New information retrieval systems

L'article pretén donar una visió panoràmica de la investigació que s'ha realitzat d'aquesta nova generació de sistemes de recuperació de la informació, tot describint-ne els seus components més importants i li·lustrant-ho amb exemples basats en aquests nous principis que ja s'estiguin utilitzant.This article offers an overall view of the research that has been conducted, through descriptions of the main components of this new generation of information retrieval systems. Contains examples of systems currently in ise that are based upon these principles

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning. We present the \emph{BugHunter Dataset}: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74

Development of Agent-Based Simulation Models for Software Evolution

Software ist ein Bestandteil des alltäglichen Lebens für uns geworden. Dies ist auch mit zunehmenden Anforderungen an die Anpassungsfähigkeit an sich schnell ändernde Umgebungen verbunden. Dieser evolutionäre Prozess der Software wird von einem dem Software Engineering zugehörigen Forschungsbereich, der Softwareevolution, untersucht. Die Änderungen an einer Software über die Zeit werden durch die Arbeit der Entwickler verursacht. Aus diesem Grund stellt das Entwicklerverhalten einen zentralen Bestandteil dar, wenn man die Evolution eines Softwareprojekts analysieren möchte. Für die Analyse realer Projekte steht eine Vielzahl von Open Source Projekten frei zur Verfügung. Für die Simulation von Softwareprojekten benutzen wir Multiagentensysteme, da wir damit das Verhalten der Entwickler detailliert beschrieben können. In dieser Dissertation entwickeln wir mehrere, aufeinander aufbauende, agentenbasierte Modelle, die unterschiedliche Aspekte der Software Evolution abdecken. Wir beginnen mit einem einfachen Modell ohne Abhängigkeiten zwischen den Agenten, mit dem man allein durch das Entwicklerverhalten das Wachstum eines realen Projekts simulativ reproduzieren kann. Darauffolgende Modelle wurden um weitere Agenten, zum Beispiel unterschiedliche Entwickler-Typen und Fehler, sowie Abhängigkeiten zwischen den Agenten ergänzt. Mit diesen erweiterten Modellen lassen sich unterschiedliche Fragestellungen betreffend Software Evolution simulativ beantworten. Eine dieser Fragen beantwortet zum Beispiel was mit der Software bezüglich ihrer Qualität passiert, wenn der Hauptentwickler das Projekt plötzlich verlässt. Das komplexeste Modell ist in der Lage Software Refactorings zu simulieren und nutzt dazu Graph Transformationen. Die Simulation erzeugt als Ausgabe einen Graphen, der die Software repräsentiert. Als Repräsentant der Software dient der Change-Coupling-Graph, der für die Simulation von Refactorings erweitert wird. Dieser Graph wird in dieser Arbeit als \emph{Softwaregraph} bezeichnet. Um die verschiedenen Modelle zu parametrisieren haben wir unterschiedliche Mining-Werkzeuge entwickelt. Diese Werkzeuge ermöglichen es uns ein Modell mit projektspezifischen Parametern zu instanziieren, ein Modell mit einem Snapshot des analysierten Projektes zu instanziieren oder Transformationsregeln zu parametrisieren, die für die Modellierung von Refactorings benötigt werden. Die Ergebnisse aus drei Fallstudien zeigen unter anderem, dass unser Ansatz agentenbasierte Simulation für die Vorhersage der Evolution von Software Projekten eine geeignete Wahl ist. Des Weiteren konnten wir zeigen, dass mit einer geeigneten Parameterwahl unterschiedliche Wachstumstrends der realen Software simulativ reproduzierbar sind. Die besten Ergebnisse für den simulierten Softwaregraphen erhalten wir, wenn wir die Simulation nach einer initialen Phase mit einem Snapshot der realen Software starten. Die Refactorings betreffend konnten wir zeigen, dass das Modell basierend auf Graph Transformationen anwendbar ist und dass das simulierte Wachstum sich damit leicht verbessern lässt.Software has become a part of everyday life for us. This is also associated with increasing requirements for adaptability to rapidly changing environments. This evolutionary process of software is being studied by a software engineering related research area, called software evolution. The changes to a software over time are caused by the work of the developers. For this reason, the developer contribution behavior is central for analyzing the evolution of a software project. For the analysis of real projects, a variety of open source projects is freely available. For the simulation of software projects, we use multiagent systems because this allows us to describe the behavior of the developers in detail. In this thesis, we develop several successive agent-based models that cover different aspects of software evolution. We start with a simple model with no dependencies between the agents that can simulative reproduce the growth of a real project solely based on the developer’s contribution behavior. Subsequent models were supplemented by additional agents, such as different developer types and bugs, as well as dependencies between the agents. These advanced models can then be used to answer different questions concerning software evolution simulative. For example, one of these questions answers what happens to the software in terms of quality when the core developer suddenly leaves the project. The most complex model can simulate software refactorings based on graph transformations. The simulation output is a graph which represents the software. The representative of the software is the change coupling graph, which is extended for the simulation of refactorings. In this thesis, this graph is denoted as \emph{software graph}. To parameterize these models, we have developed different mining tools. These tools allow us to instantiate a model with project-specific parameters, to instantiate a model with a snapshot of the analyzed project, or to parameterize the transformation rules required to model refactorings. The results of three case studies show, among other things, that our approach to use agent-based simulation is an appropriate choice for predicting the evolution of software projects. Furthermore, we were able to show that different growth trends of the real software can be reproduced simulative with a suitable selection of simulation parameters. The best results for the simulated software graph are obtained when we start the simulation after an initial phase with a snapshot of real software. Regarding refactorings, we were able to show that the model based on graph transformations is applicable and that it can slightly improve the simulated growth

