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Interactive Distributed Rendering of 3D Scenes on Multiple Xbox 360 Systems and Personal Computers
(2012)
Die Forschung zur kontrovers diskutierten Robotik in der Pflege und Begleitung von Personen mit Demenz steht noch am Anfang, wenngleich bereits erste Systeme auf dem Markt sind. Der Beitrag gibt entlang beispielhafter, fallbezogener Auszüge Einblicke in das laufende multidisziplinäre Projekt EmoRobot, das sich explorativ und interpretativ mit der Erkundung des Einsatzes von Robotik in der emotionsorientierten Pflege und Versorgung von Personen mit Demenz befasst. Fokussiert werden dabei die je eigenen Relevanzen der Personen mit Demenz.
Graph drawing with spring embedders employs a V x V computation phase over the graph's vertex set to compute repulsive forces. Here, the efficacy of forces diminishes with distance: a vertex can effectively only influence other vertices in a certain radius around its position. Therefore, the algorithm lends itself to an implementation using search data structures to reduce the runtime complexity. NVIDIA RT cores implement hierarchical tree traversal in hardware. We show how to map the problem of finding graph layouts with force-directed methods to a ray tracing problem that can subsequently be implemented with dedicated ray tracing hardware. With that, we observe speedups of 4x to 13x over a CUDA software implementation.
We describe a systematic approach for rendering time-varying simulation data produced by exa-scale simulations, using GPU workstations. The data sets we focus on use adaptive mesh refinement (AMR) to overcome memory bandwidth limitations by representing interesting regions in space with high detail. Particularly, our focus is on data sets where the AMR hierarchy is fixed and does not change over time. Our study is motivated by the NASA Exajet, a large computational fluid dynamics simulation of a civilian cargo aircraft that consists of 423 simulation time steps, each storing 2.5 GB of data per scalar field, amounting to a total of 4 TB. We present strategies for rendering this time series data set with smooth animation and at interactive rates using current generation GPUs. We start with an unoptimized baseline and step by step extend that to support fast streaming updates. Our approach demonstrates how to push current visualization workstations and modern visualization APIs to their limits to achieve interactive visualization of exa-scale time series data sets.
We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.
We present a new interface for interactive comparisons of more than two alternative documents in the context of a generative design system that uses generative data-flow networks defined via directed acyclic graphs. To better show differences between such networks, we emphasize added, deleted, (un)changed nodes and edges. We emphasize differences in the output as well as parameters using highlighting and enable post-hoc merging of the state of a parameter across a selected set of alternatives. To minimize visual clutter, we introduce new difference visualizations for selected nodes and alternatives using additive and subtractive encodings, which improve readability and keep visual clutter low. We analyzed similarities in networks from a set of alternative designs produced by architecture students and found that the number of similarities outweighs the differences, which motivates use of subtractive encoding. We ran a user study to evaluate the two main proposed difference visualization encodings and found that they are equally effective.
The increasing complexity of tasks that are required to be executed by robots demands higher reliability of robotic platforms. For this, it is crucial for robot developers to consider fault diagnosis. In this study, a general non-intrusive fault diagnosis system for robotic platforms is proposed. A mini-PC is non-intrusively attached to a robot that is used to detect and diagnose faults. The health data and diagnosis produced by the mini-PC is then standardized and transmitted to a remote-PC. A storage device is also attached to the mini-PC for data logging of health data in case of loss of communication with the remote-PC. In this study, a hybrid fault diagnosis method is compared to consistency-based diagnosis (CBD), and CBD is selected to be deployed on the system. The proposed system is modular and can be deployed on different robotic platforms with minimum setup.
This work presents the preliminary research towards developing an adaptive tool for fault detection and diagnosis of distributed robotic systems, using explainable machine learning methods. Autonomous robots are complex systems that require high reliability in order to operate in different environments. Even more so, when considering distributed robotic systems, the task of fault detection and diagnosis becomes exponentially difficult.
To diagnose systems, models representing the behaviour under investigation need to be developed, and with distributed robotic systems generating large amount of data, machine learning becomes an attractive method of modelling especially because of its high performance. However, with current day methods such as artificial neural networks (ANNs), the issue of explainability arises where learnt models lack the ability to give explainable reasons behind their decisions.
This paper presents current trends in methods for data collection from distributed systems, inductive logic programming (ILP); an explainable machine learning method, and fault detection and diagnosis.
In diesem Artikel wird darüber berichtet, ob die Glaubwürdigkeit von Avataren als mögliches Modulationskriterium für die virtuelle Expositionstherapie von Agoraphobie in Frage kommt. Dafür werden mehrere Glaubwürdigkeitsstufen für Avatare, die hypothetisch einen Einfluss auf die virtuelle Expositionstherapie von Agoraphobie haben könnten sowie ein potentielles Expositionsszenario entwickelt. Die Arbeit kann innerhalb einer Studie einen signifikanten Einfluss der Glaubwürdigkeitsstufen auf Präsenz, Kopräsenz und Realismus aufzeigen.
