004 Datenverarbeitung; Informatik
Refine
Has Fulltext
- yes (78)
Is part of the Bibliography
- yes (78)
Year of publication
Document Type
- Journal article (78) (remove)
Keywords
- virtual reality (11)
- machine learning (5)
- augmented reality (3)
- immersion (3)
- Deep learning (2)
- Quadrocopter (2)
- Quadrotor (2)
- XR (2)
- artificial intelligence (2)
- automation (2)
- deep learning (2)
- education (2)
- endoscopy (2)
- exposure (2)
- fully convolutional neural networks (2)
- gastroenterology (2)
- historical document analysis (2)
- human-computer interaction (2)
- navigation (2)
- neural networks (2)
- ontology (2)
- prediction (2)
- self-aware computing (2)
- virtual environments (2)
- 3D-reconstruction methods (1)
- 3DTK toolkit (1)
- 4D-GIS (1)
- AVA (1)
- Aufwandsanalyse (1)
- Automatisierte Prüfungskorrektur (1)
- Autonomous UAV (1)
- Brüder Grimm Privatbibliothek (1)
- CLIP (1)
- Convolutional Neural Network (1)
- Cost Analysis (1)
- DNA storage (1)
- EPM (1)
- Educational Measurement (I2.399) (1)
- Entscheidungsfindung (1)
- Erkennung handschriftlicher Artefakte (1)
- Ethik (1)
- Forces (1)
- GNSS/INS integrated navigation (1)
- Grimm brothers personal library (1)
- HMD (Head-Mounted Display) (1)
- HTTP adaptive video streaming (1)
- INS/LIDAR integrated navigation (1)
- IT security (1)
- Image Aesthetic Assessment (1)
- Informatik (1)
- Intelligent Virtual Agents (1)
- InteractionSuitcase (1)
- Internet of Things (1)
- IoT (1)
- Kerneldensity estimation (1)
- Klima (1)
- Künstliche Intelligenz (1)
- LoRaWAN (1)
- Modell (1)
- Multiple-Choice Examination (1)
- Multiple-Choice Prüfungen (1)
- NP-hardness (1)
- Neuronales Netz (1)
- Optical Flow (1)
- Poisson surface reconstruction (1)
- RGB-D (1)
- Robotics (1)
- Self-Evaluation Programs (I2.399.780) (1)
- Structure-from-Motion (1)
- Terramechanics (1)
- Torque (1)
- WhatsApp (1)
- Wheel (1)
- XR-artificial intelligence combination (1)
- XR-artificial intelligence continuum (1)
- YouTube (1)
- acrophobia (1)
- adaptation models (1)
- adult learning (1)
- aerodynamics (1)
- agents (1)
- annotation (1)
- anomaly detection (1)
- anomaly prediction (1)
- ant-colony optimization (1)
- anthropomorphism (1)
- anxiety (1)
- application design (1)
- approximation algorithm (1)
- arithmetic calculations (1)
- autonomous (1)
- autonomous UAV (1)
- availability (1)
- avatar embodiment (1)
- avatars (1)
- background knowledge (1)
- baseline detection (1)
- behavior perception (1)
- binary tanglegram (1)
- biosignals (1)
- camera orientation (1)
- carbon (1)
- certifying algorithm (1)
- chain cover (1)
- channel management (1)
- climate (1)
- co-authorships (1)
- co-inventorships (1)
- coherence (1)
- collaboration (1)
- collision (1)
- communication models (1)
- communication networks (1)
- congruence (1)
- content-based image retrieval (1)
- continuous-time SLAM (1)
- convex bipartite graph (1)
- convolutional neural network (1)
- cost-sensitive learning (1)
- crossing minimization (1)
- crowdsensing (1)
- crowdsourced measurements (1)
- cultural and media studies (1)
- culturally aware (1)
- data warehouse (1)
- decision support system (1)
- decision-making (1)
- deep metric learning (1)
- deformation-based method (1)
- design (1)
- design cycle (1)
- detection time simulation (1)
- digital twin (1)
- dimensions of proximity (1)
- distributed control (1)
- dynamic programming (1)
- eHealth (1)
- educational tool (1)
- electronic health records (1)
- elevated plus-maze (1)
- embedding techniques (1)
- emotions (1)
- encryption (1)
- endurance (1)
- event detection (1)
- exercise intensity (1)
- experience (1)
- experimental evaluation (1)
- extended reality (XR) (1)
- failure prediction (1)
- fault detection (1)
- feature matching (1)
- few-shot learning (1)
- fixed-parameter tractability (1)
- food quality (1)
- foreign language learning and teaching (1)
- formation flight (1)
- fruit temperature (1)
- future energy grid exploration (1)
- games (1)
- gamification (1)
- genetic algorithm (1)
- graph algorithm (1)
- group-based communication (1)
- handwriting (1)
- handwritten artefact recognition (1)
- hierarchy (1)
- historical images (1)
- hospital data (1)
- human body weight (1)
- human computer interaction (HCI) (1)
- human-artificial intelligence interaction (1)
- human-artificial intelligence interface (1)
- human-centered design (1)
- human-centered, human-robot (1)
- human–computer interaction (1)
- illusion of self-motion (1)
- image classification (1)
- image processing (1)
- imbalanced regression (1)
- immersive classroom (1)
- immersive classroom management (1)
- immersive technologies (1)
- implicit association test (1)
- induced matching (1)
- informal education (1)
- information extraction (1)
- information systems and information technology (1)
- intelligent transportation systems (1)
- intelligent vehicles (1)
- intelligent virtual agents (1)
- intelligent voice assistant (1)
- intercultural learning and teaching (1)
- interdisciplinary education (1)
- internet traffic (1)
- invasive vascular interventions (1)
