Malware is becoming more intelligent as static methods for blocking communication with Command and Control (C&C) server are becoming obsolete. Domain Generation Algorithms (DGAs) are a common evasion technique that generates pseudo-random domain names to communicate with C&C servers in a difficult way to detect using handcrafted methods. Trying to detect DGAs by looking at the domain name is a broad and efficient approach to detect malware-infected hosts. This gives us the possibility of detecting a wider assortment of malware compared to other techniques, even without knowledge of the malware’s existence. Our study compared the effectiveness of three different machine learning classifiers: Convolutional Neural Network (CNN), Generative Adversarial Network (GAN) and Random Forest (RF) when recognizing patterns and identifying these pseudo-random domains. The result indicates that CNN differed significantly from GAN and RF. It achieved 97.46% accuracy in the final evaluation, while RF achieved 93.89% and GAN achieved 60.39%. In the future, network traffic (efficiency) could be a key component to examine, as productivity may be harmed if the networkis over burdened by domain identification using machine learning algorithms.

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Denna artikel utforskar artificiell intelligens (AI) som en kreativ partner inom digitalbildproduktion och dess potential att förändra och komplettera traditionella designmetoder.Artikeln undersöker AI:s roll inom designprocessen, främst inriktat mot AI-baserade systemsom kan syntetisera visuellt material, diskuterar dess styrkor, svagheter och begränsningarsamt reflekterar över hur AI påverkar kreativitet, effektivisering och idégenerering. Som ettkomplement till artikeln har en fotobok skapats för att på ett visuellt engagerande sättpresentera resultaten av en AI:s försök till tolkning av textbeskrivningar, med hjälp avAI-verktyget Midjourney och GPT-4. Vidare granskas hur AI-drivna designprocesser kansamverka med traditionella metoder och på så sätt belysa hur AI kan integreras i designerskreativa verktygslåda och därmed utnyttjas för att skapa intressanta och unika resultat.Artikeln bidrar till förståelsen av AI:s växande roll inom designområdet och erbjuder insikter ihur designers kan använda AI som en kreativ partner.

Motivated by successes in the image generation domain, this thesis presents a novel Hybrid VQ-VAE (H-VQ-VAE) approach for generating realistic synthetic time series data with categorical features. The primary motivation behind this work is to address the limitations of existing generative models in accurately capturing the underlying structure and patterns of time series data, especially when dealing with categorical features.  Our proposed H-VQ-VAE model builds upon the foundation of the VQ-VAE architecture and consists of two separate VQ-VAEs: the whole VQ-VAE and the sliding VQ-VAE. Both models share a ResNet-based architecture with conv1d layers to effectively capture the temporal structure within the time series data. The whole VQ-VAE focuses on entire sequences of data to learn relationships between categorical and numerical features, while the sliding VQ-VAE exclusively processes numerical features using a sliding window approach. We conducted experiments on multiple datasets to evaluate the performance of our H-VQ-VAE model in comparison with the original VQ-VAE and TimeGAN models. Our evaluation used a train-on-real and test-on-synthetic approach, focusing on metrics such as Mean Absolute Error (MAE) and Explained Variance (EV). The H-VQ-VAE model achieved a 25-50% better MAE for numerical features compared to the VQ-VAE and outperformed TimeGAN by 45-75% on the complex dataset indicating its effectiveness in capturing the underlying structure and patterns of the time series data. In conclusion, the H-VQ-VAE model offers a promising approach for generating realistic synthetic time series data with categorical features, with potential applications in various fields where accurate data generation is crucial.

Data augmentation is a technique that acquires more training data by augmenting available samples, where the training data is used to fit model parameters. Data augmentation is utilized due to a shortage of training data in certain domains and to reduce overfitting. Augmenting a training dataset for image classification with a Generative Adversarial Network (GAN) has been shown to increase classification accuracy. This report investigates if transfer learning within a GAN can further increase classification accuracy when utilizing the augmented training dataset. The method section describes a specific GAN architecture for the experiments that includes a label condition. When using transfer learning within the specific GAN architecture, a statistical analysis shows a statistically significant increase in classification accuracy for a classification problem with the EMNIST dataset, which consists of images of handwritten alphanumeric characters. In the discussion section, the authors analyze the results and motivates other use cases for the proposed GAN architecture. / Datautökning är en metod som skapar mer träningsdata genom att utöka befintlig träningsdata, där träningsdatan används för att anpassa modellers parametrar. Datautökning används på grund av en brist på träningsdata inom vissa områden samt för att minska overfitting. Att utöka ett träningsdataset för att genomföra bildklassificering med ett generativt adversarialt nätverk (GAN) har visats kunna öka precisionen av klassificering av bilder. Denna rapport undersöker om transferlärande inom en GAN kan vidare öka klassificeringsprecisionen när ett utökat träningsdataset används. Metoden beskriver en specific GANarkitektur som innehåller ett etikettvillkor. När transferlärande används inom den utvalda GAN-arkitekturen visar en statistisk analys en statistiskt säkerställd ökning av klassificeringsprecisionen för ett klassificeringsproblem med EMNIST datasetet, som innehåller bilder på handskrivna bokstäver och siffror. I diskussionen diskuteras orsakerna bakom resultaten och fler användningsområden nämns.

