• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권10호
• /
• pp.5074-5094
• /
• 2016

Screen splitting is one of the fundamental tasks in different methods including video and image compression, screen classification, screen content coding and the like. These methods in turn support various applications in data communications, remote screen sharing, remote desktop delivery to assist teaching-learning, telemedicine, Desktop as a Service etc. In the literature we find systems requiring splitting assumes a fixed size split that do not change dynamically, also there is no analysis why that split is chosen in terms of performance. By doing mathematical analysis this paper first finds the efficient splitting schemes that can be easily automated to make a system adaptive. Thereafter, taking the screen motion detection as a case study, it demonstrates the effects of various splitting methods on motion detection performance. The simulation results clearly shows how classification performances varies with different splitting which will facilitate to choose the best splitting for a specific application scenario as well as making the system adaptive by providing dynamic splitting.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2019년도 제60차 하계학술대회논문집 27권2호
• /
• pp.53-54
• /
• 2019

코딩 교육이 초등학생부터 의무화 되어 프로그래밍의 중요성이 날이 갈수록 높아지고 있고, 현재 전 세계 공용어인 영어는 필수라 할 수 있게 된 사회이다. 본 논문에서는 유아에게 코딩과 영어를 지루하고 어려운 것일 거라는 틀을 깨고 쉽게 접할 수 있도록 하는 STEAM 코딩 교육을 적용한 유아용 영어 학습 콘텐츠를 제안한다. 유아가 직접 방향 코딩을 하여 길을 찾아가는 과정에서의 창의력 발달과 목적지에 도착했을 때 해당 과일의 영단어를 확인하고 발음을 듣게 하여 언어능력발달에 도움이 될 것 수 있을 것으로 기대한다.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2020년도 제61차 동계학술대회논문집 28권1호
• /
• pp.43-46
• /
• 2020

본 논문은 코딩 교육과 영어 교육의 융합을 지향한다. 즉, 하나의 애플리케이션으로 두 가지 교육을 동시에 진행 가능할 수 있는 프로그램을 목표로 삼아 개발되었다. 시중에 공개되어있는 유아 코딩교육 애플리케이션이나 영어 교육 애플리케이션의 수는 많지만, 코딩과 영어를 동시에 교육할 수 있고, 이를 게임으로서 즐길 수 있게 만든 애플리케이션은 드물다. 특히 유아들에게 코딩과 영어에 대한 자신감과 흥미를 심어주고 고착화된 공부 방식이 아닌, 놀이를 통한 교육 방식을 제공하면서 코딩과 영어에 대한 긍정적 인식을 심어주기 위해 노력했다.

• 정보관리학회지
• /
• 제17권4호
• /
• pp.27-45
• /
• 2000

인구센서스와 같은 국가 통계정보는 국가의 미래 투자계획과 정책수립을 위한 중요한 기초데이터이다. 그러나 데이터의 코딩과정이 모두 수작업으로 이루어지기 때문에 결과의 일관성 결여와 시간과 인력이 너무 많이 소요된다는 것 등이 문제점으로 지적되고 있다. 따라서 본 연구에서는 한국 산업표준 분류표에 근거한 자동코딩시스템을 개발하여 코딩과정을 수작업으로 처리할 때 발생하는 문제점을 해결하였다. 시스템의 지식베이스로는 학습이론을 사용하여 저자가 새로이 개발한 복수의 전거어 사전들을 활용하였다. 실험한 결과, 생성률은 99.5%를, 정확률은 83.3%라는 결과를 얻었다. 따라서 이 시스템은 실제 통계데이터의 자동코딩과정에 사용될 수 있으며, 국가 통계정보의 효율적 분석에 매우 유용하게 사용될 수 있을 것이다.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제63권2호
• /
• pp.233-254
• /
• 2024

This paper delves into the symbiotic integration of coding and mathematics education, aimed at cultivating computational thinking and enriching mathematical problem-solving proficiencies. We have identified a corpus of scholarly articles (n=38) disseminated within the preceding two decades, subsequently culling a portion thereof, ultimately engendering a contemplative analysis of the extant remnants. In a swiftly evolving society driven by the Fourth Industrial Revolution and the ascendancy of Artificial Intelligence (AI), understanding the synergy between these domains has become paramount. Mathematics education stands at the crossroads of this transformation, witnessing a profound influence of AI. This paper explores the evolving landscape of mathematical cognition propelled by AI, accentuating how AI empowers advanced analytical and problem-solving capabilities, particularly in the realm of big data-driven scenarios. Given this shifting paradigm, it becomes imperative to investigate and assess AI's impact on mathematics education, a pivotal endeavor in forging an education system aligned with the future. The symbiosis of AI and human cognition doesn't merely amplify AI-centric thinking but also fosters personalized cognitive processes by facilitating interaction with AI and encouraging critical contemplation of AI's algorithmic underpinnings. This necessitates a broader conception of educational tools, encompassing AI as a catalyst for mathematical cognition, transcending conventional linguistic and symbolic instruments.

