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The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.

In this study, the authors test whether excess copper exposure has neurobehavioral effects on Hirudo verbana leeches.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.

In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!

Herring and Scott investigated how specific types of background music affected 8th and 9th graders' performance on a reading comprehension task. In the study, their results indicated that music with English lyrics led to lower reading comprehension scores, while foreign language and instrumental music was comparable to no music at all. The authors therefore recommend that teachers avoid playing English language music for students completing reading tasks in order to minimize distractions and improve work efficiency.

The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.

Machine learning offers a powerful way to analyze large astronomical datasets and identify stars with unusual properties. In this study, students used Extreme Gradient Boosting and Isolation Forest models to investigate why some red clump stars deviate from their expected luminosities. Their results show that stellar age plays a much stronger role than metallicity in driving luminosity variation, and the models successfully detected outlier stars that may affect distance measurements in galactic studies.

Here the authors investigated the impact of player ability versus starting positions in the game of Nim under imperfect play, hypothesizing that player skill becomes the primary determinant of outcomes as pile sizes grow. Through computational simulations and a mathematical model, they demonstrated that favorable starting positions lose their advantage over time and provide key insights to help improve reinforcement learning algorithms in abstract, complex decision-making environments.