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This volume contains those research papers presented at the Second International Conference on Tests and Proofs (TAP 2008) that were not included in the main conference proceedings. TAP was the second conference devoted to the convergence of proofs and tests. It combines ideas from both areas for the advancement of software quality. To prove the correctness of a program is to demonstrate, through impeccable mathematical techniques, that it has no bugs; to test a program is to run it with the expectation of discovering bugs. On the surface, the two techniques seem contradictory: if you have proved your program, it is fruitless to comb it for bugs; and if you are testing it, that is surely a sign that you have given up on any hope of proving its correctness. Accordingly, proofs and tests have, since the onset of software engineering research, been pursued by distinct communities using rather different techniques and tools. And yet the development of both approaches leads to the discovery of common issues and to the realization that each may need the other. The emergence of model checking has been one of the first signs that contradiction may yield to complementarity, but in the past few years an increasing number of research efforts have encountered the need for combining proofs and tests, dropping earlier dogmatic views of their incompatibility and taking instead the best of what each of these software engineering domains has to offer. The first TAP conference (held at ETH Zurich in February 2007) was an attempt to provide a forum for the cross-fertilization of ideas and approaches from the testing and proving communities. For the 2008 edition we found the Monash University Prato Centre near Florence to be an ideal place providing a stimulating environment. We wish to sincerely thank all the authors who submitted their work for consideration. And we would like to thank the Program Committee members as well as additional referees for their great effort and professional work in the review and selection process. Their names are listed on the following pages. In addition to the contributed papers, the program included three excellent keynote talks. We are grateful to Michael Hennell (LDRA Ltd., Cheshire, UK), Orna Kupferman (Hebrew University, Israel), and Elaine Weyuker (AT&T Labs Inc., USA) for accepting the invitation to address the conference. Two very interesting tutorials were part of TAP 2008: "Parameterized Unit Testing with Pex" (J. de Halleux, N. Tillmann) and "Integrating Verification and Testing of Object-Oriented Software" (C. Engel, C. Gladisch, V. Klebanov, and P. Rümmer). We would like to express our thanks to the tutorial presenters for their contribution. It was a team effort that made the conference so successful. We are grateful to the Conference Chair and the Steering Committee members for their support. And we particularly thank Christoph Gladisch, Beate Körner, and Philipp Rümmer for their hard work and help in making the conference a success. In addition, we gratefully acknowledge the generous support of Microsoft Research Redmond, who financed an invited speaker.
This paper describes the robots TIAGo and Lisa used by
team homer@UniKoblenz of the University of Koblenz-Landau, Germany,
for the participation at the RoboCup@Home 2019 in Sydney,
Australia. We ended up first at RoboCup@Home 2019 in the Open Platform
League and won the competition in our league now three times
in a row (four times in total) which makes our team the most successful
in RoboCup@Home. We demonstrated approaches for learning from
demonstration, touch enforcing manipulation and autonomous semantic
exploration in the finals. A special focus is put on novel system components
and the open source contributions of our team. We have released
packages for object recognition, a robot face including speech synthesis,
mapping and navigation, speech recognition interface, gesture recognition
and imitation learning. The packages are available (and new packages
will be released) on http://homer.uni-koblenz.de.
This paper describes the robots TIAGo and Lisa used by team homer@UniKoblenz of the University of Koblenz-Landau, Germany, for the participation at the RoboCup@Home 2018 in Montreal, Canada. Further this paper serves as qualification material for the RoboCup-@Home participation in 2018. A special focus is put on novel system components and the open source contributions of our team. This year the team from Koblenz won the biggest annual scientianc robot competition in Montreal in the RoboCup@Home Open Platform track for the third time and also won the RoboCup@Home German Open for the second time. As a research highlight a novel symbolic imitation learning approach was demonstrated during the annals. The TIAGo robotic research platform was used for the first time by the team. We have released packages for object recognition, a robot face including speech synthesis, mapping and navigation, speech recognition interface via android and a GUI. The packages are available (and new packages will be released) on http://wiki.ros.org/agas-ros-pkg. Further information can be found on our project page http://homer.uni-koblenz.de.
