GenAI4ELab/papercli-papers huggingface dataset:
Ensuring Class-Conditional Coverage for Pathological Workflows (Student Abstract)
Authors: Siddharth Narendra; Shubham Ojha; Aditya Narendra; Abhay Kshirsagar; Abhisek Mallick
Venue: AAAI 2025 (main)

Abstract: Conformal Prediction (CP) is an uncertainty quantification framework that provides prediction sets with a user-specified probability to include the true class in the prediction set. This guarantee on the user-specified probability is known as marginal coverage. Marginal coverage refers to the probability that the true label is included in the prediction set, averaged over all test samples. However, this can lead to inconsistent coverage across different classes, constraining its suitability for high-stakes applications such as pathological workflows. This study implements a Classwise CP method applied to two cancer datasets to achieve class-conditional coverage which ensures that each class has a user-specified probability of being included in the prediction set when it is the true label. Our results demonstrate the effectiveness of this approach through a significant reduction in the average class coverage gap compared to the Baseline CP method.
Audience Engagement with Political Messaging on YouTube Shorts (Student Abstract)
Authors: Omkar Narkar; Aman Vohra; Ashiqur R. KhudaBukhsh
Venue: AAAI 2025 (main)

Abstract: This study investigates user engagement and political polarization on YouTube Shorts, a special category of YouTube videos with a duration of 15-60 seconds. Via a substantial corpus of 38,838 videos gleaned from 100 YouTube channels focusing on political content, we contrast YouTube Shorts with long-form content in terms of user engagement, content toxicity, and polarization. Our analyses reveal that (1) YouTube Shorts receive more likes and views and fewer comments as compared to their long-form video counterparts; (2) YouTube Shorts are more toxic; and (3) considerably more polarized than long-form YouTube videos.
From Complexity to Clarity: Transforming Chest X-ray Reports with Chained Prompting (Student Abstract)
Authors: Sujoy Nath; Arkaprabha Basu; Kushal Bose; Swagatam Das
Venue: AAAI 2025 (main)

Abstract: In the rapidly advancing field of AI-assisted medical diagnosis, the generation of medical reports for Chest X-rays (CXR) has significantly improved with the increased availability of radiographs and their corresponding reports. However, these reports often contain complex medical terminology, making them difficult for patients and non-healthcare professionals to understand. In this study, we introduce a strategy called Chained Prompting for Improved Readability of Medical Reports (CPIR-MR), which translates original medical reports into more comprehensible language. Our primary contribution is the creation of a new extension to the IU X-Ray dataset, providing Simplified Medical Reports (SMRs) generated by CPIR-MR. Additionally, we demonstrate that standard methodologies can effectively produce these simplified reports by proposing a multi-modal text decoder (MTD) that combines BLIP with a classification network to generate simplified medical explanations (SMEs) when fine-tuned on SMRs.
Strategic Manipulation in Temporal Voting with Undesirable Candidates (Student Abstract)
Authors: Tzeh Yuan Neoh; Nicholas Teh
Venue: AAAI 2025 (main)

Abstract: We study a model of sequential decision-making where voters have dynamic preferences over a set of candidates that are undesirable. This models scenarios such as the implementation of projects that are overall beneficial to society, but impose individual costs on certain affected individuals. We show that while minimizing the sum of agents' disutilities can be done in polynomial time, minimizing the maximum disutility obtained by any agent is computationally intractable, even in restricted cases. We then examine the potential for agents to engage in strategic manipulation in response to these welfare objectives, offering insights into possible misconduct within such decision-making environments.
3M-Game: Multi-Modal Multi-Task Multi-Teacher Learning for Game Event Detection (Student Abstract)
Authors: Thye Shan Ng; Feiqi Cao; Soyeon Caren Han
Venue: AAAI 2025 (main)

Abstract: Esports has rapidly emerged as a global phenomenon with an ever-expanding audience on livestream platforms. However, due to the complex nature of the game, it becomes challenging for newcomers to comprehend the gaming situation. This research introduces a 3M-Game that integrates multi-modal (MM) information from the livestream platform, including chat and livestream, to uncover the event. While conventional MM models typically prioritise aligning MM data through concurrent training towards a unified objective, our framework leverages multiple independent teachers trained on different tasks to accomplish game event detection. The results show the effectiveness of the proposed framework. The code and appendix are in https://github.com/adlnlp/3m_game.
A Generative Approach at the Instance-Level for Image Segmentation Under Limited Training Data Conditions (Student Abstract)
Authors: Thanh-Danh Nguyen; Vinh-Tiep Nguyen; Tam V. Nguyen
Venue: AAAI 2025 (main)

