Browsing by study line "ingen studieinriktning"

With the increased usage of machine learning models in various tasks and domains, the demand of understanding the models is emphasized. However, often modern machine learning models are difficult to understand and therefore do not provoke trust. Models can be understood by revealing their inner logic with explanations, but explanations can be difficult to interpret for non-expert users. We introduce an interactive visual interface to help non-expert users to understand and compare machine learning models. The interface visualizes explanations for multiple models in order to help the user to understand how the models generate predictions and whether the predictions can be trusted. We also explore current research in explainable AI visualizations, in order to compare our prototype to comparable systems present in research. The contributions of this paper are a system description and a use case for an interactive visualization interface to compare and explain machine learning models, as well as providing an understanding of the current state of research in explainable AI visualization systems and recommendations for future studies. We conclude that our system enables efficient visualizations for regression models unlike the papers covered in our survey. Another conclusion is that the field lacks precise terminology.

Origin-Destination (OD) data is a crucial part of price estimation in the aviation industry, and an OD flight is any number of flights a passenger takes in a single journey. OD data is a complex set of data that is both flow and multidimensional type of data. In this work, the focus is to design interactive visualization techniques to support user exploration of OD data. The thesis work aims to find which of the two menu designs suit better for OD data visualization: breadth-first or depth-first menu design. The two menus follow Schneiderman’s Task by Data Taxonomy, a broader version of the Information Seeking Mantra. The first menu design is a parallel, breadth-first menu layout. The layout shows the variables in an open layout and is closer to the original data matrix. The second menu design is a hierarchical, depth-first layout. This layout is derived from the semantics of the data and is more compact in terms of screen space. The two menu designs are compared in an online survey study conducted with the potential end users. The results of the online survey study are inconclusive, and therefore are complemented with an expert review. Both the survey study and expert review show that the Sankey graph is a good visualization type for this work, but the interaction of the two menu designs requires further improvements. Both of the menu designs received positive and negative feedback in the expert review. For future work, a solution that combines the positives of the two designs could be considered. ACM Computing Classification System (CCS): Human-Centered Computing → Visualization → Empirical Studies in Visualization Human-centered computing → Interaction design → Interaction design process and methods → Interface design prototyping

Stacking of antiaromatic molecules leads to enhanced stability and higher conductivity due to reversed antiarotmaticity. It has been shown that cyclophenes consisting of antiaromatic Ni(II) norrcorrole subunits have a vertical current-density flux between the two metal ions. The Ni(II) meso- substituted dibenzotetraaza[14]annulene complex fulfills the Hückel rule for being antiaromatic. Upon increasing the temperature above 13 K, the effective magnetic moment of solid state Ni(II) meso-substituted dibenzotetraaza[14]annulene changes from being diamagnet to paramagnetic. A suggested explanation for this is that there might be weak interaction between the Ni atoms. In this study the possibility of the existence of vertical current-density flux between the two metal ions in the Ni(II) meso-substituted dibenzotetraaza[14]annulene is investigated. In addition, the effect of the Ni and N atoms in Ni(II) 1,5,9,13-tetraaza[16]annulene was studied by replacing Ni and Zn and N with O. Electronic motion in molecules that are under the influence of a magnetic field is investigated computationally, since at present there is no routine experimental method for doing that. TURBOMOLE, the Gauge-including Magnetically Induced Currents method and Paraview were employed in this study for structure optimization of the molecules, calculation of current-density flux and current strength in the molecules and visualisation of the current-density pathways respectively. The results of this study does not show any current transport between the subunits in the Ni(II) meso-substituted dibenzotetraaza[14]annulene complex. Both the Ni(II) 1,5,9,13-tetraaza[16]annulene and the Zn(II) 1,5,9,13-tetraaza[16]annulene are aromatic but they were not stacked due to their distorted structure. The (2Z,7Z,10Z,14Z)-1,9-dioxa-5,13-diazacyclohexadeca-2,7,10,14-tetraene-5,13-diide complexes with either Zn(II) or Ni(II) were both non-aromatic as well as the Ni(II) (2Z,7Z,10Z,14Z)-1,9-dioxa-5,13-diazacyclohexadeca-2,7,10,14-tetraene-5,13-diide dimer.

