@inproceedings{25069,
  author       = {{Adelt, Peer and Koppelmann, Bastian and Müller, Wolfgang and Kleinjohann, Bernd and Scheytt, J. Christoph}},
  booktitle    = {{Design Automation and Testing in Europe (DATE)}},
  location     = {{Lausanne, CH, Mrz. 2017}},
  title        = {{{ANALISA - A Tool for Static Instruction Set Analysis}}},
  year         = {{2017}},
}

@inproceedings{25070,
  abstract     = {{In the Image Processing domain, automated generation of complex Image Processing functionality is highly desirable; e.g., for rapid prototyping. Service composition techniques, in turn, facilitate automated generation of complex functionality based on building blocks in terms of services. For that reason, we aim for transferring the Service Composition paradigm into the Image Processing domain. In this paper, we present our symbolic composition approach that enables us to automatically generate Image Processing applications. Functionality of Image Processing services is described by means of a variant of first-order logic, which grounds on domain knowledge operationalized in terms of ontologies. A Petri-net formalism serves as basis for modeling data-flow of services and composed services. A planning-based composition algorithm automatically composes complex data-flow for a required functionality. A brief evaluation serves as proof of concept.}},
  author       = {{Jungmann, Alexander and Kleinjohann, Bernd}},
  booktitle    = {{Proceedings of the 13th IEEE International Conference on Services Computing (SCC)}},
  pages        = {{106--113}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{Automatic Composition of Service-based Image Processing Applications}}},
  year         = {{2016}},
}

@inproceedings{25073,
  abstract     = {{In this paper, we introduce an approach for combining embedded systems with Service-oriented Computing techniques based on a concrete application scenario from the robotics domain. Our proposed Service-oriented Architecture allows for incorporating computational expensive functionality as services into a distributed computing environment. Furthermore, our framework facilitates a seamless integration of embedded systems such as robots as service providers into the computing environment. The entire communication is based on so-called recipes, which can be interpreted as autonomous messages that contain all necessary information for executing compositions of services.}},
  author       = {{Jungmann, Alexander and Jatzkowski, Jan and Kleinjohann, Bernd}},
  booktitle    = {{Proceedings of the 5th IFIP International Embedded Systems Symposium (IESS)}},
  publisher    = {{Springer-Verlag}},
  title        = {{{Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study}}},
  year         = {{2015}},
}

@inproceedings{25074,
  author       = {{Jatzkowski, Jan and Kreutz, Marcio Eduardo and Rettberg, Achim}},
  booktitle    = {{Proceedings of the 5th IFIP International Embedded Systems Symposium (IESS)}},
  publisher    = {{Springer}},
  title        = {{{Hierarchical Multicore-Scheduling for Virtualization of Dependent Real-Time Systems}}},
  year         = {{2015}},
}

@inproceedings{25075,
  author       = {{Stahl, Katharina and Stöcklein, Jörg and Li, Silja}},
  booktitle    = {{Virtual, Augmented and Mixed Reality VAMR 2015 Held as Part of HCI International 2015}},
  editor       = {{Shumaker, Randall and Lackey, Stephanie}},
  location     = {{Los Angeles, CA, USA, 2. - 7. Aug. 2015}},
  pages        = {{499--512}},
  publisher    = {{Springer International Publishing Switzerland}},
  title        = {{{Evaluation of Autonomous Approaches using Virtual Environments}}},
  volume       = {{ 9179}},
  year         = {{2015}},
}

@inproceedings{25076,
  abstract     = {{Automated service composition aims at automatically generating software solutions based on services to provide more complex functionality. In this paper, we give an initial overview about why adaptivity becomes increasingly important when aiming for automated composition of service functionality in dynamic and freely accessible environments such as service markets. We systematically derive dependencies among crucial processes such as service composition and service execution in a holistic view. Furthermore, we briefly discuss the influences and effects of changes in the environment according to the derived dependencies, and discuss possible future research directions.}},
  author       = {{Jungmann, Alexander}},
  booktitle    = {{Proceedings of the IEEE 11th World Congress on Services (SERVICES)}},
  pages        = {{329--332}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{On Adaptivity for Automated Composition of Service Functionality}}},
  year         = {{2015}},
}

@inproceedings{25080,
  abstract     = {{ervices are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.}},
  author       = {{Mohr, Felix and Jungmann, Alexander and Kleine Büning, Hans}},
  booktitle    = {{Proceedings of the 12th IEEE International Conference on Services Computing (SCC)}},
  pages        = {{57--64}},
  publisher    = {{ IEEE Computer Society}},
  title        = {{{Automated Online Service Composition}}},
  year         = {{2015}},
}

