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1.
Language of Appraisal in Book Reviews: A Case Study
Katja Časar, 2020, magistrsko delo

Opis: This master’s thesis presents an analysis of appraisal in the case of ten book reviews. Their selection is based on several criteria that make them representative of this text type. The selected texts evaluate novels, novellas and short stories that were ranked top 300 according to the Open Syllabus Project 2.0 online data base. This means that they fall into the category of the most often assigned books in educational institutions. The authors of the selected texts are editors, journalists and writers, and there is an even number of male and female reviewers. The purpose of the study is the appraisal analysis of the contemporary English language; therefore, only the recently published texts were selected. The main methodology used in this master’s thesis is the appraisal theory developed by James Martin and Peter White (Martin and White). This theory evolved in the systemic functional linguistics, and it relies on the theoretical concepts of Michael Halliday (Halliday). The appraisal analysis was conducted with help of the analytical tool Catma 5.0, which enables annotation of texts, their analysis and the visualization of data. The results of the research show that the most frequently used attitudinal resources are the expressions of appreciation. Therefore, the evaluation of the story and everything associated with it is in the foreground of the book reviews. The analysis of the selected texts reveals that evaluation is mostly explicit, meaning that the reader is directly invited to engage with the book. The findings indicate that the attitudinal resources are graded more according to intensity and quantity and less according to prototypicality and marginality. This conclusion draws attention to the variety of lexical and grammatical structures in the selected texts that are assumed to be characteristic of this text type in general. The results also show that the reviewers do not include many external sources into the text, which consequently narrows down the dialogistic space and excludes alternative views and attitudes. The appraisal analysis points toward the text-structural and semantic characteristics of book reviews in general. The structure of the selected texts consists of the following elements: information about the author and the book, the plot summary and evaluation of these elements, which are often intertwined. Some reviews also include personal accounts, book details and/or numeric ratings. The most significant semantic characteristic of evaluation expressed in the selected book reviews is the critique of the Western oppressor. The reviewers judge crimes against humanity and question Western perspectives. They also imply the complicity of the readers because they are viewed as members of the Western identity. Additionally, the results of the analysis show that the book reviews are contextual and intertextual text types, which include various means for the realization of appraisal. A vast spectrum of lexical and grammatical structures makes book reviews an interesting research topic with many possibilities for further research.
Ključne besede: evaluative language, systemic functional linguistics, appraisal theory, appraisal analysis, book review.
Objavljeno v DKUM: 23.07.2020; Ogledov: 705; Prenosov: 116
.pdf Celotno besedilo (2,65 MB)

2.
Effectiveness of proactive password checker based on Markov models : doktorska disertacija
Viktor Taneski, 2019, doktorska disertacija

Opis: In this doctoral dissertation we focus on the most common method of authentication, the username-password combination. The reason for the frequent use of this authentication mechanism is its simplicity and low cost of implementation. Although passwords are so useful, they have many problems. Morris and Thompson, for the first time almost four decades ago, found that textual passwords were a weak security point of information systems. They have come to the conclusion that users are one of the biggest threats to information system’s security. Since then, we face these problems on a daily basis. Users do not perform the behaviours they need to be done in order to stay safe and secure, although they are aware of the security issues. Because this is a research area that security experts have been dealing with for a long time, in this dissertation we wanted to identify problems related to textual passwords and possible suggested solutions. For this purpose, we first performed a systematic literature review on textual passwords and their security. In doing so, we wanted to evaluate the current status of passwords in terms of their strength, ways of managing passwords, and whether users are still the “weakest link”. We found that one of the less researched solutions is proactive password checking. A proactive password checker could filter out the passwords that are easy-to-guess and only let through the passwords that are harder to guess. In order for a proactive password checking to be more effective, it is necessary for the checker to be able to check the probability that a certain password will be selected by the user. For this purpose, the better password checkers usually use certain tools to calculate password probability i.e., password strength. To find out which method is most suitable for calculating password strength, we have looked at similar solutions throughout history. We have found that Markov models are one of the most common methods used for password strength estimation, although we may encounter some problems when using them, such as sparsity and over-fitting. By reviewing similar solutions, we found that Markov models are mostly trained on only one dataset. This could limit the performance of the model in terms of correctly identifying bad or very strong passwords. As training datasets are important in the development of Markov models, it is clear that they will have some effect in the final assessment of the password’s strength. What we explore in our dissertation, is the importance of this effect on the final password strength estimation. Mainly, we focus on exploring the effect of different but similar datasets on password strength estimation. For the purposes of our study, we analysed publicly available sets of “common passwords” and processed them regarding the frequency distribution of the letters contained in these passwords. We built different Markov models based on these datasets and frequency distribution. This helped us determine if one Markov model was sufficient or if several models were needed to effectively estimate password strength for a wide range of passwords. The results showed statistical differences between the models. In more detail, we found that: - different Markov models (trained on different databases) showed statistically different results when tested on the same dataset, - more diverse datasets are needed to be able to calculate the strength of as many passwords as possible, since one “universal” model, trained on one “universal” dataset is less effective at classifying passwords in different categories (i.e., weak, medium, strong), - different Markov models of 1st and 2nd order, in most cases, give no statistically different outputs, - overall, Markov models can be used as a basis for constructing a more effective password checker that uses multiple different and specific Markov models, which could be more effective if we want to cover a wider range of passwords.
Ključne besede: passwords, password analysis, password security, password problems, password strength, systematic literature review, Markov models
Objavljeno v DKUM: 13.01.2020; Ogledov: 1039; Prenosov: 104
.pdf Celotno besedilo (1,12 MB)

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