- iowa gambling task (1)
- key-insight extraction (1)
- kinect (1)
- language-image pre-training (1)
- layout recognition (1)
- learning environments (1)
- light-gated proteins (1)
- local energy system (1)
- logistics (1)
- long-term analysis (1)
- map projections (1)
- mapping (1)
- mathematical model (1)
- measurements (1)
- media analysis (1)
- medical records (1)
- medieval manuscripts (1)
- meditation (1)
- mindfulness (1)
- misconceptions (1)
- mixed reality (1)
- mixed-cultural (1)
- mixed-cultural settings (1)
- mobile instant messaging (1)
- mobile messaging application (1)
- mobile networks (1)
- mobile streaming (1)
- model following (1)
- model output statistics (1)
- model-based diagnosis (1)
- multimodal fusion (1)
- multimodal interface (1)
- multiple myeloma (1)
- multirotors (1)
- multiscale encoder (1)
- nano-satellite (1)
- nanocellulose (1)
- natural interfaces (1)
- natural language processing (1)
- neume notation (1)
- neural architecture (1)
- non-native accent (1)
- object detection (1)
- octree (1)
- optical music recognition (1)
- passage of time (1)
- passive haptic feedback (1)
- perception (1)
- performance (1)
- performance analysis (1)
- performance prediction (1)
- place-illusion (1)
- plausibility (1)
- plausibility-illusion (1)
- point cloud (1)
- point cloud compression (1)
- point-to-plane measure (1)
- point-to-point measure (1)
- pollution (1)
- positioning (1)
- precision horticulture (1)
- precision training (1)
- presence (1)
- private chat groups (1)
- procedural fusion methods (1)
- prompt engineering (1)
- protein chip (1)
- psychophyisology (1)
- public speaking (1)
- quadcopter (1)
- quadcopters (1)
- quality assurance (1)
- quality evaluation (1)
- quality of experience (1)
- quality of experience prediction (1)
- radiology (1)
- ransomware (1)
- real world evidence (1)
- real-world application (1)
- realism (1)
- recommender system (1)
- regelbasierte Nachbearbeitung (1)
- research methods (1)
- rich vehicle routing problem (1)
- robustness (1)
- rotors (1)
- rule based post processing (1)
- sample weighting (1)
- satisfiability problems (1)
- scalability (1)
- scalable quadcopter (1)
- scheduling (1)
- science, technology and society (1)
- secure group communication (1)
- segmentation (1)
- self-adaptive (1)
- self-adaptive systems (1)
- self-aware computing systems (1)
- self-managing systems (1)
- semantic fusion (1)
- sensor fusion (1)
- sentinel (1)
- serious games (1)
- sesnsors (1)
- simulation (1)
- simulation system (1)
- single-electron transistors (1)
- sketching (1)
- smart meter data utilization (1)
- smart speaker (1)
- social VR (1)
- social interaction (1)
- social relationship (1)
- social robot (1)
- social robotics (1)
- social role (1)
- socially interactive agents (1)
- spatial presence (1)
- statistical validity (1)
- statistics and numerical data (1)
- stereotypes (1)
- stroke (1)
- student simulation (1)
- stylus (1)
- sunburn (1)
- supervised learning (1)
- surface model (1)
- survey (1)
- switching navigation (1)
- systematic literature review (1)
- systematic review (1)
- table extraction (1)
- table understanding (1)
- taxonomy (1)
- teacher education (1)
- technology-supported learning (1)
- text line detection (1)
- text supervision (1)
- theory (1)
- therapeutic application (1)
- thermal camera (1)
- thermal point cloud (1)
- time calibration (1)
- time perception (1)
- tools (1)
- trait anxiety (1)
- transformer (1)
- translational neuroscience (1)
- transport microenvironments (1)
- transportation (1)
- unmanned aerial vehicle (1)
- unmanned aerial vehicles (1)
- usability evaluation (1)
- use cases (1)
- user experience (1)
- user interaction (1)
- user study (1)
- vection (1)
- vehicle dynamics (1)
- vehicular navigation (1)
- verbal behaviour (1)
- virtual agent (1)
- virtual agent interaction (1)
- virtual humans (1)
- virtual reality training (1)
- virtual stimuli (1)
- virtual tunnel (1)
- virtual-reality-continuum (1)
- waypoint parameter (1)
- wearable (1)
Institute
- Institut für Informatik (78) (remove)
This article presents a novel method for controlling a virtual audience system (VAS) in Virtual Reality (VR) application, called STAGE, which has been originally designed for supervised public speaking training in university seminars dedicated to the preparation and delivery of scientific talks. We are interested in creating pedagogical narratives: narratives encompass affective phenomenon and rather than organizing events changing the course of a training scenario, pedagogical plans using our system focus on organizing the affects it arouses for the trainees. Efficiently controlling a virtual audience towards a specific training objective while evaluating the speaker’s performance presents a challenge for a seminar instructor: the high level of cognitive and physical demands required to be able to control the virtual audience, whilst evaluating speaker’s performance, adjusting and allowing it to quickly react to the user’s behaviors and interactions. It is indeed a critical limitation