This article describes how online lectures of the University of Tokyo, starting from April 2020, were realized, including their preparation, status, issues, and perspective.

The Japan ACM SIGCHI Chapter and CHI2020 Japan Chapter local meeting committee reports on the online presentation of the Japan local meeting of ACM CHI2020, which was cancelled due to a new coronavirus disease (COVID-19).

We have been carrying out community intervention studies with the goal of prioritizing frailty prevention for the elderly and educating them about it. Most of the activities in the area were face-to-face lectures and events. But during the infectious disease control period, all face-to-face events were canceled, and the community gathering place for the elderly was temporarily closed. If the activities up until they were canceled are suspended and the time staying home as a countermeasure for the infectious disease becomes longer, the practice amount of the three pillars of frailty prevention, which are exercise, nutrition, and social participation may decrease. This article introduces the work to promote the frailty prevention activities that were implemented during the COVID-19 infection prevention period from March 2020.

A static OS, with which the used OS objects are created at system build time, requires some method for describing the configuration information which is necessary for creating OS objects. Static API is a language for describing the configuration information of a real-time OS. It was first introduced in the μITRON4.0 Specification and improved in the TOPPERS New Generation Kernel Specification. This paper describes the requirements on static API and the specifications of static API in those specifications. This paper also describes the design and implementation of the configurator, software that processes static API, along the 20 years' history of development in the TOPPERS project.

In recent years, the field of Learning Analytics has been experiencing a surge in interest. Learning Analytics involves work such as evaluating a learner's achievement degree and predicting his future ability, by data mining and analyzing learning history using a learning management system or e-portfolio. In this research, we predict the transition of a student's performance in a programming exercise lecture, using Learning Analytics. Using the prediction result, we aim to develop an application that finds students who are failing a class at an early stage and supports their learning. Specifically, we cluster students using the data from a comprehension test, build a regression model by applying multiple regression analysis to each cluster, then predict the week when the students will pass. We implemented the proposed model and evaluated its usefulness by leave-one-out cross validation.

Probabilities occur in many applications of computer science, such as communication theory and artificial intelligence. These are critical applications that require some form of verification to guarantee the quality of their implementations. Unfortunately, probabilities are also the typical example of a mathematical theory whose abuses of notations make pencil-and-paper proofs difficult to formalize. In this paper, we experiment a new formalization of conditional probabilities that we validate with two applications. First, we formalize the foundational definitions and theorems of information theory, extending previous work with new lemmas. Second, we formalize the notion of conditional independence and its properties, paving the road for a formalization of probabilistic graphical models.