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Differential Analysis and Fingerprinting of ZombieLoads on Block Ciphers (2021)

Schlüter, Till ; Lemke-Rust, Kerstin

What's in Score for Website Users: A Data-driven Long-term Study on Risk-based Authentication Characteristics (2021)

Wiefling, Stephan ; Dürmuth, Markus ; Lo Iacono, Luigi

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.

Authentication in Ultra Large Scale REST-based Systems (2020)

Nguyen, Hoai Viet

With the digital transformation, software systems have become an integral part of our society and economy. In every part of our life, software systems are increasingly utilized to, e.g., simplify housework or to optimize business processes. All these applications are connected to the Internet, which already includes millions of software services consumed by billions of people. Applications which process such a magnitude of users and data traffic requires to be highly scalable and are therefore denoted as Ultra Large Scale (ULS) systems. Roy Fielding has defined one of the first approaches which allows designing modern ULS software systems. In his doctoral thesis, Fielding introduced the architectural style Representational State Transfer (REST) which builds the theoretical foundation of the web. At present, the web is considered as the world's largest ULS system. Due to a large number of users and the significance of software for society and the economy, the security of ULS systems is another crucial quality factor besides high scalability.

Einsatz von Künstlicher Intelligenz im internationalen Spitzensport – Eine Erhebung des Status Quo (2020)

Hammes, Fabian ; Link, Daniel ; Lames, Martin ; Hagg, Alexander ; Asteroth, Alexander ; Pfeiffer, Mark

Anhand eines Literaturreviews sowie Interviews mit ausgewählten Expert/innen aus 7 Ländern wird der aktuelle Stand der KI im internationalen Spitzensport beschrieben. Die Ergebnisse zeigen primär Aktivitäten in den Bereichen Bilderkennung, Signalverarbeitung und Regelerkennung.

Einsatzmöglichkeiten und Transfer von Künstlicher Intelligenz im internationalen Spitzensport – zwischen Small und Big Data (2020)

Hagg, Alexander ; Asteroth, Alexander ; Pfeiffer, Mark ; Hammes, Fabian ; Link, Daniel

Ausgehend von einer Sichtung aktueller Ergebnisse in der KI werden Faktoren identifiziert, die zur erfolgreichen Anwendung führen. Ein Abgleich mit den Gegebenheiten im Spitzen-sport führt zur Identifikation potenzieller Handlungsfelder.

Informationelle Selbstbestimmung am Arbeitsplatz (2021)

Lo Iacono, Luigi ; Schmitt, Hartmut

Exchange of Preparatory Information for Secure and Usable Cryptocurrency Transactions (2020)

Kattwinkel, Oliver ; Rademacher, Michael

Bond Graph Modelling for Control, Fault Diagnosis and Failure Prognosis (2021)

Borutzky, Wolfgang

This book shows in a comprehensive presentation how Bond Graph methodology can support model-based control, model-based fault diagnosis, fault accommodation, and failure prognosis by reviewing the state-of-the-art, presenting a hybrid integrated approach to Bond Graph model-based fault diagnosis and failure prognosis, and by providing a review of software that can be used for these tasks.

Benutzerfreundlicher Datenschutz in Cloud-basierten Office-Paketen (2021)

Tolsdorf, Jan ; Dehling, Florian ; Feth, Denis

Verarbeitung von Beschäftigtendaten (2021)

Polst, Svenja ; Tolsdorf, Jan ; Dehling, Florian ; Feth, Denis

Objektive Fahrdynamikuntersuchungen an Pkw mit batterieelektrischen Antrieben (2020)

Pfister, Andreas ; Geng, Andreas ; Kleeberger, Christian ; Küstenmacher, Anastassia

Objective Vehicle Dynamics Investigations on Cars with Battery Electric Drives (2020)

Pfister, Andreas ; Geng, Andreas ; Kleeberger, Christian ; Küstenmacher, Anastassia

How automatic speed control based on distance affects user behaviours in telepresence robot navigation within dense conference-like environments (2020)

Batmaz, Anil Ufuk ; Maiero, Jens ; Kruijff, Ernst ; Riecke, Bernhard E. ; Neustaedter, Carman ; Stuerzlinger, Wolfgang

Telepresence robots allow users to be spatially and socially present in remote environments. Yet, it can be challenging to remotely operate telepresence robots, especially in dense environments such as academic conferences or workplaces. In this paper, we primarily focus on the effect that a speed control method, which automatically slows the telepresence robot down when getting closer to obstacles, has on user behaviors. In our first user study, participants drove the robot through a static obstacle course with narrow sections. Results indicate that the automatic speed control method significantly decreases the number of collisions. For the second study we designed a more naturalistic, conference-like experimental environment with tasks that require social interaction, and collected subjective responses from the participants when they were asked to navigate through the environment. While about half of the participants preferred automatic speed control because it allowed for smoother and safer navigation, others did not want to be influenced by an automatic mechanism. Overall, the results suggest that automatic speed control simplifies the user interface for telepresence robots in static dense environments, but should be considered as optionally available, especially in situations involving social interactions.

An Analysis of Phenotypic Diversity in Multi-solution Optimization (2020)

Hagg, Alexander ; Preuss, Mike ; Asteroth, Alexander ; Bäck, Thomas

An investigation of regression as an avenue to find precision-runtime trade-off for object segmentation (2020)

Prabhu, Arun Rajendra

The ability to finely segment different instances of various objects in an environment forms a critical tool in the perception tool-box of any autonomous agent. Traditionally instance segmentation is treated as a multi-label pixel-wise classification problem. This formulation has resulted in networks that are capable of producing high-quality instance masks but are extremely slow for real-world usage, especially on platforms with limited computational capabilities. This thesis investigates an alternate regression-based formulation of instance segmentation to achieve a good trade-off between mask precision and run-time. Particularly the instance masks are parameterized and a CNN is trained to regress to these parameters, analogous to bounding box regression performed by an object detection network. In this investigation, the instance segmentation masks in the Cityscape dataset are approximated using irregular octagons and an existing object detector network (i.e., SqueezeDet) is modified to regresses to the parameters of these octagonal approximations. The resulting network is referred to as SqueezeDetOcta. At the image boundaries, object instances are only partially visible. Due to the convolutional nature of most object detection networks, special handling of the boundary adhering object instances is warranted. However, the current object detection techniques seem to be unaffected by this and handle all the object instances alike. To this end, this work proposes selectively learning only partial, untainted parameters of the bounding box approximation of the boundary adhering object instances. Anchor-based object detection networks like SqueezeDet and YOLOv2 have a discrepancy between the ground-truth encoding/decoding scheme and the coordinate space used for clustering, to generate the prior anchor shapes. To resolve this disagreement, this work proposes clustering in a space defined by two coordinate axes representing the natural log transformations of the width and height of the ground-truth bounding boxes. When both SqueezeDet and SqueezeDetOcta were trained from scratch, SqueezeDetOcta lagged behind the SqueezeDet network by a massive ≈ 6.19 mAP. Further analysis revealed that the sparsity of the annotated data was the reason for this lackluster performance of the SqueezeDetOcta network. To mitigate this issue transfer-learning was used to fine-tune the SqueezeDetOcta network starting from the trained weights of the SqueezeDet network. When all the layers of the SqueezeDetOcta were fine-tuned, it outperformed the SqueezeDet network paired with logarithmically extracted anchors by ≈ 0.77 mAP. In addition to this, the forward pass latencies of both SqueezeDet and SqueezeDetOcta are close to ≈ 19ms. Boundary adhesion considerations, during training, resulted in an improvement of ≈ 2.62 mAP of the baseline SqueezeDet network. A SqueezeDet network paired with logarithmically extracted anchors improved the performance of the baseline SqueezeDet network by ≈ 1.85 mAP. In summary, this work demonstrates that if given sufficient fine instance annotated data, an existing object detection network can be modified to predict much finer approximations (i.e., irregular octagons) of the instance annotations, whilst having the same forward pass latency as that of the bounding box predicting network. The results justify the merits of logarithmically extracted anchors to boost the performance of any anchor-based object detection network. The results also showed that the special handling of image boundary adhering object instances produces more performant object detectors.

