Humans perceive flicker artifacts at 500 Hz.

Humans perceive a stable average intensity image without flicker artifacts when a television or monitor updates at a sufficiently fast rate. This rate, known as the critical flicker fusion rate, has been studied for both spatially uniform lights, and spatio-temporal displays. These studies have included both stabilized and unstablized retinal images, and report the maximum observable rate as 50-90 Hz. A separate line of research has reported that fast eye movements known as saccades allow simple modulated LEDs to be observed at very high rates. Here we show that humans perceive visual flicker artifacts at rates over 500 Hz when a display includes high frequency spatial edges. This rate is many times higher than previously reported. As a result, modern display designs which use complex spatio-temporal coding need to update much faster than conventional TVs, which traditionally presented a simple sequence of natural images

E-learning adoption in the banking workplace in Indonesia: an empirical study

The main purpose of this study is to determine whether the TAM (Technology Acceptance Model) could be extended to include external variables including computer self-efficacy, prior experience, computer anxiety, management support and compatibility, to further understand the learners’ perceived usefulness and perceived ease of use of an e-learning system. The study also aims to clarify which factors are more influential in affecting the decision to use e-learning. Five factors were examined together with the TAM construct using the SEM (Structural Equation Modeling) technique. The study reveals that management support, prior experience, computer anxiety and compatibility have predictive power towards behavioral intention to use e-learning systems. The results gained from this study, which took place in the banking workplace in Indonesia, provide a conceptual framework for individuals and organizations to better understand the critical factors which influence e-learning acceptance in developing countries

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28