Korean Journal of Construction Engineering and Management
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v.5
no.5
s.21
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pp.151-162
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2004
Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.
KSII Transactions on Internet and Information Systems (TIIS)
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v.17
no.6
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pp.1657-1673
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2023
With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.
Proceedings of the Korea Information Processing Society Conference
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2023.11a
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pp.540-542
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2023
Due to the serious issues posed by facial manipulation technologies, many researchers are becoming increasingly interested in the identification of face forgeries. The majority of existing face forgery detection methods leverage powerful data adaptation ability of neural network to derive distinguishing traits. These deep learning-based detection methods frequently treat the detection of fake faces as a binary classification problem and employ softmax loss to track CNN network training. However, acquired traits observed by softmax loss are insufficient for discriminating. To get over these limitations, in this study, we introduce a novel discriminative feature learning based on Vision Transformer architecture. Additionally, a separation-center loss is created to simply compress intra-class variation of original faces while enhancing inter-class differences in the embedding space.
International Journal of Computer Science & Network Security
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v.24
no.8
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pp.1-13
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2024
Artificial intelligence (AI) describes a variety of approaches in computer applications to mimic human learning. As this technology becomes increasingly prevalent, it is inevitable that it will enter the educational environment, as both an educational tool and topic of learning. STEM education, which deals with science, technology, engineering, and math, is perhaps the most appropriate educational field in which to introduce students to this new and rapidly growing technology. In recent years, educators, AI engineers, and educational researchers have published trial results of experimental curricula implementing AI technology in student and teacher education. This systematic literature review analyzed a sample of seven such publications to identify key trends in suggested best practices for the usage of AI in STEM classrooms. The sample was analyzed for keywords using MaxQDA. The results indicated three key trends among suggested best practices. The first was that AI is best taught to students when the technology itself is the topic of education. Another trend was that simulating real world applications of AI technology was most impactful in showing students the potential, limits, and ethical implications of AI. Finally, it was found that educator's familiarity with AI is an important factor in their ability to employ it in the classroom.
The research aimed to investigate characteristics of middle school students in a biology class as science gifted education in terms of self-regulated learning abilities, personality traits and learning preferences. The twenty subject in the study responded to questionnaires of a self-regulated learning ability instrument, a personality trait tool, and a learning preference survey in March, 2009. It was found that the research subjects showed higher levels of cognitive strategies, meta-cognition, and motivation than those students in a previous study(Jung et. al., 2004), while environment was opposite. The level of cognitive strategies was significantly correlated with meta-cognition(r=.610, p=.004) and motivation (r=.538, p=.014) and meta-cognition with environment(r=.717, p=.000). Those students who showed highest levels of self-regulated learning ability displayed various personality traits. One male student with the highest level of self-regulated learning ability showed a personality of hardworking, tender-minded, and conscientious traits and wanted to be a medical doctor. The female student with the second highest level of self-regulated learning ability presented a personality as creative, abstract and divergent thinker and she showed a strong aspiration to be a world-famous biologist with breakthrough contribution. The five students with highest levels of self-regulated learning ability showed a common preference in science learning: they dislike memory-oriented and theory-centered lecture with note-taking from teacher's writings on chalkboard; they prefer science learning with inquiry-oriented laboratory work, discussion among students as well as teachers. However, reasons to prefer discussion were diverse as one student wants to listen other students' opinions while the other student want to present his opinion to other students. The most favorable science teachers appeared to be who ask questions frequently, increase student interests, behave friendly with students, and is a active person. In conclusion, science teaching for the gifted should employ individualized teaching strategies appropriate for individual personality and preferred learning styles as well as meeting with individual interests in science themes.
Trade education methods that combine practical knowledge and on-site job training have significantly contributed in improving abilities of trade experts. For instance, GTEP and LINC have contributed to a substantial expansion of SME export performane. Moreover, students' cooperation experience have led to employment outcomes as SMEs can employ customized trade workers. I have conducted a survey to 100 students about university-industry collaboration. Results show that ICT skills and foreign language ability are the highest required conditions of employment while production and technology knowledge are the lowest. Furthermore, 50 companies operating in foreign markets responded that through industry-university cooperation, capabilities of university graduates have improved and trade education cooperation scheme is a success.
