ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion

Adversarial camouflage has garnered attention for its ability to attack object detectors from any viewpoint by covering the entire object's surface. However, universality and robustness in existing methods often fall short as the transferability aspect is often overlooked, thus restricting their application only to a specific target with limited performance. To address these challenges, we present Adversarial Camouflage for Transferable and Intensive Vehicle Evasion (ACTIVE), a state-of-the-art physical camouflage attack framework designed to generate universal and robust adversarial camouflage capable of concealing any 3D vehicle from detectors. Our framework incorporates innovative techniques to enhance universality and robustness, including a refined texture rendering that enables common texture application to different vehicles without being constrained to a specific texture map, a novel stealth loss that renders the vehicle undetectable, and a smooth and camouflage loss to enhance the naturalness of the adversarial camouflage. Our extensive experiments on 15 different models show that ACTIVE consistently outperforms existing works on various public detectors, including the latest YOLOv7. Notably, our universality evaluations reveal promising transferability to other vehicle classes, tasks (segmentation models), and the real world, not just other vehicles.Comment: Accepted for ICCV 2023. Main Paper with Supplementary Material. Project Page: https://islab-ai.github.io/active-iccv2023

Vigorous Growing of Donor Plantlets by Liquid Overlay in Subcultures Is the Key to Cryopreservation of Endangered Species Pogostemon yatabeanus

Cryopreservation is a unique option for the long-term conservation of threatened plant species with non-orthodox or limitedly available seeds. However, the wide application of cryopreservation for the protection of wild flora is hampered by some reasons: limits of source material available, difficulties in in vitro propagation, needs to re-optimize protocol steps for new species, etc. In this study, using an endemic and endangered Korean species, Pogostemon yatabeanus, we investigated subculture medium and supplements on in vitro growth of donor plants: medium strength, gelling agents, liquid overlay, plant hormones, and activated charcoal. Subculture conditions of each cycle tested significantly impacted on height and dry weight of subcultured donor plantlets. Among the treatments tested, the overlay of the liquid medium on top of gellan gum-gelled medium significantly increased the growth of shoots and roots. In the droplet-vitrification procedure, the survival and regeneration of cryopreserved shoot tips were critically impacted by the dry weight of donor plantlets (CORELL = 0.85~0.95) which was affected by the following subculture conditions. Moreover, every subsequent subculture cycle before cryopreservation positively or negatively impacted post-cryopreservation regeneration. This study highlights the vigor of donor plantlets for post-cryopreservation regeneration and provides practices for the revitalization of donor plants during subcultures

Effect of a Single Multi-Vitamin and Mineral Supplement on Nutritional Intake in Korean Elderly: Korean National Health and Nutrition Examination Survey 2018–2020

Inadequate nutritional intake is common, especially among elderly individuals. Although micronutrient intake may help fill nutritional gaps, the effects of multi-vitamin and mineral supplements (MVMS) among the Korean elderly are not well known. Therefore, we investigated the nutrition-improving effects of a single MVMS. A total of 2478 people aged ≥65 years who participated in the Korea National Health and Nutrition Survey 2018–2020 were analyzed. Nutrient intake from food and supplements was measured using the 24 h recall method. We compared the nutritional intake and insufficiency between the food-only group (n = 2170) and the food and MVMS group (n = 308). We also evaluated the differences in inadequate nutritional intake after taking MVMS with food. The analysis included vitamins A and C, thiamine, riboflavin, niacin, calcium, iron, and phosphorus. The proportion of insufficient intake ranged from 6.2% to 80.5% for men and from 21.2% to 82.4% for women, depending on the nutrients. Intake of MVMS with food was associated with lower rates of inadequacy (3.8–68.5% for men and 3.3–75.5% for women) compared to the food-only group. The results suggest that micronutrient deficiency frequently occurs in the Korean elderly population and can be improved by MVMS intake

Urban Hydrogen Production Model Using Environmental Infrastructures to Achieve the Net Zero Goal

Land available for energy production is limited in cities owing to high population density. To reach the net zero goal, cities contributing 70% of overall greenhouse gas emissions need to dramatically reduce emissions and increase self-sufficiency in energy production. Environmental infrastructures such as sewage treatment and incineration plants can be used as energy production facilities in cities. This study attempted to examine the effect of using environmental infrastructure such as energy production facilities to contribute toward the carbon neutrality goal through urban energy systems. In particular, since the facilities are suitable for hydrogen supply in cities, the analysis was conducted focusing on the possibility of hydrogen production. First, the current status of energy supply and demand, and additional energy production potential in sewage treatment and incineration plants in Seoul, were analyzed. Then, the role of these environmental infrastructures toward energy self-sufficiency in the urban system was examined. This study confirmed that the facilities can contribute to the city’s energy self-sufficiency and the achievement of its net-zero goal

Urban Hydrogen Production Model Using Environmental Infrastructures to Achieve the Net Zero Goal

Land available for energy production is limited in cities owing to high population density. To reach the net zero goal, cities contributing 70% of overall greenhouse gas emissions need to dramatically reduce emissions and increase self-sufficiency in energy production. Environmental infrastructures such as sewage treatment and incineration plants can be used as energy production facilities in cities. This study attempted to examine the effect of using environmental infrastructure such as energy production facilities to contribute toward the carbon neutrality goal through urban energy systems. In particular, since the facilities are suitable for hydrogen supply in cities, the analysis was conducted focusing on the possibility of hydrogen production. First, the current status of energy supply and demand, and additional energy production potential in sewage treatment and incineration plants in Seoul, were analyzed. Then, the role of these environmental infrastructures toward energy self-sufficiency in the urban system was examined. This study confirmed that the facilities can contribute to the city’s energy self-sufficiency and the achievement of its net-zero goal

Towards Incompressible Laminar Flow Estimation Based on Interpolated Feature Generation and Deep Learning

For industrial design and the improvement of fluid flow simulations, computational fluid dynamics (CFD) solvers offer practical functions and conveniences. However, because iterative simulations demand lengthy computation times and a considerable amount of memory for sophisticated calculations, CFD solvers are not economically viable. Such limitations are overcome by CFD data-driven learning models based on neural networks, which lower the trade-off between accurate simulation performance and model complexity. Deep neural networks (DNNs) or convolutional neural networks (CNNs) are good illustrations of deep learning-based CFD models for fluid flow modeling. However, improving the accuracy of fluid flow reconstruction or estimation in these earlier methods is crucial. Based on interpolated feature data generation and a deep U-Net learning model, this work suggests a rapid laminar flow prediction model for inference of Naiver–Stokes solutions. The simulated dataset consists of 2D obstacles in various positions and orientations, including cylinders, triangles, rectangles, and pentagons. The accuracy of estimating velocities and pressure fields with minimal relative errors can be improved using this cutting-edge technique in training and testing procedures. Tasks involving CFD design and optimization should benefit from the experimental findings

Korean Covid-19 Twitter_Moral Foundation Dataset

Based on a patent: https://doi.org/10.8080/102021002875

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results