Structure-activity relationships of thiostrepton derivatives: implications for rational drug design
(2014)
Bei der Übertragung und Speicherung von Daten ist es eine wesentliche Frage, inwieweit die Daten komprimiert werden können, ohne dass deren Informationsgehalt verloren geht.
Ein Maß für den Informationsgehalt von Daten ist also von grundlegender Bedeutung. Vor etwa siebzig Jahren hat C. E. Shannon ein solches Maß eingeführt und damit das Lehr- und Forschungsgebiet der Informationstheorie begründet, welches seit dem bis heute hin wesentlich zur Konzeption und Realisierung von Informationsund Kommunikationstechnologien beigetragen hat. Etwa zwanzig Jahre später hat A. N. Kolmogorov ein anderes Maß für den Informationsgehalt von Daten eingeführt. Während die Shannonsche Informationstheorie zum Curriculum von mathematischen, informatischen und elektrotechnischen Studiengängen gehört, ist die Algorithmische Informationstheorie von Kolmogorov weit weniger bekannt und eher Gegenstand von speziellen Lehrveranstaltungen.
Seit einigen Jahren nimmt allerdings die Beschäftigung mit dieser Theorie zu, zumal in der einschlägigen Literatur von erfolgreichen praktischen Anwendungen der Theorie berichtet wird. Die vorliegende Arbeit gibt eine Einführung in grundlegende Ideen dieser Theorie und beschreibt deren Anwendungsmöglichkeiten bei einigen ausgewählten Problemstellungen der Theoretischen Informatik.
Die Ausarbeitung kann als Skript für einführende Lehrveranstaltungen in die Algorithmische Informationstheorie sowie als Lektüre zur Einarbeitung in die Thematik als Ausgangspunkt für Forschungs- und Entwicklungsarbeiten verwendet werden.
Warum beeinträchtigen bestimmte Kratzer auf einer CD nicht die Wiedergabequalität? Wie können Datenübertragungen gegen Informationsverlust gesichert werden? Warum und wie funktionieren öffentliche Verschlüsselungssysteme? Worin ist deren Sicherheit begründet? Auf welcher Grundlage werden Routing-Tabellen in Netzwerkknoten erstellt? Wie wird eine optimale Kompression von Daten erreicht?
Diese und viele andere Fragen müssen zufriedenstellend beantwortet werden können, um bestimmte Qualitäten von Informations- und Kommunikationstechnologien zu erreichen. Informatikerinnen und Informatiker aller Studienrichtungen müssen in der Lage sein, diese Technologien erfolgreich einzusetzen und weiterzuentwickeln. Dazu müssen sie die Grundlagen kennen, auf denen diese Technologien basieren.
Internet, Soziale Netzwerke, Spiele, Smartphones, DVDs, digitaler Rundfunk und digitales Fernsehen funktionieren nur deshalb, weil zu ihrer Entwicklung und Anwendung mathematisch abgesicherte Verfahren zur Verfügung stehen. Dieses Buch vermittelt Einsichten in grundlegende Konzepte und Methoden der Linearen Algebra, auf denen diese Verfahren beruhen. Am Beispiel fehlertoleranter Codierung wird einführend gezeigt, wie diese Konzepte und Methoden in der Praxis eingesetzt werden, und am Beispiel von Quantenalgorithmen, die möglicherweise in Zukunft eine Rolle spielen, wird deutlich, dass die Lineare Algebra zeitinvariante Konzepte, Methoden und Verfahren bereitstellt, mit denen IT-Technologien konzipiert, implementiert, angewendet und weiterentwickelt werden können. Wegen seiner didaktischen Elemente wie Vorgabe von Lernzielen, Zusammenfassungen, Marginalien und einer Vielzahl von Übungen mit Musterlösungen eignet sich das Buch nicht nur als Begleitlektüre zu entsprechenden Informatik- und Mathematik-Lehrveranstaltungen, sondern insbesondere auch zum Selbststudium.
Logiken, Mengen, Relationen, Funktionen, Induktion und Rekursion sind grundlegende mathematische Konzepte und Methoden, die in allen Bereichen der Informatik für die Beschreibung von Problemen und deren Lösung benötigt werden. Das Beherrschen dieser Konzepte und Methoden ist Voraussetzung für das Studium fast aller weiteren Informatik-Module, nicht nur in Bereichen der Mathematik und der Theoretischen Informatik, sondern auch in Bereichen der Praktischen Informatik, wie z.B. Programmierung, Datenstrukturen, Algorithmen und Datenbanken. Das Buch stellt die grundlegenden Begriffe, ihre Eigenschaften und Anwendungsmöglichkeiten schrittweise vor. Das Verständnis der Begriffe und deren Zusammenhang und Zusammenwirken wird u.a. durch Lernziele, integrierte Übungsaufgaben mit Musterlösungen und Marginalien unterstützt; das Buch ist zum Selbststudium geeignet.
