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There are various approaches to executing security breaches which are nowadays massively occurring in electronic communication environments, and phishing attacks are one of the most applied ones. A vast majority of phishing attacks are initiated using electronic messages, which attackers utilize to direct users to harmful or fake websites, to infect computers or to obtain personal or sensitive data for malicious purposes. Consequently, it is necessary to identify phishing messages in order to provide suitable user protection. Research and numerous studies have included machine learning algorithms and techniques from the field of artificial intelligence which predominantly depend on language-specific datasets and characteristics of phishing messages, and which have demonstrated to be effective for extracting critical information and for data-driven decision making. However, phishing datasets exist mainly for the English language. The aim of this paper is to present an information extraction pipeline that encompasses phases, such as corpus pre-processing, generating predictions of phishing messages using selected machine learning algorithms, along with a basic analysis, confusion matrices and evaluation scores for Croatian phishing messages. This type of key information can be used for teaching in higher education, e.g. in security-related courses or subjects that deal with artificial intelligence, machine learning, big data analysis, computational linguistics etc. This is essential as it can provide deeper insights into phishing attack strategies and potential countermeasures.

Cilj istraživanja je analiza zlonamjernih SMS poruka kojima se od korisnika nastoje na prijevaru izvući financijska sredstva ili osobni podaci.

Detecting phishing attacks is not straightforward, since there are many obstacles that derive from language complexity and technical aspects. Studying phishing attacks and other related issues heavily relies on computer datasets, i.e. digital corpora that reflect these linguistic and technical intricacies. Diverse studies using phishing datasets have been performed, but mainly for the English language. Research for other languages is scarce, and especially for not widely spoken languages. For the Croatian language there is an evident lack of corpora that are essential for diverse analyses and for constructing models that are capable of recognizing phishing attacks and protecting users. These datasets are necessary for natural language processing and building machine learning workflows, where results largely depend on corpora that must be specifically crafted for this purpose. Therefore, creating high-quality domain-specific corpora is of great importance in the domain of information security. Such corpora can be employed for teaching purposes in various courses in higher education, and could be analyzed in numerous ways in order to understand the underlying principles of phishing attack strategies. The aim of this paper is to demonstrate the entire process of data acquisition and corpus creation for the phishing detection domain. In addition, an analysis of the corpus is presented with regard to different aspects, such as descriptive attributes, terminology characteristics, metadata and language.

Machine learning (ML) can be used to analyze and predict student success outcome in order to avoid various problems and to plan future actions for helping students overcome difficulties during their study. This paper analyzes data from a digital system of 309 students who were enrolled in the Specialist Study in Trade Business at the Faculty of Tourism and Rural Development from 2010 to 2018. The paper explores the impact of four different data sets on the performance of ML algorithms. The first data set is with partially missing data on the length of study (around 7%), the second one uses arithmetic means in place of missing data, the third is based on median values, whereas the fourth uses the geometric mean instead. Four popular ML algorithms were considered: k-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF) and Probabilistic Neural Network (PNN). All of them are used for predicting student success based on achieved ECTS credit points. The aim of this paper is to compare and analyze the impact of missing values on the results of individual ML algorithms.

A message sent to a specific market or an audience contains certain types of information that affect the audience. For this reason, brand identity, nowadays increasingly in a digital form, plays an important role. Each state wants to create a robust, attractive, and different brand identity that will set it apart from other states and thus augment its reputation. This research aimed to determine the elements of brand identity that respondents mostly associate with Croatia and to examine their attitudes towards the belief that the brand identity that influences emotions is essential in the creation of national visual identity, as well as their attitudes towards the assumption that the brand identity of Croatia should be liked first by the inhabitants of Croatia, and only then by foreign tourists. Two research propositions were tested using the structural equation modeling, measuring the relationship between the emotional and formal elements of brand identity with the attitudes towards the emotions concerning the brand identity and the relevance of the brand identity to the country residents. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Phishing attacks have become today one of the most common security breaches performed on different communication channels. Their goal is to direct users to malicious websites or to infect a user’s computer as a means to acquire personal or sensitive data for later misuse. Phishing is often the first step in the process of cybercrime, and in order to be able to recognize potential attacks and adequately protect users, it is necessary to understand the underlying principles of attack strategies. Therefore, applying machine learning for training a system that would recognize phishing messages would be essential for increasing the level of security from cyberattacks. The aim of this paper is to give an overview of machine learning techniques used for the detection of phishing (and spam) e-mails, focusing mainly on regression and classification algorithms. In addition to the mentioned techniques, an analysis of datasets that are used for training of systems for detecting phishing attacks (and spam) is presented with regard to their size, language and accuracy scores. Different types of phishing messages are analyzed as well in this paper.

Introduction In using online medical manuals, patients that do not speak English natively are in effect “virtual immigrants”, confronted with a problem of low health literacy. Our aim was to investigate the use of diabetes key terms when the official guide for insulin pump was translated into a non-widely spoken language like Croatian. Data set and methods An official guide for insulin pump was translated by the manufacturer into Croatian and compared to its original English version for the ratio of professional and popular key diabetes terms. Analyzed professional terminology included: diabetes (Cro. dijabetes) ; blood glucose (glukoza u krvi) ; retinopathy (retinopatija) ; nephropathy (nefropatija) ; neuropathy (neuropatija) ; cardiovascular (kardiovaskularni) ; hyperglycemia (hiperglikemija) and hypoglycemia (hipoglikemija). Analyzed popular terminology included: sugar disorders (poremećaj šećera u krvi) ; blood sugar (šećer u krvi) ; eye damage (oštećenje oka) ; kidney damage (oštećenje bubrega) ; nerve damage (oštećenje živaca) ; blood system damage (oštećenje krvnih žila/srčanožilnog sustava) ; high/low levels of sugar (povišena vrijednosti šećera) and low level of sugar (niska vrijednost šećera). Results Ratio of professional vs. popular terminology was 5.8 in the English vs. 2.92 in Croatian version resulting in more professional terminology in English manual. Particular terms used more frequently in English were diabetes, blood glucose, hyperglycemia and hypoglycemia. Conclusion Medical manuals often advocate the use of professional terms in addressing the patient ; in case of this guide for insulin pump it could result in less information accessibility. Most insulin pump users have good knowledge on particular terminology but highly professional language can represent a barrier to understand information.

The aim of the analysis is to present publicly available health information sources that can be used to gain insight into health information needs. Hospital websites are widely used the access point for information search or for communication. Information presented on hospital websites should be presented in clear, up-to-date and understandable way enabling easy information access and interactive communication. The research presents Big Data analysis performed on three types of data. The research is a result of the institutional project (grant 11-931-1072).

The aim of the research is implementation of Natural Language Processing (NLP) techniques for detection of malicious (phishing) e-mails in order to augment cyber security. Fake and malicious messages, i.e. phishing messages, often represent the first step in the cyber-crime process. The research presents the use of the Natural Language Processing (NLP) techniques in order to detect malicious and fake e-mails, aiming to steal information, data or financial resources.

U prvome dijelu se prikazuje porast istraživačkog interesa za primjenom IKT u zdravstvu, u bazi Web of Science, od 2000. godine do danas. U drugome dijelu istraživanja provedena je analiza primjene različitih tehnologija (platformi, aplikacija i servisa) u komunikaciji liječnik-pacijent: zdravstvenih portala, e-mail komunikacijskih usluga, virtualnih platformi, telemedicinskih usluga za ljudsku interakciju - osobito putem videozapisa, mobilnih aplikacija (mHealth) i društvenih mreža koje se sve više koriste za interakciju između korisnika zdravstvene zaštite i pružatelja usluga. Prikazana su rješenja temeljena na umjetnoj inteligenciji.

Procjena učinkovitosti ekosustava otvorenih podataka zadobila je značajan interes istraživača širom svijeta (vidi Welle Donker & Van Loenen, 2016; Dodds and Newman, 2015; Capgemini Consulting, 2015; Public Sector Information (PSI) Scoreboard; Independent Reporting Mechanism (IRM), 2015; World Wide Web Foundation, 2015; Open Knowledge International (OKI), 2014). Ove postojeće metode procjene usredotočuju se na status komponenti ekosustava (postoji li politika otvorenih podataka, jesu li podaci u skladu s standardima, jesu li dostupni besplatno, itd.). Pristupom prema potrebama aplikacije/korisnika, ovaj projekt implementira drugačiji pristup procjeni: otvoreni podaci kao sredstvo za procjenu (fizičkog) pristupa bolničkim zgradama.

Ease of access to and low cost of hardware and software for 3D scanning have made 3D technologies increasingly popular in recent research. One of the possible 3D scanning approaches is photogrammetry which relies on using a data set consisting of photographs of the same physical object. In this paper are evaluated different 3D models generated from the same input data set by specialised software packages for photogrammetry. The main attributes of the 3D models are examined in comparative analyses and their differences highlighted. Furthermore, visual qualitative inspections are performed on the models and the results are compared.

The presentation gives overview of different AI technologies with implementation in the domain of tourism.

Human translation of literary works is a profession as ancient as the appearance of writing with a very long tradition. However, with the ever-growing improvements of technologies in the field of machine translation, the possibilities of adopting machine- translated literary texts are in constant progress. Nevertheless, human evaluation of machine-translated text is a necessity. The aim of this paper is to examine what makes machine translations appear bad in terms of error types that occur within such translations. Therefore, a human error classification analysis on a machine-translated text corpus in the domain of poetry is conducted. This study could be used for improving the methodology for machine translation evaluation of literary texts.

The focus of the research is on evaluation of terminology consistency in domain of business ommunication.

In the research, chatbot is implemented in the domain of tourism, for the selected domain of tourism.

The 4th industrial revolution has brought not only new opportunities but new demands and challenges, also in government communication to citizens and businesses. Transformations do not only concern ICT usage differences but also social, economic, educational and business changes, and bring risks known as „digital divide“. One of the services that are integrated into the new digital environment, and not yet in full deployment, is e-government. Governments are faced with challenges of digital transformation, aiming to achieve, such as enhanced public services, enhanced administration, enhanced social value, cost reductions or empowerment of citizens bringing them closer to the public policy of decision-making. Although e-government aims to provide access to individuals, businesses, interest groups or their governments, various types of “digital divide” present barriers to full implementation of e-government services. In order to investigate the utilization of e-government, we used data from the Eurostat database for 32 countries for 2018. The research is performed by the use of K-means cluster analysis and the non-parametric Kruskal-Wallis test. The aim of the research is two-fold: (i) to perform cluster analysis of e-government practice across European countries and identify the position of Croatia, and (ii) to detect possible reasons of a digital divide in relation to age and gender divide and economic development. K-means cluster analysis revealed three clusters as optimum. Results of the Kruskal-Wallis test confirm the statistically significant difference among clusters at 1% significance regarding GDP, global gender inequality index and political empowerment. Results reveal that the digital divide is one of the major challenges preventing citizens from the use of e-government services. According to existing studies, governments should perform business processes re-engineering and transform the way of work and communication, but also enable ICT training programs in order to decrease the digital divide and to exploit the full potential of e-services.

Information and communication technologies have changed the way of communication, users’ habits and expectations in all types of settings (business, education, entertainment, service production, tourism, manufacture, …), but also set new requirements for institutions. One of them is online access to digitised authentic materials, which creates added value for users and institutions.The process includes selection, digitisation, format processing, annotation and information retrieval conducted on the collection of materials from the Archives of the Faculty of Humanities and Social Sciences, University of Zagreb, consisting of minutes from the Faculty Council meetings from 1874 until the digital era. Results confirm the presented process as one of possible solutions but requires planned strategy and interdisciplinary approach.

Digitisation of archival materials is a lengthy process in which the act of digitisation is usually the shortest and the most straightforward one. It is the processing of the digitised materials that take a lot of time and effort. It is valuable to have digitised materials available online, even for the reason of not having to travel to the physical location of the originals, but the tendency of extracting additional value from the materials increases. To that end, the archival materials can be processed by new techniques such as artificial intelligence (AI) and crowdsourcing. The authors use the example of digitisation of the minutes from the Faculty (of Humanities and Social Sciences) Council meetings dating from 1913 to 1996 and the application of AI to improve the OCR results and NER for semantic enrichment as well the example of digitisation of the food rationing cards, used between 1941 and 1945 in Zagreb, Croatia and the application of AI and crowdsourcing to data extraction, analysis and visualisation.

The main goal of the paper is to research the possibilities of MT integration into AV web-based environment and to evaluate improvements by adding new digital resources (specific terminology) related to the domain. The research is performed for English-Arabic, English-Croatian for the specific domain. Evaluation is performed after the two iterations when using online MT tool (without and with additional terminology resource) by native speakers (Arabic and Croatian. Evaluation consists of two steps: human evaluation and automatic evaluation.

The lecture deals with analysis of information transfer improvement through the Quality Assurance using CAT technology.

Quality control (QC), represents the basic part of the quality management (QM) translation process. Quality control (QC) could be performed by human check or by use of quality assurance tools, in order to detect various types of error-categories. In the translation process, the special attention is given to the quality control (QC) of terminology. Detection of terminology (in)consistency in the legal domain can considerably influence readability, understanding and information transfer, important when dealing with texts created for a large number of users and for the specific user. The quality control (QC) process will be analysed at the textual level and as part of the translation workflow process, asking for specific CAT tools. Practical examples will be presented, related to terminology extraction, error-detection, terminology validation, integration and terminology evaluation. Examples will be given for English, French, and other languages.

This paper presents an idea to bring crowdsourcing to a higher level, for the purpose of acquiring valuable machine translation and natural language processing resources. In the proposed scenario, students are being educated in order to improve the quality and effectiveness of their natural language processing (NLP) related work. Their motivation is ensured by introducing an element of gamification – a ranking is kept, where the best contributing users are decorated with medals. The ranking is available at all times to all users and is always up-to-date, hence the effects of the contributions are immediately visible to the users. This scenario was applied to a group of students enrolled in Natural Language Processing course, who were presented with a task of collecting parallel corpora for less-resourced language pairs, in this case Croatian-English and English- Croatian. The whole experiment was supervised with the help of a custom-made open-source system named TMrepository, developed and maintained by the authors of this paper.

The presentation discusses the role of machine translation technology in meeting migrants’ needs, benefits and drawbacks of technology use in the communication process.

U intervju se prikazuju modućnosti suradnje između jezične industrije i akademske zajednice, razvoje vještina i protrebnih znanja, praksa, projekti i spremnost za posao.