Refine
H-BRS Bibliography
- yes (74) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (74) (remove)
Document Type
- Conference Object (36)
- Article (20)
- Preprint (7)
- Doctoral Thesis (5)
- Book (monograph, edited volume) (2)
- Part of a Book (2)
- Report (1)
- Working Paper (1)
Year of publication
- 2023 (74) (remove)
Keywords
- Virtual Reality (3)
- GDPR (2)
- Higher education (2)
- Quality diversity (2)
- Risk-based Authentication (2)
- Robotics (2)
- Survey (2)
- robot introspection (2)
- 16S rRNA gene sequencing (1)
- 3D user interface (1)
- 450 MHz (1)
- Account (Datenverarbeitung) (1)
- Agent-Based Modeling (1)
- Air Pollution (1)
- Air pollution modeling (1)
- Attention (1)
- Augmented Reality (1)
- Authentifikation (1)
- Bacteria, Anaerobic (1)
- Bayesian optimization (1)
- Behaviour-Driven Development (1)
- Big Data Analysis (1)
- CNN (1)
- Cervical cancer screening (1)
- Cervicovaginal microbiome (1)
- Climate Risks (1)
- Colposcopy (1)
- Column (1)
- Compositional Pattern Producing Networks (1)
- Computational chemistry (1)
- Computational modeling (1)
- Computer Science - Computer Vision and Pattern Recognition (1)
- Computer Science - Learning (1)
- Computer Vision (1)
- Computersicherheit (1)
- Concurrent repeated failure prognosis (1)
- Crossmedia (1)
- Cybersickness (1)
- DNA extraction protocols (1)
- DNA profile (1)
- Data Fusion (1)
- Data Protection Officer (1)
- Diagnostic bond graph-based online fault diagnosis (1)
- Digital Ecosystem (1)
- EN-12299 (1)
- ERP (1)
- ERP-Software (1)
- Efficiency (1)
- Elephantiasis (1)
- Enterprise Resource Planning (1)
- Euler–Bernoulli beam (1)
- Expert Interviews (1)
- Explainability (1)
- FOS: Computer and information sciences (1)
- Features (1)
- Forests (1)
- Funktionsmodell (1)
- Games and Simulations for Learning (1)
- HPV diagnostic (1)
- High-speed railway track (1)
- Hochschulehre (1)
- Human computer interaction (1)
- Human factors (1)
- Human-Centered Design (1)
- Human-robot interaction (1)
- Humans (1)
- IEC 104 (1)
- IEC 61850 (1)
- Implementation Challenges (1)
- Increasing fault magnitude (1)
- Inductive Logic Programming (1)
- Information Security (1)
- Informations-, Kommunikations- und Medientechnologie (1)
- Interbank Market (1)
- Intermittent faults (1)
- Interventionstudie (1)
- Java <Programmiersprache> (1)
- Knowledge representation (1)
- LTE-M (1)
- Lagerverwaltung (1)
- Large-Scale Online Services (1)
- Lattice Boltzmann Method (1)
- Leg (1)
- Lehr-Lernpsychologie (1)
- Lernen (1)
- Lernumgebung (1)
- Liquidity Crises (1)
- Locomotion (1)
- Login (1)
- Lymphedema (1)
- MQTT (1)
- Machine Learning (1)
- Machine learning (1)
- Materialwissenschaften (1)
- Model-driven engineering (1)
- Motion Sickness (1)
- Multivariate time series classification (1)
- NGS (1)
- Object-Based Image Analysis (OBIA) (1)
- OpenStack (1)
- Optimization (1)
- Outlier Detection (1)
- PCR inhibitors (1)
- PM2.5 estimation (1)
- PSD (1)
- Passwort (1)
- Privacy patterns (1)
- Prudential Regulation (1)
- Python <Programmiersprache> (1)
- RMS acceleration (1)
- RNN (1)
- Reasoning (1)
- Reconstruction Error (1)
- Remaining Useful Life (RUL) estimates (1)
- Requirements (1)
- Requirements Engineering (1)
- Review (1)
- RoboCup (1)
- Robotics (cs.RO) (1)
- Rotating Table Test (1)
- SIMPACK (1)
- SORT (1)
- Serious Games (1)
- Skin (1)
- Smart Grid (1)
- Software (1)
- Spatiotemporality (1)
- Taxonomy (1)
- Temporally-weighted (1)
- Tracking by detection (1)
- Traffic Simulations (1)
- Travel Techniques (1)
- Tree Stumps (1)
- Unmanned Aerial Vehicle (UAV) (1)
- Usable Privacy (1)
- Usable Security (1)
- Usable Security and Privacy (1)
- User experience design (1)
- User-Centered Design (1)
- User-centered privacy engineering (1)
- Virtual Agents (1)
- Virtuelle Realität (1)
- Visual Computing (1)
- Vulnerable Groups (1)
- XGBoost (1)
- YOLO (1)
- analyses (1)
- analysis (1)
- authoring tools (1)
- automation of sample processing (1)
- bioinformatics (1)
- bond graph (1)
- built environment (1)
- cellular automata (1)
- component analyses (1)
- crawling (1)
- database systems (1)
- deep learning (1)
- degraded DNA (1)
- design process (1)
- explainable gesture recognition (1)
- extraction-linked bias (1)
- forensic (1)
- frequency (1)
- guidance (1)
- haptics (1)
- high speed railway vehicle (1)
- high-throughput DNA sequencing (1)
- high-throughput sequencing (1)
- higher education (1)
- hospital environment (1)
- hospital-acquired infections (1)
- human microbiome (1)
- hybrid robot skill representation (1)
- irregularity amplitude (1)
- knowledge graphs (1)
- leaning (1)
- locomotion (1)
- massive parallel sequencing (1)
- microbial community structure (1)
- microbial ecology (1)
- microbiome (1)
- microbiome analyses (1)
- modal superposition (1)
- multi-solution optimization (1)
- multisensory (1)
- natural language processing (1)
- next generation sequencing (1)
- open educational resources (OERs) (1)
- parametric (1)
- power spectral density (1)
- pre-optimization (1)
- question answering (1)
- rds encoding (1)
- remote sensing (1)
- representation (1)
- ride comfort (1)
- robot context awareness (1)
- robot execution failures (1)
- robot failure diagnosis (1)
- robot skill execution failures (1)
- robot skill generalisation (1)
- semi-continuous locomotion (1)
- semi-supervised learning (1)
- sensory perception (1)
- short tandem repeat (1)
- skill execution models (1)
- software engineering (1)
- spatial updating (1)
- teleportation (1)
- text mining (1)
- track irregularity (1)
- traffic surveillance (1)
- transfer learning (1)
- virtual reality (1)
- web (1)
- website (1)
- wind nuisance (1)
- wireless performance (1)
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.
