GenAI4ELab/papercli-papers huggingface dataset:
MotifGPL: Motif-Enhanced Graph Prototype Learning for Deciphering Urban Social Segregation
Authors: Tengfei He; Xiao Zhou
Venue: AAAI 2025 (main)

Abstract: Social segregation in cities, spanning racial, residential, and income dimensions, is becoming increasingly diverse and severe. As urban spaces and social dynamics grow more complex, residents experience varying levels of segregation, which, if left unaddressed, could exacerbate crime rates, fuel social tensions, and lead to other societal challenges. Effectively addressing these issues requires a comprehensive analysis of the underlying structures of urban spaces and resident interactions. While previous studies have primarily focused on surface-level indicators of segregation, they often fail to explore the complexity of urban structure and mobility dynamics, leaving gaps in understanding modern segregation patterns. To fill this gap, we propose the Motif-Enhanced Graph Prototype Learning (MotifGPL) framework, offering a novel approach to studying urban segregation. The framework consists of three key modules: prototype-based graph structure extraction, motif distribution discovery, and urban graph reconstruction. Specifically, we use prototype-based learning to extract key urban graph prototypes from both spatial and origin-destination graphs, incorporating attributes such as points of interest, street images, and flow indices. The motif distribution discovery module enhances interpretability by matching each prototype to similar motifs, which represent simplified graph structures that reflect local patterns. These motifs are then used to guide the reconstruction of urban graphs, enabling a more detailed exploration of spatial structures and mobility patterns. By identifying critical motifs influencing urban segregation, MotifGPL offers insights to guide the design of urban environments that can help reduce segregation. Experimental results demonstrate that MotifGPL effectively uncovers these key motifs and provides actionable insights for mitigating segregation.
Cost-Aware Near-Optimal Policy Learning
Authors: Joy He-Yueya; Jonathan Lee; Matthew Jörke; Emma Brunskill
Venue: AAAI 2025 (main)

Abstract: It is often of interest to learn a context-sensitive decision policy, such as in contextual multi-armed bandit processes. To quantify the efficiency of a machine learning algorithm for such settings, probably approximately correct (PAC) bounds, which bound the number of samples required, or cumulative regret guarantees, are typically used. However, real-world settings often have limited resources for experimentation, and decisions/interventions may differ in the amount of resources required (e.g., money or time). Therefore, it is of interest to consider how to design an experiment strategy that reduces the experimental budget needed to learn a near-optimal contextual policy. Unlike reinforcement learning or bandit approaches that embed costs into the reward function, we focus on reducing resource use in learning a near-optimal policy without resource constraints. We introduce two resource-aware algorithms for the contextual bandit setting and prove their soundness. Simulations based on real-world datasets demonstrate that our algorithms significantly reduce the resources needed to learn a near-optimal decision policy compared to previous resource-unaware methods.
Data Augmentation Approaches for Satellite Imagery
Authors: Laurel M. Hopkins; Weng-Keen Wong; Hannah Kerner; Fuxin Li; Rebecca A. Hutchinson
Venue: AAAI 2025 (main)

Abstract: Deep learning models commonly benefit from data augmentation techniques to diversify the set of training images. When working with satellite imagery, it is common for practitioners to apply a limited set of transformations developed for natural images (e.g., flip and rotate) to expand the training set without overly modifying the satellite images. There are many techniques for natural image data augmentation, but given the differences between the two domains, it is not clear whether data augmentation methods developed for natural images are well suited for satellite imagery. This paper presents an extensive experimental study on three classification and three regression tasks over four satellite image datasets. We compare common computer vision data augmentation techniques and propose three novel satellite-specific data augmentation strategies. Across tasks and datasets, we find that geometric transformations are beneficial for satellite imagery while color transformations generally are not. Additionally, our novel Sat-SlideMix, Sat-CutMix, and Sat-Trivial methods all exhibit strong performance across all tasks and datasets.
J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain
Authors: Yiran Hu; Huanghai Liu; Qingjing Chen; Ning Zheng; Chong Wang; Yun Liu; Charles L. A. Clarke; Weixing Shen
Venue: AAAI 2025 (main)