Semantic descriptions of non-textual media available on the web can be used to facilitate retrieval and presentation of media assets and documents containing them. While technologies for multimedia semantic descriptions already exist, there is as yet no formal description of a high quality multimedia ontology that is compatible with existing (semantic) web technologies. We explain the complexity of the problem using an annotation scenario. We then derive a number of requirements for specifying a formal multimedia ontology, including: compatibility with MPEG-7, embedding in foundational ontologies, and modularisation including separation of document structure from domain knowledge. We then present the developed ontology and discuss it with respect to our requirements.
This thesis describes the implementation of a Path-planning algorithm for multi-axle vehicles using machine learning algorithms. For that purpose, a general overview over Genetic Algorithms is given and alternative machine learning algorithms are briefly explained. The software developed for this purpose is based on the EZSystem Simulation Software developed by the AG Echtzeitysteme at the University Koblenz-Landau and a path correction algorithm developed by Christian Schwarz, which is also detailed in this paper. This also includes a description of the vehicle used in these simulations. Genetic Algorithms as a solution for path-planning in complex scenarios are then evaluated based on the results of the developed simulation software and compared to alternative, non-machine learning solutions, which are also shortly presented.
E-KRHyper is a versatile theorem prover and model generator for firstorder logic that natively supports equality. Inequality of constants, however, has to be given by explicitly adding facts. As the amount of these facts grows quadratically in the number of these distinct constants, the knowledge base is blown up. This makes it harder for a human reader to focus on the actual problem, and impairs the reasoning process. We extend E-Hyper- underlying E-KRhyper tableau calculus to avoid this blow-up by implementing a native handling for inequality of constants. This is done by introducing the unique name assumption for a subset of the constants (the so called distinct object identifiers). The obtained calculus is shown to be sound and complete and is implemented into the E-KRHyper system. Synthetic benchmarks, situated in the theory of arrays, are used to back up the benefits of the new calculus.
In automated theorem proving, there are some problems that need information on the inequality of certain constants. In most cases this information is provided by adding facts which explicitly state that two constants are unequal. Depending on the number of constants, a huge amount of this facts can clutter the knowledge base and distract the author and readers of the problem from its actual proposition. For most cases it is save to assume that a larger knowledge base reduces the performance of a theorem prover, which is another drawback of explicit inequality facts. Using the unique name assumption in those reasoning tasks renders the introduction of inequality facts obsolete as the unique name assumptions states that two constants are identical iff their interpretation is identical. Implicit handling of non-identical constants makes the problems easier to comprehend and reduces the execution time of reasoning. In this thesis we will show how to integrate the unique name assumption into the E-hyper tableau calculus and that the modified calculus is sound and complete. The calculus will be implemented into the E-KRHyper theorem prover and we will show, by empiric evaluation, that the changed implementation, which is able to use the unique name assumption, is superior to the traditional version of E-KRHyper.
Avoidance of routing loops
(2009)
We introduce a new routing algorithm which can detect routing loops by evaluating routing updates more thoroughly. Our new algorithm is called Routing with Metric based Topology Investigation (RMTI), which is based on the simple Routing Information Protocol (RIP) and is compatible to all RIP versions. In case of a link failure, a network can reorganize itself if there are redundant links available. Redundant links are only available in a network system like the internet if the topology contains loops. Therefore, it is necessary to recognize and to prevent routing loops. A routing loop can be seen as a circular trace of a routing update information which returns to the same router, either directly from the neighbor router or via a loop topology. Routing loops could consume a large amount of network bandwidth and could impact the endtoend performance of the network. Our RMTI approach is capable to improve the efficiency of Distance Vector Routing.
The University of Koblenz-Landau would like to apply for participation in the RoboCup Mixed Reality League in Suzhou, China 2008. Our team is composed of ten team members and two supervisors. All members are graduate students of Computational Visualistics. Our supervisors are Ph.D. candidates currently researching in the working groups of artificial intelligence and computer graphics.
The novel mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) enables its users to collaboratively create, share, and modify semantic points of interest (POI). Semantic POIs describe geographic places with explicit semantic properties of a collaboratively created ontology. As the ontology includes multiple subclassiffcations and instantiations and as it links to DBpedia, the richness of annotation goes far beyond mere textual annotations such as tags. With the intuitive interface of csxPOI, users can easily create, delete, and modify their POIs and those shared by others. Thereby, the users adapt the structure of the ontology underlying the semantic annotations of the POIs. Data mining techniques are employed to cluster and thus improve the quality of the collaboratively created POIs. The semantic POIs and collaborative POI ontology are published as Linked Open Data.
