<div><div><div><p>Provenance collection and analysis is one of the most important techniques used in analyzing computation system behaviors. For forensic analysis in enterprise environment, existing provenance systems are limited. On one hand, they tend to log many redundant and irrelevant events causing high runtime and space overhead as well as long investigation time. On the other hand, they lack the application specific provenance data, leading to ineffective investigation process. Moreover, emerging machine learning especially deep learning based artificial intelligence systems are hard to interpret and vulnerable to adversarial attacks. Using provenance information to analyze such systems and defend adversarial attacks is potentially very promising but not well-studied yet.</p><p><br></p><div><div><div><p>In this dissertation, I try to address the aforementioned challenges. I present an effective and efficient operating system level provenance data collector, ProTracer. It features the idea of alternating between logging and tainting to perform on-the-fly log filtering and reduction to achieve low runtime and storage overhead. Tainting is used to track the dependence relationships between system call events, and logging is performed only when useful dependencies are detected. I also develop MPI, an LLVM based analysis and instrumentation framework which automatically transfers existing applications to be provenance-aware. It requires the programmers to annotate the desired data structures used for partitioning, and then instruments the program to actively emit application specific semantics to provenance collectors which can be used for multiple perspective attack investigation. In the end, I propose a new technique named NIC, a provenance collection and analysis technique for deep learning systems. It analyzes deep learning system internal variables to generate system invariants as provenance for such systems, which can be then used to as a general way to detect adversarial attacks.</p></div></div></div></div></div></div>

In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.

The aim of the present study has been to examine users' experience of Chatbot from a business perspective and a consumer perspective. The study has also focused on highlighting what limitations a Chatbot can have and possible improvements for future development. The study is based on a qualitative research method with semi-structured interviews that have been analyzed on the basis of a thematic analysis. The results of the interview material have been analyzed based on previous research and various theoretical perspectives such as Artificial Intelligence (AI), Natural Language Processing (NLP). The results of the study have shown that the experience of Chatbot can differ between businesses that offer Chatbot, which are more positive and consumers who use it as customer service. Limitations and suggestions for improvements around Chatbotar are also a consistent result of the study. / Den föreliggande studie har haft som syfte att undersöka användarnas upplevelse av Chatbot utifrån verksamhetsperspektiv och konsumentperspektiv. Studien har också fokuserat på att lyfta fram vilka begränsningar en Chatbot kan ha och eventuella förbättringar för framtida utvecklingen. Studien är baserad på en kvalitativ forskningsmetod med semistrukturerade intervjuer som har analyserats utifrån en tematisk analys. Resultatet av intervjumaterialet har analyserat utifrån tidigare forskning och olika teoretiska perspektiv som Artificial Intelligence (AI), Natural Language Processing (NLP). Resultatet av studien har visat att upplevelsen av Chatbot kan skilja sig mellan verksamheter som erbjuder Chatbot, som är mer positiva och konsumenter som använder det som kundtjänst. Begränsningar och förslag på förbättringar kring Chatbotar är också ett genomgående resultat i studien.

The recent boom of Machine Learning Network Architectures like Generative Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks (DCGAN), Self Attention Generative Adversarial Networks (SAGAN), Context Conditional Generative Adversarial Networks (CCGAN) and the development of high-performance computing for big data analysis has the potential to be highly beneficial in many domains and fittingly in the early detection of chronic diseases. The clinical heterogeneity of one such chronic auto-immune disease like Systemic Lupus Erythematosus (SLE), also known as Lupus, makes it difficult for medical diagnostics. One major concern is a limited dataset that is available for diagnostics. In this research, we demonstrate the application of Generative Adversarial Networks for data augmentation and improving the error rates of Convolution Neural Networks (CNN). Limited Lupus dataset of 30 typical ’butterfly rash’ images is used as a model to decrease the error rates of a widely accepted CNN architecture like Le-Net. For the Lupus dataset, it can be seen that there is a 73.22% decrease in the error rates of Le-Net. Therefore such an approach can be extended to most recent Neural Network classifiers like ResNet. Additionally, a human perceptual study reveals that the artificial images generated from CCGAN are preferred to closely resemble real Lupus images over the artificial images generated from SAGAN and DCGAN by 45 Amazon MTurk participants. These participants are identified as ’healthcare professionals’ in the Amazon MTurk platform. This research aims to help reduce the time in detection and treatment of Lupus which usually takes 6 to 9 months from its onset.

