University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

An overview of descriptive studies in relation to a general design research methodology

Abstract: This paper describes a general design research methodology that aims at a more rigorous approach to design research by piecing together the various existing design research areas and by encouraging collaboration with other disciplines. It discusses the challenges involved in doing design research, such as the large number of factors that influence design, and highlights the importance of descriptive studies. The papers presented in this book are used throughout as examples, and set in the context of the proposed methodology in the Conclusion and Outlook at the end of this book. These papers and others revealed some of the key issues that need addressing: the formulation of criteria for success, the use of results from descriptive studies in the development of design methods and tools, and the validation of such methods and tools against these results.

Symbol-Level Multiuser MISO Precoding for Multi-Level Adaptive Modulation

Abstract: Symbol-level precoding is a new paradigm for multiuser multiple-antenna downlink systems aimed at creating constructive interference among transmitted data streams. This can be enabled by designing the precoded signal of the multiantenna transmitter on a symbol level, taking into account both channel state information and data symbols. Previous literature has studied this paradigm for Mary phase shift keying modulations by addressing various performance metrics, such as power minimization and maximization of the minimum rate. In this paper, we extend this to generic multi-level modulations, i.e., Mary quadrature amplitude modulation by establishing connection to PHY layer multicasting with phase constraints. Furthermore, we address the adaptive modulation schemes which are crucial in enabling the throughput scaling of symbol-level precoded systems. In this direction, we design the signal processing algorithms for minimizing the required power under per-user signal to interference noise ratio or goodput constraints. Extensive numerical results show that the proposed algorithm provides considerable power and energy efficiency gains, while adapting the employed modulation scheme to match the requested data rate.

Création et validation d’une version française du questionnaire AttrakDiff pour l’évaluation de l’expérience utilisateur des systèmes interactifs

Abstract: Resume Introduction Alors que l’evaluation de l’experience utilisateur (UX) est au cœur des preoccupations dans le domaine des interactions homme–machine, aucun outil d’evaluation auto-administre valide de l’UX n’existe actuellement en langue francaise. Le questionnaire AttrakDiff 2 (Hassenzahl, Burmester, & Koller, 2003) est un outil d’evaluation de l’UX qui repose sur un modele theorique distinguant qualites pragmatiques et qualites hedoniques des systemes interactifs. Objectif Cet article decrit la traduction et la validation de la version francaise du questionnaire AttrakDiff 2 en vue de son utilisation sur des echantillons de population francophone. Methode Suivant la methodologie de validation transculturelle proposee par Vallerand (1989), le questionnaire a ete traduit par des chercheurs trilingues puis a fait l’objet d’un processus de traduction renversee et d’une validation par un comite d’experts. Un pretest a ete effectue sur 26 participants. Les caracteristiques de la version francaise de l’AttrakDiff 2 ont ensuite ete evaluees par une etude quantitative en ligne sur un echantillon de 381 utilisateurs. Resultats Les resultats des analyses effectuees confirment la structure factorielle attendue en 3 facteurs et une bonne consistance interne des sous-echelles. Les liens entre les facteurs sont consistants avec le modele theorique d’Hassenzahl (2003) ou attributs pragmatiques et hedoniques percus se combinent pour former un jugement d’attractivite. Conclusion La presente version francaise de l’AttrakDiff 2 est globalement conforme a la version initiale allemande de l’outil et presente des niveaux de validite et de fidelite satisfaisants.

Opponent models with uncertainty for strategic argumentation

Abstract: This paper deals with the issue of strategic argumentation in the setting of Dung-style abstract argumentation theory. Such reasoning takes place through the use of opponent models--recursive representations of an agent's knowledge and beliefs regarding the opponent's knowledge. Using such models, we present three approaches to reasoning. The first directly utilises the opponent model to identify the best move to advance in a dialogue. The second extends our basic approach through the use of quantitative uncertainty over the opponent's model. The final extension introduces virtual arguments into the opponent's reasoning process. Such arguments are unknown to the agent, but presumed to exist and interact with known arguments. They are therefore used to add a primitive notion of risk to the agent's reasoning. We have implemented our models and we have performed an empirical analysis that shows that this added expressivity improves the performance of an agent in a dialogue.

Web Application Vulnerability Prediction Using Hybrid Program Analysis and Machine Learning

Abstract: Due to limited time and resources, web software engineers need support in identifying vulnerable code. A practical approach to predicting vulnerable code would enable them to prioritize security auditing efforts. In this paper, we propose using a set of hybrid (static+dynamic) code attributes that characterize input validation and input sanitization code patterns and are expected to be significant indicators of web application vulnerabilities. Because static and dynamic program analyses complement each other, both techniques are used to extract the proposed attributes in an accurate and scalable way. Current vulnerability prediction techniques rely on the availability of data labeled with vulnerability information for training. For many real world applications, past vulnerability data is often not available or at least not complete. Hence, to address both situations where labeled past data is fully available or not, we apply both supervised and semi-supervised learning when building vulnerability predictors based on hybrid code attributes. Given that semi-supervised learning is entirely unexplored in this domain, we describe how to use this learning scheme effectively for vulnerability prediction. We performed empirical case studies on seven open source projects where we built and evaluated supervised and semi-supervised models. When cross validated with fully available labeled data, the supervised models achieve an average of 77 percent recall and 5 percent probability of false alarm for predicting SQL injection, cross site scripting, remote code execution and file inclusion vulnerabilities. With a low amount of labeled data, when compared to the supervised model, the semi-supervised model showed an average improvement of 24 percent higher recall and 3 percent lower probability of false alarm, thus suggesting semi-supervised learning may be a preferable solution for many real world applications where vulnerability data is missing.