Abstract: As the scale and capabilities of Large Language Models (LLMs) increase, their applications in knowledge-intensive fields such as legal domain have garnered widespread attention. However, it remains doubtful whether these LLMs make judgments based on domain knowledge for reasoning. If LLMs base their judgments solely on specific words or patterns, rather than on the underlying logic of the language, the “LLM-as-judges” paradigm poses substantial risks in the real-world applications. To address this question, we propose a method of legal knowledge injection attacks for robustness testing, thereby inferring whether LLMs have learned legal knowledge and reasoning logic. In this paper, we propose J&H: an evaluation framework for detecting the robustness of LLMs under knowledge injection attacks in the legal domain. The aim of the framework is to explore whether LLMs perform deductive reasoning when accomplishing legal tasks. To further this aim, we have attacked each part of the reasoning logic underlying these tasks (major premise, minor premise, and conclusion generation). We have collected mistakes that legal experts might make in judicial decisions in the real world, such as typos, legal synonyms, inaccurate external legal statutes retrieval. However, in real legal practice, legal experts tend to overlook these mistakes and make judgments based on logic. However, when faced with these errors, LLMs are likely to be misled by typographical errors and may not utilize logic in their judgments. We conducted knowledge injection attacks on existing general and domain-specific LLMs. Current LLMs are not robust against the attacks employed in our experiments. In addition we propose and compare several methods to enhance the knowledge robustness of LLMs. All code can be found at the link.
Component-Level Segmentation for Oracle Bone Inscription Decipherment
Authors: Zhikai Hu; Yiu-ming Cheung; Yonggang Zhang; Zhang Peiying; Tang Pui Ling
Venue: AAAI 2025 (main)

Abstract: Oracle Bone Inscriptions (OBIs), as the earliest systematically organized pictographic script in China, hold significant importance in the study of the origins of Chinese civilization. Of the approximately 4,500 excavated OBI characters, only about one-third have been deciphered, leaving the remaining characters shrouded in mystery. Over the past decade, an increasing number of researchers have attempted to leverage artificial intelligence to assist in deciphering OBIs, but these efforts have not yet fully met the demands of this challenging objective. In this paper, we identify a key task—Component-Level OBI Segmentation—based on a successful deciphering case from 2018. This task aims to help experts quickly identify specific components within OBIs, thereby accelerating the deciphering process. Accordingly, we propose a new model to accomplish this task. Our model leverages a small amount of annotated data and a large amount of weakly annotated data and incorporates expert-provided prior knowledge, i.e., stroke rules, to automatically segment OBI components. Additionally, we train a series of auxiliary classifiers to evaluate the segmentation results during the test stage. We also invite experts to conduct a professional assessment of the results, which we cross-validated against our proposed evaluation metrics. Experimental results demonstrate that our method can accurately and clearly present the segmented components to experts.
Accessible, At-Home Detection of Parkinson’s Disease via Multi-Task Video Analysis
Authors: Md Saiful Islam; Tariq Adnan; Jan Freyberg; Sangwu Lee; Abdelrahman Abdelkader; Meghan Pawlik; Cathe Schwartz; Karen Jaffe; Ruth B. Schneider; Ray Dorsey; Ehsan Hoque
Venue: AAAI 2025 (main)