Informatikerinnen und Informatiker aller Fachrichtungen müssen die grundlegenden Konzepte, Methoden und Verfahren, die der Entwicklung und dem Einsatz von Informations- und Kommunikationstechnologien zugrunde liegen, verstehen und bei der Lösung von Problemen anwenden können. Das Buch stellt die algebraischen und zahlentheoretischen Grundlagen dafür vor und wendet diese bei der Lösung praktischer Problemstellungen, wie modulare Arithmetik, Primzahltests und Verschlüsselung an. Das Verständnis der Begriffe und deren Zusammenhänge und Zusammenwirken wird u.a. durch Lernziele, integrierte Übungsaufgaben mit Musterlösungen und Marginalien unterstützt. Das Buch ist zum Selbststudium gut geeignet.
The development of mobile robotic systems is a demanding task regarding its complexity, required resources and skills in multiple fields such as software development, artificial intelligence, mechanical design, electrical engineering, signal processing, sensor technology or control theory. This holds true particularly for soccer playing robots, where additional aspects like high dynamics, cooperation and high physical stress have to be dealt with. In robot competitions such as RoboCup, additional skills in the domains of team, project and knowledge management are of importance.
An electronic display often has to present information from several sources. This contribution reports about an approach, in which programmable logic (FPGA) synchronises and combines several graphics inputs. The application area is computer graphics, especially rendering of large 3D models, which is a computing intensive task. Therefore, complex scenes are generated on parallel systems and merged to give the requested output image. So far, the transportation of intermediate results is often done by a local area network. However, as this can be a limiting factor, the new approach removes this bottleneck and combines the graphic signals with an FPGA.
Improving the study entry supports students in a decisive phase of their university education. Implementing improvements is a change process and can only be successful if the relevant stakeholders are addressed and convinced. In the described Teaching Quality Pact project evaluation data is used as a mean to discuss in the university the situation of the study programs. As these discussions were based on empirical data rather than on opinion, it was possible to achieve an open discussion about measures that are implemented. The open discussion is maintained during the project when results of the measures taken are analyzed.
This paper introduces FaceHaptics, a novel haptic display based on a robot arm attached to a head-mounted virtual reality display. It provides localized, multi-directional and movable haptic cues in the form of wind, warmth, moving and single-point touch events and water spray to dedicated parts of the face not covered by the head-mounted display.The easily extensible system, however, can principally mount any type of compact haptic actuator or object. User study 1 showed that users appreciate the directional resolution of cues, and can judge wind direction well, especially when they move their head and wind direction is adjusted dynamically to compensate for head rotations. Study 2 showed that adding FaceHaptics cues to a VR walkthrough can significantly improve user experience, presence, and emotional responses.
Digital ecosystems are driving the digital transformation of business models. Meanwhile, the associated processing of personal data within these complex systems poses challenges to the protection of individual privacy. In this paper, we explore these challenges from the perspective of digital ecosystems' platform providers. To this end, we present the results of an interview study with seven data protection officers representing a total of 12 digital ecosystems in Germany. We identified current and future challenges for the implementation of data protection requirements, covering issues on legal obligations and data subject rights. Our results support stakeholders involved in the implementation of privacy protection measures in digital ecosystems, and form the foundation for future privacy-related studies tailored to the specifics of digital ecosystems.
Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA's high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.
Risk-based Authentication (RBA) is an adaptive security measure that improves the security of password-based authentication by protecting against credential stuffing, password guessing, or phishing attacks. RBA monitors extra features during login and requests for an additional authentication step if the observed feature values deviate from the usual ones in the login history. In state-of-the-art RBA re-authentication deployments, users receive an email with a numerical code in its body, which must be entered on the online service. Although this procedure has a major impact on RBA's time exposure and usability, these aspects were not studied so far.
We introduce two RBA re-authentication variants supplementing the de facto standard with a link-based and another code-based approach. Then, we present the results of a between-group study (N=592) to evaluate these three approaches. Our observations show with significant results that there is potential to speed up the RBA re-authentication process without reducing neither its security properties nor its security perception. The link-based re-authentication via "magic links", however, makes users significantly more anxious than the code-based approaches when perceived for the first time. Our evaluations underline the fact that RBA re-authentication is not a uniform procedure. We summarize our findings and provide recommendations.
Risk-based authentication (RBA) aims to protect users against attacks involving stolen passwords. RBA monitors features during login, and requests re-authentication when feature values widely differ from those previously observed. It is recommended by various national security organizations, and users perceive it more usable than and equally secure to equivalent two-factor authentication. Despite that, RBA is still used by very few online services. Reasons for this include a lack of validated open resources on RBA properties, implementation, and configuration. This effectively hinders the RBA research, development, and adoption progress.
