GenAI4ELab/papercli-papers huggingface dataset:
To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning
Authors: Hilmy Baja; Michiel Kallenberg; Ioannis N. Athanasiadis
Venue: AAAI 2025 (main)

Abstract: Farmers rely on in-field observations to make well-informed crop management decisions to maximize profit and minimize adverse environmental impact.
However, obtaining real-world crop state measurements is labor-intensive, time-consuming and expensive.
In most cases, it is not feasible to gather crop state measurements before every decision moment.
Moreover, in previous research pertaining to farm management optimization, these observations are often assumed to be readily available without any cost, which is unrealistic.
Hence, enabling optimization without the need to have *temporally complete* crop state observations is important.
An approach to that problem is to include measuring as part of decision making.
As a solution, we apply reinforcement learning (RL) to recommend opportune moments to simultaneously measure crop features and apply nitrogen fertilizer.
With realistic considerations, we design an RL environment with explicit crop feature measuring costs.
While balancing costs, we find that an RL agent, trained with recurrent PPO, discovers adaptive measuring policies that follow critical crop development stages, with results aligned by what domain experts would consider a sensible approach. 
Our results highlight the importance of measuring when crop feature measurements are not readily available.
ScamNet: Toward Explainable Large Language Model-Based Fraudulent Shopping Website Detection
Authors: Marzieh Bitaab; Alireza Karimi; Zhuoer Lyu; Ahmadreza Mosallanezhad; Adam Oest; Ruoyu Wang; Tiffany Bao; Yan Shoshitaishvili; Adam Doupé
Venue: AAAI 2025 (main)

Abstract: Fraudulent shopping websites pose a significant threat to online consumers and legitimate businesses: in 2023, victims of such scams reported $392 million in losses to the Federal Trade Commission. This alarming trend not only impacts individuals but also erodes societal trust in e-commerce, necessitating urgent countermeasures. While previous studies have attempted to identify these fraudulent websites at scale, they face limitations such as potential bias in data collection, overreliance on easily manipulated features, and the lack of explainable results. This study explores the potential of Large Language Models (LLMs) in identifying fraudulent shopping websites, revealing that current LLMs underperform compared to existing machine learning models. To address this, we propose ScamNet, a fine-tuned LLM for explainable fraudulent shopping website detection. Our experimental results on real-world datasets demonstrate a breakthrough in detection performance from 22.35% detection rate to 95.59%, particularly in identifying subtle deceptive tactics such as using a legitimate-looking website template. ScamNet offers interpretable insights into its decision-making process, enhancing transparency and overcoming a key limitation of previous approaches.
Evaluating Index-based Treatment Allocation in Underresourced Communities
Authors: Niclas Boehmer; Yash Nair; Sanket Shah; Lucas Janson; Aparna Taneja; Milind Tambe
Venue: AAAI 2025 (main)

Abstract: In many applications of AI for Social Impact (e.g., when allocating spots in support programs for underserved communities), resources are scarce and an allocation policy is needed to decide who receives a resource. Before being deployed at scale, a rigorous evaluation of an AI-powered allocation policy is vital. In this paper, we introduce the methods necessary to evaluate index-based allocation policies, which allocate a limited number of resources to those who need them the most. Such policies create dependencies between agents, rendering standard statistical tests invalid and ineffective. Addressing the arising practical and technical challenges, we describe an efficient estimator and methods for drawing valid statistical conclusions. Our extensive experiments validate our methodology in practical settings while also showcasing its statistical power. We conclude by proposing and empirically verifying extensions of our methodology that enable us to reevaluate a past randomized control trial conducted with 10000 beneficiaries for a mHealth program for pregnant women. Our new methodology allows us to draw previously invisible conclusions when comparing two different ML allocation policies.
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
Authors: Nikolaos Ioannis Bountos; Arthur Ouaknine; Ioannis Papoutsis; David Rolnick
Venue: AAAI 2025 (main)

