Topic Discovery from Textual Data: Machine Learning and Natural Language Processing for Knowledge Discovery in the Fisheries Domain

It is estimated that the world’s data will increase to roughly 160 billion terabytes by 2025, with most of that data occurring in an unstructured form. Today, we have already reached the point where more data is being produced than can be physically stored. To ingest all this data and to construct valuable knowledge from it, new computational tools and algorithms are needed, especially since manual probing of the data is slow, expensive, and subjective. For unstructured data, such as text in documents, an ongoing field of research is probabilistic topic models. Topic models are techniques to automatically uncover the hidden or latent topics present within a collection of documents. Topic models can infer the topical content of thousands or millions of documents without prior labeling or annotation. This unsupervised nature makes probabilistic topic models a useful tool for applied data scientists to interpret and examine large volumes of documents for extracting new and valuable knowledge. This dissertation scientifically investigates how to optimally and efficiently apply and interpret topic models to large collections of documents. Specifically, it shows how different types of textual data, pre-processing steps, and hyper-parameter settings can affect the quality of the derived latent topics. The results presented in this dissertation provide a starting point for researchers who want to apply topic models with scientific rigorousness to scientific publications

Fostering technically augmented human collective intelligence: With an application to human fluency in formal languages for automated deduction

This study addresses a general concern, a specific concern, and a synthesis between them. The general concern is how to foster human collective intelligence, in this work approached from a human-technological angle. The specific concern is how to foster human fluency in formal languages for automated deduction. Human collective intelligence occurs, loosely speaking, when the intelligence of a group of people is greater than the sum of the intelligence of the individuals of that group. Important motivations to foster human collective intelligence are to help people (1) deal better with problems and (2) become better, more fluent and more refined at what they are already good at. This study covers a large class of facets related to fostering collective intelligence with an abstract overarching approach, and then works out a limited, yet carefully selected set of facets in detail. The overarching approach (the Orcoba-Approach) facilitates human individuals or collectives to acquire or improve a given capability. The core of the approach consists of expressing the capability in a permanent record that enables the targeted capability to be, at least partially reproduced and examined. Such a record contains both a description of relevant human capabilities and activities, and specifications of complementary artefacts used. The approach facilitates people, or other actors, to share, add and adapt records, and does so guided by the differences in performance teams achieve with them. It does so in a way inspired by biological evolution, accelerating the improvement of the best records. The concern for fostering human fluency in formal languages for automated deduction (briefly: formal fluency) is inspired by the following. Since the second half of the 20th century much work has been done on developing and improving the application of computers to boost our capability to reason over our collective knowledge. One major branch of development involves automated deduction: knowledge that is represented in a formal language for automated deduction enable special computer programs to automatically produce knowledge-representations that are deducible from the given knowledge. This technique enables people to extend works of others to an unprecedented degree, contributing to the general concern of this work to increase human collective intelligence. Although automated deduction is nowadays widely applied for the purpose just given, we are only scratching the surface of its full potential. A way to contribute to the relation of this full potential is to foster more people reaching formal fluency. The latter is the specific concern of this study. This work’s contribution to fostering formal fluency consists of subjecting it to the specialised approaches to foster human collective intelligence developed in the first part of the work. This work presents both how to tailor these approaches to formal fluency and the lessons learnt about favourable elements for a record for formal fluency. The most sophisticated fruit of this work consists of the design and partial implementation of the serious game developed for this purpose, SWiFT

Agents with Social Norms and Values: A framework for agent based social simulations with social norms and personal values