• International Journal of Internet, Broadcasting and Communication
• /
• 제16권2호
• /
• pp.22-30
• /
• 2024

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels-referred to as color seeds-and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

• 실천공학교육논문지
• /
• 제16권3_spc호
• /
• pp.267-274
• /
• 2024

One challenge with teaching practical programming classes is the standardization of development tools on student computers. This is particularly true when a complicated setup process is required before beginning to code, or in remote classes, such as those necessitated by the COVID-19 pandemic, where the instructor cannot provide individual troubleshooting assistance. In such cases, students who encounter problems during the setup process may give up on the class altogether before even beginning to code. Therefore, this paper recommends using GitHub Codespaces as a tool for implementing standardized student development environments from day one. Codespaces provides Docker containers that an instructor can configure in such a way as to enable students to practice installing various coding tools within a controlled space, while also providing a language-specific, fully optimized development environment. In addition, Codespaces may be used more effectively in collaboration with GitHub Classroom, which helps instructors manage both the starter code and coding environment in which students work. In this paper, we compare two semesters of university Node.JS programming classes that utilized different development environments: one localized on student computers, the other containerized in Codespaces online. Then, we discuss how GitHub Codespaces and GitHub Classroom can be used to increase the effectiveness of practical programming classes while also increasing student engagement and programming confidence in class.

• 한국컴퓨터정보학회논문지
• /
• 제20권8호
• /
• pp.121-128
• /
• 2015

This study aimed to investigate the effects of an unplugged robot education system capable of computerless coding education. Specifically, this study compared this education system with PicoCricket, an educational robot that can also be used with elementary students in lower grades, using assessment tools on perceived usefulness and ease. Using random sampling and randomized assignment for more objective validation, 30 participants were assigned to the unplugged robot education system group (experimental group) and 30 participants were assigned to the PicoCricket group (control group), for a total of 60 study participants. The research procedure included verification of the equivalence of the two groups by conducting a pretest after a 2-hour basic training session on algorithms and programming. The experimental and control groups learned the same content using different educational tools in accordance with software training guidelines for a total of 12 hours. Then, the difference in perceived usefulness and ease between the two groups was examined using a post-treatment test. The study results showed that scores on both dependent variables, perceived usefulness and perceived ease, were significantly higher in the experimental group than the control group. Moreover, scores on all sub-variables of the dependent variables were significantly higher in the experimental group than the control group. These results suggest that learners using the unplugged robot education system found it more useful and easier to use than learners using the existing educational robot, PicoCricket. This study's findings are significant, as according to the technology acceptance model, the perceived usefulness and ease of an educational tool are important variables that determine the acceptance of the tool (i.e., persistence of learning).

• 한국수학교육학회지시리즈E:수학교육논문집
• /
• 제38권2호
• /
• pp.231-246
• /
• 2024

본 논문에서는 텍스트 코딩을 활용하여 최근 개발한 중등수학 모바일(Mobile) 콘텐츠에 관하여 소개한다. 해당 콘텐츠는 복잡한 계산에 대한 부담을 덜어주고 함수의 그래프를 쉽게 그리는 등의 실습이 가능하도록 설계되어, 학생들이 단순 문제 풀이 시간을 절약하는 대신 확보한 시간을 활용해 수학 문제의 본질을 이해하고 응용하는 능력을 기름으로써 자신감을 향상시키고자 하는 의도로 기획되었다. 또한 코드를 통해 문제를 해결하는 과정과 절차를 보다 잘 인지할 수 있도록 함으로써, 컴퓨팅 사고력(Computatonal Thinking)과 알고리즘적 사고 향상에 도움을 주고자 하였다. 두 차례 각기 다른 수준과 다른 배경을 가진 학생들을 대상으로 본 콘텐츠를 시범 적용한 사례(대학생 대상 대학 미분적분학 학습 전 복습, 고등학생 대상 수학 과목 예습)에서 얻은 데이터와 프로젝트 결과물을 바탕으로 본 콘텐츠가 중·고등학교 수학을 효율적으로 예습·복습한다거나, 지필로 불가능한 복잡한 계산 및 시뮬레이션을 통한 결과 예측 등의 활동을 수행하는 데 활용될 수 있음을 확인하였다.

• International Journal of Computer Science & Network Security
• /
• 제22권10호
• /
• pp.73-82
• /
• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.