A Compact 16 Channel Embedded System with High Dynamic Range Readout and Heater Management for Semiconducting Metal Oxide Gas Sensors (2020)

Hammer, Christof ; Warmer, Johannes ; Maurer, Stephan ; Kaul, Peter ; Thoelen, Ronald ; Jung, Norbert

The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100Ω to 5 GΩ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance.

Black-Box Optimization of Object Detector Scales (2020)

Muthuraja, Mohandass ; Arriaga, Octavio ; Plöger, Paul ; Kirchner, Frank ; Valdenegro-Toro, Matias

Object detectors have improved considerably in the last years by using advanced CNN architectures. However, many detector hyper-parameters are generally manually tuned, or they are used with values set by the detector authors. Automatic Hyper-parameter optimization has not been explored in improving CNN-based object detectors hyper-parameters. In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES. We show that by tuning the input image size and prior box anchor scale on Faster R-CNN mAP increases by 2% on PASCAL VOC 2007, and by 3% with SSD. On the COCO dataset with SSD there are mAP improvement in the medium and large objects, but mAP decreases by 1% in small objects. We also perform a regression analysis to find the significant hyper-parameters to tune.

Perception for Autonomous Systems (PAZ) (2020)

Arriaga, Octavio ; Valdenegro-Toro, Matias ; Muthuraja, Mohandass ; Devaramani, Sushma ; Kirchner, Frank

In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of abstraction in accordance to their requirements or skill level. More specifically, PAZ is divided into three hierarchical levels which we refer to as pipelines, processors, and backends. These abstractions allows users to compose functions in a hierarchical modular scheme that can be applied for preprocessing, data-augmentation, prediction and postprocessing of inputs and outputs of machine learning (ML) models. PAZ uses these abstractions to build reusable training and prediction pipelines for multiple robot perception tasks such as: 2D keypoint estimation, 2D object detection, 3D keypoint discovery, 6D pose estimation, emotion classification, face recognition, instance segmentation, and attention mechanisms.

Can Reinforcement Learning for Continuous Control Generalize Across Physics Engines? (2020)

Mohammed, Aaqib Parvez ; Valdenegro-Toro, Matias

Reinforcement learning (RL) algorithms should learn as much as possible about the environment but not the properties of the physics engines that generate the environment. There are multiple algorithms that solve the task in a physics engine based environment but there is no work done so far to understand if the RL algorithms can generalize across physics engines. In this work, we compare the generalization performance of various deep reinforcement learning algorithms on a variety of control tasks. Our results show that MuJoCo is the best engine to transfer the learning to other engines. On the other hand, none of the algorithms generalize when trained on PyBullet. We also found out that various algorithms have a promising generalizability if the effect of random seeds can be minimized on their performance.

Perceiving jittering self-motion in a field of lollipops from ages 4 to 95 (2020)

Bury, Nils-Alexander ; Jenkin, Michael R. ; Allison, Robert S. ; Harris, Laurence R.

An internal model of self-motion provides a fundamental basis for action in our daily lives, yet little is known about its development. The ability to control self-motion develops in youth and often deteriorates with advanced age. Self-motion generates relative motion between the viewer and the environment. Thus, the smoothness of the visual motion created will vary as control improves. Here, we study the influence of the smoothness of visually simulated self-motion on an observer's ability to judge how far they have travelled over a wide range of ages. Previous studies were typically highly controlled and concentrated on university students. But are such populations representative of the general public? And are there developmental and sex effects? Here, estimates of distance travelled (visual odometry) during visually induced self-motion were obtained from 466 participants drawn from visitors to a public science museum. Participants were presented with visual motion that simulated forward linear self-motion through a field of lollipops using a head-mounted virtual reality display. They judged the distance of their simulated motion by indicating when they had reached the position of a previously presented target. The simulated visual motion was presented with or without horizontal or vertical sinusoidal jitter. Participants' responses indicated that they felt they travelled further in the presence of vertical jitter. The effectiveness of the display increased with age over all jitter conditions. The estimated time for participants to feel that they had started to move also increased slightly with age. There were no differences between the sexes. These results suggest that age should be taken into account when generating motion in a virtual reality environment. Citizen science studies like this can provide a unique and valuable insight into perceptual processes in a truly representative sample of people.

OSC data (2020)

Bury, Nils

OSC data file

Accelerating Evolutionary Design Exploration with Predictive and Generative Models (2020)

Gaier, Adam

Optimization plays an essential role in industrial design, but is not limited to minimization of a simple function, such as cost or strength. These tools are also used in conceptual phases, to better understand what is possible. To support this exploration we focus on Quality Diversity (QD) algorithms, which produce sets of varied, high performing solutions. These techniques often require the evaluation of millions of solutions -- making them impractical in design cases. In this thesis we propose methods to radically improve the data-efficiency of QD with machine learning, enabling its application to design. In our first contribution, we develop a method of modeling the performance of evolved neural networks used for control and design. The structures of these networks grow and change, making them difficult to model -- but with a new method we are able to estimate their performance based on their heredity, improving data-efficiency by several times. In our second contribution we combine model-based optimization with MAP-Elites, a QD algorithm. A model of performance is created from known designs, and MAP-Elites creates a new set of designs using this approximation. A subset of these designs are the evaluated to improve the model, and the process repeats. We show that this approach improves the efficiency of MAP-Elites by orders of magnitude. Our third contribution integrates generative models into MAP-Elites to learn domain specific encodings. A variational autoencoder is trained on the solutions produced by MAP-Elites, capturing the common “recipe” for high performance. This learned encoding can then be reused by other algorithms for rapid optimization, including MAP-Elites. Throughout this thesis, though the focus of our vision is design, we examine applications in other fields, such as robotics. These advances are not exclusive to design, but serve as foundational work on the integration of QD and machine learning.

Porosity effects on post-buckling behavior of geometrically imperfect metal foam doubly-curved shells with stiffeners (2020)

Mirjavadi, Seyed Sajad ; Forsat, Masoud ; Yahya, Yahya Zakariya ; Barati, Mohammad Reza ; Jayasimha, Anirudh Narasimamurthy ; Hamouda, A. M.S.

The role of bond graph modeling and simulation in mechatronics systems (2002)

Granda, Jose J.