Artificial Intelligence (AI) is reshaping how businesses, governments, organizations and individuals operate. Most businesses are now moving away from traditional systems of operation into employing and leveraging on technologies like AI to deliver superior goods and services to their clients. Today, there are calls for a shift from the traditional auditing techniques of sampling to the use of advanced technology with the capability to analyze all the operating activities of a business to improve audit quality. There is no better time for auditing to merge with AI than now as it is increasingly becoming too challenging for human auditors to analyze huge volumes of structured and unstructured data in various locations to gain the relevant financial and non-financial information that they may need to form their opinions. The purpose of this research was to examine the impact of AI on auditors’ skills and competencies, audit process and audit quality. The researchers used the qualitative research methodology and reviewed literature to identify gaps in the literature. Participants (practicing auditors) were interviewed to gather data for analysis. The findings from the empirical data concludes that AI has had a significant impact on auditors’ skills and competencies, with many respondents affirming that the core skills now needed by auditors is IT skills. Audit process and audit quality have also been positively affected by AI.

Organizations in all industries have reached a transition point because of the rapid development of digital technology. Digitalization and AI has therefore become the driving force for transformation within today's organizations to remain competitive in the digital era. The forest industry is no exception. However, digital transformation through AI within organizations is synonymous with high complexity and the forestry industry faces unique challenges to overcome because of the industry's traditional approach and the corporate culture that comes with it. This approach creates challenges for technology to be able to take an active and leading role. Problems that can emerge with digital transformation are one of the most important topics that have been researched for a long time. But few studies have examined digital transformation through AI in the forestry industry. Against this background, the purpose of this study is to analyze the barriers that can inhibit AI-Readiness in the forest industry. To answer our research question, we have used a qualitative case study with semi-structured interviews. The semi-structured interviews are based on the framework Technological Frames which intends to examine interpretation about information technologies. The results shows that barriers that can inhibit AI-Readiness exist which can be linked to organizational culture, user experiences and IT-strategies.

Popular social media platforms use recommender systems (RS) to improve users’ experience. However, these systems pose potential issues including political polarization and fake news spread. In addition, social media RS may raise many ethical issues, such as Privacy, Opacity, Fairness and Social Effects, regarding the collection and processing of data. The research problem of this study is the lack of knowledge regarding user concerns toward these issues. Therefore, this study attempts to address the research gap by studying user attitudes toward the aforementioned ethical issues. The main research is: What are users’ concerns about ethical issues of using recommender systems in social media platforms? For this study, a survey was chosen as the research strategy. A questionnaire was developed and distributed online to collect data. The data analysis is based on descriptive statistics using measures of central tendency by finding the median (the middle point). Key findings indicate that users are familiar and concerned with the major issues. In particular, users are concerned about how and what types of private data are collected. However, users are most concerned about the spread of fake news. Users also express concern about political polarization, although they do not believe they are personally affected. However, users would prefer that recommender algorithms use less private data, even if it compromises news feed quality. The study has limitations including a geographic focus on Europe and demographics. Nevertheless, the findings offer insights into user concerns about RS, paving the way for future research.

Mathematical statistical models are insufficient for describing complex phenomena. In contrast, Artificial Neural Networks (ANNs), have been used across various complex problem domains for solving problems. ANNs can learn complex patterns and capture non-linear relationships between parameters. Using ANNs to gain an understanding of complex problem domains can reveal hidden truths and lead to scientific discoveries not possible before with mathematical statistical models. In this thesis, a fully connected feed-forward neural network was built to analyse the parameter influence in the complex problem domain of football. The aim of this work was to demonstrate that a simple artificial neural network could be used to analyse parameter influence in complex problem domains. The investigation centred around the question of: How well can the fully connected feed-forward neural network be used for analysing parameter influence. To conduct this research, free publicly available statistical match data was gathered from online sources. Subsequently, an ANN model was built and trained to predict the outcomes of the Spanish La Liga matches during the 2021/2022 season. The network could achieve an average accuracy of 51.57\%, comparable to similar models in related studies. After the network was trained the weights were analysed to understand the influence of parameters on the outcomes of matches. The results obtained were random, indicating that this specific approach taken, requires a larger dataset. A different approach with a different type of network would be more suitable for this undertaking.

