Online continual learning for image classification is crucial for models to adapt to new data while retaining knowledge of previously learned tasks. This capability is essential to address real-world challenges involving dynamic environments and evolving data distributions. Traditional approaches predominantly employ Convolutional Neural Networks, which are limited to processing images as grids and primarily capture local patterns rather than relational information. Although the emergence of transformer architectures has improved the ability to capture relationships, these models often require significantly larger resources. In this paper, we present a novel online continual learning framework based on Graph Attention Networks (GATs), which effectively capture contextual relationships and dynamically update the task-specific representation via learned attention weights. Our approach utilizes a pre-trained feature extractor to convert images into graphs using hierarchical feature maps, representing information at varying levels of granularity. These graphs are then processed by a GAT and incorporate an enhanced global pooling strategy to improve classification performance for continual learning. In addition, we propose the rehearsal memory duplication technique that improves the representation of the previous tasks while maintaining the memory budget. Comprehensive evaluations on benchmark datasets, including SVHN, CIFAR10, CIFAR100, and MiniImageNet, demonstrate the superiority of our method compared to the state-of-the-art methods.

Shadeeb Hossain Engineering Technology and Information Sciences DeVry University New York, USA [ORCID ID: 0000 - 0002 - 5224 - 7684 ] Abstract -- This paper explores advancements in Artificial Intelligence (AI) technologies to enhance classroom learning, highlighting contributions from companies like IBM, Microsoft, Google, and ChatGPT, as well as the potential of brain signal analysis. The focus is on improving students' learning experiences by using Machine Learning (ML) algorithms to (i) identify a student's preferred learning style (visual or auditory) and (ii) predict academic dropout risk. A Logistic Regression algorithm is applied for binary classification using six predictor variables, such as assessment scores, lesson duration, and preferred learning style, to accurately identify learning preferences. In comparison, the Stochastic Gradient Descent (SGD) classifier achieved an accuracy of 83.1% on the same dataset Individual feedback to students and customized learning materials has a significant impact on their learning ability and have been areas of active research focus [1]. However, in the United States, due to the vast diversity in classroom populations, it becomes inherently difficult for educators to customize lessons and address individual students' problems [2]. V arious factors contribute to the effectiveness of individual learning processes [3,4]. Questionnaires have often been used as a tool to predict an individual's learning style [5 - 8]. Learning analytics, which involves the collection, analysis, and use of da ta, has been suggested to improve students' learning experiences [9]. In most cases, these assessments have been used to generalize the overall learning patterns of a classroom rather than addressing the needs of individual students. The concept of a SMART classroom incorporates both hardware and software components to adapt to dynamic learning patterns in a classroom, and it has been an area of ongoing research [10,11].

The rapid evolution of large language models (LLMs) has transformed human-computer interaction (HCI), but the interaction with LLMs is currently mainly focused on text-based interactions, while other multi-model approaches remain under-explored. This paper introduces VTutor, an open-source Software Development Kit (SDK) that combines generative AI with advanced animation technologies to create engaging, adaptable, and realistic APAs for human-AI multi-media interactions. VTutor leverages LLMs for real-time personalized feedback, advanced lip synchronization for natural speech alignment, and WebGL rendering for seamless web integration. Supporting various 2D and 3D character models, VTutor enables researchers and developers to design emotionally resonant, contextually adaptive learning agents. This toolkit enhances learner engagement, feedback receptivity, and human-AI interaction while promoting trustworthy AI principles in education. VTutor sets a new standard for next-generation APAs, offering an accessible, scalable solution for fostering meaningful and immersive human-AI interaction experiences. The VTutor project is open-sourced and welcomes community-driven contributions and showcases.

Prompt learning has attracted increasing attention in the graph domain as a means to bridge the gap between pretext and downstream tasks. Existing studies on heterogeneous graph prompting typically use feature prompts to modify node features for specific downstream tasks, which do not concern the structure of heterogeneous graphs. Such a design also overlooks information from the meta-paths, which are core to learning the high-order semantics of the heterogeneous graphs. To address these issues, we propose CLEAR, a Cluster-based prompt LEARning model on heterogeneous graphs. We present cluster prompts that reformulate downstream tasks as heterogeneous graph reconstruction. In this way, we align the pretext and downstream tasks to share the same training objective. Additionally, our cluster prompts are also injected into the meta-paths such that the prompt learning process incorporates high-order semantic information entailed by the meta-paths. Extensive experiments on downstream tasks confirm the superiority of CLEAR. It consistently outperforms state-of-the-art models, achieving up to 5% improvement on the F1 metric for node classification.

Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning, with applications spanning historical document preservation to modern data entry and accessibility solutions. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to develop robust recognition systems. This survey examines the evolution of HTR models, tracing their progression from early heuristic-based approaches to contemporary state-of-the-art neural models, which leverage deep learning techniques. The scope of the field has also expanded, with models initially capable of recognizing only word-level content progressing to recent end-to-end document-level approaches. Our paper categorizes existing work into two primary levels of recognition: (1) \emph{up to line-level}, encompassing word and line recognition, and (2) \emph{beyond line-level}, addressing paragraph- and document-level challenges. We provide a unified framework that examines research methodologies, recent advances in benchmarking, key datasets in the field, and a discussion of the results reported in the literature. Finally, we identify pressing research challenges and outline promising future directions, aiming to equip researchers and practitioners with a roadmap for advancing the field.

Diffusion policies have achieved superior performance in imitation learning and offline reinforcement learning (RL) due to their rich expressiveness. However, the vanilla diffusion training procedure requires samples from target distribution, which is impossible in online RL since we cannot sample from the optimal policy, making training diffusion policies highly non-trivial in online RL. Backpropagating policy gradient through the diffusion process incurs huge computational costs and instability, thus being expensive and impractical. To enable efficient diffusion policy training for online RL, we propose Soft Diffusion Actor-Critic (SDAC), exploiting the viewpoint of diffusion models as noise-perturbed energy-based models. The proposed SDAC relies solely on the state-action value function as the energy functions to train diffusion policies, bypassing sampling from the optimal policy while maintaining lightweight computations. We conducted comprehensive comparisons on MuJoCo benchmarks. The empirical results show that SDAC outperforms all recent diffusion-policy online RLs on most tasks, and improves more than 120% over soft actor-critic on complex locomotion tasks such as Humanoid and Ant.

Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a treestructure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.

As artificial intelligence (AI) technologies begin to permeate diverse fields--from healthcare to education--consumers, researchers and policymakers are increasingly raising concerns about whether and how AI is regulated. It is therefore reasonable to anticipate that alignment with principles of'ethical' or'responsible' AI, as well as compliance with law and policy, will form an increasingly important part of AI development. Yet, for the most part, the conventional computer science curriculum is ill-equipped to prepare students for these challenges. To this end, we seek to explore how new educational content related to AI ethics and AI policy can be integrated into both ethics-and technical-focused courses. This paper describes a two-lecture AI policy module that was piloted in a graduate-level introductory machine learning course in 2024. The module, which includes an in-class active learning game, is evaluated using data from student surveys before and after the lectures, and pedagogical motivations and considerations are discussed. We find that the module is successful in engaging otherwise technically-oriented students on the topic of AI policy, increasing student awareness of the social impacts of a variety of AI technologies and developing student interest in the field of AI regulation. Introduction The explosive growth of artificial intelligence (AI) technologies is widely documented and increasingly evident in everyday life: some responses from the search engine Google now include an "AI Overview" inserted before the first webpage link; companies like Tesla and Waymo have seen success in implementing partial or full autonomous driving in vehicles on live roads; and "Apple Intelligence" was the flagship feature for the launch of Apple's new smartphone in fall 2024. Yet what legal or policy response this technological growth will precipitate is less certain [1, 2]. Nevertheless, it should be expected that the development and enactment of regulatory frameworks for AI will demand AI engineers with a command not only of the technical intricacies of AI models, but also of the policy and regulatory landscape for AI development and compliance [3].

Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evaluate the collaborative potential of human-machine interaction and identify the psychological and practical factors critical to successful collaboration. Specifically, we investigate how varying model reliability and cognitive framing influence human trust, cognitive load, and annotation behavior in HITL systems. We demonstrate that model reliability and psychological framing significantly impact annotators' trust, engagement, and consistency, offering insights into optimizing HITL frameworks. Through three experimental scenarios with 29 participants--baseline model reliability (S1), fabricated errors (S2), and cognitive bias introduced by negative framing (S3)--we analyzed behavioral and qualitative data. Reliable predictions in S1 yielded high trust and annotation consistency, while unreliable outputs in S2 led to increased critical evaluations but also heightened frustration and response variability. Negative framing in S3 revealed how cognitive bias influenced participants to perceive the model as more relatable and accurate, despite misinformation regarding its reliability. These findings highlight the importance of both reliable machine outputs and psychological factors in shaping effective human-machine collaboration. By leveraging the strengths of both human oversight and automated systems, this study establishes a scalable HITL framework for emotion annotation and lays the foundation for broader applications in adaptive learning and human-computer interaction.

We present a theoretical analysis of GPU memory consumption during the training of DeepSeek models such as DeepSeek-v2 and DeepSeek-v3. Our primary objective is to clarify the device-level memory requirements associated with various distributed training configurations. Specifically, we examine critical factors influencing memory usage, including micro-batch size, activation recomputation policies, 3D parallelism, and ZeRO optimizations. It is important to emphasize that the training policies discussed in this report are not representative of DeepSeek's official configurations. Instead, they are explored to provide a deeper understanding of memory dynamics in training of large-scale mixture-of-experts model.