In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by approximating the fine-grained variable as continuous, evaluated using MSE. Furthermore, three models are evaluated on a balanced and unbalanced dataset in order to investigate the effects of class imbalance. The results show that the SVR performs better on unbalanced fine-grained data, with the best fine-grained model reaching a MSE 4.12, compared to the balanced SVR (6.84). The binary SVM model reaches an accuracy of 86.37% and weighted F1 macro of 86.36% on the unbalanced data, while the balanced binary SVM model reaches approximately 80% for both measures. The multinomial model shows the worst performance due to the inability to handle class imbalance, despite the implementation of class weights. Furthermore, results from feature engineering shows that SVR benefits marginally from certain regex conversions, and tf-idf weighting shows better performance on the balanced sets compared to the unbalanced sets.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第23405号 / 理博第4740号 / 新制||理||1679(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 彰二, 教授 川口 真也, 准教授 立川 正志 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18117号 / 理博第3995号 / 新制||理||1576(附属図書館) / 30975 / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 彰二, 教授 大野 睦人, 准教授 土井 知子 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18986号 / 工博第4028号 / 新制||工||1620(附属図書館) / 31937 / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 辻 伸泰, 教授 田中 功, 教授 乾 晴行 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18987号 / 工博第4029号 / 新制||工||1620(附属図書館) / 31938 / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 辻 伸泰, 教授 白井 泰治, 教授 松原 英一郎 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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No description available.

Data conversion for evolving events in an Event Sourcing System is a complex issue and needs to be maintainable. There are suggested ways handling data conversion today which combine different methods into a framework. However, there is a lack of exploration of different and alternative methods to handle the complicated matter.This thesis explores data conversion with fine-grained events. The purpose is to explore methods and broaden knowledge for handling data conversion while using attribute driven events called fine-grained events. The goal was to build a proof-of-concept that preserves the attributes reliability and availability and can handle data conversion of these specific events.The results found by using fine-grained events are a decrease in terms of system complexity and a proof-of-concept that maintains the desired attributes. / Datakonvertering för utvecklande händelser i ett Event Sourcing System är en komplex fråga som kräver att systemet är enkelt underhållning. Det finns förslag på sätt att hantera datakonvertering idag, vilket kombinerar olika metoder i ett ramverk. Det finns emellertid en brist på undersökning av olika och alternativa metoder för att hantera den komplexa orsaken.Denna avhandling undersöker datakonvertering med finkorniga händelser. Syftet är att utforska metoder och utvidga kunskap för hantering av datakonvertering genom att använda attributdrivna händelser som kallas finkorniga händelser. Målet var att bygga ett proof-of-concept som bevarar egenskaperpålitlighet och tillgängligt och som dessa specifika händelser.även hanterar datakonvertering förResultaten som hittas genom att använda finkorniga händelser är en minskning av systemkomplexiteten och ett bevis på koncept som upprätthåller de önskade egenskaperna.

The Industry 4.0 revolution relies heavily on data to generate value, innovation, new services, and optimize current processes [1]. Technologies such as Internet of Things (IoT), machine learning, digital twins, and much more depend directly on data to bring value and innovation to both discrete manufacturing and process industries. The origin of data may vary from sensor data to financial statements and even strictly confidential user or business data. In data-driven ecosystems, collaboration between different actors is often needed to provide services such as analytics, logistics, predictive maintenance, process improvement, and more. Data therefore cannot be considered a corporate internal asset only. Hence, data needs to be shared among organizations in a data-driven ecosystem for it to be used as a strategic resource for creating desired values, innovations, or process improvements [2]. When sharing business critical and sensitive data, the access to the data needs to be accurately controlled to prevent leakage to authorized users and organizations.  Access control is a mechanism to control actions of users over objects, e.g., to read, write, and delete files, accessing data, writing over registers, and so on. This thesis studies one of the latest access control mechanisms in Attribute Based Access Control (ABAC) for industrial data sharing. ABAC emerges as an evolution of the commonly industry-wide used Role-based Access Control. ABAC presents the idea of attributes to create access policies, rather than manually assigned roles or ownerships, enabling for expressive fine-granular access control policies. Furthermore, this thesis presents approaches to implement ABAC into industrial IoT data sharing applications, with special focus on the manageability and granularity of the attributes and policies.  The thesis also studies the implications of outsourced data storage on third party cloud servers over access control for data sharing and explores how to integrate cryptographic techniques and paradigms into data access control. In particular, the combination of ABAC and Attribute-Based Encryption (ABE) is investigated to protect privacy over not-fully trusted domains. In this, important research gaps are identified. / Arrowhead Tools