Exploring the Effect of Automation Failure on the Human’s Trustworthiness in Human-Agent Teamwork

Collaboration in teams composed of both humans and automations has an interdependent nature, which demands calibrated trust among all the teammembers. For building suitable autonomous teammates, we need to study how trust and trustworthiness function in such teams. In particular, automations occasionally fail to do their job, which leads to a...

Identifying and Visualizing Computational Hotspots in Path Tracing

Path tracing is a well-known light transport algorithm used to render photo-realistic images. However, it is an expensive algorithm with an active area of research for improving its efficiency. In our work, we present a method to measure and visualize the regions of high computational cost for unidirectional path tracers. We have defined metrics...

Efficient direct lighting calculations for area lights with light portals

Direct lighting calculation is an essential part of photorealistic rendering. Standard importance sampling techniques converge slowly in scenes where a light source is only visible through small openings as visibility is not considered. This problem is often addressed by manually placing light portals, marking the openings to the light. However,...

Efficient Emitter Sampling for Spectral Path Tracing

Spectral Monte-Carlo methods are powerful physically-based techniques for simulating wavelength-dependent phenomena such as dispersion. However, compared to tristimulus rendering, they involve sampling the spectral domain, which adds substantial overhead, requiring significantly more samples for noise-free, realistic-looking renders. Thereby, we...

Ambient Light Caching via Approximate Photon Mapping

Indirect illumination is an essential part of realistic computer-generated imagery. However, accurate calculation of indirect illumination comes at high compute costs. To this end, we replace lengthy indirect illumination paths by employing an ambient light cache based on photon mapping principles. By only performing cache queries after a...

Achieving Perceptually-Acceptable Early Renders in Spectral Progressive Rendering by Introducing Bias

Spectral Monte-Carlo rendering can simulate advanced light phenomena (e.g., dispersion, caustics, or iridescence), but require significantly more samples compared to trichromatic rendering to obtain noise-free images. Therefore, its progressive variant typically exhibits an extreme amount of chromatic noise in early renders. To that end, we...

Adversarial Traffic Modifications for the Network Intrusion Detection Domain: A Practical Adversarial Network Traffic Crafting Approach

Adversarial attacks pose a risk to machine learning (ML)-based network intrusion detection systems (NIDS). In this manner, it is of great significance to explore to what degree these methods can be viably utilized by potential adversaries. The majority of adversarial techniques are designed for unconstrained domains such as the image recognition...

Evaluating the performance of TDNN-BLSTM on Mandarin read and spontaneous speech

A limitation of current ASR systems is the so-called out-of-vocabulary words. The solution to overcome this limitation is to use APR systems. Previous research on Dutch APR systems identified Time Delayed Bidirectional Long-Short Term Memory Neural Network (TDNN-BLSTM) as one of best performing state-of-the-art NN architecture for PR. The goal...

Training and testing the TDNN-OPGRU acoustic model on English read and spontaneous speech

Automatic phoneme recognition (APR) is the process of recognizing phonemes (spoken sounds) in a recording of speech. It can be used for any application requiring fast and accurate transcription, i.e. a courthouse. This research creates such a model using the TDNN-OPGRU architecture and trains it on two datasets of recorded English speech - ...

Evaluation of phoneme recognition through TDNN-OPGRU on Mandarin speech

This research expands past research on implementing the TDNN-OPGRU network for Automatic Phoneme Recognition on Dutch speech by implementing and testing the TDNN-OPGRU network on Mandarin speech. The goal of this research is to investigate the performance of the TDNN-OPGRU architecture when decoding phonemes in Mandarin prepared and spontaneous...

Investigating the performance of Generative Adversarial Networks on Fabric Pattern Generation

Generative adversarial networks (GANs) are a popular method for image generation, capable of state-of-the-art. Despite this popularity, the generation of fabric patterns remains somewhat unexplored. A possible reason for this could be that there is no publicly shared dataset large enough to train models. However, research into this topic could...

Assessing the performance of the TDNN-BLSTM architecture for phoneme recognition of English speech

This research studies the Projected Bidirectional Long Short-Term Memory Time Delayed Neural Network (TDNN-BLSTM) model for English phoneme recognition. It contributes to the field of phoneme recognition by analyzing the performance of the TDNN-BLSTM model based on the TIMIT corpus and the Buckeye corpus, respectively containing read speech and...

Smart Teddy: Elderly monitoring and support system using ambient intelligence: Human Interaction and Integration

In September 2018, the Smart Teddy project was founded by a group of researchers within the Hague University of Applied Sciences1 in the Netherlands. The Smart Teddy project is a multidisciplinary project aiming to create an interactive system, using a teddy bear as a focus point, which collects the data needed in order to enable seniors with...

The Smart Teddy Project: Design of a data acquisition system to monitor seniors with dementia and detect dangerous situations

The amount of people dealing with dementia is rising globally. The amount of caretakers is, however, not. Therefore, technological aids are needed to support people dealing with dementia and relieve the stress on their caretakers. Current solutions provide tracking of people with dementia. Also, different robots exist that provide people with...

Evaluation of lane change predictions for schedule-driven intersection control

Optimization of traffic signal control has been widely investigated by means of model-based strategies. In 2012 a new model-based controller was published, named Schedule-driven Intersection Control (SCHIC). This controller uses a job-scheduling algorithm to minimize the cumulative delay for all observed vehicles. The algorithms of SCHIC are at...

Exploring the effects of conditioning Independent Q-Learners on the sufficient plan-time statistic for Dec-POMDPs

The Decentralized Partially Observable Markov Decision Process is a commonly used framework to formally model scenarios in which multiple agents must collaborate using local information. A key difficulty in a Dec-POMDP is that in order to coordinate successfully, an agent must decide on actions not only using its own information, but also by...