Exploring Users' Pointing Performance on Virtual and Physical Large Curved Displays

Large curved displays have emerged as a powerful platform for collaboration, data visualization, and entertainment. These displays provide highly immersive experiences, a wider field of view, and higher satisfaction levels. Yet, large curved displays are not commonly available due to their high costs. With the recent advancement of Head Mounted Displays (HMDs), large curved displays can be simulated in Virtual Reality (VR) with minimal cost and space requirements. However, to consider the virtual display as an alternative to the physical display, it is necessary to uncover user performance differences (e.g., pointing speed and accuracy) between these two platforms. In this paper, we explored users' pointing performance on both physical and virtual large curved displays. Specifically, with two studies, we investigate users' performance between the two platforms for standard pointing factors such as target width, target amplitude as well as users' position relative to the screen. Results from user studies reveal no significant difference in pointing performance between the two platforms when users are located at the same position relative to the screen. In addition, we observe users' pointing performance improves when they are located at the center of a semi-circular display compared to off-centered positions. We conclude by outlining design implications for pointing on large curved virtual displays. These findings show that large curved virtual displays are a viable alternative to physical displays for pointing tasks.Comment: In 29th ACM Symposium on Virtual Reality Software and Technology (VRST 2023

Exploring Users Pointing Performance on Large Displays with Different Curvatures in Virtual Reality

Large curved displays inside Virtual Reality environments are becoming popular for visualizing high-resolution content during analytical tasks, gaming or entertainment. Prior research showed that such displays provide a wide field of view and offer users a high level of immersion. However, little is known about users' performance (e.g., pointing speed and accuracy) on them. We explore users' pointing performance on large virtual curved displays. We investigate standard pointing factors (e.g., target width and amplitude) in combination with relevant curve-related factors, namely display curvature and both linear and angular measures. Our results show that the less curved the display, the higher the performance, i.e., faster movement time. This result holds for pointing tasks controlled via their visual properties (linear widths and amplitudes) or their motor properties (angular widths and amplitudes). Additionally, display curvatures significantly affect the error rate for both linear and angular conditions. Furthermore, we observe that curved displays perform better or similar to flat displays based on throughput analysis. Finally, we discuss our results and provide suggestions regarding pointing tasks on large curved displays in VR.Comment: IEEE Transactions on Visualization and Computer Graphics (2023

Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalized scheme. This paper intends to discuss our machine learning model, which can make a significant amount of profit in the US stock market by performing live trading in the Quantopian platform while using resources free of cost. Our top approach was to use ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor about making trading decisions in the stock market