Abstract: Forests are vital to ecosystems, supporting biodiversity and
essential services, but are rapidly changing due to land use
and climate change. Understanding and mitigating negative
effects requires parsing data on forests at global scale from
a broad array of sensory modalities, and using them in diverse forest monitoring applications. Such diversity in data
and applications can be effectively addressed through the
development of a large, pre-trained foundation model that
serves as a versatile base for various downstream tasks. However, remote sensing modalities, which are an excellent fit for
several forest management tasks, are particularly challenging considering the variation in environmental conditions,
object scales, image acquisition modes and spatio-temporal
resolutions, etc. With that in mind, we present the first unified Forest Monitoring Benchmark (FoMo-Bench), carefully
constructed to evaluate foundation models with such flexibility. FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data, covering a variety of geographical regions, and including multispectral, red-green-blue, synthetic aperture radar and LiDAR data with various temporal, spatial and spectral resolutions. FoMo-Bench includes multiple types of forest-monitoring tasks, spanning
classification, segmentation, and object detection. To enhance task and geographic diversity in FoMo-Bench, we introduce TalloS, a global dataset combining satellite imagery with ground-based annotations for tree species classification
across 1,000+ categories and hierarchical taxonomic levels.
Finally, we propose FoMo-Net, a pre-training framework to
develop foundation models with the capacity to process any
combination of commonly used modalities and spectral bands
in remote sensing. This work aims to inspire research collaborations between machine learning and forest biology researchers in exploring scalable multi-modal and multi-task models for forest monitoring and beyond. All code, data and
appendices are published in the repository and on ArXiv.
PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detection
Authors: Tri Cao; Chengyu Huang; Yuexin Li; Wang Huilin; Amy He; Nay Oo; Bryan Hooi
Venue: AAAI 2025 (main)

Abstract: Phishing attacks are a major threat to online security, exploiting user vulnerabilities to steal sensitive information. Various methods have been developed to counteract phishing, each with varying levels of accuracy, but they also face notable limitations. In this study, we introduce PhishAgent, a multimodal agent that combines a wide range of tools, integrating both online and offline knowledge bases with Multimodal Large Language Models (MLLMs). This combination leads to broader brand coverage, which enhances brand recognition and recall. Furthermore, we propose a multimodal information retrieval framework designed to extract the relevant top k items from offline knowledge bases, using available information from a webpage, including logos and HTML. Our empirical results, based on three real-world datasets, demonstrate that the proposed framework significantly enhances detection accuracy and reduces both false positives and false negatives, while maintaining model efficiency. Additionally, PhishAgent shows strong resilience against various types of adversarial attacks.
Learning Production Functions for Supply Chains with Graph Neural Networks
Authors: Serina Chang; Zhiyin Lin; Benjamin Yan; Swapnil Bembde; Qi Xiu; Chi Heem Wong; Yu Qin; Frank Kloster; Xi Luo; Raj Palleti; Jure Leskovec
Venue: AAAI 2025 (main)

Abstract: The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to better understand and improve supply chains, and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture these hidden relationships between nodes’ inputs and outputs. Here, we introduce a new class of models for this setting, by combining temporal GNNs with a novel inventory module, which learns production functions via attention weights and a special loss function. We evaluate our models extensively on real supply chains data, along with data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, outperforming the strongest baseline by 6-50% (across datasets), and forecast future transactions, outperforming the strongest baseline by 11-62%.
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
Authors: Khaoula Chehbouni; Martine de Cock; Gilles Caporossi; Afaf Taik; Reihaneh Rabbany; Golnoosh Farnadi
Venue: AAAI 2025 (main)

Abstract: The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which is the use of strategies by predators to lure children into sexual exploitation. Previous efforts to detect grooming in industry and academia have involved accessing and monitoring private conversations through centrally-trained models or sending private conversations to a global server. In this work, we implement a privacy-preserving pipeline for the early detection of sexual predators. We leverage federated learning and differential privacy in order to create safer online spaces for children while respecting their privacy. We investigate various privacy-preserving implementations and discuss their benefits and shortcomings. Our extensive evaluation using real-world data proves that privacy and utility can coexist with only a slight reduction in utility.
Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulation
Authors: Zirong Chen; Elizabeth Chason; Noah Mladenovski; Erin Wilson; Kristin Mullen; Stephen Martini; Meiyi Ma
Venue: AAAI 2025 (main)

Abstract: Emergency response services are vital for enhancing public safety by safeguarding the environment, property, and human lives. 
As frontline members of these services, 9-1-1 dispatchers have a direct impact on response times and the overall effectiveness of emergency operations. However, traditional dispatcher training methods, which rely on role-playing by experienced personnel, are labor-intensive, time-consuming, and often neglect the specific needs of underserved communities. To address these challenges, we introduce Sim911, the first training simulation for 9-1-1 dispatchers powered by Large Language Models (LLMs). Sim911 enhances training through three key technical innovations: (1) knowledge construction, which utilizes archived 9-1-1 call data to generate simulations that closely mirror real-world scenarios;  (2) context-aware controlled generation, which employs dynamic prompts and vector bases to ensure that LLM behavior aligns with training objectives; and (3) validation with looped correction, which filters out low-quality responses and refines the system performance. Experimental results show Sim911's superior performance in effectiveness and equity.  Beyond its technical advancements, Sim911 delivers significant social impacts. Successfully deployed in the Metro X of Emergency Communications (MXDEC)(PS: To ensure a double-blind review, we refer to the city as 'City X,' a mid-sized U.S. city with a population of over 700,000. Its Metro Department of Emergency Communications (MXDEC) employs around 80 dispatchers and call-takers. For the rest of the paper, we refer to MXDEC as 'DEC.')  Sim911 has been integrated into multiple training sessions, saving time for dispatchers. By supporting a diverse range of incident types and caller tags, Sim911 provides more realistic and inclusive training experiences. In a conducted user study, 90.00 percent of participants found Sim911 to be as effective or even superior to traditional human-led training, making it a valuable tool for emergency communications centers nationwide, particularly those facing staffing challenges.
Uncertainty-aware Knowledge Tracing
Authors: Weihua Cheng; Hanwen Du; Chunxiao Li; Ersheng Ni; Liangdi Tan; Tianqi Xu; Yongxin Ni
Venue: AAAI 2025 (main)