of a number of existing systems that they rely on a Wizard of Oz approach, where the tutor drives the audience in reaction to the user’s performance. We address this problem by integrating with a VAS a high-level control component for tutors, which allows using predefined audience behavior rules, defining custom ones, as well as intervening during run-time for finer control of the unfolding of the pedagogical plan. At its core, this component offers a tool to program, select, modify and monitor interactive training narratives using a high-level representation. The STAGE offers the following features: i) a high-level API to program pedagogical narratives focusing on a specific public speaking situation and training objectives, ii) an interactive visualization interface iii) computation and visualization of user metrics, iv) a semi-autonomous virtual audience composed of virtual spectators with automatic reactions to the speaker and surrounding spectators while following the pedagogical plan V) and the possibility for the instructor to embody a virtual spectator to ask questions or guide the speaker from within the Virtual Environment. We present here the design, and implementation of the tutoring system and its integration in STAGE, and discuss its reception by end-users.
Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.
Cover contact graphs
(2012)
We study problems that arise in the context of covering certain geometric objects called seeds (e.g., points or disks) by a set of other geometric objects called cover (e.g., a set of disks or homothetic triangles). We insist that the interiors of the seeds and the cover elements are pairwise disjoint, respectively, but they can touch. We call the contact graph of a cover a cover contact graph (CCG). We are interested in three types of tasks, both in the general case and in the special case of seeds on a line: (a) deciding whether a given seed set has a connected CCG, (b) deciding whether a given graph has a realization as a CCG on a given seed set, and (c) bounding the sizes of certain classes of CCG’s. Concerning (a) we give efficient algorithms for the case that seeds are points and show that the problem becomes hard if seeds and covers are disks. Concerning (b) we show that this problem is hard even for point seeds and disk covers (given a fixed correspondence between graph vertices and seeds). Concerning (c) we obtain upper and lower bounds on the number of CCG’s for point seeds.
Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.
An innovative framework has been developed for teamwork of two quadcopter formations, each having its specified formation geometry, assigned task, and matching control scheme. Position control for quadcopters in one of the formations has been implemented through a Linear Quadratic Regulator Proportional Integral (LQR PI) control scheme based on explicit model following scheme. Quadcopters in the other formation are controlled through LQR PI servomechanism control scheme. These two control schemes are compared in terms of their performance and control effort. Both formations are commanded by respective ground stations through virtual leaders. Quadcopters in formations are able to track desired trajectories as well as hovering at desired points for selected time duration. In case of communication loss between ground station and any of the quadcopters, the neighboring quadcopter provides the command data, received from the ground station, to the affected unit. Proposed control schemes have been validated through extensive simulations using MATLAB®/Simulink® that provided favorable results.
In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%.
To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.
In many real world settings, imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on model training compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.
A binary tanglegram is a drawing of a pair of rooted binary trees whose leaf sets are in one-to-one correspondence; matching leaves are connected by inter-tree edges. For applications, for example, in phylogenetics, it is essential that both trees are drawn without edge crossings and that the inter-tree edges have as few crossings as possible. It is known that finding a tanglegram with the minimum number of crossings is NP-hard and that the problem is fixed-parameter tractable with respect to that number.
We prove that under the Unique Games Conjecture there is no constant-factor approximation for binary trees. We show that the problem is NP-hard even if both trees are complete binary trees. For this case we give an O(n 3)-time 2-approximation and a new, simple fixed-parameter algorithm. We show that the maximization version of the dual problem for binary trees can be reduced to a version of MaxCut for which the algorithm of Goemans and Williamson yields a 0.878-approximation.
Dynamic point cloud compression based on projections, surface reconstruction and video compression
(2021)
In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.