On-screen interactive presentations have got immense popularity in the domain of attentive interfaces recently. These attentive screens adapt their behavior according to the user's visual attention. This thesis aims to introduce an application that would enable these attentive interfaces to change their behavior not just according to the gaze data but also facial features and expressions. The modern era requires new ways of communications and publications for advertisement. These ads need to be more specific according to people's interests, age, and gender. When advertising, it's important to get a reaction from the user but not every user is interested in providing feedback. In such a context more, advance techniques are required that would collect user's feedback effortlessly. The main problem this thesis intends to resolve is, to apply advanced techniques of gaze and face recognition to collect data about user's reactions towards different ads being played on interactive screens. We aim to create an application that enables attentive screens to detect a person's facial features, expressions, and eye gaze. With eye gaze data we can determine the interests and with facial features, age and gender can be specified. All this information will help in optimizing the advertisements.
Concept for a Knowledge Base on ICT for Governance and Policy Modelling regarding eGovPoliNet
(2013)
Abstract The EU project eGovPoliNet is engaged in research and development in the field of information and communication technologies (ICT) for governance and policy modelling. Numerous communities pursue similar goals in this field of IT-based, strategic decision making and simulation of social problem areas. Though, the existing research approaches and results so far are quite fragmented. The aim of eGovPoliNet is to overcome the fragmentation across disciplines and to establish an international, open dialogue by fostering the cooperation between research and practice. This dialogue will advance the discussion and development of various problem areas with the help of researchers from different disciplines, who share knowledge, expertise and best practice supporting policy analysis, modelling and governance. To support this dialogue, eGovPoliNet will provide a knowledge base, which's conceptual development is the subject of this thesis. The knowledge base is to be filled with content from the area of ICT for strategic decision making and social simulation, such as publications, ICT solutions and project descriptions. This content needs to be structured, organised and managed in a way, so that it generates added value and the knowledge base is used as source of accumulated knowledge, which consolidates the previously fragmented research and development results in a central location.
The aim of this thesis is the development of a concept for a knowledge base, which provides the structure and the necessary functionalities to gather and process knowledge concerning ICT solutions for governance and policy modelling. This knowledge needs to be made available to users and thereby motivate them to contribute to the development and maintenance of the knowledge base.
Regarding the rapidly growing amount of data produced every year and the increasing acceptance of Enterprise 2.0 enterprises have to care about the management of their data more and more. Content created and stored in an uncoordinated manner can lead to data-silos (Williams & Hardy 2011, p.57), which result in long search times, inaccessible data and in consequence monetary losses. The "expanding digital universe" forces enterprises to develop new archiving solutions and records management policies (Gantz et al. 2007, p.13). Enterprise Content Management (ECM) is the research field that deals with these challenges. It is placed in the scientific context of Enterprise Information Management. This thesis aims to find out to what extent current Enterprise Content Management Systems (ECMS) support these new requirements, especially concerning the archiving of Enterprise 2.0 data. For this purpose, three scenarios were created to evaluate two different kinds of ECMS (one Open Source - and one proprietary system) chosen on the basis of a short marketrnresearch. The application of the scenarios reveals that the system vendors actually face the industry- concerns: both tools provide functionality for the archiving of data arising from online collaboration and also business records management capabilities but the integration of those topics is not, or is only inconsistently solved. At this point new questions - such as, "Which datarngenerated in an Enterprise 2.0 is worth being a record?" - arise and should be examined in future research.
