Refine
H-BRS Bibliography
- yes (80)
Departments, institutes and facilities
- Fachbereich Informatik (80) (remove)
Document Type
- Conference Object (38)
- Article (19)
- Preprint (7)
- Doctoral Thesis (4)
- Part of a Book (3)
- Research Data (3)
- Report (3)
- Book (monograph, edited volume) (1)
- Conference Proceedings (1)
- Contribution to a Periodical (1)
Year of publication
- 2021 (80) (remove)
Keywords
- Usable Security (4)
- Big Data Analysis (3)
- Machine Learning (3)
- AML (2)
- Augmented Reality (2)
- Authentication features (2)
- Cognitive robot control (2)
- Explainable robotics (2)
- Generative Models (2)
- HSP90 (2)
- Human-Computer Interaction (2)
- Learning from experience (2)
- LoRa (2)
- LoRaWAN (2)
- Low-Power Wide Area Network (LP-WAN) (2)
- Measurement (2)
- Path Loss (2)
- Quality diversity (2)
- Risk-based Authentication (RBA) (2)
- Robotics (2)
- Urban (2)
- 3D navigation (1)
- AD (1)
- AES (1)
- API Documentation (1)
- API Gebrauchstauglichkeit (1)
- API usability (1)
- Adaptive Control (1)
- Artificial Intelligence (1)
- Assistive robots (1)
- Auditory Cueing (1)
- BPMS (1)
- Benchmarking (1)
- Bioinformatics (1)
- Block cipher (1)
- Bond Graph Modelling (1)
- Branch and cut (1)
- CC (1)
- CEHL (1)
- Cache line fingerprinting (1)
- Classifiers (1)
- Clustering (1)
- Co-creative processes (1)
- Cognitive informatics (1)
- Cognitive robotics (1)
- Compliant fingers (1)
- Computational creativity (1)
- Computing methodologies (1)
- Content Security Policies (1)
- Continual robot learning (1)
- Correlative Microscopy (1)
- Cortex-M3 (1)
- Creative Commons (1)
- DPA (1)
- Datenbanksysteme (1)
- Developer Centered Security (1)
- Differential analysis (1)
- Digitale Lehre (1)
- Dimensionality reduction (1)
- Divergent optimization (1)
- Drug (1)
- E-Health (1)
- ELM (1)
- Earth Observation (1)
- Employee data protection (1)
- Evolutionary optimization (1)
- Explainable Machine Learning (1)
- Failure Prognosis (1)
- Fault Detection & Diagnosis (1)
- Fault Diagnosis (1)
- Feature extraction (1)
- Fluency (1)
- Foveated rendering (1)
- GDPR (1)
- GLI (1)
- Gabor filter (1)
- Global illumination (1)
- HSP70 (1)
- HTTP (1)
- Head-mounted Display (1)
- Header whitelisting (1)
- Heat Shock Protein (1)
- Hochleistungssport (1)
- Hochschullehre (1)
- Human centered computing (1)
- Human computer interaction (1)
- Human factors (1)
- Hybrid Failure Prognosis (1)
- Hybrid Systems (1)
- Hyperspectral image (1)
- Inductive Logic Programming (1)
- Informationsflüsse (1)
- Informationsgewinnung (1)
- Informationsverarbeitung (1)
- Integer programming (1)
- Intelligent Autonomous Systems (1)
- Intermediaries (1)
- Knowledge Graphs (1)
- Künstliche Intelligenz (1)
- Language learning (1)
- Large high-resolution displays (1)
- Leistungsdiagnostik (1)
- Leistungssport (1)
- Leukemia (1)
- MBZ (1)
- Machine-learning (1)
- Mebendazole (1)
- Memory-Constrained Devices (1)
- Methodik (1)
- Microarchitectural Data Sampling (MDS) (1)
- Mixed (1)
- Model-based Fault Diagnosis (1)
- Modelling (1)
- Molecular dynamics (1)
- Multimodal Microspectroscopy (1)
- NISTPQC (1)
- Natural Language Processing (1)
- OER (1)
- Object detection (1)
- Ontology (1)
- Open Educational Ressources (1)
- Out-of-view Objects (1)
- PDSTSP (1)
- PHR (1)
- Parallel drone scheduling traveling salesman problem (1)
- Password (1)
- Personal Health Record (1)
- Post-Quantum Signatures (1)
- Privacy engineering (1)
- Process Models (1)
- Process views (1)
- Pronunciation (1)
- QoS (1)
- Quality control (1)
- Quantum mechanics (1)
- Radiance caching (1)
- Reflectance modeling (1)
- Registration Refinement (1)
- Risk-based Authentication (1)
- Robot failure diagnosis (1)
- Robot learning (1)
- Robot software (1)
- Robotics competitions (1)
