Incheon National University

Web 3.0 is a blockchain technology-based internet paradigm that is transparent and decentralized. But, Web 3.0 streaming services require modifications to the existing service structure due to certain technical issues. Now, researchers propose 'Retriever,' an Inter-Planetary File System (IPFS) based Web 3.0 technology and non-fungible token marketplace. The proposed marketplace reduces service delay and improves user experience by deploying their own IPFS nodes and controlling the blockchain. Web 3.0 is an internet paradigm that is based around blockchain technology, an advanced database mechanism. Compared to Web 2.0, the current internet paradigm, Web 3.0 provides some added advantages, such as transparency and decentralized control structures. This is because Web 3.0 is designed to work over trustless and permissionless networks. Unfortunately, owing to certain technical difficulties, the implementation of Web 3.0 media streaming requires modifications to the service architecture of existing media streaming services. These difficulties include the degradation of user experience and Web 3.0's incompatibility with certain operating softwares and browsers. To address these issues, a team of researchers, led by Assistant Professor Gi Seok Park from Incheon National University undertook a novel project. Their findings were made available on 22 August 2023 and recently published in Volume 16, Issue 6 of the journal IEEE Transactions on Services Computing in November-December 2023. In this study, the researchers proposed an end-to-end system architecture that is specifically designed for Web 3.0 streaming services. They made use of Inter-Planetary (IPFS), a type of Web 3.0 peer-to-peer (P2P) data storage technology, to reduce service delays and improve user experience. Web 3.0 services have also been implemented using the application programming interfaces of third-party service providers called IPFS pinning service. Unfortunately, they limit performance. Taking this into consideration, the team designed a system in which they were able to fully control the blockchain nodes by deploying their own IPFS nodes that ran directly on their system. They also implemented new protocols that cached content and scheduled chunks on their IPFS nodes, which enabled the nodes to collaborate with each other and quickly download data. The researchers found that their proposed system was compatible with IPFS nodes and still ran on IPFS P2P networks. They also launched Retriever, a media non-fungible token (NFT) marketplace that was developed using Web 3.0 technologies. Retriever allowed users to watch video content, ensure data privacy, and was found to be compatible with multiple mobile devices. "Our service can allow creators to monetize their video content and even sell their video content if they wish to. This is because each content will now be managed as an NFT. More importantly, this entire process will be fair and transparent," says Dr. Park, while speaking about Retriever. When asked about the real-life implications of this study, Dr. Park explains, " Our proposed service would establish digital trust from users. Moreover, thanks to blockchain technology, web services will no longer need to force trust on users in the future. All transactions will be made fairly through smart contracts and recorded transparently through the blockchain ledger."

In Korea, workers are being provided with virtual reality (VR)-based safety training content to mitigate the increase in occupational accidents. However, the current training evaluation methods suffer from a lack of immediate feedback from participants for personal learning performance evaluation. To address this, a team of researchers has now developed a new framework that uses real-time biometric data during VR training for improving personalized safety and preventing occupational hazards. In Korea, occupational hazards are on the rise, particularly in the construction sector. According to a report on the 'Occupational Safety Accident Status' by Korea's Ministry of Employment and Labor, the industry accounted for the highest number of accidents and fatalities among all sectors in 2021. To address this rise, the Korea Occupational Safety and Health Agency has been providing virtual reality (VR)-based construction safety content to daily workers as part of their educational training initiatives. Nevertheless, current VR-based training methods grapple with two limitations. Firstly, VR-based construction safety training is essentially a passive exercise, with learners following one-way instructions that fail to adapt to their judgments and decisions. Secondly, there is an absence of an objective evaluation process during VR-based safety training. To address these challenges, researchers have introduced immersive VR-based construction safety content to promote active worker engagement and have conducted post-written tests. However, these post-written tests have limitations in terms of immediacy and objectivity. Furthermore, among the individual characteristics that can affect learning performance, including personal, academic, social, and cognitive aspects, cognitive characteristics may undergo changes during VR-based safety training. To address this, a team of researchers led by Associate Professor Choongwan Koo from the Division of Architecture & Urban Division at Incheon National University, Korea, has now proposed a groundbreaking machine learning approach for forecasting personal learning performance in VR-based construction safety training that uses real-time biometric responses. Their paper was made available online on October 7, 2023, and will be published in Volume 156 of the journal Automation in Construction in December 2023. "While traditional methods of evaluating learning outcomes that use post-written tests may lack objectivity, real-time biometric responses, collected from eye-tracking and electroencephalogram (EEG) sensors, can be used to promptly and objectively evaluate personal learning performances during VR-based safety training," explains Dr. Koo. The study involved 30 construction workers undergoing VR-based construction safety training. Real-time biometric responses, collected from eye-tracking and EEG to monitor brain activity, were gathered during the training to assess the psychological responses of the participants. Combining this data with pre-training surveys and post-training written tests, the researchers developed machine-learning-based forecasting models to evaluate the overall personal learning performance of the participants during VR-based safety training. The team developed two models -- a full forecast model (FM) that uses both demographic factors and biometric responses as independent variables and a simplified forecast model (SM) which solely relies on the identified principal features as independent variables to reduce complexity. While the FM exhibited higher accuracy in predicting personal learning performance than traditional models, it also displayed a high level of overfitting. In contrast, the SM demonstrated higher prediction accuracy than the FM due to a smaller number of variables, significantly reducing overfitting. The team thus concluded that the SM was best suited for practical use. Explaining these results, Dr. Koo emphasizes, "This approach can have a significant impact on improving personal learning performance during VR-based construction safety training, preventing safety incidents, and fostering a safe working environment." Further, the team also emphasizes the need for future research to consider various accident types and hazard factors in VR-based safety training. In conclusion, this study marks a significant stride in enhancing personalized safety in construction environments and improving the evaluation of learning performance!