TSEM: Temporally-Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
(2023)
The non-filarial and non-communicable disease podoconiosis affects around 4 million people and is characterized by severe leg lymphedema accompanied with painful intermittent acute inflammatory episodes, called acute dermatolymphangioadenitis (ADLA) attacks. Risk factors have been associated with the disease but the mechanisms of pathophysiology remain uncertain. Lymphedema can lead to skin lesions, which can serve as entry points for bacteria that may cause ADLA attacks leading to progression of the lymphedema. However, the microbiome of the skin of affected legs from podoconiosis individuals remains unclear. Thus, we analysed the skin microbiome of podoconiosis legs using next generation sequencing. We revealed a positive correlation between increasing lymphedema severity and non-commensal anaerobic bacteria, especially Anaerococcus provencensis, as well as a negative correlation with the presence of Corynebacterium, a constituent of normal skin flora. Disease symptoms were generally linked to higher microbial diversity and richness, which deviated from the normal composition of the skin. These findings show an association of distinct bacterial taxa with lymphedema stages, highlighting the important role of bacteria for the pathogenesis of podoconiosis and might enable a selection of better treatment regimens to manage ADLA attacks and disease progression.
Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best-performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation of the dataset, we refer to this article.
Skill generalisation and experience acquisition for predicting and avoiding execution failures
(2023)
For performing tasks in their target environments, autonomous robots usually execute and combine skills. Robot skills in general and learning-based skills in particular are usually designed so that flexible skill acquisition is possible, but without an explicit consideration of execution failures, the impact that failure analysis can have on the skill learning process, or the benefits of introspection for effective coexistence with humans. Particularly in human-centered environments, the ability to understand, explain, and appropriately react to failures can affect a robot's trustworthiness and, consequently, its overall acceptability. Thus, in this dissertation, we study the questions of how parameterised skills can be designed so that execution-level decisions are associated with semantic knowledge about the execution process, and how such knowledge can be utilised for avoiding and analysing execution failures. The first major segment of this work is dedicated to developing a representation for skill parameterisation whose objective is to improve the transparency of the skill parameterisation process and enable a semantic analysis of execution failures. We particularly develop a hybrid learning-based representation for parameterising skills, called an execution model, which combines qualitative success preconditions with a function that maps parameters to predicted execution success. The second major part of this work focuses on applications of the execution model representation to address different types of execution failures. We first present a diagnosis algorithm that, given parameters that have resulted in a failure, finds a failure hypothesis by searching for violations of the qualitative model, as well as an experience correction algorithm that uses the found hypothesis to identify parameters that are likely to correct the failure. Furthermore, we present an extension of execution models that allows multiple qualitative execution contexts to be considered so that context-specific execution failures can be avoided. Finally, to enable the avoidance of model generalisation failures, we propose an adaptive ontology-assisted strategy for execution model generalisation between object categories that aims to combine the benefits of model-based and data-driven methods; for this, information about category similarities as encoded in an ontology is integrated with outcomes of model generalisation attempts performed by a robot. The proposed methods are exemplified in terms of various use cases - object and handle grasping, object stowing, pulling, and hand-over - and evaluated in multiple experiments performed with a physical robot. The main contributions of this work include a formalisation of the skill parameterisation problem by considering execution failures as an integral part of the skill design and learning process, a demonstration of how a hybrid representation for parameterising skills can contribute towards improving the introspective properties of robot skills, as well as an extensive evaluation of the proposed methods in various experiments. We believe that this work constitutes a small first step towards more failure-aware robots that are suitable to be used in human-centered environments.
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s work on evaluating saliency methods for classification models illustrate certain explanation methods fail the model and data randomization tests. However, on extending the tests for various state of the art object detectors we illustrate that the ability to explain a model is more dependent on the model itself than the explanation method. We perform sanity checks for object detection and define new qualitative criteria to evaluate the saliency explanations, both for object classification and bounding box decisions, using Guided Backpropagation, Integrated Gradients, and their Smoothgrad versions, together with Faster R-CNN, SSD, and EfficientDet-D0, trained on COCO. In addition, the sensitivity of the explanation method to model parameters and data labels varies class-wise motivating to perform the sanity checks for each class. We find that EfficientDet-D0 is the most interpretable method independent of the saliency method, which passes the sanity checks with little problems.
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.