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In recent years, ransomware attacks have grown dramatically. New variants continually emerging make tracking and mitigating these threats increasingly difficult using traditional detection methods. As the landscape of ransomware evolves, there is a growing need for more advanced detection techniques. Neural networks have gained popularity as a method to enhance detection accuracy, by leveraging low-level hardware information such as hardware events as features for identifying ransomware attacks. In this paper, we investigated several state-of-the-art supervised learning models, including XGBoost, LightGBM, MLP, and CNN, which are specifically designed to handle time series data or image-based data for ransomware detection. We compared their detection accuracy, computational efficiency, and resource requirements for classification. Our findings indicate that particularly LightGBM, offer a strong balance of high detection accuracy, fast processing speed, and low memory usage, making them highly effective for ransomware detection tasks. 

In collaborative problem-solving (CPS), students work together to solve problems using their collective knowledge and social interactions to understand the problem and progress towards a solution. This study focuses on how students engage in CPS while working in pairs in a STEM+C (Science, Technology, Engineering, Mathematics, and Computing) environment that involves open-ended computational modeling tasks. Specifically, we study how groups with different prior knowledge in physics and computing concepts differ in their information pooling and consensus-building behaviors. In addition, we examine how these differences impact the development of their shared understanding and learning. Our study consisted of a high school kinematics curriculum with 1D and 2D modeling tasks. Using an exploratory approach, we performed in-depth case studies to analyze the behaviors of groups with different prior knowledge distributions across these tasks. We identify effective information pooling and consensus-building behaviors in addition to difficulties students faced when developing a shared understanding of physics and computing concepts. 

Research into autonomous vehicles has focused on purpose-built vehicles with Lidar, camera, and radar systems. Many vehicles on the road today have sensors built into them to provide advanced driver assistance systems. In this paper we assess the ability of low-end automotive radar coupled with lightweight algorithms to perform scene segmentation. Results from a variety of scenes demonstrate the viability of this approach that complement existing autonomous driving systems.