This report is done in collaboration with WirelessCar for the master of science thesis at Halmstad University. Many different parameters influence fuel consumption. The objective of the report is to evaluate if Graph neural networks are a practical model to perform fuel consumption prediction on areas. The model uses a partitioning of geographical locations of trip observations to capture their spatial information. The project also proposes a method to capture the non-stationary behavior of vehicles by defining a vehicle node as a separate entity. The model then captures their different features in a dense layer neural network and utilizes message passing to capture context about neighboring nodes. The model is compared to a baseline neural network with a similar network architecture as the graph neural network. The data is partitioned to define an area with Kmeans and static gridnet partition with and without terrain details. This partition is used to structure a homogeneous graph that is underperforming. The practical drawbacks of the initial homogeneous graph are inspected and addressed to develop a heterogeneous graph that can outperform the neural network baseline.

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In this thesis, anomalies are defined as data points whose value differs significantly from the normal pattern of the data set. Anomalousobservations on time series measured on satellites has a growing need of being detected directly on board the space-orbit systems to for example prevent malfunction and have efficient data management. Unibap's service Spacecloud Framework (SCFW) is developed to allow the deployment of machine learning applications directly on the satellite systems. Neural Networks (NNs) is therefore a candidate for the possibility to predict anomalies on satellite time series. The work described in this reportaims to implement and create a benchmark for Convolutional Autoencoder NN (CNN) and a Long Short-term Memory Autoencoder NN (LSTM). These implementations are used to determine which NN can be applied in Unibap's SCFW and detect anomalies with accuracy.  The NNs are trained and tested using a public data-sets which containreal and artificial time-series with labelled anomalies. The anomaliesare detected by reconstructing the time series and creating a threshold between the output and the input. The algorithms classify a data pointas an anomaly if it lies above the threshold. The networks are evaluated based on accuracy, execution time and size, to assess whether they are suited for implementation in SCFW. The results from the NNs indicatethat CNN is best suited for further application. On this basis, anattempt to implement CNN in SCFW is performed, but failed due to time and documentation limitations. Therefore, further research is needed to identify whether CNN can be implemented in SCFW and successfully detect anomalies.

Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification (SDSM). These three methods are compared across four artificial pattern recognition problems, generating only fifty liquids for each problem. Each of these algorithms shows overall improvements to LSMs with SDSM demonstrating the greatest improvement. SDSM is also shown to generalize well and outperforms traditional LSMs when presented with speech data obtained from the TIMIT dataset.

Training a deep neural network from scratch can be very expensive in terms of resources.In addition, training a neural network on a new task is usually done by training themodel form scratch. Recently there are new approaches in machine learning which usesthe knowledge from a pre-trained deep neural network on a new task. The technique ofreusing the knowledge from previously trained deep neural networks is called Transferlearning. In this paper we are going to evaluate transfer learning capabilities of deep neuralnetwork architectures for image classification. This research attempts to implementtransfer learning with different datasets and models in order to investigate transfer learningin different situations.

The size and speed of computer networks continue to expand at a rapid pace, as do the corresponding errors, failures, and faults inherent within such extensive networks. This thesis introduces a novel approach to interface Border Gateway Protocol (BGP) computer networks with neural networks to learn the precursor connectivity patterns that emerge prior to a node failure. Details of the design and construction of a framework that utilizes neural networks to learn and monitor BGP connection states as a means of detecting and predicting BGP peer node failure are presented. Moreover, this framework is used to monitor a BGP network and a suite of tests are conducted to establish that this neural network approach as a viable strategy for predicting BGP peer node failure. For all performed experiments both of the proposed neural network architectures succeed in memorizing and utilizing the network connectivity patterns. Lastly, a discussion of this framework's generic design is presented to acknowledge how other types of networks and alternate machine learning techniques can be accommodated with relative ease.