Social norms govern collective behaviour by guiding individual behaviour in the absence of a central enforcing authority, which makes them powerful self-regulating mechanisms for societies. This is in stark contrast to policy or legislative norms - also targeted at governing behaviour in society - which are issued by a central authority who also then needs to enforce compliance. Enforcing compliance is expensive. Also, these norms might come into conflict with existing social norms, which causes issues. It is therefore not surprising that much research is aimed at understanding existing social norms around behaviours connected to important issues like health or climate change. Designing policy that piggybacks on existing norms to promote behaviour is faster and cheaper than using the classic carrot-and-stick approach of most policy design. The modelling community has invested quite a bit of effort into developing normative frameworks, models and simulations. Yet, very little of this effort has been directed towards the study of the norm life-cycle. Besides, these research efforts have omitted explicit representation of norms and the assessment of norm stability and reactivity in the face of some environmental changes. Values as a stabilizing factor, must be considered while studying the reactivity and stability of social norms. Without such stabilizing elements, modeled norms react swiftly to any change in the environment and are mere behavioural patterns rather than social norms. In this thesis, I use values as drivers of behavioural choices, and Schwartz’s theory of abstract values as a basis. As these values are very abstract, there is a need to translate them to more concrete values and assign behavioural choices to them. A theory or methodology for this step has not been developed in a way that is widely applicable. Thus, a precise way of such a translation is necessary for practical purposes. I designed a practical but formal framework that can be used to study the value-driven behaviour of agents in social simulations. I showed how this formal design can be used in practice to implement multi-agent simulations. Then, I continued with proposing a social norm framework that is focused on finding an explanation for norm dynamics - their emergence, perpetuation, and eventual disappearance. I operationalized the framework by way of a multi-agent simulation in the context of environmental change and absence of sanctions for deviant behaviour. I showed that the values are an intuitive stabilizing factor that allow norms to persist through changes in the agents’ environment and perpetuate and spread even in the absence of punishment. A norm will, however, change, evolve or disappear altogether if it becomes impossible to perform or if the value priorities of the agents change. I explained the norm dynamic and its strong connection to values by implementing various multi-agent simulation scenarios

Evolution of Low-Code Platforms

Our world is driven by software, from everything we do online to the cars we drive. The role software plays is so large that you could say that every company is a software company. Companies have to partake in this transformation to a software-driven world but face various challenges in doing so. There is a scarcity of software engineers which only appears to be increasing. Next to that, software development is complex and requires a joint effort of software engineers and domain experts. Finally, when the software is developed, the process does not stop. Companies have to constantly update their software to comply with changing wishes and demands. A recent development, low-code platforms, could be the solution to these challenges. Through the introduction of higher-level abstractions, such as domain-specific models, these platforms enable professionals without specific software development training to develop software systems. Enabling untrained professionals to participate in the software development process not only means that fewer trained IT personnel are needed, but that the business side is also automatically more involved in the development of the software. Low-code platforms have to live up to certain expectations to be successful. They have to support the development of modern, cloud-based applications that are available from every device. Not only are they accessible for humans, we expect these applications to be open for communication with other systems. Finally, maintaining software should be as easy as creating new software. This dissertation presents, in three parts, the evolution of low-code platforms and how they support the new generation of digital companies. In each of these parts the software architect and his role in the development of low-code platforms stands central. The first part discusses software evolution in event sourced systems. Event sourcing is a form of event-driven architecture that offers a lot of benefits for the software system by storing every change as an event. An increased flexibility in making future changes is gained because the full history of a system is stored. Various evolution techniques are presented that can be applied to confront the evolution challenges experienced by software architects. API management in software ecosystems is central in the second part. Modern software systems are open, which means that external parties can connect with a software system to exchange data. Through these connections software ecosystems can grow. These connections are created APIs. Low-code platforms have to support the management processes and enable them for citizen developers to be successful. The API-m-FAMM gives software architects a tool to evaluate and plan the improvement of their API management capabilities. Evolution supporting architecture is the third and final part. Changes that are made within a low-code platform have an impact on other parts of the platform or even on complementors. The analysis of the impact is difficult because of the higher-level abstractions offered by low-code platforms. The Impact Analysis for Low-Code Development Platforms framework allows software architects to design the process of software evolution, making sure that companies stay in control of their systems

