Facial expressions are crucial to human social communication, but the extent to which they are innate and universal versus learned and culture dependent is a subject of debate. Two studies explored the effect of culture and learning on facial expression understanding. In Experiment 1, Japanese and U.S. participants interpreted facial expressions of emotion. Each group was better than the other at classifying facial expressions posed by members of the same culture. In Experiment 2, this reciprocal in-group advantage was reproduced by a neurocomputational model trained in either a Japanese cultural context or an American cultural context. The model demonstrates how each of us, interacting with others in a particular cultural context, learns to recognize a culture-specific facial expression dialect.

Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

Optical fire sensors, sometimes called “volumetric” sensors, are complementary to conventional point sensors such as smoke and heat detectors in providing people with early warnings of fire incidents. Cameras combined with image processing software hold the promise of detecting fire incidents more quickly than point sensors and can also provide size, growth, and direction information more readily than their conventional counterparts. In this paper, we present QuickBlaze, a flame and smoke detection system based on vision sensors aimed at early detection of fire incidents for open or closed indoor and outdoor environments. We use simple image and video processing techniques to compute motion and color cues, enabling segmentation of flame and smoke candidates from the background in real time. We begin with color balancing, then separate smoke and flame detection streams operate on the image. Both streams identify candidate regions based on color information then perform morphological image processing on the candidates. The smoke detection stream then filters candidate regions based on turbulence flow rate analysis, and the flame detection stream filters based on growth and flow rate information. QuickBlaze does not require any offline training, although manual adjustment of parameters during a calibration phase is required to cater to the particular camera’s depth of view and the surrounding environment. In an extensive empirical evaluation benchmarking QuickBlaze against commercial fire detection software, we find that it has a better response time, is 2.66 times faster, and better localizes fire incidents. Detection of fire using our real-time video processing approach early on in the burning process holds the potential to decrease the length of the critical period from combustion to human response in the event of a fire.