Glyph-based visualization is one of the main techniques for visualizing complex multivariate data. With small glyphs, data variables are typically encoded with relatively low visual and perceptual precision. Glyph designers have to contemplate the trade-offs in allocating visual channels when there is a large number of data variables. While there are many successful glyph designs in the literature, there is not yet a systematic method for assisting visualization designers to evaluate different design options that feature different types of trade-offs. In this paper, we present an evaluation scheme based on the multi-criteria decision analysis (MCDA) methodology. The scheme provides designers with a structured way to consider their glyph designs from a range of perspectives, while rendering a semi-quantitative template for evaluating different design options. In addition, this work provides guideposts for future empirical research to obtain more quantitative measurements that can be used in MCDA-aided glyph design processes.

This paper proposes a new investment strategy with deep-learning market prediction for mutual fund portfolio optimization. Our strategy uses the capital asset pricing model (CAPM) that applies macroeconomic factors to predict whether the market is bull or bear. Then, we develop a robo-advisor (RA) to predict future market, optimize portfolio and automate investment. Experiments use 22 years' data of S&P500 and mutual funds of U.S. to validate our strategy. Results show that the accuracy of our market prediction method can reach 84.3% and the rate-of-return of our RA is 13.S7%. Our model is more accurate and profitable than other algorithms.