Abstract: High-accuracy image segmentation models require abundant training annotated data which is costly for pixel-level annotations. Our work addresses a high-cost manual annotating process or the lack of detailed annotations via a generative approach. In particular, our approach (1) proposes the conditional instance-level synthesis to enrich the limited data to enhance the segmentation performance, and (2) employs the generative architectures to complete the segmentation task under few-shot learning concepts. The initial results on the Cityscapes benchmark emphasize our potential generative solution on the instance segmentation task given limited data.
Counterfactual Explanations of Time Varying Rankings (Student Abstract)
Authors: Ryusei Ohtani; Yuko Sakurai; Satoshi Oyama
Venue: AAAI 2025 (main)

Abstract: Counterfactual explanations in Explainable AI (XAI) identify which features to change to alter an outcome, but existing methods adjust only the features of a single agent. We present a new approach to re-evaluating rankings that is based on predictions of future features of the other agents in a ranking system. It uses an algorithm that provides a more realistic counterfactual explanation of changing the ranking of a particular agent. Computer experiments demonstrated that the proposed algorithm can capture the time variation of the entire ranking system in the inference results.
A Modal Logic of Optimality (Student Abstract)
Authors: James T. Oswald; Brandon Rozek; Thomas Macaulay Ferguson; Selmer Bringsjord
Venue: AAAI 2025 (main)

Abstract: We present our work on a new modal logic of optimality, OPT, whose semantics are modeled in terms of optimal paths through reward-weighted transition systems. We prove some basic properties of OPT, including its status as a normal modal logic, as well as its relation to some of the standard modal axioms. We end with a discussion of applications to AI and future research directions and extensions.
Hateful Meme Detection through Context-Sensitive Prompting and Fine-Grained Labeling (Student Abstract)
Authors: Rongxin Ouyang; Kokil Jaidka; Subhayan Mukerjee; Guangyu Cui
Venue: AAAI 2025 (main)

Abstract: The prevalence of multi-modal content on social media complicates automated moderation strategies. This calls for an enhancement in multi-modal classification and a deeper understanding of understated meanings in images and memes. Although previous efforts have aimed at improving model performance through fine-tuning, few have explored an end-to-end optimization pipeline that accounts for modalities, prompting, labelling, and fine-tuning. In this study, we propose an end-to-end conceptual framework for model opti- mization in complex tasks. Experiments support the efficacy of this traditional yet novel framework, achieving the highest accuracy and AUROC. Ablation experiments demonstrate that isolated optimisations are not ineffective on their own.
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)
Authors: Christianah Titilope Oyewale; Rafiat M. Bamimore Akodu; Faith Adeoluwa Adeyemi
Venue: AAAI 2025 (main)

Abstract: Assessing 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.
Enhancing Molecular Representation Learning Through the Combination of 3D and 2D Graph Machine Learning (Student Abstract)
Authors: Ian Tong Pan; Joseph D. Romano
Venue: AAAI 2025 (main)

Abstract: Molecular machine learning has broad applications across multiple domains such as drug development, environmental toxicology, and materials science. Various pre-trained frameworks using self-supervised representation learning have emerged to tackle the difficulty of obtaining large molecular datasets useful for training high-performing molecular machine learning models. In this study, we explore a novel representation learning framework trained using both 2D and 3D molecular data. Specifically, a 3D invariant graph neural network to learn how to capture 3D atomic information and then pass these atomic representations into a regular 2D graph neural network which can leverage molecular topology. Results from experiments demonstrate the representations produced by our method using both 3D and 2D molecular information lead to strong performance in downstream tasks.
Acoustic-to-Hyper-Spectral: Hyper-Spectral Image Construction from Frequency Spectrums Through Simulated Annealing (Student Abstract)
Authors: Ruth-Emely Pierau; Alaster Meehan; Hamid Rezatofighi; Peter J. Stuckey
Venue: AAAI 2025 (main)

