Telehealth is an online health care system that is extensively used in the current pandemic situation. Our proposed technique is considered a fog computing-based attack detection architecture to protect IoT Telehealth Networks. As for IoT Telehealth Networks, the sensor/actuator edge devices are considered the weakest link in the IoT system and are obvious targets of attacks such as botnet attacks. In this thesis, we introduce a novel framework that employs several machine learning and data analysis techniques to detect those attacks. We evaluate the effectiveness of the proposed framework using two publicly available datasets from real-world scenarios. These datasets contain a variety of attacks with different characteristics. The robustness of the proposed framework and its ability, to detect and distinguish between the existing IoT attacks that are tested by combining the two datasets for cross-evaluation. This combination is based on a novel technique for generating supplementary data instances, which employs GAN (generative adversarial networks) for data augmentation and to ensure that the number of samples and features are balanced. / Graduate

Despite recent advances in deep-learning based semantic segmentation, automatic building detection from remotely sensed imagery is still a challenging problem owing to large variability in the appearance of buildings across the globe. The errors occur mostly around the boundaries of the building footprints, in shadow areas, and when detecting buildings whose exterior surfaces have reflectivity properties that are very similar to those of the surrounding regions. To overcome these problems, we propose a generative adversarial network based segmentation framework with uncertainty attention unit and refinement module embedded in the generator. The refinement module, composed of edge and reverse attention units, is designed to refine the predicted building map. The edge attention enhances the boundary features to estimate building boundaries with greater precision, and the reverse attention allows the network to explore the features missing in the previously estimated regions. The uncertainty attention unit assists the network in resolving uncertainties in classification. As a measure of the power of our approach, as of January 5, 2022, it ranks at the second place on DeepGlobe’s public leaderboard despite the fact that main focus of our approach — refinement of the building edges — does not align exactly with the metrics used for leaderboard rankings. Our overall F1-score on DeepGlobe’s challenging dataset is 0.745. We also report improvements on the previous-best results for the challenging INRIA Validation Dataset for which our network achieves an overall IoU of 81.28% and an overall accuracy of 97.03%. Along the same lines, for the official INRIA Test Dataset, our network scores 77.86% and 96.41% in overall IoU and accuracy. We have also improved upon the previous best results on two other datasets: For the WHU Building Dataset, our network achieves 92.27% IoU, 96.73% precision, 95.24% recall and 95.98% F1-score. And, finally, for the Massachusetts Buildings Dataset, our network achieves 96.19% relaxed IoU score and 98.03% relaxed F1-score over the previous best scores of 91.55% and 96.78% respectively, and in terms of non-relaxed F1 and IoU scores, our network outperforms the previous best scores by 2.77% and 3.89% respectively.

No description available.

No description available.

Game engines such as Unreal Engine 5 are widely used to create photo-realistic renderings. To run these renderings at high quality without experiencing any performance issues,high-performance hardware is often required. In situations where the hardware is lacking,users may be forced to lower the quality and resolution of renderings to maintain goodperformance. While this may be acceptable in some situations, it limits the benefit that apowerful tool like Unreal Engine 5 can provide. This thesis aims to explore the possibilityof using a Real-ESRGAN, fine-tuned on a custom data set, to increase both the resolutionand quality of screenshots taken in Unreal Engine 5. By doing this, users can lower theresolution and quality of their Unreal Engine 5 rendering while still being able to generatehigh quality screenshots similar to those produced when running the rendering at higherresolution and higher quality. To accomplish this, a custom data set was created by randomizing camera positionsand capturing screenshots in an Unreal Engine 5 rendering. This data set was used to finetune a pre-trained Real-ESRGAN model. The fine-tuned model could then generate imagesfrom low resolution and low quality screenshots taken in Unreal Engine 5. The resultingimages were analyzed and evaluated using both quantitative and qualitative methods.The conclusions drawn from this thesis indicate that images generated using the finetuned weights are of high quality. This conclusion is supported by quantitative measurements, demonstrating that the generated images and the ground truth images are similar.Furthermore, visual inspection conducted by the authors confirms that the generated images are similar to the reference images, despite occasional artifacts.

At present, state-of-the-art deep learning music generation systems require a lot time and hardware resources to develop. This means that they are almost exclusively available to large companies. In order to reduce these requirements, more efficient techniques and methods need to be utilised.  This project aims to investigate various approaches by developing a music generation system using generative adversarial networks, comparing different techniques and their effect on the system's performance.  Our results show the difficulties in generating music in a more resource-constrained environment. We find that structuring the input space with conditional model constraints improves the systems' ability to conform to musical standards. The results also indicate the importance of a patch-based discriminator for evaluating the texture of the generated music. Finally, we propose a similarity loss as a way of reducing mode collapse in the generator, thus stabilising the training process.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24821号 / 情博第837号 / 新制||情||140(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 熊田 孝恒, 教授 西田 眞也, 教授 内田 由紀子, 准教授 中島 亮一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<div>Brain tumor sub region segmentation is a challenging problem in Magnetic Resonance imaging. The tumor regions tend to suffer from lack of homogeneity, textural differences, variable location, and their ability to proliferate into surrounding tissue. </div><div> The segmentation task thus requires an algorithm which can be indifferent to such influences and robust to external interference. In this work we propose a conditional generative adversarial network which learns off multiple planes of reference. Using this learning, we evaluate the quality of the segmentation and back propagate the loss for improving the learning. The results produced by the network show competitive quality in both the training and the testing data-set.</div><div><br></div>

