Security in Multimedia Information Systems: Analysis and Prediction

Objective: The student's humanistic position is not only the result of the teacher's personal influence but also a controlled process. As a result of the implementation of this process, the student receives an understanding of what should relate to the humanistic process and the humanistic function of society. Background: The basis for the formation of humanism for all participants in the students' educational process is determined either by the teacher's position or by the installation in the pedagogical process. So, any impact on the part of the teacher can be perceived as an exceptional factor of influencing the student's personality, which can be perceived as a compulsory education requirement. Method: Assessment of pedagogical effectiveness was carried out by means of an expert survey. Diagnostic and processing of empirical data was carried out according to the methodology for assessing the importance of each individual requirement for the proposed educational materials. Results: The study examines the components that form the student’s humanistic position on the basis of the traditional components of the formation of the pedagogical teaching strategy. The practical significance of the study is determined by the fact that it is possible to implement the concept of the formation of active skills in students during certain actions and the formation of other humanitarian positions. Conclusion: The study shows that the level of formation of the students' humanistic position concerning all participants in the educational process depends on updating the educational process of training specialists based on the developed theoretical and methodological foundations.

HOW MUCH ICT SECURITY HAS IMPROVED DURING THE LAST DECADE

The paper describes experiments that investigated the use of auditory stimuli (speech and non-speech sound) to communicate data in two information systems. These experiments were performed under a multimedia stock control system and a multimedia e-mail tool. Experiments using the multimedia stock control information system investigated rhythms and timbre to communicate windows and their associated functions as well as the simultaneous rise of speech and sound to communicate a large amount of information to users. Experiments using the multimedia e-mail tool investigated the use of tunes and short pitch sequences with visual stimuli to communicate information in browsing e-mail data. The paper also integrates experimental results front both systems and discusses the incorporation of auditory stimuli in interactive audio-visual or multimedia information systems. Auditory stimuli helped to communicate, complement or reinforce information that users received via the visual channel. These auditory technologies could also benefit visually impaired users of such systems. Results also indicated that the simultaneous use of sound and speech can be useful in the design of interactive multimedia information systems. The inclusion of auditory stimuli (including speech) in the design of information systems has the potential to maximise the volume of information communicated to users.

Smart Contracts (SCs) in Ethereum can automate tasks and provide different functionalities to a user. Such automation is enabled by the ‘Turing-complete’ nature of the programming language (Solidity) in which SCs are written. This also opens up different vulnerabilities and bugs in SCs that malicious actors exploit to carry out malicious or illegal activities on the cryptocurrency platform. In this work, we study the correlation between malicious activities and the vulnerabilities present in SCs and find that some malicious activities are correlated with certain types of vulnerabilities. We then develop and study the feasibility of a scoring mechanism that corresponds to the severity of the vulnerabilities present in SCs to determine if it is a relevant feature to identify suspicious SCs. We analyze the utility of the severity score towards detection of suspicious SCs using unsupervised machine learning (ML) algorithms across different temporal granularities and identify behavioral changes. In our experiments with on-chain SCs, we were able to find a total of 1094 benign SCs across different granularities which behave similar to malicious SCs, with the inclusion of the smart contract vulnerability scores in the feature set.KeywordsBlockchainMLSuspect identification

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

Abstract. Introduction. Higher education institutions are a multicomponent centers of professional development of future specialist and centers of formation of human and social capital of country. An important condition for ensuring the competitive functioning of education institution, its ability to increase spiritual and intellectual potential of country is the development of organizational culture of institution. Organizational culture is a collective characteristic of the sociopsychological environment of educational institution, formation and development of which is determined by adaptability to external changes. Effective organizational culture contributes to the achievement of intellectual and cultural social goals by clearly defining the mission, goals and development strategy of higher education institution. Purpose. The purpose of the article is investigation the formation and development of powerful organizational culture of higher education institutions and presentation of its role in achieving competitive advantages of organizations at educational services market. Results. The essence of organizational culture of higher education institution is investigated. The views of scholars on interpretation of the terms «organizational culture» and «corporate culture» are presented and generalized. There is noted that organizational culture is a basis for formation of corporate culture of organization. Factors influencing the formation and development of organizational culture of higher education institution are identified and characterized, such as: professional activity of management, personal and professional development of teachers and students, macroeconomic environment, structural and organizational characteristics of institution. The influence of organizational culture on achievement of higher educational institutions' competitive advantages due to improvement of effective indicators of educational and scientific activity is presented. Modern tendencies in educational services market caused by migratory moods of youth, aging of the population are presented. The main measures to improve the organizational culture of higher education institutions at different levels (administrative, teaching, student and service) are described, which will increase the competitive position of organization. Conclusions. Organizational culture is very important for ensuring the quality and effectiveness of educational, scientific and cultural processes of higher education institutions, uniting the values, goals and norms of behavior and communication between all participants in educational process, regardless of positions and social roles. The full development of organizational culture of higher education institutions requires the coordination of values, skills and abilities of all representatives of organization to achieve a synergistic effect.

The purpose of information systems (IS) security behavior, represented by the difference between work and personal purpose, has been neglected in the research of user's IS security behavior. This is especially important for the organizations that allow or inevitably allow their employees to do personal tasks during work hours, because it may bring big threats to an organization's IS. Unfortunately, few empirical studies have examined if different behavioral purpose, typically, work/personal purpose, affects users' IS security behavior decision. Based on boundary theory and the idea of multidimensional rationality, we argue that people demarcate a psychological boundary for work and personal usages of IS and thus apply different rationalities to make IS security-related decisions. Our empirical investigation indicates that in a work usage context, people apply rule-based rationality and therefore are more influenced by mandatoriness and facilitating conditions. Whereas in a personal usage context, people apply outcome-based rationality and therefore are more concerned with task benefits and costs. The findings imply that organizations and vendors may devise different measures to ensure users' IS security behavior in different contexts.

Information security awareness is important among users because it can influence user's behavior towards complying with organization information security policies. Therefore, the current study was conducted to examine information security awareness factors affecting user's compliance behavior towards Health Information System (HIS) security policies based on extended Health Belief Model. The questionnaires were distributed to the respondents at selected public hospitals in Malaysia (N = 454). Statistical results confirm that perceived work experience, perceived severity, perceived benefit, cues to action, self-efficacy and perceived barrier were significant predictors of health information system's security policies compliance behaviour while perceived susceptibility was insignificant. Our findings will prove to be beneficial to fellow researchers and policy makers, especially related to the medical sectors in improving user's behavior toward practicing information security.

Managing the security of information systems (SIS) is at once a tedious task and the cornerstone of business in a highly competitive environment. Successful control of the SIS in a business requires many years of experience, expertise and continuous improvement. This implies real know-how for the employees who are responsible for SIS control. This article focuses on the encapsulation of this know-how into UML models through profiles according to the meta-object facility (MOF) standards from the object management group (OMG). The main idea is to understand, manipulate and exploit this delicate and valuable know-how without being necessarily an expert on SIS. This challenge is threefold: firstly, to find a common language, as well as approaches and tools for Engineering of both IS and SIS; secondly, to generate earnings and make use of the enormous progress in Engineering of IS, to establish and improve Engineering of SIS; and thirdly, to achieve homogeneous management and follow-up of IT projects and their security. This paper presents the context of the Engineering of Security of Information Systems, its importance and the feasibility of this challenge.

Machine learning (ML) algorithms seek to extract the most beneficial information out of the raw data. As mentioned in previous chapters, preprocess data preparation actions might be needed in order to make the data set ready to be fed into the ML algorithms. ML algorithms have gotten the attention of researchers all over the world during the last few decades. In traditional algorithms, the analyst had to define the rules, which was not always accessible or easily possible, in order to obtain the output. ML algorithms develop the models or rules based on the training data set which include input and output data points. ML algorithms try to understand more beneficial information regarding the system based on the training data set. The built model can be tested using the verification data set which does not have any overlap with the training sets. If the model acquires the acceptable performance measures, it can be applied for other cases to perform the prediction process. It should be considered that ML algorithms are not supposed to do the magic. Indeed, they are supposed to explore and analyze the training data, which the human brain is not able to handle that, to develop a predictive model in which its performance is acceptable for the verification data set. Predictive models should be generalized which means performing satisfactorily for both training and verifying data sets.

The study focuses on reforms in Ukrainian modern education industry, in particular, on the attempt to outline the functional state of one of the most important phenomena of changes, i.e. the concept of “education quality”. The authors point to the possibility of analysing the phenomenon from several perspectives: terminological, conceptual and ideological. They envisage that such an approach can help to parameterize the contemporary educational phenomenon, which is too complex to define unequivocally. The text of the article demonstrates the complexity of the problem to analyse in one way as it is multicomponent and multidimensional. Most researchers engaged in assessing education quality within the contemporary Ukrainian educational space focus their attention on two or more dimensions, which is projected on educational activities in terms of analysing its process or its outcome. According to the authors, strategic inaccuracy and blurred approach to the phenomenon are the reasons for numerous attempts to conceptualize a complex concept unidimensionally at the same time losing its multidimensionality. One of the reasons for this is the dynamics of educational processes under the conditions of information society formation. The authors consider the problem of “education quality” concepts functioning against the background of rapid development of educational communication processes during modern digital epoch. Most current approaches used to define the concept of “education quality” give grounds for justifying the multidimensionality of the phenomenon, which can be explained by reforming the educational communication itself, the dynamics of global processes of cognition and information change in the methods of interaction between educational process participants. The dynamics of information process development in modern society requires systematic and reasonable steps in regard of implementing educational innovations, and the matter of “education quality” phenomenon necessitates a new and integrated target-driven research in order to establish clear boundaries of this too broad and multidimensional concept. Key words: education, education quality, information technologies in education, pedagogical communication, information society, terminology.

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

The controversy surrounding whether serum total cholesterol is a risk factor for the graded progression of knee osteoarthritis (KOA) has prompted this study to develop an authentic prediction model using a machine learning (ML) algorithm. The objective was to investigate whether serum total cholesterol plays a significant role in the progression of KOA. This cross-sectional study utilized data from the public database DRYAD. LASSO regression was employed to identify risk factors associated with the graded progression of KOA. Additionally, six ML algorithms were utilized in conjunction with clinical features and relevant variables to construct a prediction model. The significance and ranking of variables were carefully analyzed. The variables incorporated in the model include JBS3, Diabetes, Hypertension, HDL, TC, BMI, SES, and AGE. Serum total cholesterol emerged as a significant risk factor for the graded progression of KOA in all six ML algorithms used for importance ranking. XGBoost algorithm was based on the combined best performance of the training and validation sets. The ML algorithm enables predictive modeling of risk factors for the progression of the KOA K-L classification and confirms that serum total cholesterol is an important risk factor for the progression of KOA.

An Information System (IS) security programme consists of several essential security controls. In order to verify and maintain the effectiveness of an IS security programme, it is pertinent to identify how security controls are compared to each other in terms of their practical implications. This study reports the results from an experience survey to offer insights of the roles of security controls in IS security programmes. Four clusters of security controls were identified with technical security controls (such as authentication and confidentiality) being high on respondents' perceived importance. An approach to develop SAI with weighted security controls is explained to help security administrators conduct security assessments. The results of this study contribute to IS security budget planning and resource allocation. Managerial implications are also reported.

Protection motivation theory (PMT) is one of the most commonly used theories to examine information security behaviors. Our systematic review of the application of PMT in information systems (IS) security and the comparison with its application for decades in psychology identified five categories of important issues that have not yet been examined in IS security research. Discussing these issues in terms of why they are relevant and important for IS security, and to what extent IS research has not considered them, offers new research opportunities associated with the study of PMT and IS security threats. We suggest how future studies can approach each of the open issues to provide a new road map for quantitative and qualitative IS scholars.