Tourism and recreation spots in Korea have been developed metropolitan cities-oriented that facility construction has too much importance in local tourist site development more than satisfaction and experience. Tourism hardly seems to play its role as a motive power even in locals where tourism occupies much in their economic development. Therefore, the ministry of culture & tourism has introduced a plan to discover cultural and tourism resources as a development alternative which handles theme-ability and specialization. However, most projects of local tourism resources developed since 1999 have resulted similar features comparing to previous and existing tourist spots. And the main objectives of this paper have not been realized very well. This research hence forth suggests a program-based model development in tourism resources, with a case study of Gokun-gugok, one of the historical and cultural sites and is located in Hwachon-gun, Gangwon Province. Main points include: Since the Gokun-gugok landscape has been undermined and been loosed the traditional cultural value due to the road development, this study intends to plan to make the adventure of tourism destination including restoring the site as a cultural place. The Gokun-gugok site needs to develop combining various types of tours and adventures with instructive and educational programs to meet the visitors' needs. This research also intends to precede a development plan based on harmonizing natural, historical and cultural assets of the Gokun-gugok with facility maintains and tourism development. Meanwhile, the study stresses on realizing development of tourism resources categorizing programs by seasonality, visitor's economic class, and visit duration. Asa consequence, the research presents a "Culture & Tourism Academy" which deals with these types of adventure programs and informative educations. To assess feasibility of the development plan in terms of economy, environment and policy, the research conducted the site inspection and examined the site's surroundings, land properties and inundation. 145,000 square meters have been extracted as a feasible development area out of total 392,500 square meters. Finally, the study segmentizes target markets basedon the result of the survey on visitors and local residents. The more segmentized markets employ facilities according to their traditional characters.
We examine the relationship between firms' environmental (E), social (S), and governance (G) factors, with their financial performance in order to provide an empirical rationale for CSV (creating shared value) pursuing both of firms' profitability and CSR (corporate social responsibility). The financial performance is classified into four aspects such as profitability, stability, efficiency, and cash-flow, and each of these aspects is measured by two financial ratios respectively. To measure the firms' ESG performance, we employ the published performance grades by the Korea Corporate Governance Service for a three year span, from 2011 to 2013. Total of eight regression analyses are performed. The results show that firms' non-financial performance in general has statistically significant positive relationships with return on assets, return on net sales, and cash-flow from operating activities ratio, while it has negative relationships with net working capital ratio, asset turnover ratio, and cash-flow from investing activities ratio. It has no significant relationships with debt ratio and equity turnover ratio. The results imply that firms' non-financial performance may have a negative impact on some financial performance such as liquidity and efficiency in a short term, but it would eventually improve the firms' profitability and cash-generating ability, which provides an empirical evidence for the concept of CSV, and motivates the firms to participate in social contribution activities without sacrificing their profitability for their respective sustainablity management.
To adjust the discrepancy between Light Detection and Ranging (LIDAR) strips, previous researches generally have been conducted using conjugate features, which are called feature-based approaches. However, irrespective of the type of features used, the adjustment process relies upon the existence of suitable conjugate features within the overlapping area and the ability of employed methods to detect and extract the features. These limitations make the process complex and sometimes limit the applicability of developed methodologies because of a lack of suitable features in overlapping areas. To address these drawbacks, this paper presents a methodology using area-based algorithms. This approach is based on the scheme that discrepancies make complex the local height variations of LIDAR data whithin overlapping area. This scheme can be helpful to determine an appropriate transformation for adjustment in the way that minimizes the geographical complexity. During the process, the contour tree (CT) was used to represent the geological characteristics of LIDAR points in overlapping area and the Iterative Closest Points (ICP) algorithm was applied to automatically determine parameters of transformation. After transformation, discrepancies were measured again and the results were evaluated statistically. This research provides a robust methodology without restrictions involved in methods that employ conjugate features. Our method also makes the overall adjustment process generally applicable and automated.
With the women's education level higher and gender equality more promoted, women's opportunity of taking part in business is increasing. Entering the emotional age which counts self-image as being important, there is a view that woman's appearance has a direct relation to their social life. The research is to examine the hypothesis and important factors for women workers at banks for their successful career, that is, to verify the effects of women's appearances on their social life, furthermore, to help female applicants for a bank and the bank clerks have a desirable social life. The subjects were 200 male and 100 female bank clerks and the research was analyzed on the basis of a total of 300 questionnaires. The results are as follows: first, 52 percent of male subjects responded that the factors of "sociability" and "character and good manners" are the most important in social life, while 47 percent of female subjects answered the factor of "ability" is so. Second, 61.5 percent of male and 84 percent of female respondents answered that woman's appearance has a potent influence on their social life. Third, 76 percent of male and 90.9 percent of female respondents answered that when the companies employ woman workers, they take woman's appearance into consideration. It shows even at job interviews that good-looking applicants are in a better position, for bankers must consult with many customers and so their neat and tidy appearance such as their natural make-up and decent hair style (short-hair style) is one of significant factors to perform their jobs. Therefore, appearance managements are primary for women bankers to work at their workplace.
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