The thesis presents and evaluates a model for detecting changes in discourses in diachronic text corpora. Detecting and analyzing discourses that typically evolve over a period of time and differ in their manifestations in individual documents is a challenging task, and existing approaches like topic modeling are often not able to reach satisfactory results. One key problem is the difficulty of properly evaluating the results of discourse detection methods, due in large part to the lack of annotated text corpora. The thesis proposes a solution where synthetic datasets containing non-stable discourse patterns are generated from a corpus of news articles. Using the news categories as a proxy for discourses allows both to control the complexity of the data and to evaluate the model results based on the known discourse patterns. The complex task of extracting topics from texts is commonly performed using generative models, which are based on simplifying assumptions regarding the process of data generation. The model presented in the thesis explores instead the potential of deep neural networks, combined with contrastive learning, to be used for discourse detection. The neural network model is first trained using supervised contrastive loss function, which teaches the model to differentiate the input data based on the type of discourse pattern it belongs to. This pretrained model is then employed for both supervised and unsupervised downstream classification tasks, where the goal is to detect changes in the discourse patterns at the timepoint level. The main aim of the thesis is to find out whether contrastive pretraining can be used as a part of a deep learning approach to discourse change detection, and whether the information encoded into the model during contrastive training can generalise to other, closely related domains. The results of the experiments show that contrastive pretraining can be used to encode information that directly relates to its learning goal into the end products of the model, although the learning process is still incomplete. However, the ability of the model to generalise this information in a way that could be useful in the timepoint level classification tasks remains limited. More work is needed to improve the model performance, especially if it is to be used with complex real world datasets.

In the modern era, using personalization when reaching out to potential or current customers is essential for businesses to compete in their area of business. With large customer bases, this personalization becomes more difficult, thus segmenting entire customer bases into smaller groups helps businesses focus better on personalization and targeted business decisions. These groups can be straightforward, like segmenting solely based on age, or more complex, like taking into account geographic, demographic, behavioral, and psychographic differences among the customers. In the latter case, customer segmentation should be performed with Machine Learning, which can help find more hidden patterns within the data. Often, the number of features in the customer data set is so large that some form of dimensionality reduction is needed. That is also the case with this thesis, which includes 12802 unique article tags that are desired to be included in the segmentation. A form of dimensionality reduction called feature hashing is selected for hashing the tags for its ability to be introduced new tags in the future. Using hashed features in customer segmentation is a balancing act. With more hashed features, the evaluation metrics might give better results and the hashed features resemble more closely the unhashed article tag data, but with less hashed features the clustering process is faster, more memory-efficient and the resulting clusters are more interpretable to the business. Three clustering algorithms, K-means, DBSCAN, and BIRCH, are tested with eight feature hashing bin sizes for each, with promising results for K-means and BIRCH.

This thesis presents DeepGT, a 3D Convolutional Neural Network designed to enhance the spatial resolution of GNSS Tropospheric Tomography, a technique for estimating atmospheric water vapor distribution using GNSS signals. By utilizing Slant Wet Delays from dense GNSS networks and boundary meteorological data from Numerical Weather Prediction models, DeepGT refines low-resolution tomographic wet refractivity fields. The proposed method quadruples the horizontal resolution, while improving the accuracy of the tomographic reconstruction. Two experiments are conducted to validate this: one with real-world SWEPOS data and another with a hypothetical dense GNSS network. The results demonstrate the potential of deep learning models such as DeepGT in enhancing GNSS Meteorology, with implications for improved weather forecasting and climate studies.