This research was about the continuous intention of healthcare professionals to use internet of medical things (IoMT) embedded with artificial intelligence (AI). IoMT and AI are evolving innovations and diffusing at the same time. It was not known in what way the two complex technologies diffusing concurrently could influence continuous intention to use IoMT. In addition, behavioural aspects namely motivation and training to use IoMT have been argued to intervene in the relationship between an AI based IoMT and continuous intention to use IoMT. Diffusion of Innovation theory was applied to explain the relationship between diffusion factors that aid the diffusion of AI based IoMT and continuous intention to use IoMT. The five factors relative advantage, compatibility, complexity, observability and trialability were chosen as determinants of continuous intention to use IoMT using DoI theory. Self-determination theory and theory of planned behaviour were used to introduce the interventions in the relationship between diffusion factors and continuous intention to use IoMT. UTAUT was used to explain the influence of the moderators artificial intelligence awareness, novelty seeking behaviour and age of healthcare professionals. The central issue investigated was the determinants of continuous intention of healthcare professionals to use IoMT with behavioural attributes of motivation and training conceived as mediators of the relationship between diffusion factors and continuous intention to use IoMT in the presence of moderators. Quantitative research methodology was used to test the research model developed to understand the relationship between the five diffusion of innovation theory factors and continuous intention to use IoMT when AI based IoMT is still diffusing. The concurrent diffusion of two new technologies was investigated using a research model that was developed for studying the healthcare professionals and their intention. The research was conducted in Bahrain in the healthcare sector. A sample of 354 healthcare professionals participated in the research. Structural equation modelling was used to analyse the data and test the hypothesis. The research showed that healthcare professionals will continue to use concurrently diffusing technologies depending on the relative advantage, complexity and compatibility of the innovations that diffuse. In addition, the results show that healthcare professionals will be motivated by the compatibility of AI-based IoMT if they have to continuously use IoMT. Furthermore, training enables both the organization and the healthcare professionals to overcome dilemma in case they have to continue to use an innovation during its diffusion or when new innovation surface in the market. Finally, artificial intelligence awareness is able to moderate the relationship between relative advantage, complexity and training to use IoMT. Thus, this research contributes to the discipline of behavioural intention of healthcare professionals in determining the influence of an artificial intelligence based IoMT on continuous intention to use IoMT when artificial intelligence embedded in IoMT diffuses concurrently with IoMT. Where IoMT diffusion factors can be used as a determine of continuous intention to use IoMT, artificial intelligence could be understood as a moderator of the relationship between diffusion factors and training to use IoMT, thus demonstrating the combined diffusion of the two technologies diffusing concurrently.

The Internet of Things (IoT) is a network of interconnected devices and objects that have various functions,such as sensing, identifying, computing, providing services and communicating. It is estimated that by the year 2030, there will be approximately 29.42 billion IoT devices globally, facilitating extensive data exchange among them. In response to this rapid growth of IoT, Artificial Intelligence (AI) has become a pivotal technology in automating key aspects of IoT systems, including decision-making, predictive data analysis among others. The widespread use of AI across various industries has brought about significant transformations in business ecosystems. Despite its immense potential, IoT systems still face several challenges. These challenges encompass concerns related to privacy and security, data management, standardization issues, trust among others. Looking at these challenges, AI emerges as an essential enabler, enhancing the intelligence and sophistication of IoT systems. Its diverse applications offer effective solutions to address the inherent challenges within IoT systems. This, in turn, leads to the optimization of processes and the development of more intelligent and smart IoT systems.This thesis presents a semi-systematic literature review (SSLR) that aims to explore the role of AI in IoT systems. A systematic search was performed on three (3) databases (Scopus, IEEE-Xplore and the ACM digital library), 29 scientific and peer reviewed studies published between 2018-2022 were selected and examined to provide answers to the research questions. This study also encompasses an additional study within the context of AI and trustworthiness in IoT systems, user acceptance within IoT systems and AIoT's impact on sustainable economic growth across industries. This thesis also presents the DIMACERI Framework which encompasses eight dimensions of IoT challenges and concludes with recommendations for stakeholders in AIoT systems. AI is strategically integrated across the DIMACERI dimensions to create reliable, secure and efficient IoT systems.