People Analytics: Was Organisationen vom Profifußball lernen können (2020)

Dum, Thorsten ; Sierau, Axel

More Than Just Good Passwords? A Study on Usability and Security Perceptions of Risk-based Authentication (2020)

Wiefling, Stephan ; Dürmuth, Markus ; Lo Iacono, Luigi

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.

On the Diagnosability of Actions Performed by Contemporary Robotic Systems (2020)

Mitrevski, Alex ; Abdelrahman, Ahmed Faisal ; Narasimamurthy, Anirudh ; Plöger, Paul G.

When a robotic agent experiences a failure while acting in the world, it should be possible to discover why that failure has occurred, namely to diagnose the failure. In this paper, we argue that the diagnosability of robot actions, at least in a classical sense, is a feature that cannot be taken for granted since it strongly depends on the underlying action representation. We specifically define criteria that determine the diagnosability of robot actions. The diagnosability question is then analysed in the context of a handle manipulation action, such that we discuss two different representations of the action – a composite policy with a learned success model for the action parameters, and a neural network-based monolithic policy – both of which exist on different sides of the diagnosability spectrum. Through this comparison, we conclude that composite actions are more suited to explicit diagnosis, but representations with less prior knowledge are more flexible. This suggests that model learning may provide balance between flexibility and diagnosability; however, data-driven diagnosis methods also need to be enhanced in order to deal with the complexity of modern robots.

Comparing Non-Visual and Visual Guidance Methods for Narrow Field of View Augmented Reality Displays (2020)

Marquardt, Alexander ; Trepkowski, Christina ; Eibich, Tom David ; Maiero, Jens ; Kruijff, Ernst ; Schöning, Johannes

HeadJoystick: Improving Flying in VR using a Novel Leaning-Based Interface (2020)

Hashemian, Abraham ; Lotfaliei, Matin ; Adhikari, Ashu ; Kruijff, Ernst ; Riecke, Bernhard

Gone But Not Forgotten: Evaluating Performance and Scalability of Real-Time Mesoscopic Agents (2020)

Seele, Sven ; Pätzold, Luisa ; Herpers, Rainer

FeetBack: Augmenting Robotic Telepresence with Haptic Feedback on the Feet (2020)

Jones, Brennan ; Maiero, Jens ; Mogharrab, Alireza ; Aguilar, Ivan A. ; Adhikari, Ashu ; Riecke, Bernhard E. ; Kruijff, Ernst ; Neustaedter, Carman ; Lindeman, Robert W.

Telepresence robots allow people to participate in remote spaces, yet they can be difficult to manoeuvre with people and obstacles around. We designed a haptic-feedback system called “FeetBack," which users place their feet in when driving a telepresence robot. When the robot approaches people or obstacles, haptic proximity and collision feedback are provided on the respective sides of the feet, helping inform users about events that are hard to notice through the robot’s camera views. We conducted two studies: one to explore the usage of FeetBack in virtual environments, another focused on real environments.We found that FeetBack can increase spatial presence in simple virtual environments. Users valued the feedback to adjust their behaviour in both types of environments, though it was sometimes too frequent or unneeded for certain situations after a period of time. These results point to the value of foot-based haptic feedback for telepresence robot systems, while also the need to design context-sensitive haptic feedback.

Deep Semantic Image Segmentation for UAV-UGV Cooperative Path Planning: A Car Park Use Case (2020)

Vasić, Mirela Kundid ; Drak, Ahmad ; Bugarin, Nediljko ; Music, Josip ; Pomrehn, Christoph ; Schöbel, Maximilian ; Johenneken, Maximilian ; Stancic, Ivo ; Papic, Vladan ; Kruzic, Stanko ; Herpers, Rainer

Damage Analysis of Grassland from Aerial Images Applying Convolutional Neural Networks (2020)

Johenneken, Maximilian ; Drak, Ahmad ; Herpers, Rainer

Voting for Authorship Attribution Applied to Dark Web Data (2020)

Sennewald, Britta ; Herpers, Rainer ; Hülsmann, Marco ; Kent, Kenneth B.

Investigation of Encrypted and Obfuscated Network Traffic Utilizing Machine Learning (2020)

Boldt, Kay ; Kent, Kenneth B. ; Herpers, Rainer

This paper utilizes machine learning to investigate the classification of encryption applied to network traffic and the underlying activities. It is firstly motivated by the difficulty of traditional traffic classification caused by additional encryption as ports and headers are hidden. Secondly, the results also present the effectiveness of currently available privacy-enhancing technologies. A new dataset is created, containing Pure (without additional encryption), Tor, Tor with obfuscation, VPN and VPN+Tor network traffic. Additionally, there are five different activities performed during each kind of traffic recording, namely audio streaming, browsing, P2P/SFTP file transfers and video conferencing. The traffic is classified by extracting features based on flows calculated by ARGUS and CICFlowMeter, combining three classifiers with seven feature selection algorithms. The results for the classification of the encryption clearly indicate the possibility of using this detection system in a modified fashion within a practical application. For the detection of activities inside encrypted network traffic, the results show that the disguise is ineffective. Overall, this reveals the need to improve the resistance of commonly used techniques for the protection of network traffic against machine learning.

Accelerating Force-Directed Graph Drawing with RT Cores (2021)

Zellmann, Stefan ; Weier, Martin ; Wald, Ingo

Graph drawing with spring embedders employs a V ×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 4× to 13× over a CUDA software implementation.

A Comparative Study of Uncertainty Estimation Methods in Deep Learning Based Classification Models (2020)

Manivannan, Iswariya

Deep learning models produce overconfident predictions even for misclassified data. This work aims to improve the safety guarantees of software-intensive systems that use deep learning based classification models for decision making by performing comparative evaluation of different uncertainty estimation methods to identify possible misclassifications. In this work, uncertainty estimation methods applicable to deep learning models are reviewed and those which can be seamlessly integrated to existing deployed deep learning architectures are selected for evaluation. The different uncertainty estimation methods, deep ensembles, test-time data augmentation and Monte Carlo dropout with its variants, are empirically evaluated on two standard datasets (CIFAR-10 and CIFAR-100) and two custom classification datasets (optical inspection and RoboCup@Work dataset). A relative ranking between the methods is provided by evaluating the deep learning classifiers on various aspects such as uncertainty quality, classifier performance and calibration. Standard metrics like entropy, cross-entropy, mutual information, and variance, combined with a rank histogram based method to identify uncertain predictions by thresholding on these metrics, are used to evaluate uncertainty quality. The results indicate that Monte Carlo dropout combined with test-time data augmentation outperforms all other methods by identifying more than 95% of the misclassifications and representing uncertainty in the highest number of samples in the test set. It also yields a better classifier performance and calibration in terms of higher accuracy and lower Expected Calibration Error (ECE), respectively. A python based uncertainty estimation library for training and real-time uncertainty estimation of deep learning based classification models is also developed.

Evaluation of a "Smart" Pedestrian Counting System Based on Echo State Networks (2009)

Mathews, Emi ; Poigné, Axel

We have designed an inexpensive intelligent pedestrian counting system. The pedestrian counting system consists of several counters that can be connected together in a distributed fashion and communicate over the wireless channel. The motion pattern is recorded using a set of passive infrared (PIR) sensors. Each counter has one wireless sensor node that processes the PIR sensor data and transmits it to a base station. Then echo state network, a special kind of recurrent neural network, is used to predict the pedestrian count from the input pattern. The evaluation of the performance of such networks in a novel kind of application is one focus of this work. The counter gave a performance of 80.4% which is better than the commercially available low-priced pedestrian counters. The article reports the experiments we did for analyzing the counterperformance and lists the strengths and limitations of the current implementation. It will also report the preliminary test results obtained by substituting the PIR sensors with low-cost active IR distance sensors which can improve the counter performance further.