To close this gap, we provide the first long-term RBA analysis on a real-world large-scale online service. We collected feature data of 3.3 million users and 31.3 million login attempts over more than 1 year. Based on the data, we provide (i) studies on RBA’s real-world characteristics plus its configurations and enhancements to balance usability, security, and privacy; (ii) a machine learning–based RBA parameter optimization method to support administrators finding an optimal configuration for their own use case scenario; (iii) an evaluation of the round-trip time feature’s potential to replace the IP address for enhanced user privacy; and (iv) a synthesized RBA dataset to reproduce this research and to foster future RBA research. Our results provide insights on selecting an optimized RBA configuration so that users profit from RBA after just a few logins. The open dataset enables researchers to study, test, and improve RBA for widespread deployment in the wild.
Login Data Set for Risk-Based Authentication
Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.
This data sets aims to foster research and development for <a href="https://riskbasedauthentication.org">Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.
Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users. To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
Risk-based Authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional features during login, and when observed feature values differ significantly from previously seen ones, users have to provide additional authentication factors such as a verification code. RBA has the potential to offer more usable authentication, but the usability and the security perceptions of RBA are not studied well.
We present the results of a between-group lab study (n=65) to evaluate usability and security perceptions of two RBA variants, one 2FA variant, and password-only authentication. Our study shows with significant results that RBA is considered to be more usable than the studied 2FA variants, while it is perceived as more secure than password-only authentication in general and comparably secure to 2FA in a variety of application types. We also observed RBA usability problems and provide recommendations for mitigation. Our contribution provides a first deeper understanding of the users' perception of RBA and helps to improve RBA implementations for a broader user acceptance.
Risk-based authentication (RBA) is an adaptive security measure to strengthen password-based authentication against account takeover attacks. Our study on 65 participants shows that users find RBA more usable than two-factor authentication equivalents and more secure than password-only authentication. We identify pitfalls and provide guidelines for putting RBA into practice.
Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users.
To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
Risikobasierte Authentifizierung (RBA) ist ein adaptiver Ansatz zur Stärkung der Passwortauthentifizierung. Er überwacht eine Reihe von Merkmalen, die sich auf das Loginverhalten während der Passworteingabe beziehen. Wenn sich die beobachteten Merkmalswerte signifikant von denen früherer Logins unterscheiden, fordert RBA zusätzliche Identitätsnachweise an. Regierungsbehörden und ein Erlass des US-Präsidenten empfehlen RBA, um Onlineaccounts vor Angriffen mit gestohlenen Passwörtern zu schützen. Trotz dieser Tatsachen litt RBA unter einem Mangel an offenem Wissen. Es gab nur wenige bis keine Untersuchungen über die Usability, Sicherheit und Privatsphäre von RBA. Das Verständnis dieser Aspekte ist jedoch wichtig für eine breite Akzeptanz.
Diese Arbeit soll ein umfassendes Verständnis von RBA mit einer Reihe von Studien vermitteln. Die Ergebnisse ermöglichen es, datenschutzfreundliche RBA-Lösungen zu schaffen, die die Authentifizierung stärken bei gleichzeitig hoher Menschenakzeptanz.
Advances in computer graphics enable us to create digital images of astonishing complexity and realism. However, processing resources are still a limiting factor. Hence, many costly but desirable aspects of realism are often not accounted for, including global illumination, accurate depth of field and motion blur, spectral effects, etc. especially in real‐time rendering. At the same time, there is a strong trend towards more pixels per display due to larger displays, higher pixel densities or larger fields of view. Further observable trends in current display technology include more bits per pixel (high dynamic range, wider color gamut/fidelity), increasing refresh rates (better motion depiction), and an increasing number of displayed views per pixel (stereo, multi‐view, all the way to holographic or lightfield displays). These developments cause significant unsolved technical challenges due to aspects such as limited compute power and bandwidth. Fortunately, the human visual system has certain limitations, which mean that providing the highest possible visual quality is not always necessary. In this report, we present the key research and models that exploit the limitations of perception to tackle visual quality and workload alike. Moreover, we present the open problems and promising future research targeting the question of how we can minimize the effort to compute and display only the necessary pixels while still offering a user full visual experience.
In recent years, a variety of methods have been introduced to exploit the decrease in visual acuity of peripheral vision, known as foveated rendering. As more and more computationally involved shading is requested and display resolutions increase, maintaining low latencies is challenging when rendering in a virtual reality context. Here, foveated rendering is a promising approach for reducing the number of shaded samples. However, besides the reduction of the visual acuity, the eye is an optical system, filtering radiance through lenses. The lenses create depth-of-field (DoF) effects when accommodated to objects at varying distances. The central idea of this article is to exploit these effects as a filtering method to conceal rendering artifacts. To showcase the potential of such filters, we present a foveated rendering system, tightly integrated with a gaze-contingent DoF filter. Besides presenting benchmarks of the DoF and rendering pipeline, we carried out a perceptual study, showing that rendering quality is rated almost on par with full rendering when using DoF in our foveated mode, while shaded samples are reduced by more than 69%.
