Summary Real-Time Forecasting of Keyboard and Mouse Actions arxiv.org
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One Line
This paper explores the use of RNNs and computer vision to accurately predict real-time keyboard and mouse actions with a 34.63% accuracy.
Slides
Slide Presentation (9 slides)
Key Points
- User input can be represented as user actions, which describe the action taken and can be reenacted later.
- Keystrokes can be identified as user actions, while modifier keys require combinations with other keys to have meaning.
- Computer vision is used to identify interactive areas on the screen and extract image patches for reenactment.
- Recurrent neural networks (RNNs) are trained to predict the next user action based on previous actions.
- The system achieves an accuracy of 34.63% in predicting the next user action from a set of almost 500 possible actions.
Summaries
19 word summary
This paper examines using RNNs and computer vision to predict keyboard and mouse actions in real-time, achieving 34.63% accuracy.
70 word summary
This paper explores using recurrent neural networks (RNNs) and computer vision for real-time prediction of keyboard and mouse actions. User actions are defined as reenactable representations of user input. Computer vision identifies interactive areas clicked on the screen and extracts image patches. RNNs are trained on user activity to predict the next action, achieving 34.63% accuracy. This has potential for improving workflows, automating repetitive input, and aiding visually impaired individuals.
145 word summary
This paper discusses the use of recurrent neural networks (RNNs) and computer vision to predict keyboard and mouse actions in real-time. The authors define a "user action" as a representation of user input that can be reenacted later. They use computer vision to identify the interactive area clicked on the screen and extract an image patch of that area. These patches are stored in a database and can be searched through and extended on the fly. The authors train RNNs on a user's activity to predict the next action from a set of almost 500 possible actions, achieving an accuracy of 34.63% with minimal training. The predictions can improve computer workflows, automate repetitive input, and make frequently used buttons accessible for visually impaired individuals. The study demonstrates the feasibility and value of real-time prediction of keyboard and mouse actions for future research in this field.
287 word summary
This paper explores the use of recurrent neural networks (RNNs) and computer vision to predict keyboard and mouse actions in real-time. The goal is to learn from a user's repetitive input patterns and use that knowledge to assist the user in various ways. The authors define the concept of a "user action" as a representation of user input that is independent of the application and can be reenacted at a later time. Simple keystrokes can be identified as their own user actions, but modifier keys only have meaning when used in combination with other keys. Every key-modifier combination is considered a distinct user action. The authors use computer vision to identify which interactive area on the screen was clicked and extract an image patch of that area. These image patches are stored in a database and can be searched through and extended on the fly. The authors train RNNs, specifically LSTM and GRU models, on a user's activity over approximately a week to predict the next action from a set of almost 500 possible actions. They achieve an accuracy of 34.63% with minimal training. The predictions can be leveraged to improve and speed up computer workflows, such as automatically completing repetitive input or making frequently used buttons accessible for visually impaired individuals. The authors also demonstrate how the predictions can be used to attract the cursor to the buttons the user is most likely to click. However, there are limitations to the system, such as buttons that change size or shape and buttons with notification badges displayed on top of them. Overall, this study demonstrates the feasibility and value of predicting keyboard and mouse actions in real-time and provides a reference for future research in this domain.