- Robust grasping (1)
- SAML (1)
- SOAP (1)
- SVM (1)
- Secure Coding Practices (1)
- Semantic gap (1)
- Separation algorithm (1)
- Sicherheits-APIs (1)
- Side channel attack (1)
- Signature Verification (1)
- Slippage detection (1)
- Smartphone (1)
- Softwareentwicklung (1)
- Spielanalyse (1)
- Streaming (1)
- Support Vector Machine (1)
- Surrogate-assistance (1)
- Synergetik (1)
- Tautomers (1)
- Touchscreens (1)
- Trainingssteuerung (1)
- Transformers (1)
- Unidirectional thermoplastic composites (1)
- Usable Privacy (1)
- Usable Security and Privacy (1)
- User interface (1)
- Variational Autoencoder (1)
- Virtual Reality (1)
- Virtual reality (1)
- Visual Cueing (1)
- Visual Discrimination (1)
- Visualization design and evaluation methods (1)
- Visualization systems and tools (1)
- Web (1)
- Wettkampfanalyse (1)
- XML Signature (1)
- XML Signature Wrapping (1)
- YAWL (1)
- ZombieLoad (1)
- architectural distortion (1)
- breast cancer (1)
- component based (1)
- convolutional neural networks (1)
- developer centered security (1)
- domain adaptation (1)
- entwicklerzentrierte Sicherheit (1)
- extreme learning machine (1)
- indicators calculation (1)
- information flows (1)
- informational self-determination (1)
- leaning-based interfaces (1)
- learning traces (1)
- locomotion interface (1)
- mebendazole (1)
- mental models (1)
- multi robot systems (1)
- navigational search (1)
- privacy at work (1)
- property-based testing for robots (1)
- reuse of indicators (1)
- security (1)
- security APIs (1)
- simulation-based robot testing (1)
- software development (1)
- spatial orientation (1)
- spatial updating (1)
- trace model (1)
- trace-based system (1)
- transfer learning (1)
- unsupervised learning (1)
- usable privacy controls (1)
- verification and validation of robot action execution (1)
- virtual reality (1)
Auch die mittlerweile siebte Ausgabe des wissenschaftlichen Workshops “Usable Security und Privacy” auf der Mensch und Computer 2021 wird aktuelle Forschungs- und Praxisbeiträge präsentiert und anschließend mit allen Teilnehmer:innen diskutiert. Zwei Beiträge befassen sich dieses Jahr mit dem Thema Privatsphäre, zwei 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.
Künstliche Intelligenz (KI) ist aus der heutigen Gesellschaft kaum noch wegzudenken. Auch im Sport haben Methoden der KI in den letzten Jahren mehr und mehr Einzug gehalten. Ob und inwieweit dabei allerdings die derzeitigen Potenziale der KI tatsächlich ausgeschöpft werden, ist bislang nicht untersucht worden. Der Nutzen von Methoden der KI im Sport ist unbestritten, jedoch treten bei der Umsetzung in die Praxis gravierende Probleme auf, was den Zugang zu Ressourcen, die Verfügbarkeit von Experten und den Umgang mit den Methoden und Daten betrifft. Die Ursache für die, verglichen mit anderen Anwendungsgebieten, langsame An- bzw. Übernahme von Methoden der KI in den Spitzensport ist nach Hypothese des Autorenteams auf mehrere Mismatches zwischen dem Anwendungsfeld und den KI-Methoden zurückzuführen. Diese Mismatches sind methodischer, struktureller und auch kommunikativer Art. In der vorliegenden Expertise werden Vorschläge abgeleitet, die zur Auflösung der Mismatches führen können und zugleich neue Transfer- und Synergiemöglichkeiten aufzeigen. Außerdem wurden drei Use Cases zu Trainingssteuerung, Leistungsdiagnostik und Wettkampfdiagnostik exemplarisch umgesetzt. Dies erfolgte in Form entsprechender Projektbeschreibungen. Dabei zeigt die Ausarbeitung, auf welche Art und Weise Probleme, die heute noch bei der Verbindung zwischen KI und Sport bestehen, möglichst ausgeräumt werden können. Eine empirische Umsetzung des Use Case Trainingssteuerung erfolgte im Radsport, weshalb dieser ausführlicher dargestellt wird.
