Population Dynamics of Five Anopheles Species of the Hyrcanus Group in Northern Gyeonggi-do, Korea

To investigate the population densities of potential malaria vectors, Anopheles species were collected by light traps in malaria endemic areas, Paju and Gimpo, Gyeonggi-do of Korea. Five Anopheles Hyrcanus sibling species (An. sinensis, An. pullus, An. lesteri, An. kleini, and An. belenrae) were identified by PCR. The predominant species, An. pullus was collected during the late spring and mid-summer, while higher population consists of An. sinensis were collected from late summer to early autumn. These 2 species accounted for 92.1% of all Anopheles mosquitoes collected, while the other 3 species accounted for 7.9%. Taking into account of these population densities, late seasonal prevalence, and long-term incubation period (9-13 months) of the Korean Plasmodium vivax strain, An. sinensis s.s is thought to play an important role in the transmission of vivax malaria in the study areas

Collaborative billiARds: Towards the ultimate gaming experience

Abstract. In this paper, we identify the features that enhance gaming experience in Augmented Reality (AR) environments. These include Tangible User Interface, force-feedback, audio-visual cues, collaboration and mobility. We base our findings on lessons learnt from existing AR games. We apply these results to billiARds which is an AR system that, in addition to visual and aural cues, provides force-feedback. billiARds supports interaction through a visionbased tangible AR interface. Two users can easily operate the proposed system while playing Collaborative billiARds game around a table. The users can collaborate through both virtual and real objects. User study confirmed that the resulting system delivers enhanced gaming experience by supporting the five features highlighted in this paper

Collaborative billiARds: Towards the Ultimate Gaming Experience

Abstract. In this paper, we identify the features that enhance gaming experience in Augmented Reality (AR) environments. These include Tangibl

Robustness of Deep Learning Models for Vision Tasks

In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks. Thus, the importance of improving robustness against adversarial attacks has emerged as an urgent issue in vision tasks. This article aims to provide a historical summary of the evolution of adversarial attacks and defense methods on CNN-based models and also introduces studies focusing on brain-inspired models that mimic the visual cortex, which is resistant to adversarial attacks. As the origination of CNN models was in the application of physiological findings related to the visual cortex of the time, new physiological studies related to the visual cortex provide an opportunity to create more robust models against adversarial attacks. The authors hope this review will promote interest and progress in artificially intelligent security by improving the robustness of deep learning models for vision tasks