Generative models are broadly used in many subfields of DL. DNNs have recently developed a core approach to solving data-centric problems in image classification, translation, etc. The latest developments in parameterizing these models using DNNs and stochastic optimization algorithms have allowed scalable modeling of complex, high-dimensional data, including speech, text, and image. This dissertation proposal presents our state-the-art probabilistic bases and DL algorithms for generative models, including VAEs, GANs, and RNN-based encoder-decoder. The proposal also discusses application areas that may benefit from deep generative models in both NLP and computer vision. In NLP, we proposed an Arabic poetry generation model with extended phonetic and semantic embeddings (Phonetic CNN_subword embeddings). Extensive quantitative experiments using BLEU scores and Hamming distance show notable enhancements over strong baselines. Additionally, a comprehensive human evaluation confirms that the poems generated by our model outperform the base models in criteria including meaning, coherence, fluency, and poeticness. We proposed a generative video model using a hybrid VAE-GAN model in computer vision. Besides, we integrate two attentional mechanisms with GAN to get the essential regions of interest in a video, focused on enhancing the visual implementation of the human motion in the generated output. We have considered quantitative and qualitative experiments, including comparisons with other state-of-the-arts for evaluation. Our results indicate that our model enhances performance compared with other models and performs favorably under different quantitive metrics PSNR, SSIM, LPIPS, and FVD.Recently, mimicking biologically inspired learning in generative models based on SNNs has been shown their effectiveness in different applications. SNNs are the third generation of neural networks, in which neurons communicate through binary signals known as spikes. Since SNNs are more energy-efficient than DNNs. Moreover, DNN models have been vulnerable to small adversarial perturbations that cause misclassification of legitimate images. This dissertation shows the proposed ``VAE-Sleep'' that combines ideas from VAE and the sleep mechanism leveraging the advantages of deep and spiking neural networks (DNN--SNN).On top of that, we present ``Defense–VAE–Sleep'' that extended work of ``VAE-Sleep'' model used to purge adversarial perturbations from contaminated images. We demonstrate the benefit of sleep in improving the generalization performance of the traditional VAE when the testing data differ in specific ways even by a small amount from the training data. We conduct extensive experiments, including comparisons with the state–of–the–art on different datasets.

