본 논문에서는 OpenCV와 MediaPipe를 활용하여 실시간 영상의 운동 자세(스쿼트, 팔굽혀펴기)를 추정해 운동 자세교정 내용과 운동횟수를 안내하는 프로그램을 제안한다. 구체적으로 프로그램은 실시간으로 웹캠을 이용하여 획득한 영상에서 사용자 신체의 landmark를 추정하고, 올바른 자세 여부 판단에 필요한 몸의 각도, 수치를 계산하여 사용자가 잘못된 자세를 취했을 때 교정에 필요한 내용을 안내할 수 있다. 또한, 사용자가 올바른 자세를 취했을 때 운동횟수를 증가시키고 안내한다. 자세교정을 위한 안내 문구가 화면에 표시되고 운동횟수가 화면과 음성으로 안내된다. 이 프로그램은 집에서 운동 트레이너의 도움 없이 자신의 자세를 확인하면서 올바른 자세로 운동할 수 있도록 돕는다.

We developed RoboSTEM, a portal for open educational resources for the use of robotics in teaching and learning, to help teachers learn how to design and implement lessons using robotics. In this article, the design and functionality of RoboSTEM and the theoretical foundations behind its design are described. Two usability testing studies are also presented. Participants were 13 pre- and in-service teachers. Results from both quantitative and qualitative data analyses show that (a) usability survey ratings were positive overall and improved after revisions from the first usability study to the second; (b) data from participants’ screen activities and interviews were aligned with the survey results; (c) high levels of behavioral, cognitive, and emotional engagement in RoboSTEM were observed; and (d) the positive impact of RoboSTEM on example-based learning was acknowledged among teachers. Applications of RoboSTEM as well as future research and development directions are discussed.

There is a growing need for systems that can be used to effectively detect and classify intrusions in extensive network data exchanges. To this end, we propose Hi-MLIC , a hierarchical multilayer lightweight intrusion classification model that has been designed to address various intrusion types. This study highlights the challenges involved in classifying intrusions due to data imbalance across different types of intrusion data along with the complex nature of consolidating multiple benchmark datasets into cohesive datasets for real-time detection. To address these issues, we consolidated packet capture data from two widely used benchmark datasets, CIC-IDS2017 and UNSW-NB15, into two newer and more comprehensive datasets, CM-CIC-IDS2017 and CM-UNSW-NB15, respectively. This consolidation enables the identification and classification of a broader range of intrusion types. Our hierarchical approach achieves improved classification accuracy by effectively addressing the class imbalance that is inherent in non-hierarchical models. Layer-1 separates network traffic into benign and malicious categories. Layer-2 further classifies malicious traffic into four groups, while Layer-3 identifies 23 specific intrusion types. We reduced the model complexity and processing time by performing misclassification analysis and eliminating unnecessary features. Our model ultimately achieved a recall metric of up to 98.8%, thus demonstrating its effectiveness and efficiency in intrusion detection and classification. Altogether, the proposed Hi-MLIC represents a significant advancement in addressing the challenges of real-time network intrusion detection.

The importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompanied by annotation data, including motion capture, RGB video, image, and 3D object modeling data. It features 63 action classes involving interactions with 60 common furniture and electronic devices. Each action class comprises approximately 1,000 motion capture data representing 3D skeleton data and corresponding RGB video and 3D object modeling data, resulting in 67,505 motion capture data samples. It offers comprehensive 3D structural information of the human, RGB images and videos, and point cloud data for 60 objects, collected through the participation of 126 subjects to ensure inclusivity and account for diverse human body types. To validate our dataset, we leveraged MMNet, a 3D human action recognition model, achieving Top-1 accuracy of 91.51% and 92.29% using the skeleton joint and bone methods, respectively. Beyond human action recognition, our versatile dataset is valuable for various 3D human action analysis research endeavors.

By Digital Transformation, new technologies like ML (Machine Learning), Big Data, Cloud, VR/AR are being used to video streaming technology. We choose ML to provide optimal QoE (Quality of Experience) in various network conditions. In other words, ML helps DASH in providing non-stopping video streaming. In DASH, the source video is segmented into short duration chunks of 2-10 seconds, each of which is encoded at several different bitrate levels and resolutions. We built and compared the performances of five prototypes after applying five different machine learning algorithms to DASH. The prototype consists of a dash.js, a video processing server, web servers, data sets, and five machine learning models.