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Understanding traditional culture is important. Various methods are used to achieve better cross-cultural understanding, and certain researchers have studied human behavior. However, behavior does not always represent a culture. Therefore, our study aims to understand Japanese greeting culture by classifying it through machine learning. Following are our study contributions. (1) The first study to analyze cultural differences in greeting gestures based on the politeness level of Japanese people by classifying them. (2) Classify Japanese greeting gestures eshaku, keirei, saikeirei, and waving hand. (3) Analyze the performance results of machine and deep learning. Our study noted that bowing and waving were the behaviors that could symbolize the culture in Japan. In conclusion, first, this is the first study to analyze the eshaku, keirei, saikeirei, and waving hand greeting gestures. Second, this study complements several human activity recognition studies that have been conducted but do not focus on behavior representing a culture. Third, according to our analysis, by using a small dataset, SVM and CNN methods provide better results than k-nearest neighbors (k-NN) with Euclidean distance, k-NN with DTW, logistic regression and LightGBM in classifying greeting gestures eshaku, keirei, saikeirei, and waving hand. In the future, we will investigate other behaviors from different perspectives using another method to understand cultural differences. [ABSTRACT FROM AUTHOR]

Imminent robotic and human activities on the Moon and other planetary bodies would benefit from advanced in situ Computer Vision and Machine Learning capabilities to identify and quantify microparticle terrestrial contaminants, lunar regolith disturbances, the flux of interplanetary dust particles, possible interstellar dust, β -meteoroids, and secondary impact ejecta. The YOLO-ET (ExtraTerrestrial) algorithm, an innovation in this field, fine-tunes Tiny-YOLO to specifically address these challenges. Designed for coreML model transference to mobile devices, the algorithm facilitates edge computing in space environment conditions. YOLO-ET is deployable as an app on an iPhone with LabCam® optical enhancement, ready for space application ruggedisation. Training on images from the Tanpopo aerogel panels returned from Japan's Kibo module of the International Space Station, YOLO-ET demonstrates a 90% detection rate for surface contaminant microparticles on the aerogels, and shows promising early results for detection of both microparticle contaminants on the Moon and for evaluating asteroid return samples. YOLO-ET's application to identifying spacecraft-derived microparticles in lunar regolith simulant samples and SEM images of asteroid Ryugu samples returned by Hayabusa2 and curated by JAXA's Institute of Space and Astronautical Sciences indicate strong model performance and transfer learning capabilities for future extraterrestrial applications. [ABSTRACT FROM AUTHOR]

Group activity recognition of humans is an important task for video surveillance systems. In complex situations, however, ambiguities usually arise as a result of chaotic movements in the scene, especially when it is solved based upon motion trajectory analysis involving object segmentation and tracking. In this work, we present an interaction force model (IFM) to recognize group activity using low‐level feature based on dense optical flow. The IFM used to define interaction among people consists of three principal forces: group motion force (GMF), interaction motion force (IMF), and reciprocal motion force (RMF), to model the behaviors of the activity. Our algorithm is evaluated using the well‐known NUS‐HGA and BEHAVE datasets. Results of our approach reveal good efficiency with high accuracy, regardless of the surrounding conditions, and is competitive with other state‐of‐the‐art methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

1. The relationship between benthic invertebrate assemblage composition and surrounding land use has been examined in various ways, but most studies are undertaken at a coarse scale, or they compare obviously different landscapes. In Japan, these obvious differences in landscape are rare, and humans reside even in remote montane areas. 2. In order to clarify the influence of human residence on benthic invertebrate assemblages, seasonal and site differences among benthic samples collected from a 15-km stretch of Kuroson Stream and their relation with riparian land use were examined. 3. The number of individuals and genera differed significantly both spatially and temporally. Multivariate assemblage structure also differed significantly across space and time along the stream. 4. Increase of residential areas affected the in-stream benthic invertebrate assemblage. Site differences along the stream and the effect of human residence were not masked by the potentially homogenizing influence of tributary streams. 5. The composition of surrounding vegetation was the most important factor influencing benthic community structure. The presence or absence of human residential areas and seasonal change were also important variables. 6. Benthic invertebrates may be carried and migrate to main streams from tributaries; however, this phenomenon was not observed. Assemblages of benthic invertebrates that inhabit a particular site do not change and are considered to vary seasonally across a certain range. [ABSTRACT FROM AUTHOR]