Text plays a pivotal role in our daily lives, encompassing various forms such as social media posts, news articles, books, reports, and more. Consequently, Natural Language Processing (NLP) has garnered widespread attention. This technology empowers us to undertake tasks like text classification, entity recognition, and even crafting responses within a dialogue context. However, despite the expansive utility of NLP, it frequently necessitates a critical decision: whether to place trust in a model's predictions. To illustrate, consider a state-of-the-art (SOTA) model entrusted with diagnosing a disease or assessing the veracity of a rumor. An incorrect prediction in such scenarios can have dire consequences, impacting individuals' health or tarnishing their reputation. Consequently, it becomes imperative to establish a reliable method for evaluating the reliability of an NLP model's predictions, which is our focus-uncertainty estimation on NLP. Though many works have researched uncertainty estimation or NLP, the combination of these two domains is rare. This is because most NLP research emphasizes model prediction performance but tends to overlook the reliability of NLP model predictions. Additionally, current uncertainty estimation models may not be suitable for NLP due to the unique characteristics of NLP tasks, such as the need for more fine-grained information in named entity recognition. Therefore, this dissertation proposes novel uncertainty estimation methods for different NLP tasks by considering the NLP task's distinct characteristics. The NLP tasks are categorized into natural language understanding (NLU) and natural language generation (NLG, such as text summarization). Among the NLU tasks, the understanding could be on two views, global-view (e.g. text classification at document level) and local-view (e.g. natural language inference at sentence level and named entity recognition at token level). As a result, we research uncertainty estimation on three tasks: text classification, named entity recognition, and text summarization. Besides, because few-shot text classification has captured much attention recently, we also research the uncertainty estimation on few-shot text classification. For the first topic, uncertainty estimation on text classification, few uncertainty models focus on improving the performance of text classification where human resources are involved. In response to this gap, our research focuses on enhancing the accuracy of uncertainty scores by bolstering the confidence associated with winning scores. we introduce MSD, a novel model comprising three distinct components: 'mix-up,' 'self-ensembling,' and 'distinctiveness score.' The primary objective of MSD is to refine the accuracy of uncertainty scores by mitigating the issue of overconfidence in winning scores while simultaneously considering various categories of uncertainty. seamlessly integrate with different Deep Neural Networks. Extensive experiments with ablation settings are conducted on four real-world datasets, resulting in consistently competitive improvements. Our second topic focuses on uncertainty estimation on few-shot text classification (UEFTC), which has few or even only one available support sample for each class. UEFTC represents an underexplored research domain where, due to limited data samples, a UEFTC model predicts an uncertainty score to assess the likelihood of classification errors. However, traditional uncertainty estimation models in text classification are ill-suited for UEFTC since they demand extensive training data, while UEFTC operates in a few-shot scenario, typically providing just a few support samples, or even just one, per class. To tackle this challenge, we introduce Contrastive Learning from Uncertainty Relations (CLUR) as a solution tailored for UEFTC. CLUR exhibits the unique capability to be effectively trained with only one support sample per class, aided by pseudo uncertainty scores. A distinguishing feature of CLUR is its autonomous learning of these pseudo uncertainty scores, in contrast to previous approaches that relied on manual specification. Our investigation of CLUR encompasses four model structures, allowing us to evaluate the performance of three commonly employed contrastive learning components in the context of UEFTC. Our findings highlight the effectiveness of two of these components. Our third topic focuses on uncertainty estimation on sequential labeling. Sequential labeling involves the task of assigning labels to individual tokens in a sequence, exemplified by Named Entity Recognition (NER). Despite significant advancements in enhancing NER performance in prior research, the realm of uncertainty estimation for NER (UE-NER) remains relatively uncharted but is of paramount importance. This topic focuses on UE-NER, seeking to gauge uncertainty scores for NER predictions. Previous models for uncertainty estimation often overlook two distinctive attributes of NER: the interrelation among entities (where the learning of one entity's embedding depends on others) and the challenges posed by incorrect span predictions in entity extraction. To address these issues, we introduce the Sequential Labeling Posterior Network (SLPN), designed to estimate uncertainty scores for the extracted entities while considering uncertainty propagation from other tokens. Additionally, we have devised an evaluation methodology tailored to the specific nuances of wrong-span cases. Our fourth topic focuses on an overlooked question that persists regarding the evaluation reliability of uncertainty estimation in text summarization (UE-TS). Text summarization, a key task in natural language generation (NLG), holds significant importance, particularly in domains where inaccuracies can have serious consequences, such as healthcare. UE-TS has garnered attention due to the potential risks associated with erroneous summaries. However, the reliability of evaluating UE-TS methods raises concerns, stemming from the interdependence between uncertainty model metrics and the wide array of NLG metrics. To address these concerns, we introduce a comprehensive UE-TS benchmark incorporating twenty-six NLG metrics across four dimensions. This benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model across two datasets. Additionally, it assesses the effectiveness of fourteen common uncertainty estimation methods. Our study underscores the necessity of utilizing diverse, uncorrelated NLG metrics and uncertainty estimation techniques for a robust evaluation of UE-TS methods. / Doctor of Philosophy / Text is integral to our daily activities, appearing in various forms such as social media posts, news articles, books, and reports. We rely on text for communication, information dissemination, and decision-making. Given its ubiquity, the ability to process and understand text through Natural Language Processing (NLP) has become increasingly important. NLP technology enables us to perform tasks like text classification, which involves categorizing text into predefined labels, and named entity recognition (NER), which identifies specific entities such as names, dates, and locations within text. Additionally, NLP facilitates generating coherent and contextually appropriate responses in conversational agents, enhancing human-computer interaction. However, the reliability of NLP models is crucial, especially in sensitive applications like medical diagnoses, where errors can have severe consequences. This dissertation focuses on uncertainty estimation in NLP, a less explored but essential area. Uncertainty estimation helps evaluate the confidence of NLP model predictions. We propose new methods tailored to various NLP tasks, acknowledging their unique needs. NLP tasks are divided into natural language understanding (NLU) and natural language generation (NLG). Within NLU, we look at tasks from two perspectives: a global view (e.g., document-level text classification) and a local view (e.g., sentence-level inference and token-level entity recognition). Our research spans text classification, named entity recognition (NER), and text summarization, with a special focus on few-shot text classification due to its recent prominence. For text classification, we introduce the MSD model, which includes three components to enhance uncertainty score accuracy and address overconfidence issues. This model integrates seamlessly with different neural networks and shows consistent improvements in experiments. For few-shot text classification, we develop Contrastive Learning from Uncertainty Relations (CLUR), designed to work effectively with minimal support samples per class. CLUR autonomously learns pseudo uncertainty scores, demonstrating effectiveness with various contrastive learning components. In NER, we address the unique challenges of entity interrelation and span prediction errors. We propose the Sequential Labeling Posterior Network (SLPN) to estimate uncertainty scores while considering uncertainty propagation from other tokens. For text summarization, we create a benchmark with tens of metrics to evaluate uncertainty estimation methods across two datasets. This benchmark helps assess the reliability of these methods, highlighting the need for diverse, uncorrelated metrics. Overall, our work advances the understanding and implementation of uncertainty estimation in NLP, providing more reliable and accurate predictions across different tasks.

Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly. / Ph. D. / Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. The demand of making use of the massive on-line information during decision making process becomes increasing intensive. Therefore, it is imperative to develop algorithms that automatically extract the formatted information from large volumes of the unstructured text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves detecting the event and extracting key information about the event in the article. (3) Third, the efforts concentrate on extracting the arguments of the event from the text. We found that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of current event extraction systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Our experiments on two real-world event extraction tasks (Cyber-Attack and Civil Unrest) show the effectiveness of our deep learning approaches for detecting and extracting the event information from unstructured text data.