deriving their optimized implementations from a simple specification graph using graph transformations called refinements and optimizations. In MDE-speak, our derivations were PIM-to-PSM mappings. In this paper, we show how extensions complement refinements, optimizations, and PIM-to-PSM derivations to make the process of reverse engineering complex legacy dataflow programs tractable. We explain how optional functionality in transformations can be encoded, thereby enabling us to encode product lines of transformations as well as product lines of dataflow programs. We describe the implementation of extensions in the ReFlO tool and present two non-trivial case studies as evidence of our work’s generality

Open video tools to support the production of online collaborative audiovisual projects : how to promote collective creation in e-learning

This paper analyses the use of open video editing tools to support the creation and production of online collaborative audiovisual projects for higher education. It focuses on the possibilities offered by these tools to promote collective creation in virtual environments.

MPWC : Manage projects, workers and contacts

El proyecto nace de la necesidad de implementar una herramienta de gestión a medida para la empresa Sonicon Systems S.L., dado que la herramienta actual no se adapta completamente a los requerimientos y, en algunos casos, es compleja o dispone de demasiada información.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

A Multi-Project Scheduling Procedure for Transportation Projects, Part II, HR-339, 1994

The Iowa Department of Transportation (IDOT) has been requiring Critical Path Method (CPM) schedules on some larger or more schedule sensitive projects. The Office of Construction's expectations for enhanced project control and improved communication of project objectives have not been fully met by the use of CPM. Recognizing that the current procedures might not be adequate for all projects, IDOT sponsored a research project to explore the state-of-the-art in transportation scheduling and identify opportunities for improvement. The first phase of this project identified a technique known as the Linear Scheduling Method (LSM) as an alternative to CPM on certain highway construction projects. LSM graphically displays the construction process with respect to the location and the time in which each activity occurs. The current phase of this project was implemented to allow the research team the opportunity to evaluate LSM on all small groups of diverse projects. Unlike the first phase of the project, the research team was closely involved in the project from early in the planning phase throughout the completion of the projects. The research strongly suggests that the linear scheduling technique has great potential as a project management tool for both contractors and IDOT personnel. However, before this technique can become a viable weapon in the project management arsenal, a software application needs to be developed. This application should bring to linear scheduling a degree of functionality as rich and as comprehensive as that found in microcomputer based CPM software on the market today. The research team recommends that the IDOT extend this research effort to include the development of a linear scheduling application.

Open source software in translator training

La formació de traductors implica l´ús de procediments i eines que permetin els estudiants familiaritzar-se amb contextos professionals. El software lliure especialitzat inclou eines de qualitat professional i procediments accessibles per a les institucions acadèmiques i els estudiants a distància que treballen a casa seva. Els projectes reals que utilitzen software lliure i traducció col·laborativa (crowdsourcing) constitueixen recursos indispensables en la formació de traductors.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Legal issues in promoting FOSS in R+D projects - Policy, Organisation and Management

Creating and using FLOSS in R+D projects raises several legal issues, which need to be managed as soon as possible - preferably during the project planning stage. Challenges in the areas of project structure and policy, licenses and licensing, exploitation strategies, community management, and FLOSS-friendliness in general all have their legal aspects, which are commented here. Some recommendations are made for assisting in the use of FLOSS in R+D projects, especially in multiple party consortiums.

Licensing Review in R+D Projects

FOSS packages are becoming ever more present in R&D projects carried out a variety of entities, including large corporations. I will focus on how legal risks associated with the use of FOSS licenses can be assessed and discuss measures directed to risk mitigation.

Release Process Effectiveness - Case of Operating Room Management Software

Työn päätavoitteena oli tuoda esiin tärkeimmät julkistamisprosessin tehokkuuteen vaikuttavat tekijät. Tutkimuksessa tarkasteltiin aihetta julkistamisprojektien vetäjän näkökulmasta. Kirjallinen selvitys kattaa keskeisimmät ohjelmistoprosessin, palvelun laadun sekä projektihallinnan teoriat. Kokeellisena aineistona käytettiin asiakkailta ja myynnin sekä käyttöönoton organisaatioilta tullutta palautetta ja asiantuntijahaastatteluita. Case-tuotteena tarkasteltiin suuren kansainvälisen yrityksen jälleenmyymää leikkaussalihallinnan ohjelmistoa. Tärkeimpiä julkistamisprosessin tehokkuuteen vaikuttavia tekijöitä ovat tiekartan ja julkistamispakettien sisällön hallinta, projektin aikataulujen pitäminen, rehellinen ja nopea kommunikaatio myyntikanavaan ja asiakkaille, sekä hyvin toteutettu testaus. Työssä käydään läpi esimerkkistrategioita kehittymiseen näillä alueilla.

