Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. / Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

<p>Artificial neural networks (ANNs) have achieved extraordinary performance in various domains in recent years. However, some studies reveal that ANNs may be vulnerable in three aspects: label scarcity, perturbations, and open-set emerging classes. Noisy labeling and self-supervised learning approaches address the label scarcity issues, but most of the work couldn't handle the perturbations. Adversarial training methods, topological denoising methods, and mechanism designing methods aim to mitigate the negative effects caused by perturbations. However, adversarial training methods can barely train a robust model under the circumstance of extensive label scarcity; topological denoising methods are not efficient on dynamic data structures; and mechanism designing methods often depend on heuristic explorations. Detection-based methods devote to identifying novel or anomaly instances for further downstream tasks. Nonetheless, such instances may belong to open-set new emerging classes. To embrace the aforementioned challenges, we address the robustness issues of ANNs from two aspects. First, we propose a series of Bayesian label transition models to improve the robustness of Graph Neural Networks (GNNs) in the presence of label scarcity and perturbations in the graph domain. Second, we propose a new non-exhaustive learning model, named NE-GM-GAN, to handle both open-set problems and class-imbalance issues in network intrusion datasets. Extensive experiments with several datasets demonstrate that our proposed models can effectively improve the robustness of ANNs.</p>

Autonomous vehicles rely on sensors for a clear understanding of the environment and in a heavy duty truck, the sensors are placed at multiple locations like the cabin, chassis and the trailer in order to increase the field of view and reduce the blind spot area. Usually, these sensors perform best when they are stationary relative to the ground, hence large and fast movements, which are quite common in a truck, may lead to performance reduction, erroneous data or in the worst case, a sensor failure. This enforces a need to validate the sensors before using them for making life-critical decisions. This thesis proposes Domain Adaptation as one of the strategies to co-validate Inertial Measurement Unit (IMU) sensors. The proposed Generative Adversarial Network (GAN) based framework predicts the data of one IMU using other IMUs in the truck by implicitly learning the internal dynamics. This prediction model along with other sensor fusion strategies would be used by the supervising system to validate the IMUs in real-time. Through data collected from real-world experiments, it is shown that the proposed framework is able to accurately transform raw IMU sequences across domains. A further comparison is made between Long Short Term Memory (LSTM) and WaveNet based architectures to show the superiority of WaveNets in terms of performance and computational efficiency. / Autonoma fordon förlitar sig på sensorer för att skapa en bild av omgivningen. På en tung lastbil placeras sensorerna på multipla ställen, till exempel på hytten, chassiet och på trailern för att öka siktfältet och för att minska blinda områden. Vanligtvis presterar sensorerna som bäst när de är stationära i förhållande till marken, därför kan stora och snabba rörelser, som är vanliga på en lastbil, leda till nedsatt prestanda, felaktig data och i värsta fall fallerande sensorer. På grund av detta så finns det ett stort behov av att validera sensordata innan det används för kritiskt beslutsfattande. Den här avhandlingen föreslår domänadaption som en av de strategier för att samvalidera Tröghetsmätningssensorer (IMU-sensorer). Det föreslagna Generative Adversarial Network (GAN) baserade ramverket förutspår en Tröghetssensors data genom att implicit lära sig den interna dynamiken från andra Tröghetssensorer som är monterade på lastbilen. Den här prediktionsmodellen kombinerat med andra sensorfusionsstrategier kan användas av kontrollsystemet för att i realtid validera Tröghetssensorerna. Med hjälp av data insamlat från verkliga experiment visas det att det föreslagna ramverket klarar av att med hög noggrannhet konvertera obehandlade Tröghetssensor-sekvenser mellan domäner. Ytterligare en undersökning mellan Long Short Term Memory (LSTM) och WaveNet-baserade arkitekturer görs för att visa överlägsenheten i WaveNets när det gäller prestanda och beräkningseffektivitet.