Interaction of natural flavonoid eriocitrin with β-cyclodextrin and hydroxypropyl-β-cyclodextrin: an NMR and molecular dynamics investigation (2020)

Cesari, Andrea ; Uccello Barretta, Gloria ; Kirschner, Karl N. ; Pappalardo, Matteo ; Basile, Livia ; Guccione, Salvatore ; Russotto, Clizia ; Lauro, Maria Rosaria ; Cavaliere, Francesca ; Balzano, Federica

Lazy Robot Control by Relaxation of Motion and Force Constraints (2020)

Vukcevic, Djordje

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.

Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading (2020)

Gaddipati, Sasi Kiran ; Nair, Deebul ; Plöger, Paul G.

Automatic Short Answer Grading (ASAG) is the process of grading the student answers by computational approaches given a question and the desired answer. Previous works implemented the methods of concept mapping, facet mapping, and some used the conventional word embeddings for extracting semantic features. They extracted multiple features manually to train on the corresponding datasets. We use pretrained embeddings of the transfer learning models, ELMo, BERT, GPT, and GPT-2 to assess their efficiency on this task. We train with a single feature, cosine similarity, extracted from the embeddings of these models. We compare the RMSE scores and correlation measurements of the four models with previous works on Mohler dataset. Our work demonstrates that ELMo outperformed the other three models. We also, briefly describe the four transfer learning models and conclude with the possible causes of poor results of transfer learning models.

Designing Air Flow with Surrogate-Assisted Phenotypic Niching (2020)

Hagg, Alexander ; Wilde, Dominik ; Asteroth, Alexander ; Bäck, Thomas

Accelerating Force-Directed Graph Drawing with RT Cores (2020)

Zellmann, Stefan ; Weier, Martin ; Wald, Ingo

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.

6. Usable Security und Privacy Workshop (2020)

Lo Iacono, Luigi ; Schmitt, Hartmut ; Polst, Svenja ; Heinemann, Andreas

Bei der sechsten Ausgabe des wissenschaftlichen Workshops ”Usable Security und Privacy” auf der Mensch und Computer 2020 werden wie in den vergangenen Jahren aktuelle Forschungs- und Praxisbeiträge präsentiert und anschließend mit allen Teilnehmenden diskutiert. Drei Beiträge befassen sich dieses Jahr mit dem Thema Privatsphäre, einer mit dem Thema Sicherheit. Mit dem Workshop wird ein etabliertes Forum fortgeführt und weiterentwickelt, in dem sich Expert*innen aus unterschiedlichen Domänen, z. B. dem Usability- und Security-Engineering, transdisziplinär austauschen können.

Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks! (2020)

Matin, Maryam ; Valdenegro-Toro, Matias

Facial emotion recognition is the task to classify human emotions in face images. It is a difficult task due to high aleatoric uncertainty and visual ambiguity. A large part of the literature aims to show progress by increasing accuracy on this task, but this ignores the inherent uncertainty and ambiguity in the task. In this paper we show that Bayesian Neural Networks, as approximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able to model the aleatoric uncertainty in facial emotion recognition, and produce output probabilities that are closer to what a human expects. We also show that calibration metrics show strange behaviors for this task, due to the multiple classes that can be considered correct, which motivates future work. We believe our work will motivate other researchers to move away from Classical and into Bayesian Neural Networks.

YAWL: An open source Business Process Management System from science for science (2020)

Adams, Michael ; Hense, Andreas V. ; ter Hofstede, Arthur H.M.

YAWL (Yet Another Workflow Language) is an open source Business Process Management System, first released in 2003. YAWL grew out of a university research environment to become a unique system that has been deployed worldwide as a laboratory environment for research in Business Process Management and as a productive system in other scientific domains.

Using Active Learning for Assisted Short Answer Grading (2020)

Kishaan, Jeeveswaran ; Muthuraja, Mohandass ; Nair, Deebul ; Plöger, Paul G.

Efficient and comprehensive assessment of students knowledge is an imperative task in any learning process. Short answer grading is one of the most successful methods in assessing the knowledge of students. Many supervised learning and deep learning approaches have been used to automate the task of short answer grading in the past. We investigate why assistive grading with active learning would be the next logical step in this task as there is no absolute ground truth answer for any question and the task is very subjective in nature. We present a fast and easy method to harness the power of active learning and natural language processing in assisting the task of grading short answer questions. A webbased GUI is designed and implemented to incorporate an interactive short answer grading system. The experiments show that active learning saves the time and effort of graders in assessment and reaches the performance of supervised learning with less amount of graded answers for training.

Evaluation of a Multi-Layer 2.5D display in comparison to conventional 3D stereoscopic glasses (2020)

Schäfer, Maximilian ; Kruijff, Ernst ; Hinkenjann, André

In this paper we propose and evaluate a custom-build projection-based multilayer 2.5D display, consisting of three layers of images, and compare performance to a stereoscopic 3D display. Stereoscopic vision can increase the involvement and enhance game experience, however may induce possible side effects, e.g. motion sickness and simulator sickness. To overcome the disadvantage of multiple discrete depths, in our system perspective rendering and head-tracking is used. A study was performed to evaluate this display with 20 participants playing custom-designed games. The results indicated that the multi-layer display caused fewer side effects than the stereoscopic display and provided good usability. The participants also stated a better or equal spatial perception, while the cognitive load stayed the same.

Technical Fundamentals of Blockchain Systems (2020)

Kattwinkel, Oliver ; Rademacher, Michael

This work provides a short but technical introduction to the main building blocks of a blockchain. It argues that a blockchain is not a revolutionary technology but rather a clever combination of three fields: cryptography, decentralization and game theory. In addition, it summaries the differences between a public, private and federate blockchain model and the two prominent consensus mechanism Proof-of-Work (POW) and Proof-of-Stake (POS).

Predictive Analytics und Data Mining (2020)

von der Hude, Marlis

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.

Learning to Close the Gap: Combining Task Frame Formalism and Reinforcement Learning for Compliant Vegetable Cutting (2020)

Padalkar, Abhishek ; Nieuwenhuisen, Matthias ; Schneider, Sven ; Schulz, Dirk

Compliant manipulation is a crucial skill for robots when they are supposed to act as helping hands in everyday household tasks. Still, nowadays, those skills are hand-crafted by experts which frequently requires labor-intensive, manual parameter tuning. Moreover, some tasks are too complex to be specified fully using a task specification. Learning these skills, by contrast, requires a high number of costly and potentially unsafe interactions with the environment. We present a compliant manipulation approach using reinforcement learning guided by the Task Frame Formalism, a task specification method. This allows us to specify the easy to model knowledge about a task while the robot learns the unmodeled components by reinforcement learning. We evaluate the approach by performing a compliant manipulation task with a KUKA LWR 4+ manipulator. The robot was able to learn force control policies directly on the robot without using any simulation.