Re-usable ASIC integrationverification software architecture

Mikropiirien valmistus- ja suunnittelutekniikoiden kehittyminen mahdollistaa yhä monimutkaisempien mikropiirien valmistamisen. Piirien verifioinnista onkin tullut prosessin aikaa vievin osa,sillä kompleksisuuden kasvaessa kasvaa verifioinnin tarve eksponentiaalisesti. Vaikka erinäisiä strategioita piirien integroinnin verifiointiin on esitetty, mm. verifioinnin jakaminen koko suunnitteluprosessin ajalle, jopa yli puolet koko piirin suunnitteluun ja valmistukseen käytetystä työmäärästä kuluu verifiointiin. Uudelleenkäytettävät komponentit ovat pääosassa piirin suunnittelussa, mutta verifioinnissa uudelleenkäytettävyyttä ei ole otettu kunnolla käyttöön ainakaan verifiointiohjelmistojen osalta. Tämä diplomityö esittelee uudelleenkäytettävän mikropiirien verifiointiohjelmistoarkkitehtuurin, jolla saadaan verifiointitaakkaa vähennettyä poistamalla verifioinnissa käytettävien ohjelmistojen uudelleensuunnittelun ja toteuttamisen tarvetta.

Elicitation of Customer Requirements with Group Methods in Software Engineering

Vaatimusmäärittely on tärkeä vaihe ohjelmistotuotannossa, koska virheelliset ja puutteelliset asiakasvaatimukset vaikuttavat huomattavasti asiakkaan tyytymättömyyteen ohjelmistotuotteessa. Ohjelmistoinsinöörit käyttävät useita erilaisia menetelmiä ja tekniikoita asiakasvaatimusten kartoittamiseen. Erilaisia tekniikoita asiakasvaatimusten keräämiseen on olemassa valtava määrä.Diplomityön tavoitteena oli parantaa asiakasvaatimusten keräämisprosessia ohjelmistoprojekteissa. Asiakasvaatimusten kartoittamiseen käytettävien tekniikoiden arvioinnin perusteella kehitettiin parannettu asiakasvaatimusten keräämisprosessi. Kehitetyn prosessin testaamiseksi ja parantamiseksi järjestettiin ryhmätyöistuntoja liittyen todellisiin ohjelmistokehitysprojekteihin. Tuloksena vaatimusten kerääminen eri sidosryhmiltä nopeutui ja tehostui. Prosessi auttoi muodostamaan yleisen kuvan kehitettävästä ohjelmistosta, prosessin avulla löydettiin paljon ideoita ja prosessi tehosti ideoiden analysointia ja priorisointia. Prosessin suurin kehityskohde oli fasilitaattorin ja osallistujien valmistautumisessa ryhmätyöistuntoihin etukäteen.

Influence of software development process quality on product quality

Ohjelmistojen tärkeys nykypäivän yhteiskunnalle kasvaa jatkuvasti. Monia ohjelmistoprojekteja vaivaavat ongelmat aikataulussa pysymisestä, korkean tuottavuuden ylläpitämisestä ja riittävän korkeasta laadusta. Ohjelmistokehitysprosessien parantamisessa on naiden ongelmien minimoimiseksi tehty suuria investointeja. Investointien syynä on ollut olettamus ohjelmistokehityksen kapasiteetin suora riippuvuus tuotteen laadusta. Tämän tutkimuksen tarkoituksena oli tutkia Ohjelmistokehitysprosessien parantamisen mahdollisuuksia. Olemassaolevat ohjelmistokehityksen ja Ohjelmistokehitysprosessin parantamisen mallit, tekniikat ja metodologiat esiteltiin. Esiteltyjen mallien, tekniikoiden ja metodologioiden soveltuvuus analysoitiin ja suositus mallien käytöstä annettiin.

Implementing Agile Software Development Methods in a Small Organization

Currently there is a vogue for Agile Software Development methods and many software development organizations have already implemented or they are planning to implement agile methods. Objective of this thesis is to define how agile software development methods are implemented in a small organization. Agile methods covered in this thesis are Scrum and XP. From both methods the key practices are analysed and compared to waterfall method. This thesis also defines implementation strategy and actions how agile methods are implemented in a small organization. In practice organization must prepare well and all needed meters are defined before the implementation starts. In this work three different sample projects are introduced where agile methods were implemented. Experiences from these projects were encouraging although sample set of projects were too small to get trustworthy results.

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.