Rendering techniques for design evaluation and review or for visualizing large volume data often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates. A view direction based rendering technique renders the users central field of view in high quality whereas the surrounding is rendered with a level of detail approach depending on the distance to the users central field of view thus giving the opportunity to increase rendering efficiency. We propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.
In contrast to projection-based systems, large, high resolution multi-display systems offer a high pixel density on a large visualization area. This enables users to step up to the displays and see a small but highly detailed area. If the users move back a few steps they don't perceive details at pixel level but will instead get an overview of the whole visualization. Rendering techniques for design evaluation and review or for visualizing large volume data (e.g. Big Data applications) often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates.
A view direction based (VDB) rendering technique renders the user's central field of view in high quality whereas the surrounding is rendered with a level-of-detail approach depending on the distance to the user's central field of view. This approach mimics the physiology of the human eye and conserves the advantage of highly detailed information when standing close to the multi-display system as well as the general overview of the whole scene. In this paper we propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.
We present a system that combines voxel and polygonal representations into a single octree acceleration structure that can be used for ray tracing. Voxels are well-suited to create good level-of-detail for high-frequency models where polygonal simplifications usually fail due to the complex structure of the model. However, polygonal descriptions provide the higher visual fidelity. In addition, voxel representations often oversample the geometric domain especially for large triangles, whereas a few polygons can be tested for intersection more quickly.
In dieser Arbeit wird eine Methode zur Darstellung und Generierung von natürlich wirkendem Bewuchs auf besonders großen Arealen und unter Berücksichtigung ökologischer Faktoren vorgestellt. Die Generierung und Visualisierung von Bewuchs ist aufgrund der Komplexität biologischer Systeme und des Detailreichtums von Pflanzenmodellen ein herausforderndes Gebiet der Computergrafik und ermöglicht es, den Realismus von Landschaftsvisualisierungen erheblich zu steigern. Aufbauend auf [DMS06] wird bei Silva der Bewuchs so generiert, dass die zur Darstellung benötigten Wang-Kacheln und die mit ihnen assoziierten Teilverteilungen wiederverwendet werden können. Dazu wird ein Verfahren vorgestellt, um Poisson Disk Verteilungen mit variablen Radien auf nahtlosen Wang-Kachelmengen ohne rechenintensive globale Optimierung zu erzeugen. Durch die Einbeziehung von Nachbarschaften und frei konfigurierbaren Generierungspipelines können beliebige abiotische und biotische Faktoren bei der Bewuchsgenerierung berücksichtigt werden. Die durch Silva auf Wang-Kacheln erzeugten Pflanzenverteilungen ermöglichen, die darauf aufgebauten beschleunigenden Datenstrukturen bei der Visualisierung wieder zu verwenden. Durch Multi-Level Instancing und eine Schachtelung von Kd-Bäumen ist eine Visualisierung von großen bewachsenen Arealen mit geringen Renderzeiten und geringem Memoryfootprint von Hunderten Quadratkilometern Größe möglich.
Generating and visualizing large areas of vegetation that look natural makes terrain surfaces much more realistic. However, this is a challenging field in computer graphics, because ecological systems are complex and visually appealing plant models are geometrically detailed. This work presents Silva (System for the Instantiation of Large Vegetated Areas), a system to generate and visualize large vegetated areas based on the ecological surrounding. Silva generates vegetation on Wang-tiles with associated reusable distributions enabling multi-level instantiation. This paper presents a method to generate Poisson Disc Distributions (PDDs) with variable radii on Wang-tile sets (without a global optimization) that is able to generate seamless tilings. Because Silva has a freely configurable generation pipeline and can consider plant neighborhoods it is able to incorporate arbitrary abiotic and biotic components during generation. Based on multi-levelinstancing and nested kd-trees, the distributions on the Wang-tiles allow their acceleration structures to be reused during visualization. This enables Silva to visualize large vegetated areas of several hundred square kilometers with low render times and a small memory footprint.