Research Project beyondSPAI - The Safe and Reliable Monitoring of Adaptive Safety Zones in the Proximity of Collaborating Industrial Robots Using an Intelligent InGaAs Camera System (2020)

Hammer, Christof ; Jung, Norbert

Evaluating immersive experiences during Covid-19 and beyond (2020)

Steed, Anthony ; Ortega, Francisco R. ; Williams, Adam S. ; Kruijff, Ernst ; Stuerzlinger, Wolfgang ; Batmaz, Anil Ufuk ; Won, Andrea Stevenson ; Rosenberg, Evan Suma ; Simeone, Adalberto L. ; Hayes, Aleshia

Real-time Convolutional Neural Networks for emotion and gender classification (2019)

Arriaga, Octavio ; Valdenegro-Toro, Matias ; Plöger, Paul

Emotion and gender recognition from facial features are important properties of human empathy. Robots should also have these capabilities. For this purpose we have designed special convolutional modules that allow a model to recognize emotions and gender with a considerable lower number of parameters, enabling real-time evaluation on a constrained platform. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset, while requiring a computation time of less than 0.008 seconds on a Core i7 CPU. All our code, demos and pre-trained architectures have been released under an open-source license in our repository at https://github.com/oarriaga/face classification.

Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds (2020)

K, Swaroop Bhandary ; Hochgeschwender, Nico ; Plöger, Paul ; Kirchner, Frank ; Valdenegro-Toro, Matias

Deep learning models are extensively used in various safety critical applications. Hence these models along with being accurate need to be highly reliable. One way of achieving this is by quantifying uncertainty. Bayesian methods for UQ have been extensively studied for Deep Learning models applied on images but have been less explored for 3D modalities such as point clouds often used for Robots and Autonomous Systems. In this work, we evaluate three uncertainty quantification methods namely Deep Ensembles, MC-Dropout and MC-DropConnect on the DarkNet21Seg 3D semantic segmentation model and comprehensively analyze the impact of various parameters such as number of models in ensembles or forward passes, and drop probability values, on task performance and uncertainty estimate quality. We find that Deep Ensembles outperforms other methods in both performance and uncertainty metrics. Deep ensembles outperform other methods by a margin of 2.4% in terms of mIOU, 1.3% in terms of accuracy, while providing reliable uncertainty for decision making.

Remote Fault Diagnosis of Robots Using a Robotic Black Box (2016)

Drak, Ahmad ; Youssef, Youssef ; Plöger, Paul ; Kuestenmacher, Anastassia

Autonomous mobile robots comprise of several hardware and software components. These components interact with each other continuously in order to achieve autonomity. Due to the complexity of such a task, a monumental responsibility is bestowed upon the developer to make sure that the robot is always operable. Hence, some means of detecting faults should be readily available. In this work, the aforementioned fault-detection system is a robotic black box (RBB) attached to the robot which acquires all the relevant measurements of the system that are needed to achieve a fault-free robot. Due to limited computational and memory resources on-board the RBB, a distributed diagnosis is proposed. That is, the fault diagnosis task (detection and isolation) is shared among an on-board component (the black box) and an off-board component (an external computer). The distribution of the diagnosis task allows for a non-intrusive method of detecting and diagnosing faults, in addition to the ability of remotely diagnosing a robot and potentially issuing a repair command. In addition to decomposing the diagnosis task and allowing remote diagnosability of the robot, another key feature of this work is the addition of expert human knowledge to aid in the fault detection process.

Algorithmische Informationstheorie: Berechenbarkeit und Komplexität verstehen (2020)

Witt, Kurt-Ulrich ; Müller, Martin Eric

Dieses Lehrbuch behandelt zunächst zentrale Themen der klassischen Theoretischen Informatik und führt darauf aufbauend in die Grundlagen der Algorithmischen Informationstheorie ein.

Representation and Experience-Based Learning of Explainable Models for Robot Action Execution (2021)

Mitrevski, Alex ; Plöger, Paul G. ; Lakemeyer, Gerhard

For robots acting in human-centered environments, the ability to improve based on experience is essential for reliable and adaptive operation; however, particularly in the context of robot failure analysis, experience-based improvement is only useful if robots are also able to reason about and explain the decisions they make during execution. In this paper, we describe and analyse a representation of execution-specific knowledge that combines (i) a relational model in the form of qualitative attributes that describe the conditions under which actions can be executed successfully and (ii) a continuous model in the form of a Gaussian process that can be used for generating parameters for action execution, but also for evaluating the expected execution success given a particular action parameterisation. The proposed representation is based on prior, modelled knowledge about actions and is combined with a learning process that is supervised by a teacher. We analyse the benefits of this representation in the context of two actions – grasping handles and pulling an object on a table – such that the experiments demonstrate that the joint relational-continuous model allows a robot to improve its execution based on experience, while reducing the severity of failures experienced during execution.

Teaching Technical Journalism with an Engineering Foundation (2020)

Kirschner, Karl N. ; Keil, Susanne ; Seuser, Katharina ; Siefer, Christine

Evaluation of Risk-based Re-Authentication Methods (2020)

Wiefling, Stephan ; Patil, Tanvi ; Dürmuth, Markus ; Lo Iacono, Luigi

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.

Domestic Robotics (2016)

Prassler, Erwin ; Munich, Mario E. ; Pirjanian, Paolo ; Kosuge, Kazuhiro

Domestic Robotics (2008)

Prassler, Erwin ; Kosuge, Kazuhiro

Are There Extended Cognitive Improvements from Different Kinds of Acute Bouts of Physical Activity? (2020)

Haeger, Mathias ; Bury, Nils ; Endres, Christian ; Klatt, Stefanie

Acute bouts of physical activity of at least moderate intensity have shown to enhance cognition in young as well as older adults. This effect has been observed for different kinds of activities such as aerobic or strength and coordination training. However, only few studies have directly compared these activities regarding their effectiveness. Further, most previous studies have mainly focused on inhibition and have not examined other important core executive functions (i.e., updating, switching) which are essential for our behavior in daily life (e.g., staying focused, resisting temptations, thinking before acting), as well. Therefore, this study aimed to directly compare two kinds of activities, aerobic and coordinative, and examine how they might affect executive functions (i.e., inhibition, updating, and switching) in a test-retest protocol. It is interesting for practical implications, as coordinative exercises, for example, require little space and would be preferable in settings such as an office or a classroom. Furthermore, we designed our experiment in such a way that learning effects were controlled. Then, we tested the influence of acute bouts of physical activity on the executive functioning in both young and older adults (young 16–22 years, old 65–80 years). Overall, we found no differences between aerobic and coordinative activities and, in fact, benefits from physical activities occurred only in the updating tasks in young adults. Additionally, we also showed some learning effects that might influence the results. Thus, it is important to control cognitive tests for learning effects in test-retest studies as well as to analyze effects from physical activity on a construct level of executive functions.

