• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.203-211
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• 2021

The article studies the discourse and a legal uncertainty of the popular and generally understandable concept of cybercrime. The authors reveal the doctrinal approaches to the definition of cybercrime, cyberspace, computer crime. The analysis of international legal acts and legislation of Ukraine in fighting cybercrime is carried out. The conclusion is made about the need to improve national legislation and establish international cooperation to develop the tools for countering cybercrime and minimizing its negative outcomes. The phenomenon of nicknames is studied as a semantic source, which potentially generates a number of threats and troubles - the crisis of traditional anthroponymic culture, identity crisis, hidden sociality, and indefinite institutionalization, incognito style, a range of manifestations of loneliness - from voluntary solitude to traumatic isolation and forced detachment. The core idea is that it is the phenomenon of incognito and hidden name (nickname and other alternatives) that is the motivational stimulus for the fact of information trouble or crime.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.99-102
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• 2021

The article considers one of the areas of development of artificial intelligence where there is the development of computer intelligent systems capable of performing functions traditionally considered intelligent - language comprehension, inference, use of accumulated knowledge, learning, pattern recognition, as well as learn and explain their decisions. It is found that informational intellectual systems are promising in their development. The article is devoted to intelligent information systems and technologies in educational activities, ie issues of organization, design, development and application of systems designed for information processing, which are based on the use of artificial intelligence methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.383-388
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• 2022

This thesis explores the principle of a performer's organic action by means of his/her bodily responses on stage. This research has been developed to define the nature of a performer's central task in order to constitute empirical understanding of acting and the purpose of training in addressing the question of what sort of qualitative bodily training is necessary to be in a state of the full body involvement. This study investigates to articulate a performer's fundamental task at the most rudimentary level by utilizing those theatre artists' concepts with practical assumptions. In particular, the key terms, happen and change signifies the quality of a performer's body that has to fit into the given environment in which the performer's body can be subordinated through the moment on stage. Here, we argue that a performer's essential task parallel to make the following moment to happen and change by means of progressing a set of the next moment. In this manner, we also argue that a moment of displaying the performer's conscious effort, forceful and externalizing the visible elements under the use of erroneous language leads his/her body not to function on stage, a state of disengagement from his/her body. Finally, we provide a way to facilitate a performer's organic action by focused on his/her lived experience to create the functional moment which is opposite to the predominance of a representation, maintaining the performer's intellectual sense.

• Structural Engineering and Mechanics
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• v.85 no.6
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• pp.729-742
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• 2023

It is a recent attraction to the mechanical scientists to investigate state of wave propagation, buckling and vibration in the sport balls to observe the importance of different parameters on the performance of the players and the quality of game. Therefore, in the present study, we aim to investigate the wave propagation in handball game ball in term of mass of the ball and geometrical parameters wit incorporation of the viscoelastic effects of the ball material into account. In this regard, the ball is modeled using thick shell structure and classical elasticity models is utilized to obtain the equation of motion via Hamilton's principle. The displacement field of the ball model is obtained using first order shear deformation theory. The resultant equations are solved with the aid of generalized differential quadrature method. The results show that mass of the ball and viscoelastic coefficient have considerable influence on the state of wave propagation in the ball shell structure.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.633-652
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• 2024

Software testing is an important phase as it ensures the software quality. The software testing process comprises of three steps: generation, execution, and evaluation of test cases. Literature claims the usage of single and multiple 'Unified Modeling Language' (UML) diagrams to generate test cases. Using multiple UML diagrams increases test case coverage. However, the existing approaches show limitations in test case generation from UML diagrams. Therefore, in this research study, we propose an approach to generate the test cases using UML State Chart Diagram (SCD), Activity Diagram (AD), and Sequence Diagram (SD). The proposed approach transforms UML diagrams into intermediate forms: SCD Graph, AD Graph, and SD Graph respectively. Furthermore, by integrating these three graphs, a System Testing Graph (STG) is formed. Finally, test cases are identified from STG by using a traversal algorithm such as Depth First Search (DFS) that is an optimization method. The results show that the proposed approach is better compared to existing approaches in terms of coverage and performance. Moreover, the generated test cases have the ability to detect faults at the unit level, integration, and system level testing.

• Annual Conference of KIPS
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• 2024.05a
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• pp.494-496
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• 2024

현재 소프트웨어 산업에서 자연어 요구사항의 정확한 분석 연구는 활발히 진행되고 있다. 그러나, 문법적인 분석만을 통해 해석하는 것이 일반적이다. 본 연구는 요구공학과 언어학 그리고 카툰 공학을 접목을 제안한다. 이를 위해서, 1) 언어학적 관점에는 촘스키의 구문 구조 분석 이론과 필모어의 의미역 이론을 결합하여 문법적, 의미적 분석을 수행한다. 2) 요구공학 관점에서는 요구사항 분석으로 상태 모델 속성 추출 및 접목한다. 3) 카툰 공학에서는 3D 이미지 생성한다. 또한, 해결 못했던 동사와 형용사에 대해 분석하여 범위를 확장한다. 즉 언어학적 분석을 바탕으로 UML 상태 다이어그램을 추출하고, 이를 3D 상태 이미지 생성한다. 본 연구는 AI 기술(Text to Image)에 소프트웨어 공학적 방법에서의 절차적인 공정과 재사용 적용함으로써, AI 내부 작동 원리에 대해 체계적으로 연구하고자 한다.

• Journal of Public Diplomacy
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• v.3 no.2
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• pp.46-55
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• 2023

Agenda-setting theory of the media describes the relationships between how news media present issues and how important those issues are to news consumers. Applying agenda setting theory, we ask, does media coverage of international political events relate to a nation's brand? We leverage a domestic conflict index variable from the Cross National Time-Series (CNTS) dataset of New York Times articles, which are considered influencers of global news content. Our dependent variable is Anholt's NBI index from consumer surveys from 2005 through 2007, whose data include an aggregate nation brand index score as well as a disaggregated score for different categories such as tourism and exports. To investigate these relationships, we employ general additive models (GAM), which account for non-linearity using cubic splines, finding generally a negative relationship between media coverage of conflict and consumer sentiment of a country. To confirm our findings, we compare the results with both the Global Database of Events, Language and Tone (GDELT) dataset of political events and the Upsala Conflict Data Program (UCDP) measure of reported conflict fatalities in a country.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4745-4749
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• 2012

The diagnosis of malignant mesothelioma (MM) remains a clinical challenge and the fluorescence in situ hybridization (FISH) assay has been reported to be one promising tool. The present meta-analysis aimed to establish the overall diagnostic accuracy of FISH for diagnosing MM. After a systematic review of English language studies, the sensitivity, specificity and other measures of accuracy of FISH in the diagnosis of MM were pooled using random-effects models. Summary receiver operating characteristic curves were applied to summarize overall test performance. Nine studies met our inclusion criteria, the pooled sensitivity and specificity for FISH for diagnosing MM being 0.72 (95% CI 0.67-0.76) and 1.00 (95% CI 0.98-1.00), respectively. The positive likelihood ratio was 34.5 (95% CI 14.5-82.10), the negative likelihood ratio was 0.24 (95% CI 0.16-0.36), and the diagnostic odds ratio was 204.9 (95% CI 76.8-546.6), the area under the curve being 0.99. Our data suggest that the FISH assay is likely to be a useful diagnostic tool for confirming MM. However, considering the limited studies and patients included, further large scale studies are needed to confirm these findings.