Abstract: Limited accessibility to neurological care leads to under-diagnosed Parkinson's Disease (PD), preventing early intervention. Existing AI-based PD detection methods primarily focus on unimodal analysis of motor or speech tasks, overlooking the multifaceted nature of the disease. To address this, we introduce a large-scale, multi-task video dataset of 1102 sessions (each containing videos of finger tapping, facial expression, and speech tasks captured via webcam) from 845 participants (272 with PD). We propose a novel Uncertainty-calibrated Fusion Network (UFNet) that leverages this multimodal data to enhance diagnostic accuracy. UFNet employs independent task-specific networks, trained with Monte Carlo Dropout for uncertainty quantification, followed by self-attended fusion of features, with attention weights dynamically adjusted based on task-specific uncertainties. We randomly split the participants into training (60%), validation (20%), and test (20%) sets to ensure patient-centered evaluation. UFNet significantly outperformed single-task models in terms of accuracy, area under the ROC curve (AUROC), and sensitivity while maintaining non-inferior specificity. Withholding uncertain predictions further boosted the performance, achieving 88.0 +- 0.3% accuracy, 93.0 +- 0.2% AUROC, 79.3 +- 0.9% sensitivity, and 92.6 +- 0.3% specificity, at the expense of not being able to predict for 2.3 +- 0.3% data (+- denotes 95% confidence interval). Further analysis suggests that the trained model does not exhibit any detectable bias across sex and ethnic subgroups and is most effective for individuals aged between 50 and 80. By merely requiring a webcam and microphone, our approach facilitates accessible home-based PD screening, especially in regions with limited healthcare resources.
Pre-trained Behavioral Model for Malicious User Prediction on Social Platform
Authors: Meng Jiang; Wenjie Wang; Shaofeng Hu; Kaishen Ou; Zhenjing Zheng; Fuli Feng
Venue: AAAI 2025 (main)

Abstract: The proliferation of malicious users on social platforms poses significant financial and psychological threats, with activities ranging from scams to the dissemination of illicit content. Existing malicious user prediction comprises supervised and self-supervised learning methods. However, the former relies on extensive labeled malicious users for training, while the latter typically focuses on one form of malicious activity and depends heavily on manually crafted rules and features during pre-training. Moreover, existing pre-training methods fail to effectively capture the crucial repetitive and sporadic behavior patterns of malicious users. To address these limitations, we propose a Malicious User Behavior Pre-training framework (MaP) to build pre-trained behavior models. MaP integrates malicious pattern recognition with behavior consistency augmentation and local disruption augmentation strategies for contrastive learning to capture repetitive and sporadic malicious patterns, respectively. We instantiate MaP on a billion-level behavior pre-training scenario within an industry context. Both online and offline evaluations validate the superior performance of MaP in malicious user detection and classification.
VidSole: A Multimodal Dataset for Joint Kinetics Quantification and Disease Detection with Deep Learning
Authors: Archit Kambhamettu; Samantha Snyder; Maliheh Fakhar; Samuel Audia; Ross Miller; Jae Kun Shim; Aniket Bera
Venue: AAAI 2025 (main)

Abstract: Understanding internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis; however, current methods of measuring joint risk factors are time-consuming, expensive, and restricted to lab settings. In this paper, we enable the large-scale, cost-effective biomechanical analysis of joint loading via three key contributions: the development and deployment of novel instrumented insoles, the creation of a large multimodal biomechanics dataset (VidSole), and a baseline deep learning pipeline to predict internal joint loading factors. Our novel instrumented insole measures the tri-axial forces and moments across five high-pressure points under the foot. VidSole consists of the forces and moments measured by these insoles along with corresponding RGB video from two viewpoints, 3D body motion capture, and force plate data for over 2,600 trials of 52 diverse participants performing four fundamental activities of daily living (sit-to-stand, stand-to-sit, walking, and running). We feed the insole data and kinematic parameters extractable from video (i.e., pose, knee angle) into a deep learning pipeline consisting of an ensemble Gated Recurrent Unit (GRU) activity classifier followed by activity-specific Long Short Term Memory (LSTM) regression networks to estimate knee adduction moment (KAM), a biomechanical risk factor for knee osteoarthritis. The successful classification of activities at an accuracy of 99.02 percent and KAM estimation with mean absolute error (MAE) less than 0.5 percent*body weight*height, the current threshold for accurately detecting knee osteoarthritis with KAM, illustrates the usefulness of our dataset for future research and clinical settings.
Fields of The World: A Machine Learning Benchmark Dataset for Global Agricultural Field Boundary Segmentation
Authors: Hannah Kerner; Snehal Chaudhari; Aninda Ghosh; Caleb Robinson; Adeel Ahmad; Eddie Choi; Nathan Jacobs; Chris Holmes; Matthias Mohr; Rahul Dodhia; Juan M Lavista Ferres; Jennifer Marcus
Venue: AAAI 2025 (main)

