Open Access

Flying in virtual reality (VR) using standard handheld controllers can be cumbersome and contribute to unwanted side effects such as motion sickness and disorientation. This paper investigates a novel hands-free flying interface - HeadJoystick, where the user moves their head similar to a joystick handle toward the target direction to control virtual translation velocity. The user sits on a regular office swivel chair and rotates it physically to control virtual rotation using 1:1 mapping. We evaluated short-term (Study 1) and extended usage effects through repeated usage (Study 2) of the HeadJoystick versus handheld interfaces in two within-subject studies, where participants flew through a sequence of increasingly difficult tunnels in the sky. Using the HeadJoystick instead of handheld interfaces improved both user experience and performance, in terms of accuracy, precision, ease of learning, ease of use, usability, long-term use, presence, immersion, sensation of self-motion, workload, and enjoyment in both studies. These findings demonstrate the benefits of using leaning-based interfaces for VR flying and potentially similar telepresence applications such as remote flight with quadcopter drones. From a theoretical perspective, we also show how leaning-based motion cueing interacts with full physical rotation to improve user experience and performance compared to the gamepad.

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.

Damage to grasslands is mainly caused by wild boar during foraging. Farmers in Germany thereby register yield losses and expenses for damage repair. This contribution analyzes the acquisition and processing of aerial images to orthomosaics and image segmentation to perform spatial measurements of damaged patches in grasslands. A sample set of manually annotated orthomosaics is analyzed. Preliminary classification results applying a convolutional neural network approach to segment damaged patches are presented. First results show the applicability of the applied methods in the detection of damage caused by wild boar and suggest that other damage causes (e.g., mole damage) should be considered to improve results.

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.

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.

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.

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.

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.

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.

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).

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.