A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models (2020)

Bui, Duong Minh ; Le, Phuc Duy ; Cao, Tien Minh ; Nguyen, Hung ; Pham, Trang Thi ; Pham, Duy Anh

Vision: Shred If Insecure – Persuasive Message Design as a Lesson and Alternative to Previous Approaches to Usable Secure Email Interfaces (2020)

Tolsdorf, Jan ; Lo Iacono, Luigi

Despite the advances in research on usable secureemail, the majority of mail user agents found in practicestill violates best practices in UI design and uses ineffectiveand inhomogeneous design strategies to communicate and let users control the security status of an email message. We propose a novel interaction and design concept that we refer to as persuasive message design. Our approach is derived from heuristics and a systematic meta-study ofexisting HCI literature on email management, usable secureemail and phishing research. Concluding on this body ofknowledge we propose the design of interfaces that sup-presses weak cues and instead manipulates the display of emails according to their technical security level. Persuasive message design addresses several shortcomings of currentsecure email user interfaces and provides a consistent user experience that can be deployed even by email providers.

Black-Box Optimization of Object Detector Hyper-Parameters (2020)

Muthuraja, Mohandass

Object detectors have improved considerably in the last years by using advanced Convolutional Neural Networks (CNNs) architectures. However, many detector hyper-parameters are not generally tuned, and they are used with values set by the detector authors. Blackbox optimization methods have gained more attention in recent years because of its ability to optimize the hyper-parameters of various machine learning algorithms and deep learning models. However, these methods are not explored in improving CNN-based object detector's hyper-parameters. In this research work, we propose the use of blackbox optimization methods such as Gaussian Process based Bayesian Optimization (BOGP), Sequential Model-based Algorithm Configuration (SMAC), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to tune the hyper-parameters in Faster R-CNN and Single Shot MultiBox Detector (SSD). In Faster R-CNN, tuning the input image size, prior box anchor scales and ratios using BOGP, SMAC, and CMA-ES has increased the performance around 1.5% in terms of Mean Average Precision (mAP) on PASCAL VOC. Tuning the anchor scales of SSD has increased the mAP by 3% on PASCAL VOC and marine debris datasets. On the COCO dataset with SSD, mAP improvement is observed in the medium and large objects, but mAP decreases by 1% in small objects. The experimental results show that the blackbox optimization methods have proved to increase the mAP performance by optimizing the object detectors. Moreover, it has achieved better results than the hand-tuned configurations in most of the cases.

Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method (2020)

Bui, Duong Minh ; Le, Phuc Duy ; Cao, Minh Tien ; Pham, Trang Thi ; Pham, Duy Anh

Finite element based post-buckling analysis of refined graphene oxide reinforced concrete beams with geometrical imperfection (2020)

Mirjavadi, Seyed Sajad ; Forsat, Masoud ; Yahya, Yahya Zakariya ; Barati, Mohammad Reza ; Jayasimha, Anirudh Narasimamurthy ; Khan, Imran

Analysis of post-buckling of higher-order graphene oxide reinforced concrete plates with geometrical imperfection (2020)

Mirjavadi, Seyed Sajad ; Forsat, Masoud ; Yahya, Yahya Zakariya ; Barati, Mohammad Reza ; Jayasimha, Anirudh Narasimamurthy ; Khan, Imran

Maßgefertigte Abläufe (2017)

Hense, Andreas

Designing Air Flow with Surrogate-assisted Phenotypic Niching (2020)

Hagg, Alexander ; Wilde, Dominik ; Asteroth, Alexander ; Bäck, Thomas

In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself. But what if we could quickly understand the complex behaviors that can emerge in said domains instead? We introduce surrogate-assisted phenotypic niching, a quality diversity algorithm which allows to discover a large, diverse set of behaviors by using computationally expensive phenotypic features. In this work we discover the types of air flow in a 2D fluid dynamics optimization problem. A fast GPU-based fluid dynamics solver is used in conjunction with surrogate models to accurately predict fluid characteristics from the shapes that produce the air flow. We show that these features can be modeled in a data-driven way while sampling to improve performance, rather than explicitly sampling to improve feature models. Our method can reduce the need to run an infeasibly large set of simulations while still being able to design a large diversity of air flows and the shapes that cause them. Discovering diversity of behaviors helps engineers to better understand expensive domains and their solutions.

Discovering Representations for Black-box Optimization (2020)

Gaier, Adam ; Asteroth, Alexander ; Mouret, Jean-Baptiste

The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge between exploring a wide variety of solutions, and ensuring that those solutions are useful. Our main insight is that this process can be automated by generating a dataset of high-performing solutions with a quality diversity algorithm (here, MAP-Elites), then learning a representation with a generative model (here, a Varia-tional Autoencoder) from that dataset. Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions-but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators. We demonstrate these capabilities by learning an low-dimensional encoding for the inverse kinemat-ics of a thousand joint planar arm. The results show that learned representations make it possible to solve high-dimensional problems with orders of magnitude fewer evaluations than the standard MAP-Elites, and that, once solved, the produced encoding can be used for rapid optimization of novel, but similar, tasks. The presented techniques not only scale up quality diversity algorithms to high dimensions, but show that black-box optimization encodings can be automatically learned, rather than hand designed.

FaceHaptics: Robot Arm based Versatile Facial Haptics for Immersive Environments (2020)

Wilberz, Alexander ; Leschtschow, Dominik ; Trepkowski, Christina ; Maiero, Jens ; Kruijff, Ernst ; Riecke, Bernhard

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.

Abschlussbericht zum BMBF-Fördervorhaben Enabling Infrastructure for HPC-Applications (EI-HPC) (2020)

Berrendorf, Rudolf ; Asteroth, Alexander ; Hinkenjann, André ; Plöger, Paul G. ; Reith, Dirk

Conformations and three-dimensional structures of selected SARS-CoV-2 drug candidates (2020)

Kirschner, Karl N. ; Razzaq, Javed ; Berrendorf, Rudolf ; Heiden, Wolfgang

Quantum mechanical theories are used to search and optimized the conformations of proposed small molecule candidates for treatment of SARS-CoV-2. These candidate compounds are taken from what is reported in the news and in other pre-peer-reviewed literature (e.g. ChemRxiv, bioRxiv). The goal herein is to provided predicted structures and relative conformational stabilities for selected drug and ligand candidates, in the hopes that other research groups can make use of them for developing a treatment.

A Hybrid Bond Graph Model-based - Data Driven Method for Failure Prognostic (2020)

Borutzky, Wolfgang

Failure prognostic builds up on constant data acquisition and processing and fault diagnosis and is an essential part of predictive maintenance of smart manufacturing systems enabling condition based maintenance, optimised use of plant equipment, improved uptime and yield and to prevent safety problems. Given known control inputs into a plant and real sensor outputs or simulated measurements, the model-based part of the proposed hybrid method provides numerical values of unknown parameter degradation functions at sampling time points by the evaluation of equations that have been derived offline from a bicausal diagnostic bond graph. These numerical values are computed concurrently to the constant monitoring of a system and are stored in a buffer of fixed length. The data-driven part of the method provides a sequence of remaining useful life estimates by repeated projection of the parameter degradation into the future based on the use of values in a sliding time window. Existing software can be used to determine the best fitting function and can account for its random parameters. The continuous parameter estimation and their projection into the future can be performed in parallel for multiple isolated simultaneous parametric faults on a multicore, multiprocessor computer. The proposed hybrid bond graph model-based, data-driven method is verified by an offline simulation case study of a typical power electronic circuit. It can be used to implement embedded systems that enable cooperating machines in smart manufacturing to perform prognostic themselves.

