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A plethora of architectural patterns and elements for developing service-oriented applications can be gathered from the state-of-the-art. Most of these approaches are merely applicable for single-tenant applications. However, less methodical support is provided for scenarios, in which multiple different tenants with varying requirements access the same application stack concurrently. In order to fill this gap, both novel and existing architectural patterns, architectural elements, as well as fundamental design decisions must be considered and integrated into a framework that leverages the devel- opment of multi-tenant application. This paper addresses this demand and presents the SOAdapt framework. It promotes the development of adaptable multi-tenant applications based on a service-oriented architecture that is capable of incorporating specific requirements of new tenants in a flexible manner.
Recent work in image captioning and scene-segmentation has shown significant results in the context of scene-understanding. However, most of these developments have not been extrapolated to research areas such as robotics. In this work we review the current state-ofthe- art models, datasets and metrics in image captioning and scenesegmentation. We introduce an anomaly detection dataset for the purpose of robotic applications, and we present a deep learning architecture that describes and classifies anomalous situations. We report a METEOR score of 16.2 and a classification accuracy of 97 %.
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 % and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training procedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back-propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regularization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre-trained architectures have been released under an open-source license in our public repository.
Diese Arbeit beschäftigt sich mit der Effizienz der Seitenkanal-Kryptanalyse. In Teil II dieser Arbeit demonstrieren wir, wie die Laufzeit der wichtigsten Analysewerkzeuge mit Hilfe der CUDA Plattform erheblich gesteigert werden kann. Zweitens untersuchen wir neue Ansätze der profilierenden Seitenkanal-Kryptanalyse. Der Forschungszweig des maschinellen Lernens kann für deutliche Verbesserungen adaptiert werden, wurde jedoch wenig dahingehend untersucht. In Teil III dieser Arbeit präsentieren wir zwei neue Methoden, die einige Gemeinsamkeiten jedoch auch einige Unterschiede aufbieten, sodass sich Prüfergebnisse in einem vollständigeren Bild zeigen lassen. Darüber hinaus schlagen wir in Teil IV eine Seitenkanalanwendung zum Schutz geistigen Eigentums (IP) vor. In Teil V beschäftigen wir uns tiefergehend mit praktischer Seitenkanal-Kryptanalyse, indem wir Attacken auf einen Sicherheitsmikrokontroller durchführen, der Anwendung in einer, in Deutschland weit verbreiteten, EC Karte findet.
Human butyrylcholinesterase (BChE) is a glycoprotein capable of bioscavenging toxic compounds such as organophosphorus (OP) nerve agents. For commercial production of BChE, it is practical to synthesize BChE in non-human expression systems, such as plants or animals. However, the glycosylation profile in these systems is significantly different from the human glycosylation profile, which could result in changes in BChE's structure and function. From our investigation, we found that the glycan attached to ASN241 is both structurally and functionally important due to its close proximity to the BChE tetramerization domain and the active site gorge. To investigate the effects of populating glycosylation site ASN241, monomeric human BChE glycoforms were simulated with and without site ASN241 glycosylated. Our simulations indicate that the structure and function of human BChE are significantly affected by the absence of glycan 241.
RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark.
This paper proposes a novel approach to the generation of state equations from a bond graph (BG) of a mode switching linear time invariant model. Fast state transitions are modelled by ideal or non-ideal switches. Fixed causalities are assigned following the Standard Causality Assignment Procedure such that the number of storage elements in integral causality is maximised. A system of differential and algebraic equations (DAEs) is derived from the BG that holds for all system modes. It is distinguished between storage elements with mode independent causality and those that change causality due to switch state changes.
Integrating Bond Graph-Based Fault Diagnosis and Fault Accommodation Through Inverse Simulation
(2017)
Systemunterstützung für wissensintensive Geschäftsprozesse – Konzepte und Implementierungsansätze
(2017)
Studienverläufe von Studenten weichen nicht selten vom offiziell geplanten Curriculum ab. Für eine den Studienerfolg verbessernde Planung und Weiterentwicklung von Studiengängen und Curricula fehlen den Verantwortlichen häufig Erkenntnisse über tatsächliche sowie typischerweise erfolgreiche und weniger erfolgreiche Studienverlaufsmuster. Process-Mining-Techniken können helfen, mehr Transparenz bei der Auswertung von Studienverläufen zu schaffen und so die Erkennung typischer Studienverlaufsmuster, die Überprüfung der Übereinstimmung der konkreten Studienverläufe mit dem vorgegebenen Curriculum sowie eine zielgerechte Verbesserung des Curriculums zu unterstützen.
Das Cutting sticks-Problem ist in seiner allgemeinen Formulierung ein NP-vollständiges Problem mit Anwendungspotenzialen im Bereich der Logistik. Unter der Annahme, dass P ungleich NP (P != NP) ist, existieren keine effizienten, d.h. polynomiellen Algorithmen zur Lösung des allgemeinen Problems.
In diesem Papier werden Ansätze aufgezeigt, mit denen bestimmte Instanzen des Problems effizient berechnet werden können. Für die Berechnung wichtige Parameter werden charakterisiert und deren Beziehung untereinander analysiert.
Das Cutting sticks-Problem ist in seiner allgemeinen Formulierung ein NP-vollständiges Problem mit Anwendungspotenzialen im Bereich der Logistik. Unter der Annahme, dass P ungleich NP (P != NP) ist, existieren keine effizienten, d.h. polynomiellen Algorithmen zur Lösung des allgemeinen Problems.
In diesem Papier werden für eine Reihe von Instanzen effiziente Lösungen angegeben.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
A new method for design space exploration and optimization, Surrogate-Assisted Illumination (SAIL), is presented. Inspired by robotics techniques designed to produce diverse repertoires of behaviors for use in damage recovery, SAIL produces diverse designs that vary according to features specified by the designer. By producing high-performing designs with varied combinations of user-defined features a map of the design space is created. This map illuminates the relationship between the chosen features and performance, and can aid designers in identifying promising design concepts. SAIL is designed for use with compu-tationally expensive design problems, such as fluid or structural dynamics, and integrates approximative models and intelligent sampling of the objective function to minimize the number of function evaluations required. On a 2D airfoil optimization problem SAIL is shown to produce hundreds of diverse designs which perform competitively with those found by state-of-the-art black box optimization. Its capabilities are further illustrated in a more expensive 3D aerodynamic optimization task.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.