Deploying machine learning models is found to be a massive issue in the field. DevOps and Continuous Integration and Continuous Delivery (CI/CD) has proven to streamline and accelerate deployments in the field of software development. Creating CI/CD pipelines in software that includes elements of Machine Learning (MLOps) has unique problems, and trail-blazers in the field solve them with the use of proprietary tooling, often offered by cloud providers. In this thesis, we describe the elements of MLOps. We study what the requirements to automate the CI/CD of Machine Learning systems in the MLOps methodology. We study if it is feasible to create a state-of-the-art MLOps pipeline with existing open-source and cloud-native tooling in a cloud provider agnostic way. We designed an extendable and cloud-native pipeline covering most of the CI/CD needs of Machine Learning system. We motivated why Machine Learning systems should be included in the DevOps methodology. We studied what unique challenges machine learning brings to CI/CD pipelines, production environments and monitoring. We analyzed the pipeline’s design, architecture, and implementation details and its applicability and value to Machine Learning projects. We evaluate our solution as a promising MLOps pipeline, that manages to solve many issues of automating a reproducible Machine Learning project and its delivery to production. We designed it as a fully open-source solution that is relatively cloud provider agnostic. Configuring the pipeline to fit the client needs uses easy-to-use declarative configuration languages (YAML, JSON) that require minimal learning overhead.

Today, Global Navigation Satellite Systems (GNSS) provide services that many critical systems [1] as well as normal users, need in everyday life. These signals are threatened by unintentional and intentional interference. The received satellite signals are complex-valued by nature, however, state-of-the-art anomaly detection approaches operate in the real domain. Changing the anomaly detection into the complex domain allows for preserving the phase component of the signal data. In this thesis, I developed and tested a fully complex-valued Long Short-Term Memory (LSTM) based autoencoder for anomaly detection. I also developed a method for scaling of complex-numbers that forces both real and imaginary units into the range [-1,1] and does not change the direction of a complex vector. The model is trained and tested both in the time and frequency domains, and the frequency domain is divided into two parts: real and complex domain. The developed model’s training data consists only of clean sample data, and the output of the model is the reconstruction of the model’s input. In testing, it can be determined whether the output is clean or anomalous based on the reconstruction error and the computed threshold value. The results show that the autoencoder model in the real domain outperforms the model trained in the complex domain. This does not indicate that the anomaly detection in the complex domain does not work; rather, the model’s architecture needs improvements, and the amount of training data must be increased to reduce the overfitting of the complex domain and thus improve the anomaly detection capability. It was also investigated that some anomalous sample sequences contain a few large valued spikes while other values in the same data snapshot are smaller. After scaling, the values other than in the spikes get closer to zero. This phenomenon causes small reconstruction errors in the model and yields false predictions in the complex domain.

This study focused on detecting horizontal and vertical collusion within Indonesian government procurement processes, leveraging data-driven techniques and statistical methods. Regarding horizontal collusion, we applied clustering techniques to categorize companies based on their supply patterns, revealing clusters with similar bidding practices that may indicate potential collusion. Additionally, we identified patterns where specific supplier groups consistently won procurements, raising questions about potential competitive advantages or strategic practices that need further examination for collusion. For vertical collusion, we examined the frequency of associations between specific government employees and winning companies. While high-frequency collaborations were observed, it is essential to interpret these results with caution as they do not definitively indicate collusion, and legitimate factors might justify such associations. Despite revealing important patterns, the study acknowledges its limitations, including the representativeness of the dataset and the reliance on quantitative methods. Nevertheless, our findings carry substantial implications for enhancing procurement monitoring, strengthening anti-collusion regulations, and promoting transparency in Indonesian government procurement processes. Future research could enrich these findings by incorporating qualitative methods, exploring additional indicators of collusion, and leveraging machine learning techniques to detect collusion.

The literature study of this thesis focuses on the different analytical methods used to analyse amino acids in food and beverage samples. Amino acids are essential organic molecules and their concentrations in foods and beverages constitute, inter alia, the product’s nutritional value, quality, freshness, and flavour. Amino acid analysis of foodstuff has various applications, which exploit several analytical methods. These reviewed methods are founded on academic articles published during the past two decades. This literature review discusses the different sample matrixes, sample preparation methods, ways to derivate analytes, and different separation and detection methods utilized in the recent amino acid studies. The experimental part of this thesis was a modification of L-asparagine and L-aspartic acid test (L-Asp/L-AspAc) in Thermo Fisher Scientific Oy industrial R&D laboratory. An enzymatic photometric method is used to determine L-Asp/L-AspAc amino acids in food samples. The modification process entailed pre-testing of several candidate methods, from which the most suitable one was selected. The feasibility of the chosen test was affirmed before verification and validation of the modified test.