Computer graphics research strives to synthesize images of a high visual realism that are indistinguishable from real visual experiences. While modern image synthesis approaches enable to create digital images of astonishing complexity and beauty, processing resources remain a limiting factor. Here, rendering efficiency is a central challenge involving a trade-off between visual fidelity and interactivity. For that reason, there is still a fundamental difference between the perception of the physical world and computer-generated imagery. At the same time, advances in display technologies drive the development of novel display devices. The dynamic range, the pixel densities, and refresh rates are constantly increasing. Display systems enable a larger visual field to be addressed by covering a wider field-of-view, due to either their size or in the form of head-mounted devices. Currently, research prototypes are ranging from stereo and multi-view systems, head-mounted devices with adaptable lenses, up to retinal projection, and lightfield/holographic displays. Computer graphics has to keep step with, as driving these devices presents us with immense challenges, most of which are currently unsolved. Fortunately, the human visual system has certain limitations, which means that providing the highest possible visual quality is not always necessary. Visual input passes through the eye’s optics, is filtered, and is processed at higher level structures in the brain. Knowledge of these processes helps to design novel rendering approaches that allow the creation of images at a higher quality and within a reduced time-frame. This thesis presents the state-of-the-art research and models that exploit the limitations of perception in order to increase visual quality but also to reduce workload alike - a concept we call perception-driven rendering. This research results in several practical rendering approaches that allow some of the fundamental challenges of computer graphics to be tackled. By using different tracking hardware, display systems, and head-mounted devices, we show the potential of each of the presented systems. The capturing of specific processes of the human visual system can be improved by combining multiple measurements using machine learning techniques. Different sampling, filtering, and reconstruction techniques aid the visual quality of the synthesized images. An in-depth evaluation of the presented systems including benchmarks, comparative examination with image metrics as well as user studies and experiments demonstrated that the methods introduced are visually superior or on the same qualitative level as ground truth, whilst having a significantly reduced computational complexity.
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
Friction effects impose a requirement for the supplementary amount of torque to be produced in actuators for a robot to move, which in turn increases energy consumption. We cannot eliminate friction, but we can optimize motions to make them more energy efficient, by considering friction effects in motion computations. Optimizing motions means computing efficient joint torques/accelerations based on different friction torques imposed in each joint. Existing friction forces can be used for supporting certain types of arm motions, e.g standing still.
Reducing energy consumption of robot's arms will provide many benefits, such as longer battery life of mobile robots, reducing heat in motor systems, etc.
The aim of this project is extending an already available constrained hybrid dynamic solver, by including static friction effects in the computations of energy optimal motions. When the algorithm is extended to account for static friction factors, a convex optimization (maximization) problem must be solved.
The author of this hybrid dynamic solver has briefly outlined the approach for including static friction forces in computations of motions, but without providing a detailed derivation of the approach and elaboration that will show its correctness. Additionally, the author has outlined the idea for improving the computational efficiency of the approach, but without providing its derivation.
In this project, the proposed approach for extending the originally formulated algorithm has been completely derived and evaluated in order to show its feasibility. The evaluation is conducted in simulation environment with one DOF robot arm, and it shows correct results from the computation of motions. Furthermore, this project presents the derivation of the outlined method for improving the computational efficiency of the extended solver.
Human and robot tasks in household environments include actions such as carrying an object, cleaning a surface, etc. These tasks are performed by means of dexterous manipulation, and for humans, they are straightforward to accomplish. Moreover, humans perform these actions with reasonable accuracy and precision but with much less energy and stress on the actuators (muscles) than the robots do. The high agility in controlling their forces and motions is actually due to "laziness", i.e. humans exploit the existing natural forces and constraints to execute the tasks.
The above-mentioned properties of the human lazy strategy motivate us to relax the problem of controlling robot motions and forces, and solve it with the help of the environment. Therefore, in this work, we developed a lazy control strategy, i.e. task specification models and control architectures that relax several aspects of robot control by exploiting prior knowledge about the task and environment. The developed control strategy is realized in four different robotics use cases. In this work, the Popov-Vereshchagin hybrid dynamics solver is used as one of the building blocks in the proposed control architectures. An extension of the solver’s interface with the artificial Cartesian force and feed-forward joint torque task-drivers is proposed in this thesis.
To validate the proposed lazy control approach, an experimental evaluation was performed in a simulation environment and on a real robot platform.
Diese Theorie-Einführung hat konsequent praktische Anwendungen im Blick. Seien es Workflow-Systeme, Web Services, Verschlüsselung von Informationen, Authentifizierungsprotokolle oder selbstfahrende Autos - all diese Technologien haben enge Bezüge zu den theoretischen Grundlagen der Informatik. So trägt das Buch dazu bei, dass Studierende die Grundlagen der Theoretischen Informatik nicht nur verstehen, sondern auch anwenden können, um effektiv und produktiv an informationstechnischen Problemlösungen mitwirken zu können. Wegen seiner speziellen inhaltlichen und didaktischen Qualität ist das Buch neben dem Einsatz in der Lehre auch für das Selbststudium geeignet.
Zur Schätzung der Exposition von Oberflächengewässern durch Pflanzenschutzmittel werden PEC-Werte mit Hilfe eines probabilistischen Verfahrens ermittelt. Hierfür werden zunächst verschiedene Regressionsanalysen zur Modellierung der Abdrift durchgeführt. Anschließend wird die ausgewählte Abdriftverteilung mit verschiedenen Verteilungsansätzen für die Aufwandmenge und das Gewässervolumen kombiniert.
