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1.
Violence against the elderly - analysis of further research needs based on a systematic review of research
Teja Primc, Branko Lobnikar, Kaja Prislan, 2021, pregledni znanstveni članek

Opis: Purpose of the Paper: The purpose of this paper is to investigate the current state of research on the topic of elder abuse both in the institutional and domestic environment and to present the key findings of existing studies. The paper also aims to shed light on areas requiring additional research and present the directions for further research. Methods: A systematic literature review was conducted using the PRISMA method in the Web of Science and Scopus databases. The review included 29 papers published between 2010 and 2021. Findings: Elder abuse is an under-researched topic. The lack of research is particularly apparent in the incidence and nature of the problem in the home environment. Previous studies focus on the various forms of abuse, its perpetrators and risk factors. Rates of psychological abuse, neglect and physical abuse are high in nursing homes and among older visitors to ambulatory care facilities and emergency departments, while self-neglect, neglect and psychological abuse are prevalent in the home environment. Women between the ages of 70 and 75, who are socially isolated and require daily care, are more exposed to abuse. The perpetrators of the abuse are most often family members. Limitations/Applicability of the Research: The ‘paper’s findings provide insight into research on elder abuse and can serve as a basis for planning research into this phenomenon. The paper highlights research gaps and introduces opportunities for improvements in the systemic approaches when addressing the issue. Originality/Relevance of the Paper: The paper presents the systematic and up to date analysis of past studies on elder abuse. The results are intended for professionals and researchers working in the field.
Ključne besede: violence, abuse, neglect, elderly, systematic literature review
Objavljeno v DKUM: 26.01.2022; Ogledov: 221; Prenosov: 12
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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: 966; Prenosov: 97
.pdf Celotno besedilo (1,12 MB)

3.
Business process model and notation : the current state of affairs
Mateja Kocbek, Gregor Jošt, Marjan Heričko, Gregor Polančič, 2015, izvirni znanstveni članek

Opis: With business process modelling, companies and organizations can gain explicit control over their processes. Currently, there are many notations in the area of business process modelling, where Business Process Model and Notation (BPMN) is denoted as the de facto standard. Aims: The aim of this research is to provide the state-of-the-art results addressing the acceptance of BPMN, while also examining the purposes of its usage. Furthermore, the advantages, disadvantages and other interests related to BPMN were also investigated. Method: To achieve these objectives, a Systematic Literature Review (SLR) and a semantic examination of articles% citations was conducted. Results: After completing SLR, out of a total of 852 articles, 31 were deemed relevant. The majority of the articles analyzed the notation and compared it with other modelling techniques. The remainder evaluated general aspects of the notation, e.g. history and versions of the standard, usage of the notation or tools. Conclusion: Our findings demonstrate that there are empirical insights about the level of BPMN acceptance. They suggest that BPMN is still widely perceived as the de facto standard in the process modelling domain and its usage is ever-increasing. However, many studies report that only a limited set of elements are commonly used and to this end, several extensions were proposed. The main purpose of BPMN remains the description of business processes.
Ključne besede: business process modeling, BPMN, acceptance, use, systematic literature review
Objavljeno v DKUM: 06.07.2017; Ogledov: 1157; Prenosov: 554
.pdf Celotno besedilo (841,57 KB)
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