Procedural Content Generation methods enable the creation of varied content algorithmically. Wave Function Collapse (WFC) is one such method. It is a tile-based local constraint solver commonly applied to world and map generation for grid-based content; it is able to create varied output from the same set of rules. While WFC is versatile, content generated with it is i) limited to one grid, ii) based on uniform tiles and iii) must use the same tile set and constraints throughout the grid. Due to these limitations, large classes of content are excluded, such as structured objects. Structured objects consist of an assembly of multiple components, each possibly based on a different tile set. We therefore propose Expressive Wave Function Collapse (XWFC), a major extension of WFC that enables solving and combining multiple grids with different Non-Uniform Tile (NUT) sets. Additionally, we can guarantee NUT shape and size preservation even under WFC’s Overlapping Model. With these generalizations, new domains are within reach for structured objects based on NUT sets, such as Tetris or LEGO.

Face clustering is a subfield of computer vision and pattern recognition with many applications such as face recognition and surveillance. Accurate clustering of faces can also help us to create labeled datasets. However, in the domain of comics, face clustering is not well studied. Therefore, it is uncertain which methods of feature extraction and clustering perform well on faces of comic characters. In this paper, we investigate the effectiveness of comic face clustering. To conduct our investigation, we implement two pipelines: one that automatically extracts character faces from comic strips, and another that clusters the extracted faces. Using Dilbert Comics for our experiments, we examine the performance of various feature extraction and clustering methods. Additionally, we experiment with combining feature extraction methods and removing noisy samples to increase the clustering accuracy. We show that using color information is crucial for accurate clustering, and combining color with shape features further improves accuracy. However, our experiments indicate that accuracy improvement is not guaranteed for every combination of feature extraction methods. We also demonstrate that removing noisy samples using hierarchical clustering can increase clustering precision. Using our findings, we achieve an F1 score of 0.752 based on our Dilbert Comics dataset of 77,768 face images. We obtain this result by clustering 20,988 non-noisy face images into 35 clusters with a precision of 0.886.

Synthetic art is the end result of artificial intelligence models that have been trained to generate images from text prompts. "Comic synthesis" is one such use case, where comic illustrations are produced from textual descriptions. Previous attempts at comic synthesis have utilized conditional Generative Adversarial Networks (cGANs), but this approach has encountered challenges in generating consistent and visually appealing comic panels. Strict data requirements and quality limitations have left room for improvement. We propose a novel approach to comic synthesis using Stable Diffusion, a powerful generative modelling technique. The study investigates the fine-tuning of the stable diffusion model specifically for the generation of Dilbert Comics from textual prompts. We explore different techniques to fine-tune the stable diffusion model for comic synthesis including Dreambooth and LoRA. Through extensive experimentation and analysis, with an FID score of 123, results produced using the Lora technique outperformed Dreambooth, excelling in understanding the Dilbert style, while Dreambooth struggled with multiple-subject training. Results are also compared with previous approaches based on conditional GANs. While the quality and detail greatly improved, the transition from conditionals to text descriptions meant the results were less accurate. The results show the potential of stable diffusion in generating appealing Dilbert Comic panels while highlighting the need for further exploration to enhance the alignment between textual descriptions and the generated images.

Contrastive Language-Image Pretraining (CLIP) has gained vast interest due to its impressive performance on a variety of computer vision tasks: image classification, image retrieval, action recognition, feature extraction, and more. The model learns to associate images with their descriptions, a powerful method which allows it to perform well on unseen domains. Often, the descriptions fail to capture text which is contained within the image, a source of information which could prove useful for a handful of computer vision tasks. This limitation requires finetuning in domains where contained text is important. In fact, CLIP has mixed performance on Optical Character Recognition (OCR). This paper proposes a novel architecture: OSBC (OCR Sentence BERT CLIP), which combines CLIP and a custom text extraction pipeline, composed of an OCR model, and a Natural Language Processing (NLP) model. OSBC uses the text contained within images as an additional feature when performing image classification and retrieval. We tested the model on multiple datasets for each task, occasionally outperforming CLIP when images contained text, while maintaining finetunability, and improving the model's robustness. In addition, OSBC was designed to be generalizable, meaning it is expected to perform well on unseen domains without finetuning, though this was not achieved in practice.

