Research Data

Simulated and approximated values for the article "Analysis of semi-open queueing networks using lost customers approximation with an application to robotic mobile fulfilment systems".

Many embedded systems are safety-critical real-time systems that have to meet hard deadlines (e.g., airbag or flight control systems). When designing such real-time systems, it is of utmost importance to guarantee that all tasks of a system meet their given deadlines. For this purpose, dedicated timing analyses are required that examine the worst-case behavior of a system and are able to provide such guarantees. In the case that deadlines are not met, optimizations need to be applied in order to modify the code of the system such that timing constraints are nevertheless finally met. Research on such analyses and optimizations for hard real-time systems is an extremely lively area where new results are presented regularly and at a very fast pace. Naturally, the evaluation of such analyses and optimizations plays a very important role. Nowadays, evaluation typically relies on benchmarking such that new analyses or optimizations are applied to existing collections of applications, tasks or program codes. The currently used benchmarks are, however, highly limited and not sufficient in order to perform a sound and scientific evaluation, especially if massively parallel multi-task systems are considered. For a well-founded and reproducible evaluation of analyses and optimizations, there is a strong demand for universally applicable benchmark approaches that are freely available for the entire scientific community. Benchmarks should satisfy the needs and requirements of various branches of research (e.g., schedulability analysis, WCET analysis, compiler optimization) on the one hand, but should also, on the other hand, realistically represent different application domains like, e.g., control or signal processing applications. This project aims at the realization of a flexible and parameterizable benchmark generator that produces benchmark programs in an automated, pseudo-randomized and reproducible fashion. This benchmark generator will in particular cover the system and the code level by producing both complete task sets and also actual program codes for the individual tasks. In order to enable a widespread use of the generator and a broad collaboration with arbitrary interested people and groups, this project will be inclusive and the developed software will be openly available right from the beginning. In the end, this project shall lead to a methodology for benchmarking-based evaluation that describes clearly and reproducibly for the different real-time communities, how to use the benchmark generator in order to obtain plausible, sound and scientifically accepted evaluation results. In order to be able to generate realistic and useful benchmarks, it is necessary to characterize key features of real-life applications and benchmarks, and to classify such applications according to their respective application domains. For this purpose, this archive contains datasets of the classification of existing ANSI-C benchmarks into their respective application domains, as well as trained DGCNN models of such classifiers.

Many embedded systems are safety-critical real-time systems that have to meet hard deadlines (e.g., airbag or flight control systems). When designing such real-time systems, it is of utmost importance to guarantee that all tasks of a system meet their given deadlines. For this purpose, dedicated timing analyses are required that examine the worst-case behavior of a system and are able to provide such guarantees. In the case that deadlines are not met, optimizations need to be applied in order to modify the code of the system such that timing constraints are nevertheless finally met. Research on such analyses and optimizations for hard real-time systems is an extremely lively area where new results are presented regularly and at a very fast pace. Naturally, the evaluation of such analyses and optimizations plays a very important role. Nowadays, evaluation typically relies on benchmarking such that new analyses or optimizations are applied to existing collections of applications, tasks or program codes. The currently used benchmarks are, however, highly limited and not sufficient in order to perform a sound and scientific evaluation, especially if massively parallel multi-task systems are considered. For a well-founded and reproducible evaluation of analyses and optimizations, there is a strong demand for universally applicable benchmark approaches that are freely available for the entire scientific community. Benchmarks should satisfy the needs and requirements of various branches of research (e.g., schedulability analysis, WCET analysis, compiler optimization) on the one hand, but should also, on the other hand, realistically represent different application domains like, e.g., control or signal processing applications. This project aims at the realization of a flexible and parameterizable benchmark generator that produces benchmark programs in an automated, pseudo-randomized and reproducible fashion. This benchmark generator will in particular cover the system and the code level by producing both complete task sets and also actual program codes for the individual tasks. In order to enable a widespread use of the generator and a broad collaboration with arbitrary interested people and groups, this project will be inclusive and the developed software will be openly available right from the beginning. In the end, this project shall lead to a methodology for benchmarking-based evaluation that describes clearly and reproducibly for the different real-time communities, how to use the benchmark generator in order to obtain plausible, sound and scientifically accepted evaluation results. In order to be able to generate realistic and useful benchmarks, it is necessary to characterize key features of real-life applications and benchmarks, and to classify such applications according to their respective application domains. For this purpose, this archive contains a large collection of already existing and freely available ANSI-C benchmark collections which will serve as templates for a future classification and as blueprints for a future, randomized code generation.

