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Your webcam may know your ce, but your keyboard knows your mputer models can predict with . accuracy whether a man or woman is typing, according to a new study. To conduct the research, computer engineers installed keystrokelogging software onto the personal computers of volunteers men, womenwhich monitored their daily computer use for months. The researchers then used a program they created, called ISqueezeU to calculate the relative helpfulness of different typing features for determining genderthings like the time between two specific keystrokes, or the amount of time a key is pressed down during a single keystroke. A few features stood out as being more useful than others. For example, the average time between pressing the N key to pressing the O key was the most helpful, followed by the average time between pressing the M and O keys. The program isnt capable of specifying whether a man or woman s those keys ster or more oftenonly that there is a difference. The researchers then tested the programs findings using five machine learning models, which are computer programs that build models based on what they learn from existing data. All five models were able to predict gender accurately more than of the time, with the most successful model being more than accurate, the engineers report this week in. The team proposes the use of keystroke dynamics as a costefficient and nonintrusive way to identify the gender of unknown computer users in criminal investigations, such as in cases of cyberstalking or identity theft. The researchers plan to expand their data collection with more volunteers, and see whether incorporating other variables such as handedness or education level can increase accuracy.
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