Architectural design is a complex subject involving many different aspects that need to be considered. Drafting a floor plan from a blank slate can require iterating over several designs in the early phases of planning, and it is likely an even more daunting task for non-architects to tackle. This thesis investigates the opportunities of using conditional generative adversarial networks to generate floor plans for living spaces. The pix2pixHD method is used to learn a mapping between building boundaries and color-mapped floor plan layouts from the RPLAN dataset consisting of over 80k images. Previous work has mainly focused on either preserving an input boundary or generating layouts based on a set of conditions. To give potential users more control over the generation process, it would be useful to generate floor plans that respect both an input boundary and some high-level client requirements. By encoding requirements about desired room types and their locations in colored centroids, and stacking this image with the boundary input, we are able to train a model to synthesize visually plausible floor plan images that adapt to the given conditions. This model is compared to another model trained on only the building boundary images that acts as a baseline. Results from visual inspection, image properties, and expert evaluation show that the model trained with centroid conditions generates samples with superior image quality to the baseline model. Feeding additional information to the networks is therefore not only a way to involve the user in the design process, but it also has positive effects on the model training. The results from this thesis demonstrate that floor plan generation with generative adversarial networks can respect different kinds of conditions simultaneously, and can be a source of inspiration for future work seeking to make computer-aided design a more collaborative process between users and models. / Arkitektur och design är komplexa områden som behöver ta hänsyn till ett flertal olika aspekter. Att skissera en planritning helt från början kan kräva flera iterationer av olika idéer i de tidiga stadierna av planering, och det är troligtvis en ännu mer utmanande uppgift för en icke-arkitekt att angripa. Detta examensarbete syftar till att undersöka möjligheterna till att använda conditional generative adversarial networks för att generera planritningar för boendeytor. Pix2pixHD-metoden används för att lära en modell ett samband mellan gränsen av en byggnad och en färgkodad planritning från datasamlingen RPLAN bestående av över 80 tusen bilder. Tidigare arbeten har främst fokuserat på att antingen bevara en given byggnadsgräns eller att generera layouts baserat på en mängd av villkor. För att ge potentiella slutanvändare mer kontroll över genereringsprocessen skulle det vara användbart att generera planritningar som respekterar både en given byggnadsgräns och några klientbehov på en hög nivå. Genom att koda krav relaterade till önskade rumstyper och deras placering som färgade centroider, och sedan kombinera denna bild med byggnadsgränsen, kan vi träna en modell som kan framställa visuellt rimliga bilder på planritningar som kan anpassa sig till de givna villkoren. Denna modell jämförs med en annan modell som tränas endast på byggnadsgränser och som kan agera som en baslinje. Resultat från inspektion av genererade bilder och deras egenskaper, samt expertevaluering visar att modellen som tränas med centroidvillkor genererar bilder med högre bildkvalitet jämfört med baslinjen. Att ge mer information till modellen kan därmed både involvera användaren mer i designprocessen och bidra till positiva effekter på träningen av modellen. Resultaten från detta examensarbete visar att generering av planritningar med generative adversarial networks kan respektera olika typer av villkor samtidigt, och kan vara en källa till inspiration för framtida arbete som syftar till att göra datorstödd design en mer kollaborativ process mellan användare och modeller.

<p dir="ltr">X-ray computed tomography (CT) is one of the most widely used imaging modalities for medical diagnosis. Improving the quality of clinical CT images while keeping the X-ray dosage of patients low has been an active area of research. Recently, there have been two major technological advances in the commercial CT systems. The first is the use of Deep Neural Networks (DNN) to denoise and sharpen CT images, and the second is use of photon counting detectors (PCD) which provide higher spectral and spatial resolution compared to the conventional energy-integrating detectors. While both techniques have potential to improve the quality of CT images significantly, there are still challenges to improve the quality further.</p><p dir="ltr"><br></p><p dir="ltr">A denoising or sharpening algorithm for CT images must retain a favorable texture which is critically important for radiologists. However, commonly used methodologies in DNN training produce over-smooth images lacking texture. The lack of texture is a systematic error leading to a biased estimator.</p><p><br></p><p dir="ltr">In the first portion of this thesis, we propose three algorithms to reduce the bias, thereby to retain the favorable texture. The first method proposes a novel approach to designing a loss function that penalizes bias in the image more while training a DNN, producing more texture and detail in results. Our experiments verify that the proposed loss function outperforms the commonly used mean squared error loss function. The second algorithm proposes a novel approach to designing training pairs for a DNN-based sharpener. While conventional sharpeners employ noise-free ground truth producing over-smooth images, the proposed Noise Preserving Sharpening Filter (NPSF) adds appropriately scaled noise to both the input and the ground truth to keep the noise texture in the sharpened result similar to that of the input. Our evaluations show that the NPSF can sharpen noisy images while producing desired noise level and texture. The above two algorithms merely control the amount of texture retained and are not designed to produce texture that matches to a target texture. A Generative Adversarial Network (GAN) can produce the target texture. However, naive application of GANs can introduce inaccurate or even unreal image detail. Therefore, we propose a Texture Matching GAN (TMGAN) that uses parallel generators to separate anatomical features from the generated texture, which allows the GAN to be trained to match the target texture without directly affecting the underlying CT image. We demonstrate that TMGAN generates enhanced image quality while also producing texture that is desirable for clinical application.</p><p><br></p><p dir="ltr">In the second portion of this research, we propose a novel algorithm for the optimal statistical processing of photon-counting detector data for CT reconstruction. Current reconstruction and material decomposition algorithms for photon counting CT are not able to utilize simultaneously both the measured spectral information and advanced prior models. We propose a modular framework based on Multi-Agent Consensus Equilibrium (MACE) to obtain material decomposition and reconstructions using the PCD data. Our method employs a detector agent that uses PCD measurements to update an estimate along with a prior agent that enforces both physical and empirical knowledge about the material-decomposed sinograms. Importantly, the modular framework allows the two agents to be designed and optimized independently. Our evaluations on simulated data show promising results.</p>

Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. This similarity measure gives rise to the possibility of interpolating in the data which has been done successfully in the past. Herein we propose a new stochastic interpolation method for GANs where the interpolation is forced to adhere to the data distribution by implementing a sequential Monte Carlo algorithm for data sampling. The results show that the new method outperforms previously known interpolation methods for the data set LINES; compared to the results of other interpolation methods there was a significant improvement measured through quantitative and qualitative evaluations. The developed interpolation method has met its expectations and shown promise, however it needs to be tested on a more complex data set in order to verify that it also scales well. / Generative adversarial networks (GANs) är ett slags generativ modell som har fått mycket uppmärksamhet de senaste åren sedan de upptäcktes för sin potential att återskapa komplexa högdimensionella datafördelningar. Dessa förser en komprimerad representation av datan där enbart de karaktäriserande egenskaperna är bevarade, vilket följdaktligen inducerar ett avståndsmått på datarummet. Detta avståndsmått möjliggör interpolering inom datan vilket har åstadkommits med framgång tidigare. Häri föreslår vi en ny stokastisk interpoleringsmetod för GANs där interpolationen tvingas följa datafördelningen genom att implementera en sekventiell Monte Carlo algoritm för dragning av datapunkter. Resultaten för studien visar att metoden ger bättre interpolationer för datamängden LINES som användes; jämfört med resultaten av tidigare kända interpolationsmetoder syntes en märkbar förbättring genom kvalitativa och kvantitativa utvärderingar. Den framtagna interpolationsmetoden har alltså mött förväntningarna och är lovande, emellertid fordras att den testas på en mer komplex datamängd för att bekräfta att den fungerar väl även under mer generella förhållanden.

In this project we investigate the susceptibility ofreinforcement rearning (RL) algorithms to adversarial attacks.Adversarial attacks have been proven to be very effective atreducing performance of deep learning classifiers, and recently,have also been shown to reduce performance of RL agents.The goal of this project is to evaluate adversarial attacks onagents trained using deep reinforcement learning (DRL), aswell as to investigate how to detect these types of attacks. Wefirst use DRL to solve two environments from OpenAI’s gymmodule, namely Cartpole and Lunarlander, by using DQN andDDPG (DRL techniques). We then evaluate the performanceof attacks and finally we also train neural networks to detectattacks. The attacks was successful at reducing performancein the LunarLander environment and CartPole environment.The attack detector was very successful at detecting attacks onthe CartPole environment, but performed not quiet as well onLunarLander.We hypothesize that continuous action space environmentsmay pose a greater difficulty for attack detectors to identifypotential adversarial attacks. / I detta projekt undersöker vikänsligheten hos förstärknings lärda (RL) algotritmerför attacker mot förstärknings lärda agenter. Attackermot förstärknings lärda agenter har visat sig varamycket effektiva för att minska prestandan hos djuptförsärknings lärda klassifierare och har nyligen visat sigockså minska prestandan hos förstärknings lärda agenter.Målet med detta projekt är att utvärdera attacker motdjupt förstärknings lärda agenter och försöka utföraoch upptäcka attacker. Vi använder först RL för attlösa två miljöer från OpenAIs gym module CartPole-v0och ContiniousLunarLander-v0 med DQN och DDPG.Vi utvärderar sedan utförandet av attacker och avslutarslutligen med ett möjligt sätt att upptäcka attacker.Attackerna var mycket framgångsrika i att minskaprestandan i både CartPole-miljön och LunarLandermiljön. Attackdetektorn var mycket framgångsrik medatt upptäcka attacker i CartPole-miljön men presteradeinte lika bra i LunarLander-miljön.Vi hypotiserar att miljöer med kontinuerligahandlingsrum kan innebära en större svårighet fören attack identifierare att upptäcka attacker mot djuptförstärknings lärda agenter. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