Many embedded systems are safety-critical real-time systems that have to meet hard deadlines (e.g., airbag or flight control systems). When designing such real-time systems, it is of utmost importance to guarantee that all tasks of a system meet their given deadlines. For this purpose, dedicated timing analyses are required that examine the worst-case behavior of a system and are able to provide such guarantees. In the case that deadlines are not met, optimizations need to be applied in order to modify the code of the system such that timing constraints are nevertheless finally met. Research on such analyses and optimizations for hard real-time systems is an extremely lively area where new results are presented regularly and at a very fast pace. Naturally, the evaluation of such analyses and optimizations plays a very important role. Nowadays, evaluation typically relies on benchmarking such that new analyses or optimizations are applied to existing collections of applications, tasks or program codes. The currently used benchmarks are, however, highly limited and not sufficient in order to perform a sound and scientific evaluation, especially if massively parallel multi-task systems are considered. For a well-founded and reproducible evaluation of analyses and optimizations, there is a strong demand for universally applicable benchmark approaches that are freely available for the entire scientific community. Benchmarks should satisfy the needs and requirements of various branches of research (e.g., schedulability analysis, WCET analysis, compiler optimization) on the one hand, but should also, on the other hand, realistically represent different application domains like, e.g., control or signal processing applications. This project aims at the realization of a flexible and parameterizable benchmark generator that produces benchmark programs in an automated, pseudo-randomized and reproducible fashion. This benchmark generator will in particular cover the system and the code level by producing both complete task sets and also actual program codes for the individual tasks. In order to enable a widespread use of the generator and a broad collaboration with arbitrary interested people and groups, this project will be inclusive and the developed software will be openly available right from the beginning. In the end, this project shall lead to a methodology for benchmarking-based evaluation that describes clearly and reproducibly for the different real-time communities, how to use the benchmark generator in order to obtain plausible, sound and scientifically accepted evaluation results. In order to be able to generate realistic and useful benchmarks, it is necessary to characterize key features of real-life applications and benchmarks, and to classify such applications according to their respective application domains. For this purpose, this archive contains various feature extractors which are implemented as LLVM passes. These extractors translate ANSI-C benchmark programs into LLVM code and extract a number of numerical and structural features out of LLVM. Examples of simple, numerical features include: - number of global variables, - number of defined compound types, - number of function declarations, - number of basic blocks, - number of instructions. Examples of more complex, structural features extracted by these LLVM passes include: - number of neighbors of control flow graph nodes, - number of various instruction types (e.g., integer arithmetics, floating-point arithmetics, memory loads/stores), - average/minimal/maximal loop nesting depths, - average/minimal/maximal basic block sizes, - number of occurrences of data types as function arguments, - number of occurrences of data types as function return values.

PalLoc6D contains 50 000 synthetically generated images of a photorealistic pallet in a domain-radomized environment. PalLoc6D includes annotations of the pallets' 6D pose. The data was created using the NVIDIA Dataset Synthesizer (NDDS, https://github.com/NVIDIA/Dataset_Synthesizer). Additionally, a photorealistic 3D model of a Euro pallet is provided. PalLoc6D can be used to train neural networks for RGB camera-based 6D pallet pose estimation, such as Nvidia's "Deep Object Pose Estimation" (DOPE, https://github.com/NVlabs/Deep_Object_Pose). Furthermore, the weights of the DOPE algorithm, trained with the annotated images are included in PalLoc6D to allow a quick start for experimenting with 6D pose estimation. PalLoc6D was published as part of the paper "Estimating the Pose of a Euro Pallet with an RGB Camera based on Synthetic Training Data", which was presented at the WGTL Fachkolloquium 2022 in Bremen and will be subsequently published in the Logistics Journal. The purpose, creation, and validation of the dataset are further elaborated in the publication. Paper abstract: "Estimating the pose of a pallet and other logistics objects is crucial for various use cases, such as automatized material handling or tracking. Innovations in computer vision, computing power, and machine learning open up new opportunities for device-free localization based on cameras and neural networks. Large image datasets with annotated poses are required for training the network. Manual annotation, especially of 6D poses, is an extremely labor-intensive process. Hence, newer approaches often leverage synthetic training data to automatize the process of generating annotated image datasets. In this work, the generation of synthetic training data for 6D pose estimation of pallets is presented. The data is then used to train the Deep Object Pose Estimation (DOPE) algorithm. The experimental validation of the algorithm proves that the 6D pose estimation of a standardized Euro pallet with a Red-Green-Blue (RGB) camera is feasible. The comparison of the results from three varying datasets under different lighting conditions shows the relevance of an appropriate dataset design to achieve an accurate and robust localization. The quantitative evaluation shows an average position error of less than 20 cm for the preferred dataset. The validated training dataset and a photorealistic model of a Euro pallet are publicly provided."

"Automated needle steering in soft tissue" is an open problem in both surgery robotics and formal verification. Challenges that are common to both fields include the interaction between needle and tissue, which depends on the coupled effects of needle deflection, friction and force, and tissue deformation; navigation, collision avoidance, and path planning, which are faced with inhomogeneous, partially unknown tissue; and also adaptive updates and feedback, which are required in real time. For each experiment, we record the feed of the needle in millimeters, the x, y, and z position of the needle in millimeters, and the rotation angle of the alignment of the angled needle tip in degree. The setup allows the needle to be pushed forwards and backward in the y direction. The experiments we restrict to a two-dimensional setting, but three-dimensional position data are recorded. Accordingly, we execute only motion plans with half rotations by 180 degree rotations. We do not execute motion plans with backward movement. Also, we do not allow more than two rotations within one motion plan.

The 'Social Engineering Poetry Slam' is a format to enable the ethical hacker community to talk about Social Engineering in a favourable and entertaining environment. This self-organised session was recorded at the 33C3 Chaos Communication Congress in 2016. One contribution was removed upon slammer's request. It was announced as follows: "Listen to social engineering attack stories from fellow hackers. Presented in a poetry slam style! A poetry slam can be a novel research approach to find stories of social engineering attacks, fictional or experienced. This slam will give us a new platform to discover and discuss social engineering. Or present your social engineering experience or fictional story on how to deceive or manipulate people in the attacker's malicious interest. How did you get social engineered? Did you hear from a social engineering incident or know someone who managed to detect and mitigate it?"