Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”

AI KRegression by CHIN-CHENG CHU (20718575)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Tree cover limits occupancy of a declining game bird by Bradley Kubecka (7387181)

“…Northern bobwhite (<em>Colinus virginianus; </em>bobwhite) is a popular but declining game bird which is often considered an umbrella and indicator species for savanna ecosystems. …”

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

<b>Future Fairness Concerns: Influences on Punishment and Compensation Strategies in Fairness Maintenance</b> by Zhuang Li (10217024)

“…Our study examines how concerns about future fairness affect strategies for maintaining fairness by manipulating game frequency and deterrence conditions. We find that in public settings, where the actions for maintaining fairness are visible to others, an increase in game frequency and proximity to the beginning of the game lead to more frequent use of punishment. …”

<b>Future Fairness Concerns: Influences on Punishment and Compensation Strategies in Fairness Maintenance</b> by Zhuang Li (10217024)

“…Our study examines how concerns about future fairness affect strategies for maintaining fairness by manipulating game frequency and deterrence conditions. We find that in public settings, where the actions for maintaining fairness are visible to others, an increase in game frequency and proximity to the beginning of the game lead to more frequent use of punishment. …”

K-means results. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Feature importance result of SHAP. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Definition of variables. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Result of random forest. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

A game-theoretic analysis for creative mid-roll ads on a content platform by Shaofu Du (3359846)

“…Moreover, paid members are also a target group for CMAs, which may conflict with their interests and potentially reduce their viewing volume. We develop a game-theoretical model to examine the platform's advertising pricing and the advertiser's mode selection process. …”

Feature engineering. by Youli Jiang (15249070)

Expected payoffs of <i>P</i>_<i>x</i>(<i>x</i>|<i>y</i>) in . by Hitoshi Yamamoto (738861)

“…Results suggest gradual reputation dynamics that increase and decrease gradually due to cooperation or defection. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)