Cancer is one of the leading causes of death in the world. While there have been many studies investigating the development and progression of cancer in human tissues using host omics data or microbial data, there is a lack of research combining both types of data, even though both modalities have been shown to affect cancer morphology and aetiology. Studies which do combine these modalities often use simple methods or do not consider the relation between the two modalities and disease phenotypes. Such an integrated approach could offer additional insights and lead to the discovery of new disease biomarkers and better treatment strategies and therapies.

In this paper, we investigated whether such a holo-genomic approach offers additional information compared to using the modalities separately, by comparing the performances of prediction models built using the individual and integrated modalities for various prediction endpoints. To do this, we used TCGA gene expression data for the host omics modality and bacterial genus abundance data from the TCGA-mined Cancer Microbiome Atlas (TCMA) for the microbiome modality.

We found no improvement when integrating host gene expression with microbial abundance information compared to using the gene expression data individually, and the microbial data provided the least amount of diagnostic information. This is likely due to the information density of gene expression data, high variation of the microbiome, and the quantity, specificity and validation of the TCMA data. These results suggest that the holo-omics approach might not provide additional utility in certain contexts, that additional considerations have to be made when choosing microbial and host omic datasets for holo-omic integration, and provide an insight into the usability of the TCMA data set.

Visible Light Communication is a method of wireless communication that avoids the oversaturated frequencies used by radio communication. Prior research typically uses the camera on smartphone for communication, but using a camera is energy-intensive and inefficient. Some alternatives are photodiodes and ambient light sensors, however the former is often not available on phones and the latter is too slow to be useful. This thesis introduces a system, JAB, which combines two sensors to achieve high throughput while keeping energy usage low. The impact of distance, modulation frequency and light are measured on the system implemented on an Android smartphone. Overall, the system is usable and does greatly improve the energy effiency of using VLC. An important next step is creating a custom hardware setup with a kernel driver implementation for a full system test.

Visible Light Communication (VLC) is becoming an important research area where Visible Light Sources such as LED, Halogen Lamps and even the sun can be used for Wireless Communication. LED-to-Camera Communication is a form of VLC, where the camera can notice intermittent stimuli changes above a certain threshold frequency (flicker fusion frequency) which human eyes cannot no- tice. There exist numerous methods to encode data (= Modulation Schemes) for LED-to-Camera Communication, however, with some modulation schemes, VLC cannot support any practical appli- cations because the achievable data rate either is too low or the system is difficult to be implemented on off-the-shelf devices.
In this paper, we will propose a system design for LED-to-Camera Communication with Color Shift Keying (CSK) as a modulation scheme to encode the transmitted data. In Color Shift Keying, the sys- tem makes use of colors to encode bits, the receiver will then decode the received color into the corre- sponding bits. The proposed design provides a sys- tem outline which may obtain a more practical data rate by using CSK as a modulation method and a CMOS image sensor as receiver. A CMOS image sensor consists of a matrix of photodiodes which combined make up the captured image. The rolling shutter effect makes use of this matrix-like struc- ture by scanning each line separately, by using the rolling shutter effect the camera captures lines with different colors which can be decoded into bits.

Increased urbanisation has led to significant challenges for public transport operators. Inconsistent demand leads to peaks in passenger activity on the network. Moreover, the COVID-19 pandemic has introduced a need for social distancing as well, limiting the desired capacity of vehicles. To combat this, intelligent real-time and data-driven decision making is required. In many cases, the data required is lacking or not available in real-time. Our research addresses these challenges by providing means to gain insight into the passenger load of public transport vehicles. The focus of this research is to investigate how using in-vehicle sensor data can help in constructing an estimate of the passenger load and evaluate its contribution.

