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
Qualitative tracking of human and animation motions reveals differences in their walking gaits
In their attempt to evoke a greater emotional connection with viewers, animators have strived to replicate human movements in their animations. However, animation movements still appear distinct from human movements. With a focus on walking, we hypothesized that animations, unaffected by real external forces (e.g. gravity), would move with a universally distinct, gliding gait that is discernible from humans.
Read More...Antibacterial activity of homemade Indian tomato tamarind soup (rasam) against common pathogens
Systematic consumption of traditional foods is a popular way of treating diseases in India. Rasam, a soup of spices and tomato with a tamarind base, is a home remedy for viral infections such as the common cold. Here, we investigate if rasam, prepared under household conditions, exhibits antibacterial activity against Escherichia coli and Staphylococcus aureus, two common pathogenic bacteria. Our results show rasam prepared under household conditions lacks antibacterial activity despite its ingredients possessing such properties.
Read More...Calculating the dynamic viscosity of a fluid using image processing of a falling ball
The authors measure changes in the viscosity of glycerol with increasing temperature using the falling ball approach.
Read More...SOS-PVCase: A machine learning optimized lignin peroxidase with polyvinyl chloride (PVC) degrading properties
The authors looked at the primary structure of lignin peroxidase in an attempt to identify mutations that would improve both the stability and solubility of the peroxidase protein. The goal is to engineer peroxidase enzymes that are stable to help break down polymers, such as PVC, into monomers that can be reused instead of going to landfills.
Read More...Digestion products of bread and cheese cause addictive behavior in a planaria model
The authors looked at two peptides, gluteomorphin and casomorphin, that are present after the digestion of bread and cheese. As these peptides can bind opioid receptors the authors want to know if they could be addictive in the same way as conventional opioids (i.e., morphine) are known to be. Their results in a planaria model suggest that both of these peptides are addictive.
Read More...Reduced psoriasis skin irritation symptoms through the effects of Chinese herbal medicines on planarians
The authors looked at whether traditional Chinese medicine remedies that target the lungs and liver would reduce inflammation in a planaria model. They found that the two active compounds they tested were able to decrease induced inflammation by 97-98%.
Read More...Epileptic seizure detection using machine learning on electroencephalogram data
The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.
Read More...Exploring the effects of diverse historical stock price data on the accuracy of stock price prediction models
Algorithmic trading has been increasingly used by Americans. In this work, we tested whether including the opening, closing, and highest prices in three supervised learning models affected their performance. Indeed, we found that including all three prices decreased the error of the prediction significantly.
Read More...