Although there has been great progress in the field of Natural language processing (NLP) over the last few years, particularly with the development of attention-based models, less research has contributed towards modeling keystroke log data. State of the art methods handle textual data directly and while this has produced excellent results, the time complexity and resource usage are quite high for such methods. Additionally, these methods fail to incorporate the actual writing process when assessing text and instead solely focus on the content. Therefore, we proposed a framework for modeling textual data using keystroke-based features. Such methods pay attention to how a document or response was written, rather than the final text that was produced. These features are vastly different from the kind of features extracted from raw text but reveal information that is otherwise hidden. We hypothesized that pairing efficient machine learning techniques with keystroke log information should produce results comparable to transformer techniques, models which pay more or less attention to the different components of a text sequence in a far quicker time. Transformer-based methods dominate the field of NLP currently due to the strong understanding they display of natural language. We showed that models trained on keystroke log data are capable of effectively evaluating the quality of writing and do it in a significantly shorter amount of time compared to traditional methods. This is significant as it provides a necessary fast and cheap alternative to increasingly larger and slower LLMs.
Read More...Browse Articles
Polluted water tested from the Potomac River affects invasive species plant growth
Here recognizing the potential for pollution to impact the ecosystems of local waterways, the authors investigated the growth of tiger lilies, which are invasive to the Potomac River, in relation to the level of pollution. The authors report that increasing levels of pollution led to increased growth of the invasive species based on their study.
Read More...Rubik’s cube: What separates the fastest solvers from the rest?
In this study, the authors assess the factors that allow some speedcubers to solve Rubik's Cubes faster than others.
Read More...The Effect of Music on Heart Rate
Different songs can seem to evoke different emotions. Here the authors demonstrate that different songs can have a significant effect on the heart rate of listeners. A slower song slows heart rate, and a faster song increases it.
Read More...The Dependence of CO2 Removal Efficiency on its Injection Speed into Water
Recent research confirms that climate change, driven by CO2 emissions from burning fossil fuels, poses a significant threat to humanity. In response, authors explore methods to remove CO2 from the atmosphere, including breaking its molecular bonds through high-speed collisions.
Read More...An optimal pacing approach for track distance events
In this study, the authors use existing mathematical models to how high school athletes pace 800 m, 1600 m, and 3200 m distance track events compared to elite athletes.
Read More...Effect of mass and center of gravity on vehicle speed and braking performance
In this study, the authors test whether a gravity vehicle, which is a vehicle powered by its own gravity on a ramp, could be designed to move faster when mathematical calculations for optimal mass and center of gravity were applied in the design.
Read More...Caffeine: Does Drinking Coffee Alter Performance and RPE Levels of a Teenage Athlete in both Aerobic and Anaerobic Exercises?
Caffeine is widely consumed across the globe and is most appreciated for its effects as a stimulant. Here the authors investigate whether caffeine consumption affects performance during endurance or strength training. Their results suggest that caffeine consumption enhances endurance training, but not strength training.
Read More...A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring
Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.
Read More...The Effect of Different Fructose Diets on the Lifespan of C. elegans
High-fructose diets consumed widely in modern societies predisposes to metabolic diseases such as diabetes. Using the worm C. elegans, the authors of this study investigated the effect of fructose on the worm's survival rates. They found that worms fed 15% fructose had a lower life expectancy than those on a fructose-free diet. These results suggest that, like in humans, fructose has a negative effect on worm survival, which makes them an easy, attractive model to study the effects of fructose on health.
Read More...