Computer Science Software engineering

Researchers from The University of Osaka, Kyushu University, and the University of Victoria have developed MV-SZZ, a new method that accurately identifies defect-inducing software commits. By combining detailed code tracking with a majority voting system, the approach reduces false positives and outperforms existing techniques. This improvement could help developers debug software more efficiently and build more reliable systems.

- Extraction of key behavioral elements from videos - Robustness against variations in action speed and duration - Selected for Oral Presentation (acceptance rate: 0.74%) at CVPR, the top computer vision conference

A collaboration team of researchers from the Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, the Institute of Transformative Bio-Molecules (WPI-ITbM), Graduate School of Science at Nagoya University, and the RIKEN Center for Computational Science (R-CCS) reports in ACS Nano an integrative modeling workflow to understand with atomistic precision biomolecular dynamics from high-speed atomic force microscopy experiments.

Lightweight and high-performance framework for remote sensing object detection

All-in-one OS developed in Japan is fully customizable from set-up through operation and is available via GitHub

- Professor Sung-hoon Lim’s team at DGIST has developed a metric learning technology that increases 3D object estimation performance, a key self-driving technology - Commercialization of self-driving is expected due to the reduced self-driving framework installation cost

- Professor Sunghoon Im's team at DGIST has developed depth-sensing technology that can be reliably used in diverse environments by analyzing the relationship between the neural network structure and distance sensing in autonomous driving. - The new technology is expected to contribute to the development of a wide range of AI fields, such as autonomous driving, which must be performed safely in various urban settings and weather conditions

Atomic force microscopy (AFM) allows to visualize the dynamics of single biomolecules during their functional activity. All observations are, however, restricted to regions accessible by a fairly big probing tip during scanning. Hence, AFM images only the biomolecular surface with limited spatial resolution, missing important information required for a detailed understanding of the observed phenomena.

Researchers at The University of Tokyo use the mathematics of adaptive learning and artificial intelligence to describe how T helper cells adjust the response of the vertebrate immune system, which may lead to new vaccines and treatments for infections

Concepts for boosting WiFi coverage could also help car sensors distinguish walking pedestrians from other slowly moving objects.

Harnessing the power of virtual reality will help to visualize data and improve statistical models.