Abstract: Crop field boundaries are foundational datasets for agricultural monitoring and assessments but are expensive to collect manually. Machine learning (ML) methods for automatically extracting field boundaries from remotely sensed images could help realize the demand for these datasets at a global scale. However, current ML methods for field instance segmentation lack sufficient geographic coverage, accuracy, and generalization capabilities. Further, research on improving ML methods is restricted by the lack of labeled datasets representing the diversity of global agricultural fields. We present Fields of The World (FTW)---a novel ML benchmark dataset for agricultural field instance segmentation spanning 24 countries on four continents (Europe, Africa, Asia, and South America). FTW is an order of magnitude larger than previous datasets with 70,462 samples, each containing instance and semantic segmentation masks paired with multi-date, multi-spectral Sentinel-2 satellite images. We provide results from baseline models for the new FTW benchmark, show that models trained on FTW have better zero-shot and fine-tuning performance in held-out countries than models that aren't pre-trained with diverse datasets, and show positive qualitative zero-shot results of FTW models in a real-world scenario -- running on Sentinel-2 scenes over Ethiopia.
Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes
Authors: Joonhyuk Ko; Juba Ziani; Saswat Das; Matt Williams; Ferdinando Fioretto
Venue: AAAI 2025 (main)

Abstract: Statistical agencies rely on sampling techniques to collect socio-demographic data crucial for policy-making and resource allocation. This paper shows that surveys of important societal relevance introduce sampling errors that unevenly impact group-level estimates, thereby compromising fairness in downstream decisions. To address these issues, this paper introduces an optimization approach modeled on real-world survey design processes, ensuring sampling costs are optimized while maintaining error margins within prescribed tolerances. Additionally, privacy-preserving methods used to determine sampling rates can further impact these fairness issues. This paper explores the impact of differential privacy on the statistics informing the sampling process, revealing a surprising effect: not only is the expected negative effect from the addition of noise for differential privacy negligible, but also this privacy noise can in fact reduce unfairness as it positively biases smaller counts. These findings are validated over an extensive analysis using datasets commonly applied in census statistics.
FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion Generation
Authors: Jinlin Li; Xintong Li; Xiao Zhou
Venue: AAAI 2025 (main)

Abstract: As global populations age rapidly, incorporating age-specific considerations into urban planning has become essential to addressing the urgent demand for age-friendly built environments and ensuring sustainable urban development. However, current practices often overlook these considerations, resulting in inadequate and unevenly distributed elderly services in cities. There is a pressing need for equitable and optimized urban renewal strategies to support effective age-friendly planning. To address this challenge, we propose a novel framework, Fairness-driven Age-friendly community Planning via Conditional Diffusion generation (FAP-CD). FAP-CD leverages a conditioned graph denoising diffusion probabilistic model to learn the joint probability distribution of aging facilities and their spatial relationships at a fine-grained regional level. Our framework generates optimized facility distributions by iteratively refining noisy graphs, conditioned on the needs of the elderly during the diffusion process. Key innovations include a demand-fairness pre-training module that integrates community demand features and facility characteristics using an attention mechanism and min-max optimization, ensuring equitable service distribution across regions. Additionally, a discrete graph structure captures walkable accessibility within regional road networks, guiding model sampling. To enhance information integration, we design a graph denoising network with an attribute augmentation module and a hybrid graph message aggregation module, combining local and global node and edge information. Empirical results across multiple metrics demonstrate the effectiveness of FAP-CD in balancing age-friendly needs with regional equity, achieving an average improvement of 41% over competitive baseline models.
KD-MSLRT: Lightweight Sign Language Recognition Model Based on Mediapipe and 3D to 1D Knowledge Distillation
Authors: Yulong Li; Bolin Ren; Ke Hu; Changyuan Liu; Zhengyong Jiang; Kang Dang; Jionglong Su
Venue: AAAI 2025 (main)