The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. The method does not use images, heatmaps, 3D pose annotations, paired/unpaired 2Dto3D skeletons, 3D priors, synthetic 2D skeletons, multiview or temporal information in any shape or form. The 2D skeleton input is taken by a VAE that encodes it in a latent space and then decodes that latent representation to a 3D pose. The 3D pose is then reprojected to 2D for a constrained, selfsupervised optimization using the input 2D pose. Parallelly, the 3D pose is also randomly rotated and reprojected to 2D to generate a ’novel’ 2D view for unconstrained adversarial optimization using a discriminator network. The combination of the optimizations of the original and the novel 2D views of the predicted 3D pose results in a ’realistic’ 3D pose generation. The thesis shows that the encoding and decoding process of the VAE addresses the major challenge of erroneous and incomplete skeletons from 2D detection networks as inputs and that the variance of the VAE can be altered to get various plausible 3D poses for a given 2D input. Additionally, the latent representation could be used for crossmodal training and many downstream applications. The results on Human3.6M datasets outperform previous unsupervised approaches with less model complexity while addressing more hurdles in scaling the task to the real world. / Uppsatsen föreslår en oövervakad metod för representationslärande för att förutsäga en 3Dpose från ett 2D skelett med hjälp av ett VAE GAN (Variationellt Autoenkodande Generativt Adversariellt Nätverk) hybrid neuralt nätverk. Metoden lär sig att utvidga poser från 2D till 3D genom att använda självövervakning och adversariella inlärningstekniker. Metoden använder sig vare sig av bilder, värmekartor, 3D poseannotationer, parade/oparade 2D till 3D skelett, a priori information i 3D, syntetiska 2Dskelett, flera vyer, eller tidsinformation. 2Dskelettindata tas från ett VAE som kodar det i en latent rymd och sedan avkodar den latenta representationen till en 3Dpose. 3D posen är sedan återprojicerad till 2D för att genomgå begränsad, självövervakad optimering med hjälp av den tvådimensionella posen. Parallellt roteras dessutom 3Dposen slumpmässigt och återprojiceras till 2D för att generera en ny 2D vy för obegränsad adversariell optimering med hjälp av ett diskriminatornätverk. Kombinationen av optimeringarna av den ursprungliga och den nya 2Dvyn av den förutsagda 3Dposen resulterar i en realistisk 3Dposegenerering. Resultaten i uppsatsen visar att kodningsoch avkodningsprocessen av VAE adresserar utmaningen med felaktiga och ofullständiga skelett från 2D detekteringsnätverk som indata och att variansen av VAE kan modifieras för att få flera troliga 3D poser för givna 2D indata. Dessutom kan den latenta representationen användas för crossmodal träning och flera nedströmsapplikationer. Resultaten på datamängder från Human3.6M är bättre än tidigare oövervakade metoder med mindre modellkomplexitet samtidigt som de adresserar flera hinder för att skala upp uppgiften till verkliga tillämpningar.

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Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision<br>making in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.<br>

Gallium nitride (GaN) light emitting diodes (LED) embody a large field of research that aims to replace inefficient, conventional light sources with LEDs that have lower power, higher luminosity, and longer lifetime. This thesis presents an international collaboration effort between the State Key Laboratory for Mesoscopic Physics in Peking University (PKU) of Beijing, China and the Electrical Engineering Department of California Polytechnic State University, San Luis Obispo. Over the course of 2 years, Cal Poly’s side has simulated GaN LEDs within the pure blue wavelength spectrum (460nm), focusing specifically on the effects of reflection gratings, transmission gratings, top and bottom gratings, error gratings, 3-fold symmetric photonic crystal, and 2-fold symmetric nano-imprinted gratings. PKU used our simulation results to fabricate GaN high brightness LEDs from the results of our simulation models. We employed the use of the finite difference time domain (FDTD) method, a computational electromagnetic solution to Maxwell’s equations, to measure light extraction efficiency improvements of the various grating structures. Since the FDTD method was based on the differential form of Maxwell’s equations, it arbitrarily simulated complex grating structures of varying shapes and sizes, as well as the reflection, diffraction, and dispersion of propagating light throughout the device. We presented the optimized case, as well as the optimization trend for each of the single grating structures within a range of simulation parameters on the micron scale and find that single grating structures, on average, doubled the light extraction efficiency of GaN LEDs. Photonic crystal grating research in the micron scale suggested that transmission gratings benefit most when grating cells tightly pack together, while reflection gratings benefit when grating cells space further apart. The total number of grating cells fabricated on a reflection grating layer still affects light extraction efficiency. For the top and bottom grating structures, we performed a partial optimization of the grating sets formed from the optimized single grating cases and found that the direct pairing of optimized single grating structures decreases overall light extraction efficiency. However, through a partial optimization procedure, top and bottom grating designs could improve light extraction efficiency by 118% for that particular case, outperforming either of the single top or bottom grating cases alone. Our research then explored the effects of periodic, positional perturbation in grating designs and found that at a 10-15% randomization factor, light extraction efficiency could improve up to 230% from the original top and bottom grating case. Next, in an experiment with PKU, we mounted a 2-fold symmetric photonic crystal onto a PDMS hemi-cylinder by nano-imprinting to measure the transmission of light at angles from near tangential to normal. Overall transmission of light compared with the non-grating design increases overall light extraction efficiency when integrated over the range of angles. Finally, our research focused on the 3-fold symmetric photonic crystal grating structure and employed the use of 3-D FDTD methods and incoherent light sources to better study the effects of higher-ordered symmetry in grating design. Grating cells were discovered as the source of escaping light from the GaN LED model. The model revealed that light extraction efficiency and the far-field diffraction pattern could be estimated by the position of grating cells in the grating design.