Association for the Advancement of Artificial Intelligence: AAAI Publications
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Exploring the Impact of Age and Class Level on Emotional Perception: A Study of Nigerian Secondary School Girls Using the Reading the Mind in the Eyes Test (Student Abstract)
No full textAssessing social cognition in adolescents by understanding emotional perception is crucial, especially through tasks like the Reading the Mind in the Eyes Test (RMET). This ongoing research investigates the emotional perception skills of Nigerian high school girls through the Reading the Mind in the Eyes Test (RMET). Preliminary analysis looks at how age and class level (SS1 and SS2) affect RMET scores, with 20% of the data (n = 215) already gathered. ANOVA findings display a notable distinction among class levels (p = 0.024), while regression analysis suggests that age effectively predicts RMET scores (β = 1.06, p = 0.037), showcasing that older students achieve higher scores. These preliminary results indicate that age is a significant factor in how emotions are perceived, and more data is being collected and analyzed to gain deeper understanding. These findings can guide strategies to enhance social skills in education and improve AI models for emotion recognition in diverse and age-sensitive contexts
Generative Flow Networks for Lead Optimization in Drug Design (Student Abstract)
No full textThis paper investigates the application of Generative Flow Networks (GFlowNets) to lead optimization in drug discovery. GFlowNets provide a novel framework for generating diverse molecular structures while optimizing for desired properties, addressing the limitations of traditional methods in exploring vast chemical spaces. We adapt GFlowNets to incrementally modify lead compounds, integrating domain-specific heuristics to guide the generation process. Our method employs the trajectory balance objective on a graph neural network (GNN), to learn a policy that samples fragments based on a multi-objective reward. The reward function ensures increase in cell permeability and similarity to the starting molecule. The results on benchmark datasets of activity cliffs demonstrate that GFlowNets can generate diverse modifications, producing optimized candidate molecules with improvement in cell permeability. This work can be extended with other pharmacokinetic properties for lead optimization in early-stage drug development, potentially accelerating the discovery of novel therapeutics
When to Learn and When to Stop: Quitting at the Optimal Time (Student Abstract)
No full textArtificial neural networks (ANNs) struggle with continual learning, sacrificing performance on previously learned tasks to acquire new task knowledge. Here we propose a new approach allowing to mitigate catastrophic forgetting during continuous task learning. Typically a new task is trained until it reaches maximal performance, causing complete catastrophic forgetting of the previous tasks. In our new approach, termed Optimal Stopping (OS), network training on each new task continues only while the mean validation accuracy across all the tasks (current and previous) increases. The stopping criterion creates an explicit balance: lower performance on new tasks is accepted in exchange for preserving knowledge of previous tasks, resulting in higher overall network performance. The overall performance is further improved when OS is combined with Sleep Replay Consolidation (SRC), wherein the network converts to a Spiking Neural Network (SNN) and undergoes unsupervised learning modulated by Hebbian plasticity. During the SRC, the network spontaneously replays activation patterns from previous tasks, helping to maintain and restore prior task performance. This combined approach offers a promising avenue for enhancing the robustness and longevity of learned representations in continual learning models, achieving over twice the mean accuracy of baseline continuous learning while maintaining stable performance across tasks
Understanding Annotator Perception: Modeling Psychological Inference from First- and Third-Person Annotations (Student Abstract)
No full textLarge language models (LLMs) are trained on vast amounts of publicly available text. However, the current training frameworks take for granted that these annotations are accurate reflections of the authors’ true intents. This study questions that assumption by examining the gaps between writers’ actual psychological states and the inferences made by third-party annotators. We explore how readers interpret psychological cues in text and demonstrate that third-person annotations often fail to align with first-person realities. By integrating both first- and third-person annotations, we develop computational models that reveal significant biases in how psychological states are perceived and the downstream effects these perceptions have on reader behavior. Our findings challenge the foundational assumptions of LLM training, suggesting that the reliance on potentially flawed third-person annotations could impact model accuracy and real-world applications
Giving Cars Eyes: Using Computer Vision to Prevent Road Traffic Accidents in Nigeria
No full textThis paper explores the application of computer vision technology as a proactive solution to prevent Road Traffic Accidents in Nigeria. By leveraging machine learning algorithms and real-time video analysis, computer vision can reduce incidents caused by human error. The research focuses on designing an autonomous but elaborate system that monitors traffic patterns, road irregularities and triggers automated interventions when risky conditions are detected. The aim is to suggest that computer vision can be pivotal in enhancing road safety and reducing traffic-related fatalities in Nigeria
AI-Driven Personalized Fall Prevention for Older Adults
No full textFalls among older adults pose a significant public health challenge, impacting quality of life and healthcare costs. This research proposal aims to develop an innovative AI-driven personalized fall prevention system for older adults, leveraging advanced machine learning techniques in computer vision, natural language processing, and reinforcement learning. The proposed system will encompass five key components: (1) Advanced pose estimation and activity recognition using HRNet with attention mechanisms and hybrid LSTM-GCN models; (2) Personalized risk assessment through multi-modal deep learning, combining CNNs, RNNs, and federated learning for privacy-preserving distributed training; (3) Adaptive intervention strategies employing Deep Q-Networks and model-based reinforcement learning with GAN-simulated environments; (4) Human-AI interaction utilizing SHAP values for explainable AI and fine-tuned GPT-3 for natural language communication; and (5) Privacy-preserving techniques including differential privacy and homomorphic encryption. The research will be conducted over a five-year period, involving data collection, model development, large-scale testing, and clinical trials. Expected outcomes include a scalable, privacy-preserving AI system capable of significantly reducing fall incidents among older adults, thereby improving quality of life and reducing healthcare costs. This interdisciplinary research contributes to advancing AI techniques in real-world healthcare applications while addressing critical ethical and privacy concerns, potentially transforming elderly care on a global scale
Pancreatic Cancer Diagnosis System
No full textMy research direction is about the pancreatic cancer diagnosis system. Pancreatic cancer, as one of the cancers with the highest mortality rate, has always been a difficult problem in world medicine. I hope that through my efforts, I can contribute to the integration of AI and medicine, and contribute to increasing the probability of early diagnosis of pancreatic cancer
Bidirectional Human-AI Learning in Real-Time Disoriented Balancing
No full textWe present a real-time system that enables bidirectional human-AI learning and teaching in a balancing task that is a realistic analogue of disorientation during piloting and spaceflight. A human subject and autonomous AI model of choice guide each other in maintaining balance using a visual inverted pendulum (VIP) display. We show how AI assistance changes human performance and vice versa
SuBiTO: Synopsis-based Training Optimization for Continuous Real-Time Neural Learning over Big Streaming Data
No full textIn machine learning applications over Big streaming Data, Neural Networks (NNs) are continuously and rapidly trained over voluminous data arriving at high speeds. As soon as a new version of the NN becomes available, it gets deployed for prediction purposes (e.g. classification). The real-time character of such applications greatly depends on the volume and velocity of the data streams, as well as the NN complexity. Training on large volume of ingested streams or using complex NNs, potentially increases accuracy, but may compromise the real-time character of those applications. In this work, we present SuBiTO, a framework that automatically and continuously learns the training time vs accuracy trade-offs as new data stream in and fine tunes: (i) the number, size and type of NN layers; (ii) the size of the ingested data via stream synopses specific parameters; and (iii) the number of training epochs. Finally, SuBiTO suggests optimal sets of such parameters and detects concept drifts, enabling the human operator adapt these parameters on-the-fly, at runtime
MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs
No full textWe propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems