RESEARCH PAPERS

A Comparison between Traditional Hand-drawn Animation and Computer-generated Animation

In general, there are two kinds of animation, traditional hand-drawn animation and computer-generated animation. Miyazaki Hayao and John Lasseter are two representational directors of these two genres respectively. This essay will first examine how Miyazaki’s and Lasseter’s cultural backgrounds influenced their choices. Then, using images from animation movies of these two directors, this essay will analyze their different visual effects based on their use of colors, textures, dimensions, backgrounds, special effects and settings. Finally, this essay identifies hand-drawn animation as Modernist and computer-generated animation as Postmodernist. By looking into features of these two genres, applying Karl Marx’s theory regarding the value of commodities, and introducing the concept of immaterial labor, this essay will explore the value of hand-drawn animation and computer-generated animation.

SDH for Hearing-Impaired Children: An Analysis of SDH in Zootopia and Coco

In this essay, I am going to use Zootopia (2016) and Coco (2017) as my case studies to examine how Subtitles for the Deaf and Hard-of-hearing (SDH) are created for the DVD/Blu-ray releases of these two animated films whose target audience are children. Zootopia tells the adventure of a rabbit police officer named Judy Hopps and her friend, a fox named Nick Wilde in the city called Zootopia, which is populated by animals. Coco centers around Miguel, a little boy with a dream of becoming a musician, who mistakenly steps into the Land of the Dead and discovers the secret about his father. By examining the SDH for Zootopia and Coco, I am going to discuss how various sounds—including sound effects, diegetic music, and non-diegetic music—are conveyed through subtitles. I will also discuss how the subtitles highlight specific elements of sound, such as the emotions and tones of speech, that are considered crucial for the audience’s experience of the movie.

Identifying Profitable Day-trading Opportunities based on Machine Learning

Day trading, as a speculative trading style that involves the opening and closing of a position on a daily basis, can be affected by all sorts of variations in the market. It is desirable to build a model to predict whether a transaction can benefit at all, given the entry time, the stock information, and real-time market situations. We implemented different supervised learning models for that purpose, with a substantial amount of minute-level trading opportunity data for US stocks. This paper describes data preprocessing, modeling methodologies, comparison and evaluation of several classifiers, and further improvement and insights into the modeling result. The overall performances of all models do not differ significantly, but certain models may be recommended based on different risk-return preferences according to our performance analysis.

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