Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments. The majority of the research on video activity recognition is focused on the development of large parameter systems that require training on large video datasets. This paper develops a low-parameter, modular system with rapid inferencing capabilities that can be trained entirely on limited datasets without requiring transfer learning from large-parameter systems. The system can accurately detect and associate specific activities with the students who perform the activities in real-life classroom videos. Additionally, the paper develops an interactive web-based application to visualize human activity maps over long real-life classroom videos.

We show that deep reinforcement learning can maintain its ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples. Under such large update-to-data ratios, a recent study by Nikishin et al. (2022) suggested the emergence of a primacy bias, in which agents overfit early interactions and downplay later experience, impairing their ability to learn. In this work, we dissect the phenomena underlying the primacy bias. We inspect the early stages of training that ought to cause the failure to learn and find that a fundamental challenge is a long-standing acquaintance: value overestimation. Overinflated Q-values are found not only on out-of-distribution but also in-distribution data and can be traced to unseen action prediction propelled by optimizer momentum. We employ a simple unit-ball normalization that enables learning under large update ratios, show its efficacy on the widely used dm_control suite, and obtain strong performance on the challenging dog tasks, competitive with model-based approaches. Our results question, in parts, the prior explanation for sub-optimal learning due to overfitting on early data.

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.

Analysis of non-typical emotions, such as stress, depression and engagement is less common and more complex compared to that of frequently discussed emotions like happiness, sadness, fear, and anger. The importance of these non-typical emotions has been increasingly recognized due to their implications on mental health and well-being. Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement. We discuss the most commonly used datasets, input modalities, data processing techniques, and information fusion methods used for the computational analysis of stress, depression and engagement. A timeline and taxonomy of non-typical emotion analysis approaches along with their generic pipeline and categories are presented. Subsequently, we describe state-of-the-art computational approaches for non-typical emotion analysis, including a performance summary on the most commonly used datasets. Following this, we explore the applications, along with the associated challenges, limitations, and future research directions.

In the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication. Employing a mixed-methods approach, we conducted a one-week diary and interview study to explore users' perceptions of these tools' ability to: 1) support interpersonal communication in the short-term, and 2) lead to potential long-term effects. Our findings indicate that participants view AIMC support favorably, citing benefits such as increased communication confidence, and finding precise language to express their thoughts, navigating linguistic and cultural barriers. However, the study also uncovers current limitations of AIMC tools, including verbosity, unnatural responses, and excessive emotional intensity. These shortcomings are further exacerbated by user concerns about inauthenticity and potential overreliance on the technology. Furthermore, we identified four key communication spaces delineated by communication stakes (high or low) and relationship dynamics (formal or informal) that differentially predict users' attitudes toward AIMC tools. Specifically, participants found the tool is more suitable for communicating in formal relationships than informal ones and more beneficial in high-stakes than low-stakes communication.

Two teenage boys from Miami, Florida were arrested in December for allegedly creating and sharing AI-generated nude images of male and female classmates without consent, according to police reports obtained by WIRED via public record request. The arrest reports say the boys, aged 13 and 14, created the images of the students who were "between the ages of 12 and 13." The Florida case appears to be the first arrests and criminal charges as a result of sharing AI-generated nude images to come to light. The boys were charged with third-degree felonies--the same level of crimes such as grand theft auto or false imprisonment--under a state law passed in 2022. It makes it a felony to share "any altered sexual depiction" of a person without their consent.

To celebrate International Women's Day, we take a look back over the past 12 months and highlight some of the women we've interviewed and featured, and who've written about their research on AIhub. Elizabeth Ondula is an Electrical Engineer from the Technical University of Kenya and is currently a PhD student of Computer Science at USC. She is a member of the Autonomous Networks Research Group, and co-organizes a bi-weekly reinforcement learning group, SUITERS-RL. Prior to academia, she had roles as a Software Engineer at IBM Research in Kenya, Head of Product Development of Brave Venture Labs and Co-lead of Hardware Research at iHub Nairobi. We interviewed Elizabeth as part of our series featuring the AAAI Doctoral Consortium participants.

Five Beverly Hills eighth-graders have been expelled for their involvement in the creation and sharing of fake nude pictures of their classmates. The Beverly Hills Unified School District board of education voted at a special meeting Wednesday evening to approve stipulated agreements of expulsion with five students. According to a source close to the investigation, the expelled students were attending Beverly Vista Middle School. Under a stipulated agreement, the students and their parents do not contest the punishment and no hearing was held. The names of the students were not released, and the agreements are confidential.

This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning is a natural skill for the human brain, it is very challenging for artificial neural networks. An important reason is that, when learning something new, these networks tend to quickly and drastically forget what they had learned before, a phenomenon known as catastrophic forgetting. Especially in the last decade, continual learning has become an extensively studied topic in deep learning. This book chapter reviews the insights that this field has generated.

Asymmetric information stochastic games (\textsc{aisg}s) arise in many complex socio-technical systems, such as cyber-physical systems and IT infrastructures. Existing computational methods for \textsc{aisg}s are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to special classes of \textsc{aisg}s to avoid belief hierarchies. To address these limitations, we propose conjectural online learning (\textsc{col}), an online method for generic \textsc{aisg}s. \textsc{col} uses a forecaster-actor-critic (\textsc{fac}) architecture where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments, \textsc{col} uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by \textsc{col} are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by \textsc{col} converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate \textsc{col}'s superiority over state-of-the-art reinforcement learning methods against nonstationary attacks.