Abstract: This abstract presents a simulated annealing based approach that constructs hyper-spectral images from the frequency spectrums of a distributed acoustic sensing system and iteratively improves them through the training of learnable filters. The aim is to construct an image that represents features of signals from events while repressing noise. Hyper-spectral images are specifically created for downstream computer vision tasks such as object detection. Hyper-spectral images are images with more than three channels that are derived from a frequency spectrum to obtain the spectrum for each image pixel. Simulated annealing is used to train the filters to automatically select frequencies and bin them into frequency bands. Each frequency band is mapped into an image channel. We fully integrate our filtering method with an object detection network so that filters are trained in conjunction with the neural network. The detection model serves as both the measure and the selector. Our simulated annealing approach significantly outperforms current state-of-the-art methods by a margin of 22%. Limitations include a dependency on randomness and excluding parts of the search space prematuraly due to the design of the local moves.
Entity Only vs. Inline Approaches: Evaluating LLMs for Adverse Drug Event Detection in Clinical Text (Student Abstract)
Authors: Howard Prioleau; Saurav Aryal
Venue: AAAI 2025 (main)

Abstract: Adverse Drug Events (ADEs) are a major healthcare issue in the United States, contributing to millions of outpatient and emergency department visits and ranking as the fourth leading cause of death. While many ADEs are identified post-market, improved detection methods are crucial for enhancing patient safety. This study explores the application of large language models (LLMs) to the n2c2 task for ADE detection, evaluating optimal prompting techniques without requiring ADE-specific training data. Results indicate that an entity-only extraction approach outperforms the inline method, offering higher precision, recall, and token efficiency. This study highlights the potential of LLMs for accurate ADE detection in clinical text, improving performance while maintaining model efficiency.
LLM-based Online Prediction of Time-varying Graph Signals (Student Abstract)
Authors: Dayu Qin; Yi Yan; Ercan Engin Kuruoglu
Venue: AAAI 2025 (main)

Abstract: In this paper, we propose a novel framework that leverages Large Language Models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to infer the missing observations. Tested on the task of the online prediction of wind-speed graph signals, our model outperforms online graph filtering algorithms in terms of accuracy, demonstrating the potential of LLMs in effectively addressing partially observed signals in graphs.
A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)
Authors: Shubhanshu Rao; Gaurav Kumar; Martin Agelin-Chaab
Venue: AAAI 2025 (main)

Abstract: Achieving optimal design is a crucial aspect of any design process for safe and efficient operation. Such tasks typically require numerous simulations over many iterations, which can become computationally expensive. This paper proposes a novel method that combines Physics-informed Neural Networks (PINNs) with a Genetic Algorithm to optimize the parameters of an airfoil that aims to achieve favourable aerodynamic conditions. Traditional solvers are computationally expensive for performing such tasks, but using PINNs can significantly reduce this while keeping accuracy high. The proposed approach shows the advantage of using PINNs in optimizing complex engineering problems.
Enhancing Detection of Relationship Abuse in Text with Multi-task Learning (Student Abstract)
Authors: Caroline Rinks
Venue: AAAI 2025 (main)

Abstract: Intimate Partner Violence is a global, life-threatening public health issue that can be prevented by recognizing emotionally aggressive behaviors that signal the potential for future relationship abuse. To help identify these precursory unhealthy behaviors, this study proposes a Multi-task Learning framework for training robust models capable of detecting not only physically abusive behaviors but also emotionally abusive behaviors, such as belittling or manipulation, which historically precede physical abuse. Preliminary results indicate that Multi-task Learning can improve detection of emotional abuse and help tune detection models to particular kinds of relationship abuse.
Hybrid Quantum-Classical Style Transfer (Student Abstract)
Authors: Emily Jimin Roh; Joo Yong Shim; Soohyun Park; Joongheon Kim
Venue: AAAI 2025 (main)

Abstract: This paper proposes a novel quantum style transfer (QST) in hybrid quantum-classical computing. QST leverages quantum computing's ability to process high-dimensional data efficiently. Our approach aims to decrease both inference time and complexity while maintaining performance, presenting a viable solution that enhances the scalability and efficiency of image generation technologies.
Hide Exposures by Removing Mastermind’s External Sources on Social Network (Student Abstract)
Authors: Nilanjana Saha; Narayan Changder; Redha Taguelmimt; Samir Aknine; Animesh Dutta
Venue: AAAI 2025 (main)

Abstract: On social media, it is easy to see how people are connected and find the leader, or mastermind of a network. The mastermind is responsible for the planning of the activities in the network. Hiding the mastermind is important to carry out these activities. This raises the question for the mastermind: How effectively can the mastermind hide his connections to avoid being found? We propose an efficient heuristic algorithm called HERMES (Hide Exposures by Removing Mastermind’s External Sources) to address this. Experiments on Facebook and Google networks show that HERMES hides the mastermind more effectively than the state-of-the-art, achieving time gains of 103 and 1397 seconds, respectively, and improving influence value by up to 11.11%.
Generative Flow Networks for Lead Optimization in Drug Design (Student Abstract)
Authors: Akshat Santhana Gopalan; Sowmya Ramaswamy Krishnan
Venue: AAAI 2025 (main)

