Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

Business Models: An Information Systems Research Agenda

Abstract: The business model concept, although a relatively new topic for research, has garnered growing attention over the past decade. Whilst it has been robustly defined, the concept has so far attracted very little substantive research. In the context of the wide-spread digitization of businesses and society at large, the logic inherent in a business model has become critical for business success and, hence, a focus for academic inquiry. The business model concept is identified as the missing link between business strategy, processes, and Information Technology (IT). The authors argue that the BISE community offers distinct and unique competencies (e.g., translating business strategies into IT systems, managing business and IT processes, etc.) that can be harnessed for significant research contributions to this field. Within this research gap three distinct streams are delineated, namely, business models in IT industries, IT enabled or digital business models, and IT support for developing and managing business models. For these streams, the current state of the art, suggest critical research questions, and suitable research methodologies are outlined.

Random oracles in a quantum world

Abstract: The interest in post-quantum cryptography -- classical systems that remain secure in the presence of a quantum adversary -- has generated elegant proposals for new cryptosystems. Some of these systems are set in the random oracle model and are proven secure relative to adversaries that have classical access to the random oracle. We argue that to prove post-quantum security one needs to prove security in the quantum-accessible random oracle model where the adversary can query the random oracle with quantum state. We begin by separating the classical and quantum-accessible random oracle models by presenting a scheme that is secure when the adversary is given classical access to the random oracle, but is insecure when the adversary can make quantum oracle queries. We then set out to develop generic conditions under which a classical random oracle proof implies security in the quantum-accessible random oracle model. We introduce the concept of a history-free reduction which is a category of classical random oracle reductions that basically determine oracle answers independently of the history of previous queries, and we prove that such reductions imply security in the quantum model. We then show that certain post-quantum proposals, including ones based on lattices, can be proven secure using history-free reductions and are therefore postquantum secure. We conclude with a rich set of open problems in this area.

Formal Concept Analysis: foundations and applications

Abstract: Foundations.- Formal Concept Analysis as Mathematical Theory of Concepts and Concept Hierarchies.- Semiconcept and Protoconcept Algebras: The Basic Theorems.- Features of Interaction Between Formal Concept Analysis and Algebraic Geometry.- From Formal Concept Analysis to Contextual Logic.- Contextual Attribute Logic of Many-Valued Attributes.- Treating Incomplete Knowledge in Formal Concept Analysis.- States, Transitions, and Life Tracks in Temporal Concept Analysis.- Applications.- Linguistic Applications of Formal Concept Analysis.- Using Concept Lattices for Text Retrieval and Mining.- Efficient Mining of Association Rules Based on Formal Concept Analysis.- Galois Connections in Data Analysis: Contributions from the Soviet Era and Modern Russian Research.- Conceptual Knowledge Processing in the Field of Economics.- Software Engineering.- A Survey of Formal Concept Analysis Support for Software Engineering Activities.- Concept Lattices in Software Analysis.- Formal Concept Analysis Used for Software Analysis and Modelling.- Formal Concept Analysis-Based Class Hierarchy Design in Object-Oriented Software Development.- The ToscanaJ Suite for Implementing Conceptual Information Systems.

Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts

Abstract: The word robust has been used in many contexts in signal processing. Our treatment concerns statistical robustness, which deals with deviations from the distributional assumptions. Many problems encountered in engineering practice rely on the Gaussian distribution of the data, which in many situations is well justified. This enables a simple derivation of optimal estimators. Nominal optimality, however, is useless if the estimator was derived under distributional assumptions on the noise and the signal that do not hold in practice. Even slight deviations from the assumed distribution may cause the estimator's performance to drastically degrade or to completely break down. The signal processing practitioner should, therefore, ask whether the performance of the derived estimator is acceptable in situations where the distributional assumptions do not hold. Isn't it robustness that is of a major concern for engineering practice? Many areas of engineering today show that the distribution of the measurements is far from Gaussian as it contains outliers, which cause the distribution to be heavy tailed. Under such scenarios, we address single and multichannel estimation problems as well as linear univariate regression for independently and identically distributed (i.i.d.) data. A rather extensive treatment of the important and challenging case of dependent data for the signal processing practitioner is also included. For these problems, a comparative analysis of the most important robust methods is carried out by evaluating their performance theoretically, using simulations as well as real-world data.