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This work introduces architectural security analyses for detecting access violations and attack paths in software architectures. It integrates access control policies and vulnerabilities, often analyzed separately, into a unified approach using software architecture models. Contributions include metamodels for access control and vulnerabilities, scenario-based analysis, and two attack analyses. Evaluation demonstrates high accuracy in identifying issues for secure system development.

For long horizontal propagation distances, it is the atmosphere, not the sensor technology which is responsible for the quality of acquired image data. Atmospheric turbulence causes increased blur, reduced (color) contrast and (apparent) image motion, as well as image deformation. This work addresses the question of how best to mitigate these degrading effects and improve image data quality using suitable reconstruction techniques.

In dieser Arbeit wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. Most production processes are rigid not only by way of the physical layout of machines and their integration, but also by the custom programming of the control logic for the integration of components to a production systems. Changes are time- and resource-expensive. This makes the production of small lot sizes of customized products economically challenging. This work develops solutions for the automated adaptation of production systems based on self-organisation and distributed planning.

In this work, the authors analysed the co-dependency between models and analyses, particularly the structure and interdependence of artefacts and the feature-based decomposition and composition of model-based analyses. Their goal is to improve the maintainability of model-based analyses. They have investigated the co-dependency of Domain-specific Modelling Languages (DSMLs) and model-based analyses regarding evolvability, understandability, and reusability.

The two open access volumes LNCS 14574 and 14575 constitute the proceedings of the 27th International Conference on Foundations of Software Science and Computation Structures, FOSSACS 2024, which took place in Luxembourg in April 2024. The 24 full papers included in this book were carefully reviewed and selected from 79 submissions. They were organized in topical sections as follows: Part I: Infinite games; categorical semantics; automata and synthesis; Part II: Types and programming languages; logic and proofs; infinite-state systems.

The two open access volumes LNCS 14574 and 14575 constitute the proceedings of the 27th International Conference on Foundations of Software Science and Computation Structures, FOSSACS 2024, which took place in Luxembourg in April 2024. The 24 full papers included in this book were carefully reviewed and selected from 79 submissions. They were organized in topical sections as follows: Part I: Infinite games; categorical semantics; automata and synthesis; Part II: Types and programming languages; logic and proofs; infinite-state systems.

Interaction with dynamic image content is challenging for user perception, cognition and motor action. This dissertation identifies appropriate gaze-based interaction techniques for moving object selection in image sequences using multiple cross-sectional and one longitudinal user study. Further investigations evaluate how gaze-based interaction and automated image exploitation algorithms support human video exploitation as well as whether gaze-based user task classification is feasible.

The performance and flexibility of robot technologies make it possible to bring robots to everyday life. This creates many challenges: safety, legal regulations, reliability, reusability, and business models must be considered under special conditions. The acceptance and interaction of humans and robots are of central importance for the success of the applications. This collection summarises 15 studies from the BMBF-funded program "Robots for Assistance: Interaction in the Field.

This cumulative habilitation thesis, proposes concepts for (i) modelling and analysing dependability based on architectural models of software-intensive systems early in development, (ii) decomposition and composition of modelling languages and analysis techniques to enable more flexibility in evolution, and (iii) bridging the divergent levels of abstraction between data of the operation phase, architectural models and source code of the development phase.

Digitalization has already transformed many areas of the economy and social life. Aspects of healthcare and clinical practice are also undergoing digital transformation. In light of these developments, this dissertation sheds light on the acquisition, representation, and use of process knowledge in the context of hybrid AI methods. The central contribution is the structure-preserving back-and-forth transformation of process trees to process plans.

Business processes and information systems evolve constantly and affect each other in non-trivial ways. Aligning security requirements between both is a challenging task. This work presents an automated approach to extract access control requirements from business processes with the purpose of transforming them into a) access permissions for role-based access control and b) architectural data flow constraints to identify violations of access control in enterprise application architectures.

Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems.

Software vendors must consider confidentiality especially while creating software architectures because decisions made here are hard to change later. Our approach represents and analyzes data flows in software architectures. Systems specify data flows and confidentiality requirements specify limitations of data flows. Software architects use detected violations of these limitations to improve the system. We demonstrate how to integrate our approach into existing development processes.

Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization.

An adaptive microscope with axial chromatic encoding is designed and developed, namely the AdaScope. With the ability to confocally address any locations within the measurement volume, the AdaScope provides the hardware foundation for a cascade measurement strategy to be developed, dramatically accelerating the speed of 3D confocal microscopy.

Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene.

The solution of various tasks benefits from information fusion or even requires it. The Bayesian fusion methodology is clear, well-founded and fulfills the essential requirements for a meaningful methodology also for fusing the contributions of heterogeneous information sources. In many practically relevant tasks, Bayesian methods cause high, often unacceptable effort. In the work, novel approaches to cope with Bayesian fusion in such situations are formulated and investigated.

This Open Access book explores questions such as why and how did the first biological cells appear? And then complex organisms, brains, societies and –now– connected human societies? Physicists have good models for describing the evolution of the universe since the Big Bang, but can we apply the same concepts to the evolution of aggregated matter –living matter included? The Amazing Journey analyzes the latest results in chemistry, biology, neuroscience, anthropology and sociology under the light of the evolution of intelligence, seen as the ability of processing information. The main strength of this book is using just two concepts used in physics –information and energy– to explain: The emergence and evolution of life: procaryotes, eukaryotes and complex organisms The emergence and evolution of the brain The emergence and evolution of societies (human and not) Possible evolution of our "internet society" and the role that Artificial Intelligence is playing

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms.