• Journal of Preventive Medicine and Public Health
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• v.52 no.3
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• pp.161-169
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• 2019

Objectives: The objective of the present study was to validate a shortened transformational leadership (TL) scale (12 items) comprising core TL behaviour and to test the associations of this shortened TL scale with depressive symptoms. Methods: The study used cross-sectional data from 1632 employees of the overall workforce of a middle-sized German company (51.6% men; mean age, 41.35 years; standard deviation, 9.4 years). TL was assessed with the German version of the Transformational Leadership Inventory and depressive symptoms with the Hospital Anxiety and Depression Scale (HADS). The structural validity of the core TL scale was assessed with confirmatory factor analysis. Associations with depressive symptoms were estimated with structural equation modelling and adjusted logistic regression. Results: Confirmatory factor analysis and structural equation modelling showed better model fit for the core TL than for the full TL score. Logistic regression revealed 3.61-fold (95% confidence interval [CI], 2.20 to 5.93: women) to 4.46-fold (95% CI, 2.86 to 6.95: men) increased odds of reporting depressive symptoms (HADS score >8) for those in the lowest tertile of reported core TL. Conclusions: The shortened core TL seems to be a valid instrument for research and training purposes in the context of TL and depressive symptoms in employees. Of particular note, men reporting poor TL were more likely to report depressive symptoms.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.312-318
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• 2021

Lack of knowledge and digital skills is a threat to the information security of the state and society, so the formation and development of organizational culture of information security is extremely important to manage this threat. The purpose of the article is to assess the state of information security of the state and society. The research methodology is based on a quantitative statistical analysis of the information security culture according to the EU-27 2019. The theoretical basis of the study is the theory of defense motivation (PMT), which involves predicting the individual negative consequences of certain events and the desire to minimize them, which determines the motive for protection. The results show the passive behavior of EU citizens in ensuring information security, which is confirmed by the low level of participation in trainings for the development of digital skills and mastery of basic or above basic overall digital skills 56% of the EU population with a deviation of 16%. High risks to information security in the context of damage to information assets, including software and databases, have been identified. Passive behavior of the population also involves the use of standard identification procedures when using the Internet (login, password, SMS). At the same time, 69% of EU citizens are aware of methods of tracking Internet activity and access control capabilities (denial of permission to use personal data, access to geographical location, profile or content on social networking sites or shared online storage, site security checks). Phishing and illegal acquisition of personal data are the biggest threats to EU citizens. It have been identified problems related to information security: restrictions on the purchase of products, Internet banking, provision of personal information, communication, etc. The practical value of this research is the possibility of applying the results in the development of programs of education, training and public awareness of security issues.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.304-311
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• 2021

The relevance of the research involves outlining the need for modern professionals to acquire new competencies. In the conditions of rapid civilizational progress, in order to meet the requirements of the labor market in the knowledge society, there is a readiness for continuous training as an indicator of professional success. The purpose of the research is to identify the impact of various forms of application of information technologies for lifelong learning in order to provide the continuous self-development of each person without cultural or age restrictions and on the basis of rapid digital progress. A high level (96%) of need of the adult population in continuing education with the use of digital technologies has been established. The most effective ways to implement the concept of "lifelong learning" have been identified (educational camps, lifelong learning, mass open online courses, Makerspace activities, portfolio use, use of emoji, casual game, scientific research with iVR game, implementation of digital games, work in scientific cafes). 2 basic objectives of continuing professional education for adults have been outlined (continuous improvement of qualifications and obtaining new qualifications). The features of ICT application in adult education have been investigated by using the following methods, namely: flexibility in terms of easy access to ideas, solving various problems, orientation approach, functional learning, group or individual learning, integration of leisure, personal and professional activities, gamification. The advantages of application of information technologies for continuous education (economic, time, and adaptive) have been revealed. The concept of continuous adult learning in the context of digitalization has been concluded. The research provides a description of the structural principles of the concept of additional education; a system of information requests of the applicant, as well as basic technologies for lifelong learning. The research indicates the lack of comprehensive research in the relevant field. The practical significance of the research results lies in the possibility of using the obtained results for a wider acquaintance of the adult population with the importance of the application of lifelong learning for professional activities and the introduction of methods for its implementation in the educational policy of the state.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• SUVANNABHUMI
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• v.12 no.2
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• pp.53-80
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• 2020

Students of global development are often introduced to Southeast Asia by reading many of the influential authors whose ideas were derived from their experiences in the region. John Furnivall, Clifford Geertz, Benedict Anderson and James Scott have made Southeast Asia relevant to comprehending developments far beyond the region. It might even be added that others come to the region because it has also been the home to many key historical events and seminal social developments. However, when many of the best-known writings (and textbooks) of global history are examined, treatment of Southeast Asia is often scarce and in the worst cases non-existent. It is within this context that this paper will examine Southeast Asia's role in the interpretation of global history. The paper will consider the 'global history' as a historical production in order to depict the ways in which the construction of global narratives can be a reflection of the immediate needs of historians. Furthermore, the discussion will be historiographic, exhibiting the manner in which key global histories portrayed the significance of the region. Particular importance will be placed on the ways in which the region is used to present larger historical trajectories. Additionally, the paper will consider instances when Southeast Asia is either profoundly underrepresented in global narratives or misrepresented by global historians. Last, since the discussion will probe the nature of 'global history', it will also consider what the subject might look like from a Southeast Asian point of view. The paper will end by exploring the ways in which the region's history might be augmented to become visible to those who live outside or have little knowledge about it. Visual augmented reality offers great potential in many areas of education, training and heritage preservation. To draw upon augmented reality as a basic metaphor for enquiry (and methodology) means asking a different kind of question: how can a region be "augmented" to become (at least in this case) more prominent. That is, how can the region's nations, histories and cultures become augmented so that they can become the center of historical global narratives in their own right. Or, to put this in more familiar terms, how can the "autonomous voices" associated with the region make themselves heard?

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.11-20
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• 2022

There is a critical need for AI assistance in guard operations of Army base perimeters, which is exacerbated by changes in the national defense and security environment such as force reduction. In addition, the possibility for human error inherent to perimeter guard operations attests to the need for an innovative revamp of current systems. The purpose of this study is to propose a real-time object detection AI tailored to military CCTV surveillance with three unique characteristics. First, training data suitable for situations in which relatively small objects must be recognized is used due to the characteristics of military CCTV. Second, we utilize a data augmentation algorithm suited for military context applied in the data preparation step. Third, a noise reduction algorithm is applied to account for military-specific situations, such as camouflaged targets and unfavorable weather conditions. The proposed system has been field-tested in a real-world setting, and its performance has been verified.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.596-605
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• 2022

This study aimed to introduce research on coerced debt victimization and interventions in the context of domestic violence. To achieve the aim, this study reviewed existing studies on coerced debt conducted in the US. This study discussed the followings: First, coerced debt was theorized by coerced control theory of domestic violence and control mechanisms of economic abuse and conceptualized as fraud and force. Second, the effects of coerced debt included credit damage, economic dependence, and barriers to housing, employment, and safety. Third, to intervene the victimization, service providers should endure uncertainty and its time consuming process of recovering, provide an intense and personalized advocacy, and overcome the problems of absence of policies to support the victims. Finally, service providers should have educations and training programs on the assessment and intervention skills of coerced debt acknowledging empowerment and safety of the victims as the most important.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.427-436
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• 2022

Korea Trade-Investment Promotion Agency (KOTRA) annually publishes the trade data in South Korea under the guidance of the Ministry of Trade, Industry and Energy in South Korea. The trade data usually contains Gross domestic product (GDP), a custom tariff, business score, and the price of export items in previous and this year, with regards to the trading items and the countries. However, it is challenging to figure out the meaningful insight so as to predict the future price on trading items every year due to the significantly large amount of data accumulated over the several years under the limited human/computing resources. Within this context, this paper proposes a multi layer perception that can predict the future price of potential trading items in the next year by training large amounts of past year's data with a low computational and human cost.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.145-154
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• 2022

Based on the obtained results of the study, the most problematic issues and legal conflicts are identified, which are related to the ratio of norms of domestic and foreign legislation, taking into account the requirements of the Constitution of Ukraine and the provisions of the Law of Ukraine "On international agreements". Along with this, it is stated in this scientific article that there are a number of provisions and examples of positive practice on the specified topic abroad and in international legal acts today, which should be used by Ukraine both in improving legislation on the issues of banking activity and in increasing the level of criminal legal protection of relevant critical infrastructure facilities, especially those that are substantively related to prevention and counteraction of activity, with regard to the legalization (laundering) of criminally obtained funds, financing of terrorism and the financing of the proliferation of weapons of mass destruction, which is quite relevant for our state, given the military conflict that is taking place on its territory in the Donbass. Again, in the same context, the need for more active cooperation between Ukraine and the FATF (international body developing a policy to combat money laundering) has been proven.