Dieses Buch bietet einen leicht verständlichen Einstieg in die Thematik des Data Minings und der Prädiktiven Analyseverfahren. Als Methodensammlung gedacht, bietet es zu jedem Verfahren zunächst eine kurze Darstellung der Theorie und erklärt die zum Verständnis notwendigen Formeln. Es folgt jeweils eine Illustration der Verfahren mit Hilfe von Beispielen, die mit dem Programmpaket R erarbeitet werden.
Zum Abschluss wird eine einfache Möglichkeit präsentiert, mit der die Performancewerte verschiedener Verfahren mit statistischen Mitteln verglichen werden können. Zum Einsatz kommen hierbei geeignete Grafiken und Konfidenzintervalle.
Das Buch verzichtet nicht auf Theorie, es präsentiert jedoch so wenig Theorie wie möglich, aber so viel wie nötig und ist somit optimal für Studium und Selbststudium geeignet.
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
Virtual reality environments are increasingly being used to encourage individuals to exercise more regularly, including as part of treatment in those with mental health or neurological disorders. The success of virtual environments likely depends on whether a sense of presence can be established, where participants become fully immersed in the virtual environment. Exposure to virtual environments is associated with physiological responses, including cortical activation changes. Whether the addition of a real exercise within a virtual environment alters sense of presence perception, or the accompanying physiological changes, is not known. In a randomized and controlled study design, trials of moderate-intensity exercise (i.e. self-paced cycling) and no-exercise (i.e. automatic propulsion) were performed within three levels of virtual environment exposure. Each trial was 5-min in duration and was followed by post-trial assessments of heart rate, perceived sense of presence, EEG, and mental state. Changes in psychological strain and physical state were generally mirrored by neural activation patterns. Furthermore these change indicated that exercise augments the demands of virtual environment exposures and this likely contributed to an enhanced sense of presence.
Background: Virtual reality combined with spherical treadmills is used across species for studying neural circuits underlying navigation.
New Method: We developed an optical flow-based method for tracking treadmil ball motion in real-time using a single high-resolution camera.
Results: Tracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 ± 8 ms.
Comparison: with Existing Method(s) Optical flow based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions – such as ball surface texture, lighting conditions, or ball size – as well as camera alignment and calibration.
Conclusions: A fast system for rotational ball motion tracking suitable for virtual reality animal behavior across different scales was developed and characterized.
A Low-Cost Based 6 DoF Head Tracker for Usability Application Studies in Virtual Environments
(2008)
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques
(2022)
Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.
We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode. Additionally, similarly to weight normalization, we include a global scaling parameter. We study the initialization of the canonical form by running the power method and by drawing randomly from Gaussian or uniform distributions. Our results indicate that we can replace the power method with cheaper initializations drawn from standard distributions. The canonical re-parametrization leads to competitive normalization performance on the MNIST, CIFAR10, and SVHN data sets. Moreover, the formulation simplifies network compression. Once training has converged, the canonical form allows convenient model-compression by truncating the parameter sums.
Safety-critical applications like autonomous driving use Deep Neural Networks (DNNs) for object detection and segmentation. The DNNs fail to predict when they observe an Out-of-Distribution (OOD) input leading to catastrophic consequences. Existing OOD detection methods were extensively studied for image inputs but have not been explored much for LiDAR inputs. So in this study, we proposed two datasets for benchmarking OOD detection in 3D semantic segmentation. We used Maximum Softmax Probability and Entropy scores generated using Deep Ensembles and Flipout versions of RandLA-Net as OOD scores. We observed that Deep Ensembles out perform Flipout model in OOD detection with greater AUROC scores for both datasets.
Speech understanding is a fundamental feature for many applications focused on human-robot interaction. Although many techniques and several services for speech recognition and natural language understanding have been developed in the last years, specific implementation and validation on domestic service robots have not been performed. In this paper, we describe the implementation and the results of a functional benchmark for speech understanding in service robotics that has been developed and tested in the context of different robot competitions: RoboCup@Home, RoCKIn@Home and within the European Robotics League on Service Robots. Different approaches used by the teams in the competitions are presented and the evaluation results obtained in the competitions are discussed.
Finding good Echo State Networks to control an underwater robot using evolutionary computations
(2004)
Advanced driver assistance systems (ADAS) are technology systems and devices designed as an aid to the driver of a vehicle. One of the critical components of any ADAS is the traffic sign recognition module. For this module to achieve real-time performance, some preprocessing of input images must be done, which consists of a traffic sign detection (TSD) algorithm to reduce the possible hypothesis space. Performance of TSD algorithm is critical.
One of the best algorithms used for TSD is the Radial Symmetry Detector (RSD), which can detect both Circular [7] and Polygonal traffic signs [5]. This algorithm runs in real-time on high end personal computers, but computational performance of must be improved in order to be able to run in real-time in embedded computer platforms.
To improve the computational performance of the RSD, we propose a multiscale approach and the removal of a gaussian smoothing filter used in this algorithm. We evaluate the performance on both computation times, detection and false positive rates on a synthetic image dataset and on the german traffic sign detection benchmark [29].