Autonomous vehicles require object detection systems to navigate traffic and avoid obstacles on the road. However, current detection methods often suffer from diminished detection capabilities due to bad weather, unstructured roads, or occlusion. Now, a team of researchers has developed a novel Internet-of-Things-enabled deep learning-based end-to-end 3D object detection system with improved detection capabilities even under unfavorable conditions. This study marks a significant step in autonomous vehicle object detection technology. Autonomous vehicles hold the promise of tackling traffic congestion, enhancing traffic flow through vehicle-to-vehicle communication, and revolutionizing the travel experience by offering comfortable and safe journeys. Additionally, integrating autonomous driving technology into electric vehicles could contribute to more eco-friendly transportation solutions. A critical requirement for the success of autonomous vehicles is their ability to detect and navigate around obstacles, pedestrians, and other vehicles across diverse environments. Current autonomous vehicles employ smart sensors such as LiDARs (Light Detection and Ranging) for a 3D view of the surroundings and depth information, RADaR (Radio Detection and Ranging) for detecting objects at night and cloudy weather, and a set of cameras for providing RGB images and a 360-degree view, collectively forming a comprehensive dataset known as point cloud. However, these sensors often face challenges like reduced detection capabilities in adverse weather, on unstructured roads, or due to occlusion. To overcome these shortcomings, an international team of researchers led by Professor Gwanggil Jeon from the Department of Embedded Systems Engineering at Incheon National University (INU), Korea, has recently developed a groundbreaking Internet-of-Things-enabled deep learning-based end-to-end 3D object detection system. "Our proposed system operates in real time, enhancing the object detection capabilities of autonomous vehicles, making navigation through traffic smoother and safer," explains Prof. Jeon. Their paper was made availableonline on October 17, 2022, and published in Volume 24, Issue 11 of the journal IEEE Transactions on Intelligent Transport Systems on November 2023. The proposed innovative system is built on the YOLOv3 (You Only Look Once) deep learning object detection technique, which is the most active state-of-the-art technique available for 2D visual detection. The researchers first used this new model for 2D object detection and then modified the YOLOv3 technique to detect 3D objects. Using both point cloud data and RGB images as input, the system generates bounding boxes with confidence scores and labels for visible obstacles as output. To assess the system's performance, the team conducted experiments using the Lyft dataset, which consisted of road information captured from 20 autonomous vehicles traveling a predetermined route in Palo Alto, California, over a four-month period. The results demonstrated that YOLOv3 exhibits high accuracy, surpassing other state-of-the-art architectures. Notably, the overall accuracy for 2D and 3D object detection were an impressive 96% and 97%, respectively. Prof. Jeon emphasizes the potential impact of this enhanced detection capability: "By improving detection capabilities, this system could propel autonomous vehicles into the mainstream. The introduction of autonomous vehicles has the potential to transform the transportation and logistics industry, offering economic benefits through reduced dependence on human drivers and the introduction of more efficient transportation methods." Furthermore, the present work is expected to drive research and development in various technological fields such as sensors, robotics, and artificial intelligence. Going ahead, the team aims to explore additional deep learning algorithms for 3D object detection, recognizing the current focus on 2D image development. In summary, this groundbreaking study could pave the way for a widespread adoption of autonomous vehicles and, in turn, a more environment-friendly and comfortable mode of transport.