The term "Augmented Reality (AR)" denotes the superposition of additional virtual objects and supplementary information over real images. The joint project Enhanced Reality (ER)1 aims at a generic AR-system. The ER-project is a cooperation of six different research groups of the Department of Computer Science at the University of Koblenz-Landau. According to Ronald Azuma an AR-system combines real and virtual environments, where the real and virtual objects are registered in 3-D, and it provides interactivity in real time [Azu97]. Enhanced Reality extends Augmented Reality by requiring the virtual objects to be seamlessly embedded into the real world as photo-realistic objects according to the exact lighting conditions. Furthermore, additional information supplying value-added services may be displayed and interaction of the user may even be immersive. The short-term goal of the ER-project is the exploration of ER-fundamentals using some specific research scenarios; the long-term goal is the development of a component-based ER-framework for the creation of ER-applications for arbitrary application areas. ER-applications are developed as single-user applications for users who are moving in a real environment and are wearing some kind of visual output device like see-through glasses and some mobile end device. By these devices the user is able to see reality as it is, but he can also see the virtual objects and the additional information about some value-added service. Furthermore he might have additional devices whereby he can interact with the available virtual objects. The development of a generic framework for ER-applications requires the definition of generic components which are customizable and composable to build concrete applications and it requires a homogeneous data model which supports all components equally well. The workgroup "Software Technology"2 is responsible for this subproject. This report gives some preliminary results concerning the derivation of a component-based view of ER. There are several augmented reality frameworks like ARVIKA, AMIRE, DWARF, MORGAN, Studierstube and others which offer some support for the development of AR-applications. All of them ease the use of existing subsystems like AR-Toolkit, OpenGL and others and leverage the generation process for realistic systems by making efficient use of those subsystems. Consequently, they highly rely on them.
In dieser Ausarbeitung beschreibe ich die Ergebnisse meiner Untersuchungen zur Erweiterung des LogAnswer-Systemsmit nutzerspezifischen Profilinformationen. LogAnswer ist ein natürlichsprachliches open-domain Frage-Antwort-System. Das heißt: es beantwortet Fragen zu beliebigen Themen und liefert dabei konkrete (möglichst knappe und korrekte) Antworten zurück. Das System wird im Rahmen eines Gemeinschaftsprojekts der Arbeitsgruppe für künstliche Intelligenz von Professor Ulrich Furbach an der Universität Koblenz-Landau und der Arbeitsgruppe Intelligent Information and Communication Systems (IICS) von Professor Hermann Helbig an der Fernuniversität Hagen entwickelt. Die Motivation meiner Arbeit war die Idee, dass der Prozess der Antwortfindung optimiert werden kann, wenn das Themengebiet, auf das die Frage abzielt, im Vorhinein bestimmt werden kann. Dazu versuchte ich im Rahmen meiner Arbeit die Interessensgebiete von Nutzern basierend auf Profilinformationen zu bestimmen. Das Semantic Desktop System NEPOMUK wurde verwendet um diese Profilinformationen zu erhalten. NEPOMUK wird verwendet um alle Daten, Dokumente und Informationen, die ein Nutzer auf seinem Rechner hat zu strukturieren. Dazu nutzt das System ein sogenanntes Personal Information Model (PIMO) in Form einer Ontologie. Diese Ontologie enthält unter anderem eine Klasse "Topic", welche die wichtigste Grundlage für das Erstellen der in meiner Arbeit verwendeten Nutzerprofile bildete. Konkret wurde die RDF-Anfragesprache SPARQL verwendet, um eine Liste aller für den Nutzer relevanten Themen aus der Ontologie zu filtern. Die zentrale Idee meiner Arbeit war es nun diese Profilinformationen zur Optimierung des Ranking von Antwortkandidaten einzusetzen. In LogAnswer werden zu jeder gestellten Frage bis zu 200 potentiell relevante Textstellen aus der deutschen Wikipedia extrahiert. Diese Textstellen werden auf Basis von Eigenschaften (wie z.B. lexikalische Übereinstimmungen zwischen Frage und Textstelle) geordnet, da innerhalb des zur Verfügung stehenden Zeitlimits nicht alle Kandidaten bearbeitet werden können.
