Computational chemistry on Fujitsu vector-parallel processors: Development and performance of applications software

In this and a preceding paper, we provide an introduction to the Fujitsu VPP range of vector-parallel supercomputers and to some of the computational chemistry software available for the VPP. Here, we consider the implementation and performance of seven popular chemistry application packages. The codes discussed range from classical molecular dynamics to semiempirical and ab initio quantum chemistry. All have evolved from sequential codes, and have typically been parallelised using a replicated data approach. As such they are well suited to the large-memory/fast-processor architecture of the VPP. For one code, CASTEP, a distributed-memory data-driven parallelisation scheme is presented. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

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.

Low-cost optical tracking for collaborative applications in immersive virtual environments

We present a novel way of interacting with an immersive virtual environment which involves inexpensive motion-capture using the Wii Remote®. A software framework is also presented to visualize and share this information across two remote CAVETM-like environments. The resulting application can be used to assist rehabilitation by sending motion information across remote sites. The application’s software and hardware components are scalable enough to be used on a desktop computer when home-based rehabilitation is preferred.

Ensidesapplikationers kodkvalitet och förvaltningsbarhet : En jämförelse av förvaltningsbarhet hos single-page applications utvecklade med AngularJS och React

Single-page applications have historically been subject to strong market forces driving fast development and deployment in lieu of quality control and changeable code, which are important factors for maintainability. In this report we develop two functionally equivalent applications using AngularJS and React and compare their maintainability as defined by ISO/IEC 9126. AngularJS and React represent two distinct approaches to web development, with AngularJS being a general framework providing rich base functionality and React a small specialized library for efficient view rendering. The quality comparison was accomplished by calculating Maintainability Index for each application. Version control analysis was used to determine quality indicators during development and subsequent maintenance where new functionality was added in two steps.   The results show no major differences in maintainability in the initial applications. As more functionality is added the Maintainability Index decreases faster in the AngularJS application, indicating a steeper increase in complexity compared to the React application. Source code analysis reveals that changes in data flow requires significantly larger modifications of the AngularJS application due to its inherent architecture for data flow. We conclude that frameworks are useful when they facilitate development of known requirements but less so when applications and systems grow in size.

Guía para el desarrollo de software seguro

El presente Trabajo de Fin de Grado (TFG) es el resultado de la necesidad de la seguridad en la construcción del software ya que es uno de los mayores problemas con que se enfrenta hoy la industria debido a la baja calidad de la misma tanto en software de Sistema Operativo, como empotrado y de aplicaciones. La creciente dependencia de software para que se hagan trabajos críticos significa que el valor del software ya no reside únicamente en su capacidad para mejorar o mantener la productividad y la eficiencia. En lugar de ello, su valor también se deriva de su capacidad para continuar operando de forma fiable incluso de cara de los eventos que la amenazan. La capacidad de confiar en que el software seguirá siendo fiable en cualquier circunstancia, con un nivel de confianza justificada, es el objetivo de la seguridad del software. Seguridad del software es importante porque muchas funciones críticas son completamente dependientes del software. Esto hace que el software sea un objetivo de valor muy alto para los atacantes, cuyos motivos pueden ser maliciosos, penales, contenciosos, competitivos, o de naturaleza terrorista. Existen fuentes muy importantes de mejores prácticas, métodos y herramientas para mejorar desde los requisitos en sus aspectos no funcionales, ciclo de vida del software seguro, pasando por la dirección de proyectos hasta su desarrollo, pruebas y despliegue que debe ser tenido en cuenta por los desarrolladores. Este trabajo se centra fundamentalmente en elaborar una guía de mejores prácticas con la información existente CERT, CMMI, Mitre, Cigital, HP, y otras fuentes. También se plantea desarrollar un caso práctico sobre una aplicación dinámica o estática con el fin de explotar sus vulnerabilidades.---ABSTRACT---This Final Project Grade (TFG) is the result of the need for security in software construction as it is one of the biggest problems facing the industry today due to the low quality of it both OS software, embedded software and applications software. The increasing reliance on software for critical jobs means that the value of the software no longer resides solely in its capacity to improve or maintain productivity and efficiency. Instead, its value also stems from its ability to continue to operate reliably even when facing events that threaten it. The ability to trust that the software will remain reliable in all circumstances, with justified confidence level is the goal of software security. The security in software is important because many critical functions are completely dependent of the software. This makes the software to be a very high value target for attackers, whose motives may be by a malicious, by crime, for litigating, by competitiveness or by a terrorist nature. There are very important sources of best practices, methods and tools to improve the requirements in their non-functional aspects, the software life cycle with security in mind, from project management to its phases (development, testing and deployment) which should be taken into account by the developers. This paper focuses primarily on developing a best practice guide with existing information from CERT, CMMI, Mitre, Cigital, HP, and other organizations. It also aims to develop a case study on a dynamic or static application in order to exploit their vulnerabilities.

Developing context-aware pervasive computing applications: Models and approach

There is growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of users. However, context-awareness introduces a variety of software engineering challenges. In this paper, we address these challenges by proposing a set of conceptual models designed to support the software engineering process, including context modelling techniques, a preference model for representing context-dependent requirements, and two programming models. We also present a software infrastructure and software engineering process that can be used in conjunction with our models. Finally, we discuss a case study that demonstrates the strengths of our models and software engineering approach with respect to a set of software quality metrics.