Monte-Carlo-Simulation zur Schätzung der Exposition von Oberflächengewässern durch Abdrift von Pflanzenschutzmitteln (2004)

von der Hude, Marlis

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.

Performance Evaluation of Low-Cost Machine Vision Cameras for Image-Based Grasp Verification (2020)

Nair, Deebul ; Pakdaman, Amirhossein ; Plöger, Paul G.

Grasp verification is advantageous for autonomous manipulation robots as they provide the feedback required for higher level planning components about successful task completion. However, a major obstacle in doing grasp verification is sensor selection. In this paper, we propose a vision based grasp verification system using machine vision cameras, with the verification problem formulated as an image classification task. Machine vision cameras consist of a camera and a processing unit capable of on-board deep learning inference. The inference in these low-power hardware are done near the data source, reducing the robot's dependence on a centralized server, leading to reduced latency, and improved reliability. Machine vision cameras provide the deep learning inference capabilities using different neural accelerators. Although, it is not clear from the documentation of these cameras what is the effect of these neural accelerators on performance metrics such as latency and throughput. To systematically benchmark these machine vision cameras, we propose a parameterized model generator that generates end to end models of Convolutional Neural Networks(CNN). Using these generated models we benchmark latency and throughput of two machine vision cameras, JeVois A33 and Sipeed Maix Bit. Our experiments demonstrate that the selected machine vision camera and the deep learning models can robustly verify grasp with 97% per frame accuracy.

An Analysis of Phenotypic Diversity in Multi-Solution Optimization (2020)

Hagg, Alexander ; Preuss, Mike ; Wessing, Simon ; Asteroth, Alexander ; Bäck, Thomas

In optimization methods that return diverse solution sets, three interpretations of diversity can be distinguished: multi-objective optimization which searches diversity in objective space, multimodal optimization which tries spreading out the solutions in genetic space, and quality diversity which performs diversity maintenance in phenotypic space. We introduce niching methods that provide more flexibility to the analysis of diversity and a simple domain to compare and provide insights about the paradigms. We show that multiobjective optimization does not always produce much diversity, quality diversity is not sensitive to genetic neutrality and creates the most diverse set of solutions, and multimodal optimization produces higher fitness solutions. An autoencoder is used to discover phenotypic features automatically, producing an even more diverse solution set. Finally, we make recommendations about when to use which approach.

Untersuchung der Selbstorientierung bei veränderten Gravitationsbedingungen (kurz: SelfOG) (2019)

Felsner, Sandra ; Herpers, Rainer ; Saitov, Timur ; Scherfgen, David ; Harris, Laurence Roy ; Jenkin, Michael ; Bury, Nils

Die Wahrnehmung des perzeptionellen Aufrecht (perceptual upright, PU) variiert in Abhängigkeit der Gewichtung verschiedener gravitationsbezogener und körperbasierter Merkmale zwischen Kontexten und aufgrund individueller Unterschiede. Ziel des Vorhabens war es, systematisch zu untersuchen, welche Zusammenhänge zwischen visuellen und gravitationsbedingten Merkmalen bestehen. Das Vorhaben baute auf vorangegangen Untersuchungen auf, deren Ergebnisse indizieren, dass eine Gravitation von ca. 0,15g notwendig ist, um effiziente Selbstorientierungsinformationen bereit zu stellen (Herpers et. al, 2015; Harris et. al, 2014). In dem hier beschriebenen Vorhaben wurden nun gezielt künstliche Gravitationsbedingungen berücksichtigt, um die Gravitationsschwelle, ab der ein wahrnehmbarer Einfluss beobachtbar ist, genauer zu quantifizieren bzw. die oben genannte Hypothese zu bestätigen. Es konnte gezeigt werden, dass die zentripetale Kraft, die auf einer rotierenden Zentrifuge entlang der Längsachse des Körpers wirkt, genauso efektiv wie Stehen mit normaler Schwerkraft ist, um das Gefühl des perzeptionellen Aufrechts auszulösen. Die erzielten Daten deuten zudem darauf hin, dass ein Gravitationsfeld von mindestens 0,15 g notwendig ist, um eine efektive Orientierungsinformation für die Wahrnehmung von Aufrecht zu liefern. Dies entspricht in etwa der Gravitationskraft von 0,17 g, die auf dem Mond besteht. Für eine lineare Beschleunigung des Körpers liegt der vestibulare Schwellenwert bei etwa 0,1 m/s2 und somit liegt der Wert für die Situation auf dem Mond von 1,6 m/s2 deutlich über diesem Schwellenwert.

AErOmAt Abschlussbericht (2020)

Asteroth, Alexander ; Gaier, Adam ; Hagg, Alexander ; Meng, Jakob ; Priesnitz, Andreas ; Prochnau, Lea ; Reith, Dirk

Das Projekt AErOmAt hatte zum Ziel, neue Methoden zu entwickeln, um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen Optimierungsdomänen einzusparen. Die Hochschule Bonn-Rhein-Sieg (H-BRS) hat auf diesem Weg einen gesellschaftlich relevanten und gleichzeitig wirtschaftlich verwertbaren Beitrag zur Energieeffizienzforschung geleistet. Das Projekt führte außerdem zu einer schnelleren Integration der neuberufenen Antragsteller in die vorhandenen Forschungsstrukturen.

Effect of User Roles on the Process of Collaborative 2D Level Design on Large, High-resolution Displays (2020)

Sigitov, Anton ; Hinkenjann, André ; Staadt, Oliver

This paper presents groupware to study group behavior while conducting a creative task on large, high-resolution displays. Moreover, we present the results of a between-subjects study. In the study, 12 groups with two participants each prototyped a 2D level on a 7m x 2.5m large, high-resolution display using tablet-PCs for interaction. Six groups underwent a condition where group members had equal roles and interaction possibilities. Another six groups worked in a condition where group members had different roles: level designer and 2D artist. The results revealed that in the different roles condition, the participants worked significantly more tightly and created more assets. We could also detect some shortcomings for that configuration. We discuss the gained insights regarding system configuration, groupware interfaces, and groups behavior.

Automating Representation Discovery with MAP-Elites (2020)

Gaier, Adam ; Asteroth, Alexander ; Mouret, Jean-Baptiste

The way solutions are represented, or encoded, is usually the result of domain knowledge and experience. In this work, we combine MAP-Elites with Variational Autoencoders to learn a Data-Driven Encoding (DDE) that captures the essence of the highest-performing solutions while still able to encode a wide array of solutions. Our approach learns this data-driven encoding during optimization by balancing between exploiting the DDE to generalize the knowledge contained in the current archive of elites and exploring new representations that are not yet captured by the DDE. Learning representation during optimization allows the algorithm to solve high-dimensional problems, and provides a low-dimensional representation which can be then be re-used. We evaluate the DDE approach by evolving solutions for inverse kinematics of a planar arm (200 joint angles) and for gaits of a 6-legged robot in action space (a sequence of 60 positions for each of the 12 joints). We show that the DDE approach not only accelerates and improves optimization, but produces a powerful encoding that captures a bias for high performance while expressing a variety of solutions.

