Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%.

Purpose: The purpose of this study was to analyze the level of technology teachers’ education needs for elective high school engineering subjects in the 2022 revised technology curriculum and derive specific demands for improving the expertise of technology teachers. Methods: In order to achieve the purpose of the study, literature related to the 2022 revised curriculum and education needs analysis was reviewed. A questionnaire was developed by the research team basedon the literature review and was revised and supplemented through a review by experts. The survey subjects of this study were limited to those eligible for the 2023 level 1 technology teacher qualification training. The survey tool asked technology teachers to respond to a 5-point Likert scale regarding the importance and feasibility of performance based on achievement standards for each subject. Educational needs were defined as the difference between the importance and performance scores. The IPA matrix method was used to determine the priority of support measures based on needs analysis. Results: The results obtained through this study are as follows. First, the demand for education in the world of robotics and engineering was the highest. Second, in the case of general intellectual property subjects, there was a high demand for education in specialized content area related to intellectual property rights. Third. In the case of the creative engineering design subject, there was a high demand for developing skills related to research and development projects in the engineering field. Conclusion: Through this study, it was determined that overall learning support is required for roboticsand the world of engineering, along with intensive training in intellectual property rights in the general intellectual property course, and project-based product development and prototyping in the creative engineering design course.0

This study was conducted to develop a "High School Credit System Course Selection Practice Program" to assist high school freshmen and third-year middle school students in career planning and understanding the high school credit system. The program consists of "School Designated Course Settings," "Student Selected Course Settings," and "Student Selection Sheets," and is designed using Excel's data validation and macro functions to facilitate course offering and designation. Additionally, a career education program for third-year middle school students was developed for use in subjects such as "Technology and Home Economics," "Careers and Occupations," and creative experiential activities. It is recommended to modify this program according to the guidelines of local education offices, and subsequent research analyzing changes in students' career maturity and high school admission decision factors after applying the career education program is expected to further enhance the validity of the study.

In embodied instruction-following (EIF), the integration of pretrained language models (LMs) as task planners emerges as a significant branch, where tasks are planned at the skill level by prompting LMs with pretrained skills and user instructions.However, grounding these pretrained skills in different domains remains challenging due to their intricate entanglement with the domain-specific knowledge.To address this challenge, we present a semantic skill grounding (SemGro) framework that leverages the hierarchical nature of semantic skills.SemGro recognizes the broad spectrum of these skills, ranging from short-horizon low-semantic skills that are universally applicable across domains to long-horizon rich-semantic skills that are highly specialized and tailored for particular domains.The framework employs an iterative skill decomposition approach, starting from the higher levels of semantic skill hierarchy and then moving downwards, so as to ground each planned skill to an executable level within the target domain.To do so, we use the reasoning capabilities of LMs for composing and decomposing semantic skills, as well as their multimodal extension for assessing the skill feasibility in the target domain.Our experiments in the VirtualHome benchmark show the efficacy of SemGro in 300 cross-domain EIF scenarios.

제 4차 산업혁명 시대의 차세대 혁신 기술 도입 및 제도 개선을 통한 도로 관리 체계를 선진화 하고, 미래상을 제시할 수 있도록 최근 도로유지관리시스템의 스마트화 및 지능화 도입이 요구되어지고 있다. 특히 인공지능은 그 중에서 가장 핵심 기술로 다양한 연구가 진행되고 있으며, 이를 활용하기 위한 발전방향제시를 위해 도로 관리에서의 인공지능을 활용에 관한 이해가 필요하다. 본 연구는 동시출현단어 분석을 활용하여 도로 관리에서의 인공지능 활용에 관한 최신 연구 경향을 반영하는 지적구조를 제시하여 주제영역 구성을 밝히는 것을 목적으로 한다. 이를 위하여 2018년 1월 1일부터 2022년 12월 31일까지의 5년간의 Web of Science 기반으로 문헌 데이터를 수집하고 동시출현단어를 활용하여 분석한다. 네트워크 분석을 통해 세부 주제 영역을 파악하고, 중심성 분석을 통해 전역 및 지역 중심의 키워드를 도출하였다. 또한 군집분석을 수행하여 형성된 군집을 다차원척도지도에 표시하고, 각 키워드 간 상관관계에 따른 지적구조를 제시하였다. 향후 도로 관리에서의 보다 발전적인 인공지능 활용에 관한 연구에 도움이 될 것으로 기대한다.