From condition-specific interactions towards the differential complexome of proteins

While capturing the transcriptomic state of a cell is a comparably simple effort with modern sequencing techniques, mapping protein interactomes and complexomes in a sample-specific manner is currently not feasible on a large scale. To understand crucial biological processes, however, knowledge on the physical interplay between proteins can be more interesting than just their mere expression. In this thesis, we present and demonstrate four software tools that unlock the cellular wiring in a condition-specific manner and promise a deeper understanding of what happens upon cell fate transitions. PPIXpress allows to exploit the abundance of existing expression data to generate specific interactomes, which can even consider alternative splicing events when protein isoforms can be related to the presence of causative protein domain interactions of an underlying model. As an addition to this work, we developed the convenient differential analysis tool PPICompare to determine rewiring events and their causes within the inferred interaction networks between grouped samples. Furthermore, we present a new implementation of the combinatorial protein complex prediction algorithm DACO that features a significantly reduced runtime. This improvement facilitates an application of the method for a large number of samples and the resulting sample-specific complexes can ultimately be assessed quantitatively with our novel differential protein complex analysis tool CompleXChange.Das Transkriptom einer Zelle ist mit modernen Sequenzierungstechniken vergleichsweise einfach zu erfassen. Die Ermittlung von Proteininteraktionen und -komplexen wiederum ist in großem Maßstab derzeit nicht möglich. Um wichtige biologische Prozesse zu verstehen, kann das Zusammenspiel von Proteinen jedoch erheblich interessanter sein als deren reine Expression. In dieser Arbeit stellen wir vier Software-Tools vor, die es ermöglichen solche Interaktionen zustandsbezogen zu betrachten und damit ein tieferes Verständnis darüber versprechen, was in der Zelle bei Veränderungen passiert. PPIXpress ermöglicht es vorhandene Expressionsdaten zu nutzen, um die aktiven Interaktionen in einem biologischen Kontext zu ermitteln. Wenn Proteinvarianten mit Interaktionen von Proteindomänen in Verbindung gebracht werden können, kann hierbei sogar alternatives Spleißen berücksichtigen werden. Als Ergänzung dazu haben wir das komfortable Differenzialanalyse-Tool PPICompare entwickelt, welches Veränderungen des Interaktoms und deren Ursachen zwischen gruppierten Proben bestimmen kann. Darüber hinaus stellen wir eine neue Implementierung des Proteinkomplex-Vorhersagealgorithmus DACO vor, die eine deutlich reduzierte Laufzeit aufweist. Diese Verbesserung ermöglicht die Anwendung der Methode auf eine große Anzahl von Proben. Die damit bestimmten probenspezifischen Komplexe können schließlich mit unserem neuartigen Differenzialanalyse-Tool CompleXChange quantitativ bewertet werden

An investigation into evolving support for component reuse

It is common in engineering disciplines for new product development to be based on a concept of reuse, i.e. based on a foundation of knowledge and pre-existing components familiar to the discipline's community. In Software Engineering, this concept is known as software reuse. Software reuse is considered essential if higher quality software and reduced development effort are to be achieved. A crucial part of any engineering development is access to tools that aid development. In software engineering this means having software support tools with which to construct software including tools to support effective software reuse. The evolutionary nature of software means that the foundation of knowledge and components on which new products can be developed must reflect the changes occurring in both the software engineering discipline and the domain in which the software is to function. Therefore, effective support tools, including those used in software reuse, must evolve to reflect changes in both software engineering and the varying domains that use software. This thesis contains a survey of the current understanding of software reuse. Software reuse is defined as the use of knowledge and work components of software that already exist in the development of new software. The survey reflects the belief that domain analysis and software tool support are essential in successful software reuse. The focus of the research is an investigation into the effects of a changing domain on the evolution of support for component-based reuse and domain analysis, and on the application of software reuse support methods and tools to another engineering discipline, namely roll design. To broaden understanding of a changing domain on the evolution of support for software reuse and domain analysis, a prototype for a reuse support environment has been developed for roll designers in the steel industry

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Towards the generalisation of a case-based aiding system to facilitate the understanding of ethical and professional issues in computing

Modern computers endow users of Information and Computer Technology (ICT) with immense power. The speed of the computing revolution has enabled the novel implementation of ICT before consideration of consequent ethical issues can be made. There is now a demand by society that students, ICT novices, and professionals should be aware of the social, legal, and professional issues associated with ubiquitous use of computers. This thesis describes the development of an Internet-based tool that may be used to raise students' awareness of the ethical implications of ICT. It investigates the application, meaning, and scope of computer ethics. Theoretical foundations are developed for the construction of the tool that will classify, store, and retrieve a suitable analogous case from a collection of realworld, ethically analysed ICT case studies. These are used for comparison with ethically dubious events that may be experienced by students. The model draws upon the theoretical aspects of mechanisms for the modification of users' ethical perception. This research is novel in linking these theories to ethical understanding and case retrieval. Little information is available upon the retrieval of documents addressing ethical issues. The classification and retrieval of material using an ethical framework has some commonality with legal retrieval. Similarities are investigated, and concepts are adapted for the retrieval of ethical documents. The differences that arise present challenges for new research. The use of artificial intelligence (AI) retrieval techniques is not acceptable to meet the pedagogic aims of the retrieval tool. A model is developed, avoiding the use of AI in the reasoning process, requiring the student to consider and evaluate the ethical issues raised. The model is tested and evaluated. The research suggests that non-AI paradigms may be used for retrieval of ethical cases, and that areas for future investigation and development exist