Backdoor (Trojan) attacks are a major threat to the security of deep neural network (DNN) models. They are created by an attacker who adds a certain pattern to a portion of given training dataset, causing the DNN model to misclassify any inputs that contain the pattern. These infected classifiers are called Trojan models and the added pattern is referred to as the trigger. In image domain, a trigger can be a patch of pixel values added to the images and in text domain, it can be a set of words. In this thesis, we propose Trojan-Miner (T-Miner), a defense scheme against such backdoor attacks on text classification deep learning models. The goal of T-Miner is to detect whether a given classifier is a Trojan model or not. To create T-Miner , our approach is based on a sequence-to-sequence text generation model. T-Miner uses feedback from the suspicious (test) classifier to perturb input sentences such that their resulting class label is changed. These perturbations can be different for each of the inputs. T-Miner thus extracts the perturbations to determine whether they include any backdoor trigger and correspondingly flag the suspicious classifier as a Trojan model. We evaluate T-Miner on three text classification datasets: Yelp Restaurant Reviews, Twitter Hate Speech, and Rotten Tomatoes Movie Reviews. To illustrate the effectiveness of T-Miner, we evaluate it on attack models over text classifiers. Hence, we build a set of clean classifiers with no trigger in their training datasets and also using several trigger phrases, we create a set of Trojan models. Then, we compute how many of these models are correctly marked by T-Miner. We show that our system is able to detect trojan and clean models with 97% overall accuracy over 400 classifiers. Finally, we discuss the robustness of T-Miner in the case that the attacker knows T-Miner framework and wants to use this knowledge to weaken T-Miner performance. To this end, we propose four different scenarios for the attacker and report the performance of T-Miner under these new attack methods. / M.S. / Backdoor (Trojan) attacks are a major threat to the security of predictive models that make use of deep neural networks. The idea behind these attacks is as follows: an attacker adds a certain pattern to a portion of given training dataset and in the next step, trains a predictive model over this dataset. As a result, the predictive model misclassifies any inputs that contain the pattern. In image domain this pattern that is called trigger, can be a patch of pixel values added to the images and in text domain, it can be a set of words. In this thesis, we propose Trojan-Miner (T-Miner), a defense scheme against such backdoor attacks on text classification deep learning models. The goal of T-Miner is to detect whether a given classifier is a Trojan model or not. T-Miner is based on a sequence-to-sequence text generation model that is connected to the given predictive model and determine if the predictive model is being backdoor attacked. When T-Miner is connected to the predictive model, it generates a set of words, called perturbations, and analyses these perturbations to determine whether they include any backdoor trigger. Hence if any part of the trigger is present in the perturbations, the predictive model is flagged as a Trojan model. We evaluate T-Miner on three text classification datasets: Yelp Restaurant Reviews, Twitter Hate Speech, and Rotten Tomatoes Movie Reviews. To illustrate the effectiveness of T-Miner, we evaluate it on attack models over text classifiers. Hence, we build a set of clean classifiers with no trigger in their training datasets and also using several trigger phrases, we create a set of Trojan models. Then, we compute how many of these models are correctly marked by T-Miner. We show that our system is able to detect Trojan models with 97% overall accuracy over 400 predictive models.

<p dir="ltr">This research involves numerous network intrusion techniques through novel applications of graph representation learning and image representation learning. The methods are tested on multiple publicly available network flow datasets.</p>

<p dir="ltr">Anomaly detection is an active research field that widely applies to commercial applications to detect unusual patterns or outliers. Time series anomaly detection provides valuable insights into mission and safety-critical applications using ever-growing temporal data, including continuous streaming time series data from the Internet of Things (IoT), sensor networks, healthcare, stock prices, computer metrics, and application monitoring. While Generative Adversarial Networks (GANs) demonstrate promising results in time series anomaly detection, the opaque nature of generative deep learning models lacks explainability and hinders broader adoption. Understanding the rationale behind model predictions and providing human-interpretable explanations are vital for increasing confidence and trust in machine learning (ML) frameworks such as GANs. This study conducted a structured and comprehensive assessment of post-hoc local explainability in GAN-based time series anomaly detection using SHapley Additive exPlanations (SHAP). Using publicly available benchmarking datasets approved by Purdue’s Institutional Review Board (IRB), this study evaluated state-of-the-art GAN frameworks identifying their advantages and limitations for time series anomaly detection. This study demonstrated a systematic approach in quantifying the extent of GAN-based time series anomaly explainability, providing insights for businesses when considering adopting generative deep learning models. The presented results show that GANs capture complex time series temporal distribution and are applicable for anomaly detection. The analysis from this study shows SHAP can identify the significance of contributing features within time series data and derive post-hoc explanations to quantify GAN-detected time series anomalies.</p>