Public speaking anxiety is one of the most common social phobias. We explore the feasibility of using a conversational agent to reduce this anxiety. We developed a public-speaking tutor on the Amazon Alexa platform that enables users to engage in cognitive reconstruction exercises. We also investigated how the sociability of the agent might affect its performance as a tutor. A user study of 53 college students with fear of public speaking showed that the interaction with the agent served to assuage pre-speech state anxiety. Agent sociability improved the sense of interpersonal closeness, which was associated with lower pre-speech anxiety. Moreover, sociability of the agent increased participants' satisfaction and their willingness to continue engagement. Our findings, thus, have implications not only for addressing public speaking anxiety in a scalable way but also for the design of future conversational agents using smart speaker platforms.

As the satellite data gradually increases, the processing time of the algorithm for remote sensing also increases. It is now essential to make efforts to improve the processing performance in various accelerators such as Graphics Processing Unit (GPU). However, it is very difficult to use programming models that make programs to run on accelerators. Especially for scientists, easy programming and performance are often more important than providing many optimization functions and directives. To meet these requirements, we developed the accelerated code generator based on Open Computing Language (OpenCL) for processing ocean color remote sensing data. We easily applied our generator to atmospheric correction program for GOCI data as a case study and the program's performance achieved about 29.2x that is as good as the hand-written OpenCL program. We look forward to many scientists that want to find a tool mentioned above taking advantage of our generator.

Geostationary Ocean Color Imager-II (GOCI-II), the follow-up satellite sensor platform of GOCI, will be launched in Mar. 2019. For estimating ocean color information from this new sensor data, the dedicated ocean color algorithms will be newly developed. However, the existing ocean color algorithm development methodology without consideration for the highspeed data processing will not be employed because the amount of GOCI-II data will be significantly large and their data will have to be processed and distributed in real time. In this study, therefore, we designed the ocean color algorithm development environment (OCADE) to support high-performance computing and storage using the parallelism. Also, OCADE enables the source code projects for GOCI-II ocean color products to be automatically built and deployed. We expect that GOCI - II ocean color algorithm developers will easily create and manage their parallelized source code projects at any time and any place through the OCADE cloud service. Additionally, they will be able to validate the output and evaluate the processing performance under OCADE in real-time. We believe that OCADE will contribute to the field of massive satellite data processing.