@inproceedings{25082,
  author       = {{Jatzkowski, Jan and Kreutz, Marcio Eduardo and Rettberg, Achim}},
  booktitle    = {{Proceedings of Electronic System Level Synthesis Conference (ESLsyn)}},
  title        = {{{Towards Hierarchical Scheduling of Dependent Systems with Hypervisor-based Virtualization}}},
  year         = {{2015}},
}

@inproceedings{25083,
  author       = {{Jatzkowski, Jan and Kleinjohann, Bernd}},
  booktitle    = {{Mechatronics}},
  location     = {{Mai 2015}},
  publisher    = {{Elsevier}},
  title        = {{{Self-Reconfiguration of Real-Time Communication within Cyber-Physical Systems}}},
  year         = {{2015}},
}

@article{25107,
  abstract     = {{On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.}},
  author       = {{Jungmann, Alexander and Mohr, Felix}},
  journal      = {{Journal of Internet Services and Applications 6(1)}},
  pages        = {{1--18}},
  title        = {{{An approach towards adaptive service composition in markets of composed services}}},
  year         = {{2015}},
}

@article{25108,
  abstract     = {{Autonomous adaptation in self-adapting embedded real-time systems introduces novel risks as it may lead to unforeseen system behavior. An anomaly detection framework integrated in a real-time operating system can ease the identification of such suspicious novel behavior and, thereby, offers the potential to enhance the reliability of the considered self-x system. However, anomaly detection is based on knowledge about normal behavior. When dealing with self-reconfiguring applications, normal behavior changes. Hence, knowledge base requires adaptation or even re-construction at runtime. The stringent restrictions of real-time systems considering runtime and memory consumption make this task to a really challenging problem. We present our idea for online construction of application behavior knowledge that does not rely on training phase. The applications' behavior is defined by the application's system call invocations. For the knowledge base, we exploit suffix trees as they offer potentials to represent application behavior patterns and associated information in a compact manner. The online algorithm provided by suffix trees is a basis to construct the knowledge base with low computational effort. Anomaly detection and classification is integrated into the online construction method. New behavioral patterns do not unconditionally update the behavior knowledge base. They are evaluated in a context-related manner inspired by Danger Theory, a special discipline of artificial immune systems. Copyright © 2015 John Wiley & Sons, Ltd.}},
  author       = {{Rammig, Franz-Josef and Stahl, Katharina}},
  journal      = {{Concurrency and Computation: Practice and Experience }},
  title        = {{{Online behavior classification for anomaly detection in self-x real-time systems}}},
  year         = {{2015}},
}

@article{25109,
  author       = {{Sudhakar, Krishna and Zhao, Yuhong and Rammig, Franz-Josef}},
  journal      = {{Concurrency and Computation: Practice and Experience }},
  title        = {{{Efficient Integration of Online Model Checking into a Small-Footprint Real-Time Operating System}}},
  year         = {{2015}},
}

@article{25110,
  author       = {{Joy, M. tech. Mabel Mary and Rammig, Franz-Josef}},
  journal      = {{Int. J. of Embedded Systems}},
  title        = {{{A hybrid methodology to detect memory leaks in soft real time embedded systems software}}},
  year         = {{2015}},
}

@article{25111,
  author       = {{Khaluf, Yara and Birattari, Mauro and Rammig, Franz-Josef}},
  journal      = {{Springer Jounal Soft Computing }},
  title        = {{{Analysis of long-term swarm performance based on short-term experiments}}},
  year         = {{2015}},
}

@inproceedings{25112,
  abstract     = {{Automatically composing service-based software solutions is a challenging task. Considering context information during this service composition process is even more challenging. In domains such as image processing, however, context-sensitivity is inherent and cannot be ignored when developing techniques for automatic service composition. Formal approaches tend to create ambiguous solutions, whenever the expressive power of the applied formalism is limited. For example, services may have the same formal specification, although their actual functionality depends on the concrete context. In order to satisfy individual user requests while providing data-dependent functionality, formal approaches have to be extended. We propose to incorporate Reinforcement Learning techniques and combine them with planning based composition approaches. While planning ensures formally correct solutions, learning enables the composition process to resolve ambiguity by implicitly considering context information. Preliminary results show that our combined approach adapts to a static context while still satisfying formally specified requirements.}},
  author       = {{Jungmann, Alexander and Kleinjohann, Bernd}},
  booktitle    = {{Proceedings of the 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom)}},
  pages        = {{755--758}},
  publisher    = {{IEEE}},
  title        = {{{Towards Context-Sensitive Service Composition for Service-Oriented Image Processing}}},
  year         = {{2014}},
}