By combining in-vehicle sensor signals with historical passenger flow patterns, a novel fusion model
based on gradient boosting machines is constructed that can make real-time predictions of the passenger load using this data as input features. The evaluation shows that its estimates have a mean absolute error (MAE) score of 7.83, outperforming a random forest model baseline by 37%. Moreover, a crowding indicator analysis demonstrated that when predicting crowding indicators, the model achieves a weighted F1 score of 0.828. An ablation study found that excluding the in-vehicle features from the model reduces the model’s performance significantly, it could reduce the performance by up to 42%. In fact, the same experiment showed that having only the in-vehicle features is preferable to historical passenger flow features. Therefore, we conclude that using in-vehicle sensor data can be a feasible alternative to historical AFC data for predicting the passenger load.

The methodology has been extended by constructing a short-term forecasting model based on Seasonal ARIMA and GARCH that uses real-time signals of the passenger load to update its forecasts. The results show that while the forecasts lack accuracy initially, once the model is updated the forecasts improve up to almost a negligible error. When model predictions are used as update signals, the forecasting model is still able to improve and the results are competitive, despite the error contained in the signals.

Overall, we conclude that the proposed real-time model offers a suitable method for passenger load prediction and clearly demonstrates the effectiveness of using in-vehicle sensor data as input features. Moreover, we have presented a feasible method for using these features in a forecasting setting with a real-time model as an intermediary

Numerous papers have empirically studied the performance of deep learning based code completion models. However, none of these papers considered nor investigated whether good performance on statically typed languages translates to good performance on dynamically typed languages. A lack of available type information can make code completion more difficult, as many types are interacted with differently. However, natural language in the form of comments could compensate for a lack of available type information. This paper evaluates whether UniXcoder, a state of the NLP model, is able to perform code completion on both dynamically and statically typed languages with similar performance. Furthermore, the impact of the presence of type annotations and comments is assessed. We show that UniXcoder is able to utilize type annotations and comments in order to improve code completion performance, and that using only singleline comments yields better results than using all comments in the source code.

Code Completion is advancing constantly, with new research coming out all the time. One such advancement is CodeFill, which converts source files into token sequences for type prediction. To train the CodeFill model, a lot of source files are needed which take a long time to convert before training can begin. Converting the file the end-user is working on for completions is also essential for the total latency as longer files can affect the experience of using the model. In this study we aimed to improve the performance and success rate of this conversion. Our results indicate that we increased both the performance by 83 times or more depending on the input file length and the success rate by up to 45%.

State-of-the-art machine learning-based models provide automatic intelligent code completion based on large pre-trained language models. The theoretical accuracy of these models reaches 70%. However, the research on the practicality of these models is limited. Our paper will discuss the usefulness of UniXcoder, a machine learning-based cross-modal auto-completion model, in a normal environment through user evaluation. These models incorporate context around the requested completion and then return a prediction of code based on the context. To accomplish this, two plugins were made called 'Code4Me'. One for Visual Studio Code and PyCharm. These plugins work with a remote API that requires a segment of 3966 characters of the left and right context at the trigger point. The data collected consists of the inserted code completion, verification of the code completion, the IDE used, the trigger point, and the inference time. To evaluate the data the following metrics are used: BLUE~4, ROUGE-L, Exact Match, Edit Similarity, and the METEOR score. The results point out that developers accept once every 8 suggestions with an Exact Match of 62.5%, and the user evaluated, albeit with limited responses, are favourable towards the model and Code4Me. The accuracy of UniXcoder is lower in a real-world setting than when it is predicting source code. However, the usefulness of UniXcoder as an auto-completion model is apparent.

Automatic code completions are a widely used feature when programming code efficiently. These completions can be made by various code language models, and these can be differentiated in three categories: single token completion, statement (line) completion and block completions. These completions, and in particular statement predictions are usually created using only the left context, missing key information and context on the other side. InCoder, a novel state of the art model is capable of using both contexts. In this study we aim to show the impact of using both contexts in statement completions. The results show that on average, an improvement of 9.9% exact match and similar results for Edit Similarity, BLEU-4, ROUGE-L F1, and METEOR when using both contexts instead of only the left context.

A lot of models have been proposed to automatically complete code with promising evaluation results when tested in isolation on testing sets. This research aims to evaluate the performance of these models when used by developers when programming. Are these models still useful for actual programming and do developers even want this functionality? The model evaluated in this study is the InCoder model by Facebook, specifically the ability to complete code statements for the Python programming language. To evaluate this a plugin called Code4Me was made for PyCharm and VSC that will show code completion suggestions from the model when a keybind is pressed or a trigger point is encountered. If the user is shown a suggestion the plugin will send the actual line of code made by the developer after a delay, this can also be the suggestion itself if the user thought the suggestion was correct. When the user has used the model sufficiently they will also be asked to fill in a survey to gather opinions on the functionality that the model provides. The results show that there is a 21.95% ExactMatch, 52,73% edit similarity, and a BLEU-4 score of 36.05 for the statement completion functionality of InCoder. All users that filled in the survey preferred the automatic suggestions on trigger points but some indicated the keybind functionality was also useful. If the suggestion shown to the user was good they would use it instead of typing it themselves. The users indicated that the suggestions were a lot or a bit better than the default suggestions and using the plugin did save time programming. Overall all users were positive of the performance and thought the statement completion functionality provided useful suggestions.

Although the demand for wireless communication continuous to grow, the number of frequencies available in traditional data transmission is limited. Visible Light Communication (VLC) is a promising alternative communication method proven to work on high-end devices. However, large-scale adoption requires VLC to also work on everyday machines. Yet, no published research has proven effectiveness on low-end devices. This work describes how a communication system using light can be implement on a low-end smart phone, using a LED as a transmitter. Furthermore, it compares two methods of (de)modulation, namely On-Off Keying and Differential Manchester Code. Evaluation results demonstrate a working system with a maximum bit-rate of approximately 3000bps.

Visible Light Communication (VLC) has seen drastic improvements in recent years, one approach uses active light sources like LEDs, switching them at hight speeds to send data. Another approach uses the fundamental characteristics of liquid crystals (birefringence and thickness) to transmit data to a single pixel receiver. These characteristics allow for alterations in the color of the light. Little power is being used to transmit this data in comparison with using LEDs, and is significant faster than switching the LCs fully on or off like other research has done. This paper proposes an algorithm to decode the data transmitted by LCs with a high-end smartphone instead of the single pixel receiver. This algorithm called ChromaCam has real time transmitter detection and decoding of data. A demonstration is shown using a prototype, achieving data rates of 31 bits per second.

The state-of-the-art shows the potential of chatbots and other Machine Learning (ML) models to perform many tasks of high quality. Especially chatbots are already used by many companies to assist their customer service. However, chatbots will likely never be able to perform all tasks perfectly. Therefore, it is still the question whether such a chatbot is valuable for a business. Current research fails to describe how chatbots should be evaluated to compute the value of a chatbot for a business. In this research, we design an evaluation framework capturing the value of a chatbot in customer service. This framework consists of several key dimensions which should be computed in order to determine the value of the chatbot. To show that this evaluation framework captures the value of a chatbot, we perform a case study on water utility companies in The Netherlands. This case study showed the designed evaluation framework does capture the value of a chatbot in customer service.

In this thesis we investigate the possibilities for applying Koopman methods for reachability analysis. Reachability analysis is a verification process used to determine that a dynamical system starting in an initial set X0 cannot reach a certain set of dangerous states D within a time interval [0,T]. Koopman methods seem promising, because they predict nonlinear behaviour using linear techniques. However they have not been widely applied to reachability analysis.

We describe three different Koopman methods: data-driven, Polyflow and Carleman. We use the Polyflow method combined with ideas from several other methods to create a new reachability tool: PolyReach. Next, we analyse the performance of PolyReach by comparing it with a state-of-the-art reachability algorithm Flow* on various nonlinear systems. Finally, we summarize the strengths and weaknesses of the PolyReach tool and discuss ideas for further improvement.