Mein Ansatz verfolgte das Ziel, diesen Algorithmus durch Nutzerprofile so zu erweitern, dass Antwortkandidaten, welche für den Benutzer relevante Informationen enthalten, höher in der Rangfolge eingeordnet werden. Zur Umsetzung dieser Idee musste eine Methode gefunden werden, um zu bestimmen ob ein Antwortkandidat mit dem Profil übereinstimmt. Da sich die in einer Textstelle enthaltenen Informationen in den meisten Fällen auf das übergeordnete Thema des Artikels beziehen, ohne den Namen des Artikels explizit zu erwähnen, wurde in meiner Implementierung der Artikelname betrachtet, um zu ermitteln, zu welchem Themengebiet die Textstelle Informationen liefert. Als zusätzliches Hilfsmittel wurde außerdem die DBpedia-Ontologie eingesetzt, welche die Informationen der Wikipedia strukturiert im RDF Format enthält. Mit Hilfe dieser Ontologie war es möglich, jeden Artikel in Kategorien einzuordnen, die dann mit den im Profil enthaltenen Stichworten verglichen wurden. Zur Untersuchung der Auswirkungen des Ansatzes auf das Ranking-Verfahren wurden mehrere Testläufe mit je 200 Testfragen durchgeführt. Die erste Testmenge bestand aus zufällig ausgewählten Fragen, die mit meinem eigenen Nutzerprofil getestet wurden. Dieser Testlauf lieferte kaum nutzbare Ergebnisse, da nur bei 29 der getesteten Fragen überhaupt ein Antwortkandidat mit dem Profil in Verbindung gebracht werden konnte. Außerdem konnte eine potentielle Verbesserung der Ergebnisse nur bei einer dieser 29 Fragen festgestellt werden, was zu der Schlussfolgerung führte, dass der Einsatz von Profildaten nicht für Anwendungsfälle geeignet ist, in denen die Fragen keine Korrelation mit dem genutzten Profil aufweisen.
Da die Grundannahme meiner Arbeit war, dass Nutzer in erster Linie Fragen zu den Interessensgebieten stellen, welche sich aus ihrem Profil ableiten lassen, sollten die weiteren Testläufe genau diesen Fall beleuchten. Dazu wurden 200 Testfragen aus dem Bereich Sport ausgewählt und mit einem Profil getestet, welches Stichworte zu unterschiedlichen Sportarten enthielt. Die Tests mit den Sportfragen waren wesentlich aussagekräftiger. Auch hier deuteten die Ergebnisse darauf hin, dass der Ansatz kein großes Potential zur Verbesserung des Rankings hat. Eine genauere Betrachtung einiger ausgewählter Beispiele zeigte allerdings, dass die Integration von Profildaten für bestimmte Anwendungsfälle, wie z.B. offene Fragen für die es mehr als eine korrekte Antwort gibt, durchaus zu einer Verbesserung der Ergebnisse führen kann. Außerdem wurde festgestellt, dass viele der schlechten Ergebnisse auf Inkosistenzen in der DBpedia-Ontologie und grundsätzliche Probleme im Umgang mit Wissensbasen in natürlicher Sprache beruhen.
Die Schlussfolgerung meiner Arbeit ist, dass der in dieser Arbeit vorgestellte Ansatz zur Integration von Profilinformationen für den aktuellen Anwendungsfall von LogAnswer nicht geeignet ist, da vor allem Faktenwissen aus sehr unterschiedlichen Domänen abgefragt wird und offene Fragen nur einen geringen Anteil ausmachen.
With the Multimedia Metadata Ontology (M3O), we have developed a sophisticated model for representing among others the annotation, decomposition, and provenance of multimedia metadata. The goal of the M3O is to integrate the existing metadata standards and metadata formats rather than replacing them. To this end, the M3O provides a scaffold needed to represent multimedia metadata. Being an abstract model for multimedia metadata, it is not straightforward how to use and specialize the M3O for concrete application requirements and existing metadata formats and metadata standards. In this paper, we present a step-by-step alignment method describing how to integrate and leverage existing multimedia metadata standards and metadata formats in the M3O in order to use them in a concrete application. We demonstrate our approach by integrating three existing metadata models: the Core Ontology on Multimedia (COMM), which is a formalization of the multimedia metadata standard MPEG-7, the Ontology for Media Resource of the W3C, and the widely known industry standard EXIF for image metadata
Expert-driven business process management is an established means for improving efficiency of organizational knowledge work. Implicit procedural knowledge in the organization is made explicit by defining processes. This approach is not applicable to individual knowledge work due to its high complexity and variability. However, without explicitly described processes there is no analysis and efficient communication of best practices of individual knowledge work within the organization. In addition, the activities of the individual knowledge work cannot be synchronized with the activities in the organizational knowledge work.rnrnSolution to this problem is the semantic integration of individual knowledgernwork and organizational knowledge work by means of the patternbased core ontology strukt. The ontology allows for defining and managing the dynamic tasks of individual knowledge work in a formal way and to synchronize them with organizational business processes. Using the strukt ontology, we have implemented a prototype application for knowledge workers and have evaluated it at the use case of an architectural fifirm conducting construction projects.