CCCiCC: A Cross-Core Cache-Independent Covert Channel on AMD Family 15h CPUs (2020)

Hailfinger, Carl-Daniel ; Lemke-Rust, Kerstin ; Paar, Christof

Hash-Based Hierarchical Caching and Layered Filtering for Interactive Previews in Global Illumination Rendering (2020)

Roth, Thorsten ; Weier, Martin ; Bauszat, Pablo ; Hinkenjann, André ; Li, Yongmin

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path-traced results, but with a greatly reduced computational complexity, allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Hash-Based Hierarchical Caching and Layered Filtering for Interactive Previews in Global Illumination Rendering (2020)

Roth, Thorsten ; Weier, Martin ; Bauszat, Pablo ; Hinkenjann, André ; Li, Yongmin

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Incorporating Contextual Knowledge Into Human-Robot Collaborative Task Execution (2020)

Abdelrahman, Ahmed Faisal

An essential measure of autonomy in service robots designed to assist humans is adaptivity to the various contexts of human-oriented tasks. These robots may have to frequently execute the same action, but subject to subtle variations in task parameters that determine optimal behaviour. Such actions are traditionally executed by robots using pre-determined, generic motions, but a better approach could utilize robot arm maneuverability to learn and execute different trajectories that work best in each context. In this project, we explore a robot skill acquisition procedure that allows incorporating contextual knowledge, adjusting executions according to context, and improvement through experience, as a step towards more adaptive service robots. We propose an apprenticeship learning approach to achieving context-aware action generalisation on the task of robot-to-human object hand-over. The procedure combines learning from demonstration, with which a robot learns to imitate a demonstrator’s execution of the task, and a reinforcement learning strategy, which enables subsequent experiential learning of contextualized policies, guided by information about context that is integrated into the learning process. By extending the initial, static hand-over policy to a contextually adaptive one, the robot derives and executes variants of the demonstrated action that most appropriately suit the current context. We use dynamic movement primitives (DMPs) as compact motion representations, and a model-based Contextual Relative Entropy Policy Search (C-REPS) algorithm for learning policies that can specify hand-over position, trajectory shape, and execution speed, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We demonstrate the algorithm’s ability to learn context-dependent hand-over positions, and new trajectories, guided by suitable reward functions, and show that the current DMP implementation limits learning context-dependent execution speeds. We additionally conduct a user study involving participants assuming different postures and receiving an object from the robot, which executes hand-overs by either exclusively imitating a demonstrated motion, or selecting hand-over positions based on learned contextual policies and adapting its motion accordingly. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.

Evaluation and Optimization of IEEE802.11 multi-hop Backhaul Networks with Directional Antennas (2020)

Rademacher, Michael

A major problem for rural areas is the inaccessibility to affordable broadband Internet connections. In these areas distances are large, and digging a cable into the ground is extremely expensive, considering the small number of potential customers at the end of that cable. This leads to a digital divide, where urban areas enjoy a high-quality service at low cost, while rural areas suffer from the reverse. This work is dedicated to an alternative technical approach aiming to reduce the cost for Internet Service Provider in rural areas: WiFi-based Long Distance networks. A set of significant contributions of technology related aspects of WiFi-based Long Distance networks is described in three different fields: Propagation on long distance Wi-Fi links, MAC-layer scheduling and Interference modeling and Channel Assignment with directional antennas. For each field, the author composes and discusses the state-of-the-art. Afterwards, the author derives research questions and tackles several open issues to develop these kinds of networks further towards a suitable technology for the backhaul segment.

Analyzing power consumption of TLS ciphers on an ESP32 (2019)

Fischer, Tilo ; Linka, Hendrik ; Rademacher, Michael ; Jonas, Karl ; Loebenberger, Daniel

More and more devices will be connected to the internet [3]. Many devicesare part of the so-called Internet of Things (IoT) which contains many low-powerdevices often powered by a battery. These devices mainly communicate with the manufacturers back-end and deliver personal data and secrets like passwords.

The performance of Dunning, Jensen, and Karlsruhe basis sets on computing relative energies and geometries (2020)

Kirschner, Karl N. ; Reith, Dirk ; Heiden, Wolfgang

Applying the Popov-Vereshchagin Hybrid Dynamics Solver for Teleoperation under Instantaneous Constraints (2020)

Kulkarni, Padmaja ; Schneider, Sven ; Bennewitz, Maren ; Schulz, Dirk ; Plöger, Paul

Perception-driven rendering : techniques for the efficient visualization of 3D scenes including view- and gaze-contingent approaches (2019)

Weier, Martin

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.

Arguing Security of Autonomous Robots (2020)

Hochgeschwender, Nico ; Cornelius, Gary ; Voos, Holger

Exploiting linearity in dynamics solvers for the design of composable robotic manipulation architectures (2020)

Schneider, Sven ; Bruyninckx, Herman

Foreword to the Special Section on the Symposium on Virtual and Augmented Reality 2019 (SVR 2019) (2020)

Hinkenjann, André ; Sandor, Christian ; Teixeira, João Marcelo ; Rieder, Rafael

Context-Aware Task Execution Using Apprenticeship Learning (2020)

Abdelrahman, Ahmed Faisal ; Mitrevski, Alex ; Plöger, Paul G.

An essential measure of autonomy in assistive service robots is adaptivity to the various contexts of human-oriented tasks, which are subject to subtle variations in task parameters that determine optimal behaviour. In this work, we propose an apprenticeship learning approach to achieving context-aware action generalization on the task of robot-to-human object hand-over. The procedure combines learning from demonstration and reinforcement learning: a robot first imitates a demonstrator’s execution of the task and then learns contextualized variants of the demonstrated action through experience. We use dynamic movement primitives as compact motion representations, and a model-based C-REPS algorithm for learning policies that can specify hand-over position, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We additionally conduct a user study involving participants assuming different postures and receiving an object from a robot, which executes hand-overs by either imitating a demonstrated motion, or adapting its motion to hand-over positions suggested by the learned policy. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.

Non-Visual Cues for View Management in Narrow Field of View Augmented Reality Displays (2019)

Marquardt, Alexander ; Trepkowski, Christina ; Eibich, Tom David ; Maiero, Jens ; Kruijff, Ernst

Hash-based Hierarchical Caching for Interactive Previews in Global Illumination Rendering (2019)

Roth, Thorsten ; Weier, Martin ; Bauszat, Pablo ; Hinkenjann, André ; Li, Yongmin

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews ofstatic scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Multiresolution Analysis Pansharpening for the Fusion of Raman and Conventional Brightfield Microscopy Images (2019)

Pomrehn, Christoph ; Klein, Daniel ; Kolb, Andreas ; Kaul, Peter ; Herpers, Rainer

b-it-bots: Our Approach for Autonomous Robotics in Industrial Environments (2019)

Padalkar, Abhishek ; Wasil, Mohammad ; Mahajan, Shweta ; Kumar, Ramesh ; Bakaraniya, Dharmin ; Shirodkar, Raghuvir ; Andradi, Heruka ; Padmanabhan, Deepan ; Wiesse, Carlo ; Abdelrahman, Ahmed ; Chavan, Sushant ; Gurulingan, Naresh ; Nair, Deebul ; Thoduka, Santosh ; Awaad, Iman ; Schneider, Sven ; Plöger, Paul G. ; Kraetzschmar, Gerhard K.

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