Short summary
Accompanying dataset for our paper
A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.
Contents
The dataset includes a single zip archive, containing data from the experiment described in the paper (conducted with a Toyota HSR). The zip archive contains three subdirectories:
handle_grasping_failure_database: A dump of a MongoDB database containing data from the handle grasping experiment, including ground-truth grasping failure annotations
pre_arm_motion_images: Images collected from the robot's hand camera before moving the robot's hand towards the handle
pregrasp_images: Images collected from the robot's hand camera just before closing the gripper for grasping
The image names include the time stamp at which the images were taken; this allows matching each image with the execution data in the database.
Database usage
After unzipping the archive, the database can be restored with the command
mongorestore handle_grasping_failure_database
This will create a MongoDB database with the name drawer_handle_grasping_failures.
Code for processing the data and failure analysis can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
The dataset contains the following data from successful and failed executions of the Toyota HSR robot placing a book on a shelf.
RGB images from the robot's head camera
Depth images from the robot's head camera
Rendered images of the robot's 3D model from the point of view of the robot's head camera
Force-torque readings from a wrist-mounted force-torque sensor
Joint efforts, velocities and positions
extrinsic and intrinsic camera calibration parameters
frame-level anomaly annotations
The anomalies that occur during execution include:
the manipulated book falling down
books on the shelf being disturbed significantly
camera occlusions
robot being disturbed by an external collision
The dataset is split into a train, validation and test set with the following number of trials:
Train: 48 successful trials
Validation: 6 successful trials
Test: 60 anomalous trials and 7 successful trials
Contents
There are two zip archives included (grasping.zip and throwing.zip), corresponding to two experiments (grasping objects and throwing them in a drawer), both performed with a Toyota HSR. Each archive contains two directories - learning and generalisation - with object-specific learning and generalisation data. For each object, we provide a dump of a MongoDB database, which contains data sufficient for learning the models used in our experiments.
Usage
After unzipping the archives, each database can be restored with the command
mongorestore [data_directory_name]
This will create a MongoDB database with the name of the directory. Code for processing the data and model learning can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
In Robot-Assisted Therapy for children with Autism Spectrum Disorder, the therapists’ workload is increased due to the necessity of controlling the robot manually. The solution for this problem is to increase the level of autonomy of the system, namely the robot should interpret and adapt to the behaviour of the child under therapy. The problem that we are adressing is to develop a behaviour model that will be used for the robot decision-making process, which will learn how to adequately react to certain child reactions. We propose the use of the reinforcement learning technique for this task, where feedback for learning is obtained from the therapist’s evaluation of a robot’s behaviour.
In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines. Possible implementations of such processes are presented using novel algorithms that perform divergent search to feed the users' intuition with many examples of high quality solutions, allowing them to take influence interactively. The machine feeds and reflects upon human intuition, combining both what is possible and preferred. The machine model and the divergent optimization algorithms are the motor behind this co-creative process, in which machine and users co-create and interactively choose branches of an ad hoc hierarchical decomposition of the solution space.
The proposed co-creative process consists of several elements: a formal model for interactive co-creative processes, evolutionary divergent search, diversity and similarity, data-driven methods to discover diversity, limitations of artificial creative agents, matters of efficiency in behavioral and morphological modeling, visualization, a connection to prototype theory, and methods to allow users to influence artificial creative agents. This thesis helps putting the human back into the design loop in generative AI and optimization.
Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments
(2021)
This paper proposes a traces model in the form of an object or class model (in the UML sense) which allows the automatic calculation of indicators of various kinds and independently of the computer environment for human learning (CEHL). The model is based on the establishment of a trace-based system that encompasses all the logic of traces collecting and indicators calculation. It is im-plemented in the form of a trace database. It is an important contribution in the field of the exploitation of the traces of apprenticeship in a CEHL because it pro-vides a general formalism for modeling the traces and allowing the calculation of several indicators at the same time. Also, with the inclusion of calculated indica-tors as potential learning traces, our model provides a formalism for classifying the various indicators in the form of inheritance relationships, which promotes the reuse of indicators already calculated. Economically, the model can allow organi-zations with different learning platforms to invest only in one traces Management System. At the social level, it can allow a better sharing of trace databases be-tween the various research institutions in the field of CEHL.