Magnetic resonance (MR) tomography is an imaging method, that is used to expose the structure and function of tissues and organs in the human body for medical diagnosis. Diffusion weighted (DW) imaging is a specific MR imaging technique, which enables us to gain insight into the connectivity of white matter pathways noninvasively and in vivo. It allows for making predictions about the structure and integrity of those connections. In clinical routine this modality finds application in the planning phase of neurosurgical operations, such as in tumor resections. This is especially helpful if the lesion is deeply seated in a functionally important area, where the risk of damage is given. This work reviews the concepts of MR imaging and DW imaging. Generally, at the current resolution of diffusion weighted data, single white matter axons cannot be resolved. The captured signal rather describes whole fiber bundles. Beside this, it often appears that different complex fiber configurations occur in a single voxel, such as crossings, splittings and fannings. For this reason, the main goal is to assist tractography algorithms who are often confound in such complex regions. Tractography is a method which uses local information to reconstruct global connectivities, i.e. fiber tracts. In the course of this thesis, existing reconstruction methods such as diffusion tensor imaging (DTI) and q-ball imaging (QBI) are evaluated on synthetic generated data and real human brain data, whereas the amount of valuable information provided by the individual reconstruction mehods and their corresponding limitations are investigated. The output of QBI is the orientation distribution function (ODF), where the local maxima coincides with the underlying fiber architecture. We determine those local maxima. Furthermore, we propose a new voxel-based classification scheme conducted on diffusion tensor metrics. The main contribution of this work is the combination of voxel-based classification, local maxima from the ODF and global information from a voxel- neighborhood, which leads to the development of a global classifier. This classifier validates the detected ODF maxima and enhances them with neighborhood information. Hence, specific asymmetric fibrous architectures can be determined. The outcome of the global classifier are potential tracking directions. Subsequently, a fiber tractography algorithm is designed that integrates along the potential tracking directions and is able to reproduce splitting fiber tracts.
Social networks are ubiquitous structures that we generate and enrich every-day while connecting with people through social media platforms, emails, and any other type of interaction. While these structures are intangible to us, they carry important information. For instance, the political leaning of our friends can be a proxy to identify our own political preferences. Similarly, the credit score of our friends can be decisive in the approval or rejection of our own loans. This explanatory power is being leveraged in public policy, business decision-making and scientific research because it helps machine learning techniques to make accurate predictions. However, these generalizations often benefit the majority of people who shape the general structure of the network, and put in disadvantage under-represented groups by limiting their resources and opportunities. Therefore it is crucial to first understand how social networks form to then verify to what extent their mechanisms of edge formation contribute to reinforce social inequalities in machine learning algorithms.
To this end, in the first part of this thesis, I propose HopRank and Janus two methods to characterize the mechanisms of edge formation in real-world undirected social networks. HopRank is a model of information foraging on networks. Its key component is a biased random walker based on transition probabilities between k-hop neighborhoods. Janus is a Bayesian framework that allows to identify and rank plausible hypotheses of edge formation in cases where nodes possess additional information. In the second part of this thesis, I investigate the implications of these mechanisms - that explain edge formation in social networks - on machine learning. Specifically, I study the influence of homophily, preferential attachment, edge density, fraction of inorities, and the directionality of links on both performance and bias of collective classification, and on the visibility of minorities in top-k ranks. My findings demonstrate a strong correlation between network structure and machine learning outcomes. This suggests that systematic discrimination against certain people can be: (i) anticipated by the type of network, and (ii) mitigated by connecting strategically in the network.
The STOR project aims at the development of a scientific component system employing models and knowledge for object recognition in images. This interim report elaborates on the requirements for such a component system, structures the application area by identifying a large set of basic operations, and shows how a set of appropriate data structures and components can be derived. A small case studies exemplifies the approach.