@article{25114,
  abstract     = {{One goal of service-oriented computing is to realize future markets of composed services. In such markets, service providers offer services that can be flexibly combined with each other. However, although crucial for decision-making, market participants are usually not able to individually estimate the quality of traded services in advance. To overcome this problem, we present a conceptual design for a reputation system that collects and processes user feedback on transactions, and provides this information as a signal for quality to participants in the market. Based on our proposed concept, we describe the incorporation of reputation information into distinct decisionmaking processes that are crucial in such service markets. In this context, we present a fuzzy service matching approach that takes reputation information into account. Furthermore, we introduce an adaptive service composition approach, and investigate the impact of exchanging immediate user feedback by reputation information. Last but not least, we describe the importance of reputation information for economic decisions of different market participants. The overall output of this paper is a comprehensive view on managing and exploiting reputation information in markets of composed services using the example of On-The-Fly Computing.}},
  author       = {{Jungmann, Alexander and Brangewitz, Sonja and Petrlic, Ronald and Platenius, Marie Christin}},
  journal      = {{International Journal on Advances in Intelligent Systems 7(3&4)}},
  pages        = {{572--594}},
  title        = {{{Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services}}},
  year         = {{2014}},
}

@inproceedings{25115,
  abstract     = {{Automatically composing service-based software solutions is still a challenging task. Functional as well as nonfunctional properties have to be considered in order to satisfy individual user requests. Regarding non-functional properties, the composition process can be modeled as optimization problem and solved accordingly. Functional properties, in turn, can be described by means of a formal specification language. Statespace based planning approaches can then be applied to solve the underlying composition problem. However, depending on the expressiveness of the applied formalism and the completeness of the functional descriptions, formally equivalent services may still differ with respect to their implemented functionality. As a consequence, the most appropriate solution for a desired functionality can hardly be determined without considering additional information. In this paper, we demonstrate how to overcome this lack of information by means of Reinforcement Learning. In order to resolve ambiguity, we expand state-space based service composition by a recommendation mechanism that supports decision-making beyond formal specifications. The recommendation mechanism adjusts its recommendation strategy based on feedback from previous composition runs. Image processing serves as case study. Experimental results show the benefit of our proposed solution.}},
  author       = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd}},
  booktitle    = {{Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA)}},
  pages        = {{105--112}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{Applying Reinforcement Learning for Resolving Ambiguity in Service Composition}}},
  year         = {{2014}},
}

@article{25116,
  author       = {{Becker, Markus and Kuznik, Christoph}},
  journal      = {{Forum on Specification & Design Languages (FDL 2014)}},
  title        = {{{Fast Many-Worlds Simulation to Resolve Nondeterminism of Fault Effect Propagation}}},
  year         = {{2014}},
}

@inproceedings{25119,
  abstract     = {{In case of a disaster it is of utmost importance to obtain an overview of the affected area in order to coordinate rescue operations. Recent research and development has led to affordable unmanned aerial vehicles (UAVs) and enabled (semi-)autonomous surveillance and mapping of a disaster areas by UAVs. Mapping an area from many images incorporates image registration. Commonly used registration techniques expect static images to be registered. However, while mapping a disaster area, it is very likely that objects like vehicles or persons are moving on the ground, thus leading to dynamic scenes. We formerly introduced a fast and robust approach to register large amounts of successively arriving images, e.g., from UAVs. The approach only uses rigid-body transformations, but the usage of virtual forces enables the registration to tolerate small perspective distortions. This paper investigates the performance of our approach when applied to dynamic scenes, which are represented by nonlinear disturbances within image correspondences. We show that our approach not only tolerates small perspective distortions but also is able to register dynamic scenes.
}},
  author       = {{Stern, Claudius and Kleinjohann, Lisa}},
  booktitle    = {{Proceedings of The 2nd International Conference on Intelligent Systems and Image Processing 2014}},
  location     = {{Kitakyushu, Japan, 26. - 29. Sep. 2014}},
  pages        = {{209--215}},
  publisher    = {{ Institute of Industrial Applications Engineers}},
  title        = {{{Evaluating Influence of Nonlinear Disturbances on Image Registration Based on Virtual Forces}}},
  year         = {{2014}},
}

@inproceedings{25122,
  author       = {{Grösbrink, Stefan and Almeida, Luis}},
  booktitle    = {{19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{A Criticality-aware Mapping of Real-time Virtual Machines to Multi-core Processors}}},
  year         = {{2014}},
}