Ensidesapplikationers kodkvalitet och förvaltningsbarhet : En jämförelse av förvaltningsbarhet hos single-page applications utvecklade med AngularJS och React

Single-page applications have historically been subject to strong market forces driving fast development and deployment in lieu of quality control and changeable code, which are important factors for maintainability. In this report we develop two functionally equivalent applications using AngularJS and React and compare their maintainability as defined by ISO/IEC 9126. AngularJS and React represent two distinct approaches to web development, with AngularJS being a general framework providing rich base functionality and React a small specialized library for efficient view rendering. The quality comparison was accomplished by calculating Maintainability Index for each application. Version control analysis was used to determine quality indicators during development and subsequent maintenance where new functionality was added in two steps. The results show no major differences in maintainability in the initial applications. As more functionality is added the Maintainability Index decreases faster in the AngularJS application, indicating a steeper increase in complexity compared to the React application. Source code analysis reveals that changes in data flow requires significantly larger modifications of the AngularJS application due to its inherent architecture for data flow. We conclude that frameworks are useful when they facilitate development of known requirements but less so when applications and systems grow in size. Sammanfattning: Ensidesapplikationer har historiskt sett påverkats av starka marknadskrafter som pådriver snabba utvecklingscykler och leveranser. Detta medför att kvalitetskontroll och förändringsbar kod, som är viktiga faktorer för förvaltningsbarhet, blir lidande. I denna rapport utvecklar vi två funktionellt ekvi-valenta ensidesapplikationer med AngularJS och React samt jämför dessa applikationers förvaltningsbarhet enligt ISO/IEC 9126. AngularJS och React representerar två distinkta angreppsätt på webbutveckling, där AngularJS är ett ramverk med mycket färdig funktionalitet och React ett mindre bibliotek specialiserat på vyrendering. Kvalitetsjämförelsen utfördes genom att beräkna förvaltningsbarhetsindex för respektive applikation. Versionshanteringsanalys användes för att bestämma andra kvalitetsindikatorer efter den initiala utvecklingen samt två efterföljande underhållsarbeten. Resultaten visar inga markanta skillnader i förvaltningsbarhet för de initiala applikationerna. I takt med att mer funktionalitet lades till sjönk förvaltnings-barhetsindex snabbare för AngularJS-applikationen, vilket motsvarar en kraftigare ökning i komplexitet jämfört med React-applikationen. Versionshanteringsanalys visar att ändringar i dataflödet kräver större modifikationer för AngularJS-applikationen på grund av dess förbestämda arkitektur. Utifrån detta drar vi slutsatsen att ramverk är användbara när de understödjer utvecklingen mot kända krav men att deras nytta blir begränsad ju mer en applikation växer i storlek.

COORDINSPECTOR: a tool for extracting coordination data from legacy code

More and more current software systems rely on non trivial coordination logic for combining autonomous services typically running on different platforms and often owned by different organizations. Often, however, coordination data is deeply entangled in the code and, therefore, difficult to isolate and analyse separately. COORDINSPECTOR is a software tool which combines slicing and program analysis techniques to isolate all coordination elements from the source code of an existing application. Such a reverse engineering process provides a clear view of the actually invoked services as well as of the orchestration patterns which bind them together. The tool analyses Common Intermediate Language (CIL) code, the native language of Microsoft .Net Framework. Therefore, the scope of application of COORDINSPECTOR is quite large: potentially any piece of code developed in any of the programming languages which compiles to the .Net Framework. The tool generates graphical representations of the coordination layer together and identifies the underlying business process orchestrations, rendering them as Orc specifications

Suporte a testes automáticos em aplicações web geradas com a OutSystems Platform

Dissertação de natureza científica para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Usabilitat en aplicacions comptables

En aquest projecte es tracta de la facilitat d'ús de les aplicacions comptables, centrada en la definició d'una interfície d'usuari que faci que la utilització d'aquest tipus d'aplicacions sigui com més intuïtiu millor i permeti a l'usuari d'introduir un gran nombre d'apunts comptables en un temps limitat.

Metodologies de Desenvolupament Dirigides per Models

En aquest treball, s'introduiran dos de les metodologies de desenvolupament dirigides per models més significatives: Model Driven Architecture (MDA) i Domain Specific Modeling (DSM). Així mateix, es presentarà un estudi comparatiu d'algunes de les diferents eines existents actualment al mercat que els hi donen suport.

Reenginyeria inversa d'aplicacions J2EE

L'objectiu d'aquest Projecte Final de Carrera es el de trobar eines que a partir del codi font d'una aplicació J2EE generin diagrames que facilitin entendre el disseny de l'aplicació per tal de facilitar-ne la modificació a un nou equip que no hagués participat en la seva creació. El treball es centra en veure quins diagrames genera cada eina, i quina informació proporcionen aquests diagrames pensant en una possible modificació d'una aplicació J2EE que no haguem desenvolupat nosaltres en un futur.

Gestió del LDAP

El projecte es base en la creació d'una aplicació web que permetrà la gestió de l'inventari dels servidors i impresores d'un domini informàtic.

Gestor d'un Departament de Formació

Per dur a terme el projecte s'ha utilitzat la tecnologia J2EE a la vegada que un framework, Jakarta Struts, que permet aprofundir a l'arquitectura Model - Vista -Controlador (MVC), que és el fonament de moltes aplicacions client -servidor actuals

eJugueteLandia

En aquest projecte s'han desenvolupat tecnologies d'avantguarda, com ara la plataforma J2EE de Sun Microsystems i Jakarta Tomcat de Apache, totes de reconegut prestigi en la comunitat OpenSource. No s'han d'oblidar tampoc totes les que fan d'aquest projecte una solució professional, com ara XHTML o JavaScript.