We observed significant speedups compared to the original algorithm. Our Improved Radial Symmetry Detector is up to 5.8 times faster than the original on detecting Circles, up to 3.8 times faster on Triangle detection, 2.9 times faster on Square detection and 2.4 times faster on Octagon detection. All of this measurements were observed with better detection and false positive rates than the original RSD.
When evaluated on the GTSDB, we observed smaller speedups, in the range of 1.6 to 2.3 times faster for Circle and Regular Polygon detection, but for Circle detection we observed a decreased detection rate than the original algorithm, while for Regular Polygon detection we always observed better detection rates. False positive rates were high, in the range of 80% to 90%.
We conclude that our Improved Radial Symmetry Detector is a significant improvement of the Radial Symmetry Detector, both for Circle and Regular polygon detection. We expect that our improved algorithm will lead the way to obtain real-time traffic sign detection and recognition in embedded computer platforms.
Extraction of text information from visual sources is an important component of many modern applications, for example, extracting the text from traffic signs on a road scene in an autonomous vehicle. For natural images or road scenes this is a unsolved problem. In this thesis the use of histogram of stroke widths (HSW) for character and noncharacter region classification is presented. Stroke widths are extracted using two methods. One is based on the Stroke Width Transform and another based on run lengths. The HSW is combined with two simple region features– aspect and occupancy ratios– and then a linear SVM is used as classifier. One advantage of our method over the state of the art is that it is script-independent and can also be used to verify detected text regions with the purpose of reducing false positives. Our experiments on generated datasets of Latin, CJK, Hiragana and Katakana characters show that the HSW is able to correctly classify at least 90% of the character regions, a similar figure is obtained for non-character regions. This performance is also obtained when training the HSW with one script and testing with a different one, and even when characters are rotated. On the English and Kannada portions of the Chars74K dataset we obtained over 95% correctly classified character regions. The use of raycasting for text line grouping is also proposed. By combining it with our HSW-based character classifier, a text detector based on Maximally Stable Extremal Regions (MSER) was implemented. The text detector was evaluated on our own dataset of road scenes from the German Autobahn, where 65% precision, 72% recall with a f-score of 69% was obtained. Using the HSW as a text verifier increases precision while slightly reducing recall. Our HSW feature allows the building of a script-independent and low parameter count classifier for character and non-character regions.
Risk-Based Authentication for OpenStack: A Fully Functional Implementation and Guiding Example
(2023)
Online services have difficulties to replace passwords with more secure user authentication mechanisms, such as Two-Factor Authentication (2FA). This is partly due to the fact that users tend to reject such mechanisms in use cases outside of online banking. Relying on password authentication alone, however, is not an option in light of recent attack patterns such as credential stuffing.
Risk-Based Authentication (RBA) can serve as an interim solution to increase password-based account security until better methods are in place. Unfortunately, RBA is currently used by only a few major online services, even though it is recommended by various standards and has been shown to be effective in scientific studies. This paper contributes to the hypothesis that the low adoption of RBA in practice can be due to the complexity of implementing it. We provide an RBA implementation for the open source cloud management software OpenStack, which is the first fully functional open source RBA implementation based on the Freeman et al. algorithm, along with initial reference tests that can serve as a guiding example and blueprint for developers.
With the rising interest in vehicular communication systems many proposals for secure vehicle-to-vehicle commu- nication were made in recent years. Also, several standard- ization activities concerning the security and privacy measures in these communication systems were initiated in Europe and in US. Here, we discuss some limitations for secure vehicle- to-infrastructure communication in the existing standards of the European Telecommunications Standards Institute. Next, a vulnerability analysis for roadside stations on one side and security and privacy requirements for roadside stations on the other side are given. Afterwards, a proposal for a multi-domain public key architecture for intelligent transport systems, which considers the necessities of road infrastructure authorities and vehicle manufacturers, is introduced. The domains of the public key infrastructure are cryptographically linked based on local trust lists. In addition, a crypto agility concept is suggested, which takes adaptation of key length and cryptographic algorithms during PKI operation into account.
Secure vehicular communication has been discussed over a long period of time. Now,- this technology is implemented in different Intelligent Transportation System (ITS) projects in europe. In most of these projects a suitable Public Key Infrastructure (PKI) for a secure communication between involved entities in a Vehicular Ad hoc Network (VANET) is needed. A first proposal for a PKI architecture for Intelligent Vehicular Systems (IVS PKI) is given by the car2car communication consortium. This architecture however mainly deals with inter vehicular communication and is less focused on the needs of Road Side Units. Here, we propose a multi-domain PKI architecture for Intelligent Transportation Systems, which considers the necessities of road infrastructure authorities and vehicle manufacturers, today. The PKI domains are cryptographically linked based on local trust lists. In addition, a crypto agility concept is suggested, which takes adaptation of key length and cryptographic algorithms during PKI operation into account.