Viscuit is a programming language developed in Japan. With Viscuit, children can move and change multiple pictures on the screen using picture rewriting rules. Viscuit's distinguishing feature is that the program is expressed solely with pictures and their layouts without any letters. Therefore, Viscuit is very suitable for pre-school children. As an experiment, we carried out regular programming lessons using Viscuit in a kindergarten for one year in 2017. Through these lessons, we taught four Viscuit techniques. In this report, we analyze the programs made by 5-6 year old children in the final lesson of the 13 lessons. Our research question is how kindergarten children make Viscuit programs to express their ideas. We analyzed the kinds of programs children made to express their ideas by applying what they have learned in the previous 12 lessons. We let children make programs based on the operetta in which they were going to perform in the kindergarten graduation ceremony. As a result, 85.7% of children made valid programs and used multiple programming techniques effectively.

Viscuit is a programming language developed by one of the authors. With Viscuit, you can move and change multiple pictures on the screen using eyeglasses, which make a sequence from left to right, following from-to (picture rewriting) rules. Its distinguishing feature is that the program is expressed solely with pictures and their layouts. No letters are necessary at all. Therefore, Viscuit is perfectly suitable for pre-school children. We have been carrying out regular programming lessons in a kindergarten in Japan since 2015. We have recorded and analysed almost all of the programs made by 56 children aged 5-6 years in a series of 13 lessons held during the school year 2017. In this poster we report how the children could express their intentions through coding.

The advantages of evolutionary computation with very large populations for many-objective optimization problems are investigated. The effects of a population size of up to 1,000,000 are studied, with the number of generations fixed at 100. To overcome difficulty in computational time, we use a many-objective evolutionary algorithm designed for massive parallelization (CHEETAH) on the K supercomputer. For unimodal test problems DTLZ2 and DTLZ4, the inverted generational distance (IGD) decreases as the population increases while the generational distance (GD) is saturated with a population size of 10,000. This means an evolutionary computation with massive population size mainly contributes to improvement of diversity of obtained non-dominated solutions. Even when the total number of evaluations is fixed, this conclusion is unchanged. For the multimodal test problems DTLZ1 and DTLZ3, GD and IGD are reduced with increasing population size of up to 10,000 but are not significantly improved with population sizes larger than this. This is probably due to the difficulty in obtaining good non-dominated solutions for DTLZ1 and DTLZ3 with current CHEETAH. Because CHEETAH is bases on NSGA-II (only the non-dominated sort portion is modified for more effective many-objective optimization and parallelization), we expect that the current conclusion qualitatively stays the same for other NSGA-II-based algorithms. To take advantage of the larger population size, development of operators such as selection and crossover designed for very large population size may be required.

Optimization of interplanetary space mission trajectories have been a long standing challenge. Here a novel approach is presented that considers several aspects of the space mission simultaneously as many-objective problem. Such problem is then solved by a decomposition approach in combination with a (massive) parallelization framework employing instances of Ant Colony Optimization algorithms. Numerical results show that the here presented approach has advantages over a classical weighted sum approach and is very suitable to efficiently exploit massive parallelization.

We propose a new algorithm FHV (Fast HyperVolume) for exact hypervolume (HV) calculation using divide and conquer algorithm. FHV divides the original set of non-dominated solutions into several fractions first, calculate the value of HV of each fraction separately, sum up the each value, and finally obtain the value of HV of the original set. Therefore there are three very strong points: 1. Calculation cost is reduced significantly because each fraction contains less number of non-dominated solutions; 2. Complete parallelism is possible because each divided fraction is independent each other; 3. An arbitrary HV calculation method such as HOY (Hypervolume by Overmars and Yap) and WFG (Walking Fish Group) can be used together with FHV. Using these features computation time of HV becomes considerably reduced. For example, in a case number of objective functions is 5 and number of solutions is 1 × 106, it takes about 21 hours using WFG, however it takes only less than 30 minutes using FHV.

Many public offices and companies manage their energy consumption by Building Energy Management System (BEMS). It is not an easy task to determine whether the past energy consumption was really necessary or just wasted. Visualization for energy consumption is useful to understand the situations of energy consumption to determine their necessity. This paper presents a visualization tool for energy consumption with BEMS. The tool firstly divides the daily variation of the energy usage and environmental measurements (e.g. Temperature and humidity) into the meaningful number of patterns. It displays "long-term polyline chart" to represent the frequency of the daily pattern so that users can easily focus on particular dates at particular places. It also displays " one-day polyline chart" to represent the daily variation of the recorded values of the particular dates and places specified by users' click operations. The paper introduces the examples of visualization to demonstrate the effectiveness of the presented tool, with a real dataset of business office building.

In this paper, an algorithm for many-objective evolutionary computation, which is based on the NSGA-II with the Chebyshev preference relation, is applied to multi-objective design optimization problem of dielectric barrier discharge plasma actuator (DBDPA). The present optimization problem has four design parameters and six objective functions. The main goal of the paper is to extract useful design guidelines to predict control flow behavior based on the DBDPA parameter values using the resulting approximation Pareto set obtained by the optimization.

MOTIVATION: Although a huge amount of mammalian genomic data does become publicly available, there are still hurdles for biologists to overcome before such data can be fully exploited. One of the challenges for gaining biological insight from genomic data has been the inability to cross-reference transcriptomic and proteomic data using a single informational platform. To address this, we constructed an open-access database that enabled us to cross-reference transcriptomic and proteomic data obtained from immune cells. RESULTS: The database, named RefDIC (Reference genomics Database of Immune Cells), currently contains: (i) quantitative mRNA profiles for human and mouse immune cells/tissues obtained using Affymetrix GeneChip technology; (ii) quantitative protein profiles for mouse immune cells obtained using two-dimensional gel electrophoresis (2-DE) followed by image analysis and mass spectrometry and (iii) various visualization tools to cross-reference the mRNA and protein profiles of immune cells. RefDIC is the first open-access database for immunogenomics and serves as an important information-sharing platform, enabling a focused genomic approach in immunology. AVAILABILITY: All raw data and information can be accessed from http://refdic.rcai.riken.jp/. The microarray data is also available at http://cibex.nig.ac.jp/ under CIBEX accession no. CBX19, and http://www.ebi.ac.uk/pride/ under PRIDE accession numbers 2354-2378 and 2414.

We propose a method which visualizes irregular multidimensional time-series data as a sequence of probabilistic prototypes for detecting exceptions from medical test data. Conventional visualization methods often require iterative analysis and considerable skill thus are not totally supported by a wide range of medical experts. Our PrototypeLines displays summarized information based on a probabilistic mixture model by using hue only thus is considered to exhibit novelty. The effectiveness of the summarization is pursued mainly through use of a novel information criterion. We report our endeavor with chronic hepatitis data, especially discoveries of interesting exceptions by a nonexpert and an untrained expert.