This study is dedicated to enhancing the effectiveness of Optical Character Recognition (OCR) systems, with a special emphasis on Arabic handwritten digit recognition. The choice to focus on Arabic handwritten digits is twofold: first, there has been relatively less research conducted in this area compared to its English counterparts; second, the recognition of Arabic handwritten digits presents more challenges due to the inherent similarities between different Arabic digits.OCR systems, engineered to decipher both printed and handwritten text, often face difficulties in accurately identifying low-quality or distorted handwritten text. The quality of the input image and the complexity of the text significantly influence their performance. However, data augmentation strategies can notably improve these systems' performance. These strategies generate new images that closely resemble the original ones, albeit with minor variations, thereby enriching the model's learning and enhancing its adaptability. The research found Conditional Variational Autoencoders (C-VAE) and Conditional Generative Adversarial Networks (C-GAN) to be particularly effective in this context. These two generative models stand out due to their superior image generation and feature extraction capabilities. A significant contribution of the study has been the formulation of the Synthetic Image Evaluation Procedure, a systematic approach designed to evaluate and amplify the generative models' image generation abilities. This procedure facilitates the extraction of meaningful features, computation of the Fréchet Inception Distance (LFID) score, and supports hyper-parameter optimization and model modifications.

Deep learning models heavily rely on an abundance of data, and their performance is directly affected by data availability. In mobility pattern modeling, problems, such as next location prediction or flow prediction, are commonly solved using deep learning approaches. Despite advances in modeling techniques, complications arise when acquiring mobility data is limited by geographic factors and data protection laws. Generating highquality synthetic data is one of the solutions to get around at times when information is scarce. Trajectory generation is concerned with generating trajectories that can reproduce the spatial and temporal characteristics of the underlying original mobility patterns. The task of this project was to evaluate Generative Adversarial Network (GAN) capabilities to generate synthetic vehicle trajectory data. We extend the methodology of previous research on trajectory generation by introducing conditional trajectory duration labels and a model pretraining mechanism. The evaluation of generated trajectories consisted of a two-fold analysis. We perform qualitative analysis by visually inspecting generated trajectories and quantitative analysis by calculating the statistical distance between synthetic and original data distributions. The results indicate that extending the previous GAN methodology allows the novel model to generate trajectories statistically closer to the original data distribution. Nevertheless, a statistical base model has the best generative performance and is the only model to generate visually plausible results. We accredit the superior performance of the statistical base model to the highly predictive nature of vehicle trajectories, which must follow the road network and have the tendency to follow minimum distance routes. This research considered only one type of GAN-based model, and further research should explore other architecture alternatives to understand the potential of GAN-based models fully / Modeller för djupinlärning är starkt beroende av ett överflöd av data, och derasprestanda påverkas direkt av datatillgänglighet. I mobilitetsmönstermodellering löses problem, såsom nästa platsförutsägelse eller flödesprediktion,vanligtvis med hjälp av djupinlärningsmetoder. Trots framsteg inommodelleringsteknik uppstår komplikationer när inhämtning av mobilitetsdatabegränsas av geografiska faktorer och dataskyddslagar. Att generera syntetiskdata av hög kvalitet är en av lösningarna för att ta sig runt i tider dåinformationen är knapp. Bangenerering handlar om att generera banorsom kan reproducera de rumsliga och tidsmässiga egenskaperna hos deunderliggande ursprungliga rörlighetsmönstren. Uppgiften för detta projektvar att utvärdera GAN-kapaciteten för att generera syntetiska fordonsbanor. Viutökar metodiken för tidigare forskning om banagenerering genom att introducera villkorliga etiketter för banalängd och en modellförträningsmekanism.Utvärderingen av genererade banor bestod av en tvåfaldig analys. Viutför kvalitativ analys genom att visuellt inspektera genererade banor ochkvantitativ analys genom att beräkna det statistiska avståndet mellan syntetiskaoch ursprungliga datafördelningar. Resultaten indikerar att en utvidgningav den tidigare GAN-metoden tillåter den nya modellen att generera banorstatistiskt närmare den ursprungliga datadistributionen. Ändå har en statistiskbasmodell den bästa generativa prestandan och är den enda modellen somgenererar visuellt rimliga resultat. Vi ackrediterar den statistiska basmodellensöverlägsna prestanda till den mycket prediktiva karaktären hos fordonsbanor,som måste följa vägnätet och ha en tendens att följa minimiavståndsrutter.Denna forskning övervägde endast en typ av GAN-baserad modell, ochytterligare forskning bör utforska andra arkitekturalternativ för att förståpotentialen hos GAN-baserade modeller fullt ut