Abstract: Artificial intelligence has achieved notable results in sign language recognition and translation. However, relatively few efforts have been made to significantly improve the quality of life for the 72 million hearing-impaired people worldwide. Sign language translation models, relying on video inputs, involves with large parameter sizes, making it time-consuming and computationally intensive to be deployed. This directly contributes to the scarcity of human-centered technology in this field. Additionally, the lack of datasets in sign language translation hampers research progress in this area. To address these, we first propose a cross-modal multi-knowledge distillation technique from 3D to 1D and a novel end-to-end pre-training text correction framework. Compared to other pre-trained models, our framework achieves significant advancements in correcting text output errors. Our model achieves a decrease in Word Error Rate (WER) of at least 1.4% on PHOENIX14 and PHOENIX14T datasets compared to the state-of-the-art CorrNet. Additionally, the TensorFlow Lite (TFLite) quantized model size is reduced to 12.93 MB, making it the smallest, fastest, and most accurate model to date. We have also collected and released extensive Chinese sign language datasets, and developed a specialized training vocabulary. To address the lack of research on data augmentation for landmark data, we have designed comparative experiments on various augmentation methods. Moreover, we performed a simulated deployment and prediction of our model on Intel platform CPUs and assessed the feasibility of deploying the model on other platforms.
Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages
Authors: Zihao Li; Yucheng Shi; Zirui Liu; Fan Yang; Ali Payani; Ninghao Liu; Mengnan Du
Venue: AAAI 2025 (main)

Abstract: The development of Large Language Models (LLMs) relies on extensive text corpora, which are often unevenly distributed across languages. This imbalance results in LLMs performing significantly better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. Currently, there is a lack of quantitative methods to evaluate the performance of LLMs in these low-resource languages. To address this gap, we propose the Language Ranker, an intrinsic metric designed to benchmark and rank languages based on LLM performance using internal representations. By comparing the LLM's internal representation of various languages against a baseline derived from English, we can assess the model's multilingual capabilities in a robust and language-agnostic manner. Our analysis reveals that high-resource languages exhibit higher similarity scores with English, demonstrating superior performance, while low-resource languages show lower similarity scores, underscoring the effectiveness of our metric in assessing language-specific capabilities. Besides, the experiments show that there is a strong correlation between the LLM’s performance in different languages and the proportion of those languages in its pre-training corpus. 
These insights underscore the efficacy of the Language Ranker as a tool for evaluating LLM performance across different languages, particularly those with limited resources.
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits
Authors: Biyonka Liang; Lily Xu; Aparna Taneja; Milind Tambe; Lucas Janson
Venue: AAAI 2025 (main)

Abstract: Public health programs often provide interventions to encourage program adherence, and effectively allocating interventions is vital for producing the greatest overall health outcomes, especially in underserved communities where resources are limited. Such resource allocation problems are often modeled as restless multi-armed bandits (RMABs) with unknown underlying transition dynamics, hence requiring online reinforcement learning (RL). We present Bayesian Learning for Contextual RMABs (BCoR), an online RL approach for RMABs that novelly combines techniques in Bayesian modeling with Thompson sampling to flexibly model the complex RMAB settings present in public health program adherence problems, namely context and non-stationarity. BCoR's key strength is the ability to leverage shared information within and between arms to learn the unknown RMAB transition dynamics quickly in intervention-scarce settings with relatively short time horizons, which is common in public health applications. Empirically, BCoR achieves substantially higher finite-sample performance over a range of experimental settings, including a setting using real-world adherence data that was developed in collaboration with ARMMAN, an NGO in India which runs a large-scale maternal mHealth program, showcasing BCoR practical utility and potential for real-world deployment.