Abstract: This 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.
Combating Phone Scams with LLM-based Detection: Where Do We Stand? (Student Abstract)
Authors: Zitong Shen; Kangzhong Wang; Youqian Zhang; Grace Ngai; Eugene Yujun Fu
Venue: AAAI 2025 (main)

Abstract: Phone scams pose a significant threat to individuals and communities, causing substantial financial losses and emotional distress. Despite ongoing efforts to combat these scams, scammers continue to adapt and refine their tactics, making it imperative to explore innovative countermeasures. This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls. By analyzing the conversational dynamics between scammers and victims, LLM-based detectors can identify potential scams as they occur, offering immediate protection to users. While such approaches demonstrate promising results, we also acknowledge the challenges of biased datasets, relatively low recall, and hallucinations that must be addressed for further advancement in this field.
Comparative Analysis of Demonstration Selection Algorithms for In-Context Learning in Large Language Models (Student Abstract)
Authors: Dong Shu; Mengnan Du
Venue: AAAI 2025 (main)

Abstract: Demonstration selection algorithms play a crucial role in optimizing Large Language Models' (LLMs) in-context learning performance. Despite numerous proposed algorithms, their comparative effectiveness remains understudied. We present a comprehensive evaluation of six state-of-the-art demonstration selection algorithms across five datasets, examining both their effectiveness and computational efficiency. Our findings reveal significant trade-offs: while some demonstration selection algorithms achieve superior accuracy, they incur substantial computational costs. We also discover that increasing demonstration examples doesn't consistently improve performance, and some sophisticated algorithms struggle to outperform random selection in certain scenarios. These insights provide valuable benchmarks for future algorithm development and practical implementation. Our code is available at https://github.com/Tizzzzy/Demonstration_Selection_Overview.
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC) (Student Abstract)
Authors: Kartik Singhal; Gautam Shroff
Venue: AAAI 2025 (main)

Abstract: The Abstraction and Reasoning Corpus (ARC) poses a significant challenge to artificial intelligence, demanding broad generalization and few-shot learning capabilities that remain elusive for current deep learning methods, including large language models (LLMs) (Chollet 2019). While LLMs excel in program synthesis, their direct application to ARC yields limited success. To address this, we introduce ConceptSearch, a novel function-search algorithm that leverages LLMs for program generation and employs a concept-based scoring method to guide the search efficiently. Experimental results demonstrate that ConceptSearch outperforms direct GPT-4 prompting, with our novel scoring function boosting efficiency by ~30% compared to the baseline Hamming distance scoring. Code at https://github.com/kksinghal/concept-search
Domain-Informed Label Fusion Surpasses LLMs in Free-Living Activity Classification (Student Abstract)
Authors: Shovito Barua Soumma; Abdullah Mamun; Hassan Ghasemzadeh
Venue: AAAI 2025 (main)

Abstract: FuSE-MET addresses critical challenges in deploying human activity recognition (HAR) systems in uncontrolled environments by effectively managing noisy labels, sparse data, and undefined activity vocabularies. By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FuSE-MET optimizes label merging, reducing label complexity and improving classification accuracy. Our approach outperforms the state-of-the-art techniques, including ChatGPT-4, by balancing semantic meaning and physical intensity.
LAqua: Laplacian Pyramids for Aquatic Segmentation (Student Abstract)
Authors: Laven Srivastava; Ishaan Gakhar
Venue: AAAI 2025 (main)

Abstract: Semantic segmentation of marine environments is essential for autonomous navigation of unmanned surface vessels (USVs) as well as the detection of environmental hazards such as oil spills. To tackle the challenges of accurate environmental perception, we propose a lightweight semantic segmentation network, LAqua (Laplacians for Aquatic Segmentation), which leverages Laplacian pyramids to enhance edge detection in marine imagery. Our method drastically reduces computational requirements while maintaining high accuracy in generating semantic masks for marine environments. We evaluate LAqua on two distinct datasets: one focused on detecting oil spills in port environments and another on environmental segmentation for USVs. Results show that LAqua not only performs well across varied marine settings but also achieves comparable or superior segmentation accuracy with far fewer parameters than other models. This efficiency highlights LAqua's potential for applications in real-time detection for marine environments.