Interdisciplinary optimism? Sentiment analysis of Twitter data

Interdisciplinary research has faced many challenges, including institutional, cultural and practical ones, while it has also been reported as a ‘career risk’ and even ‘career suicide’ for researchers pursuing such an education and approach. Yet, the propagation of challenges and risks can easily lead to a feeling of anxiety and disempowerment in researchers, which we think is counterproductive to improving interdisciplinarity in practice. Therefore, in the search of ‘bright spots’, which are examples of cases in which people have had positive experiences with interdisciplinarity, this study assesses the perceptions of researchers on interdisciplinarity on the social media platform Twitter. The results of this study show researchers’ many positive experiences and successes of interdisciplinarity, and, as such, document examples of bright spots. These bright spots can give reason for optimistic thinking, which can potentially have many benefits for researchers’ well-being, creativity and innovation, and may also inspire and empower researchers to strive for and pursue interdisciplinarity in the future

A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning

Training a deep reinforcement learning-based dialogue policy with brute-force random sampling is costly. A new training paradigm was proposed to improve learning performance and efficiency by combining curriculum learning. However, attempts in the field of dialogue policy are very limited due to the lack of reliable evaluation of difficulty scores of dialogue tasks and the high sensitivity to the mode of progression through dialogue tasks. In this paper, we present a novel versatile adaptive curriculum learning (VACL) framework, which presents a substantial step toward applying automatic curriculum learning on dialogue policy tasks. It supports evaluating the difficulty of dialogue tasks only using the learning experiences of dialogue policy and skip-level selection according to their learning needs to maximize the learning efficiency. Moreover, an attractive feature of VACL is the construction of a generic, elastic global curriculum while training a good dialogue policy that could guide different dialogue policy learning without extra effort on re-training. The superiority and versatility of VACL are validated on three public dialogue datasets

Cybersecurity Maturity Assessment and Standardisation

Organisations’ cybersecurity requirements have several origins, including the need to protect their business from cyberattacks, comply with laws and regulations, and build trust. Cyber threats and new regulations emerge, thus the need to implement measures and assure compliance. Cybersecurity maturity assessments and cybersecurity standardisation can be used to implement measures and provide assurance for regulators. Therefore, this dissertation investigates cybersecurity maturity assessment and cybersecurity standardisation to improve organisations' cybersecurity. We state our research objective as follows: To support the improvement of organisations' cybersecurity by means of maturity assessment and standardisation. We employ the Design Science Research approach and investigate our problem space by identifying the stakeholders' needs, goals, and requirements using several research methodologies and propose design artifacts to solve the identified problems. The dissertation is organized into three parts: adaptivity in cybersecurity maturity assessments, cybersecurity standardisation, and the integration of cybersecurity maturity assessments and standardisation. The first part is titled “Adaptivity in cybersecurity maturity assessments”. Chapter 2 investigates the adaptivity of an existing maturity assessment model to organisational contexts. The artifact proposed in this research provides organisations with a method to adapt an existing information security maturity model to their organisational characteristics. Chapter 3 presents an assessment instrument that is adaptable by design through the posed situational questions. The questionnaire model proposed as an artifact helps organisations tailor the assessment instrument interactively by the given answers to the situational questions. Finally, in the first part, Chapter 4 investigates how organisational context affects the design of information security maturity assessment models using design principles and the proposed design requirements can be used for designing maturity assessment models. Enterprises can also use the design requirements to understand what to look for when selecting an assessment model for use within their organisation. The second part is titled, “Cybersecurity standardisation”. Chapter 5 focuses on cybersecurity standardisation and identifies gaps resulting from an international workshop organised with relevant stakeholders. We propose a reseach agenda to fill the identified gaps. Following this research, in Chapter 6, we present the cybersecurity essesntials through five standards and frameworks and a step-by-step process for SMEs to help them establish and improve their cybersecurity based on standards and frameworks. The third part is titled “Integrating cybersecurity maturity assessments and standardisation”. Chapter 7 investigates how to integrate security assessment and standardiation to meet stakeholder requirements and proposes the adaptable security maturity assessment and standardisation (ASMAS) framework. We demonstrate the ASMAS framework through a user-friendly, web-based software prototype. We conduct seven evaluation interviews with six SMEs from five countries. We used the evaluation constructs based on the Technology Acceptance Model to explain and predict the utility of the ASMAS framework. The evaluation constructs using a Likert scale (1-5), on average, score 4.29 for perceived usefulness, 4.14 for perceived ease of use, and 3.62 for intention to use evaluation constructs. These outcomes reinforce this thesis’ holistic approach to facilitate and consolidate SMEs' independent security assessment and security standardisation efforts in daily practice

A Probabilistic Deontic Logic

In this article, we introduce a logic for reasoning about probability of normative statements. We present its syntax and semantics, describe the corresponding class of models, provide an axiomatization for this logic and prove that the axiomatization is sound and complete. We also prove that our logic is decidable

Knowledge Discovery in Clinical Psychiatry: Learning from Electronic Health Records

Despite the progress that modern day health care has made in improving people's health and well-being, there are still many open questions related to disease and treatment. There is a need for new and innovative approaches, to further expand medical knowledge and to keep health care affordable. Analyzing Electronic Health Records (EHRs) is such a potential source of innovation, since EHRs often contain information hidden on an aggregated level, that can be made explicit through a knowledge discovery process. In this research, we focus on analyzing EHRs in psychiatry, the field that specializes in mental health care. We pose the following overarching research question: How can data from Electronic Health Records provide relevant insights for psychiatric care? In the first three research chapters of this work, we identify key technical, organizational and ethical challenges related to knowledge discovery in EHRs, for which we subsequently propose solutions. First, we look at collaboration between data experts, well versed in the technical part of data analysis, and practitioners, who are an excellent source of domain knowledge. We show how new knowledge and hypotheses can be found using our CRISP-IDM process, most of which were not imagined beforehand. Secondly, we investigate how to design technical infrastructure, consisting of hardware and software components, that enables using EHR data for analysis. We introduce the Capable Reuse of EHR Data (CARED) framework, which addresses nine important requirements, such as integrating data sources, support for collaboration and documentation, and privacy and security. Thirdly, we develop and validate the De-identification Method for Dutch Medical Text (DEDUCE), which aims to automatically remove information that can identify a patient from free text. It is a rule-based method that successfully removes information in categories such as person names and geographical locations. In the second part of this research, we focus on applying knowledge discovery techniques to EHR data to obtain new insights with potential to improve care. First we look at violence risk assessment, by investigating whether applying machine learning techniques to clinical notes from patients' EHRs is a fruitful novel approach. After exploring which types of models, including relatively recent deep learning models, show promise for such a classification task, we obtain two indepdendent datasets of psychiatric admissions and clinical notes from EHRs. We use these two datasets to train models that can assess violence risk based, and then evaluate their accuracy and generalizability. Our findings show that such models have definite potential for use in practice. Finally, we turn to identifying psychiatric patient subgroups, and investigate how unsupervised learning can find robust and accurate stratifications of patients. We use cluster ensembles, combinations of multiple clusterings, to obtain three significant clusters of adolescent patients, and assess their meaning and relation to other relevant clinical variables. The two parts of this dissertation combined show that learning from EHRs, after addressing key challenges related to the nature of data, is a new and interesting approach with clear potential for improving psychiatric health care

The diagnosing behaviour of intelligent tutoring systems

Intelligent Tutoring Systems (ITSs) determine the quality of student responses by means of a diagnostic process, and use this information for providing feedback and determining a student’s progress. This paper studies how ITSs diagnose student responses. In a systematic literature review we compare the diagnostic processes of 40 ITSs in various domains. We investigate what kinds of diagnoses are performed and how they are obtained, and how the processes compare across domains. The analysis identifies eight aspects that ITSs diagnose: correctness, difference, redundancy, type of error, common error, order, preference, and time. All ITSs diagnose correctness of a step. Mathematics tutors diagnose common errors more often than programming tutors, and programming tutors diagnose type of error more often than mathematics tutors. We discuss a general model for representing diagnostic processes