Abstract: Knowledge Tracing (KT) is crucial in education assessment, which focuses on depicting students' learning states and assessing students' mastery of subjects. With the rise of modern online learning platforms, particularly massive open online courses (MOOCs), an abundance of interaction data has greatly advanced the development of the KT technology. Previous research commonly adopts deterministic representation to capture students' knowledge states, which neglects the uncertainty during student interactions and thus fails to model the true knowledge state in learning process. In light of this, we propose an Uncertainty-Aware Knowledge Tracing model (UKT) which employs stochastic distribution embeddings to represent the uncertainty in student interactions, with a Wasserstein self-attention mechanism designed to capture the transition of state distribution in student learning behaviors. Additionally, we introduce the aleatory uncertainty-aware contrastive learning loss, which strengthens the model's robustness towards different types of uncertainties. Extensive experiments on six real-world datasets demonstrate that UKT not only significantly surpasses existing deep learning-based models in KT prediction, but also shows unique advantages in handling the uncertainty of student interactions.
Leveraging Computer Vision and Visual LLMs for Cost-Effective and Consistent Street Food Safety Assessment in Kolkata India
Authors: Alexey Chernikov; Klaus Ackermann; Caitlin Brown; Denni Tommasi
Venue: AAAI 2025 (main)

Abstract: Ensuring street food safety in developing countries is crucial due to the high prevalence of foodborne illnesses.  Traditional methods of food safety assessments face challenges such as resource constraints, logistical issues, and subjective biases influenced by surveyors personal lived experiences, particularly when interacting with local communities. For instance, a local food safety inspector may inadvertently overrate the quality of infrastructure due to prior familiarity or past purchases, thereby compromising objective assessment.  This subjectivity highlights the necessity for technologies that reduce human biases and enhance the accuracy of survey data across various domains.
This paper proposes a novel approach based on a combination of Computer Vision and a lightweight Visual Large Language Model (VLLM) to automate the detection and analysis of critical food safety infrastructure in street food vendor environments at a field experiment in Kolkata, India. The system utilises a three-stage object extraction pipeline from the video to identify, extract and select unique representations of critical elements such as hand-washing stations, dishwashing areas, garbage bins, and water tanks. These four infrastructure items are crucial for maintaining safe food practices, irrespective of the specific methods employed by the vendors. A VLLM then analyses the extracted representations to assess compliance with food safety standards. Notably, over half of the pipeline can be processed using a user's smartphone, significantly reducing government server workload. By leveraging this decentralised approach, the proposed system decreases the analysis cost by many orders of magnitude compared to alternatives like ChatGPT or Claude 3.5. Additionally, processing data on local government servers provides better privacy and security than cloud platforms, addressing critical ethical considerations. This automated approach significantly improves efficiency, consistency, and scalability, providing a robust solution to enhance public health outcomes in developing regions.
Optimizing Heat Alert Issuance with Reinforcement Learning
Authors: Ellen M. Considine; Rachel C. Nethery; Gregory A. Wellenius; Francesca Dominici; Mauricio Tec
Venue: AAAI 2025 (main)

Abstract: A key strategy in societal adaptation to climate change is using alert systems to prompt preventative action and reduce the adverse health impacts of extreme heat events. This paper implements and evaluates reinforcement learning (RL) as a tool to optimize the effectiveness of such systems. Our contributions are threefold. First, we introduce a new publicly available RL environment enabling the evaluation of the effectiveness of heat alert policies to reduce heat-related hospitalizations. The rewards model is trained from a comprehensive dataset of historical weather, Medicare health records, and socioeconomic/geographic features. We use scalable Bayesian techniques tailored to the low-signal effects and spatial heterogeneity present in the data. The transition model uses real historical weather patterns enriched by a data augmentation mechanism based on climate region similarity. Second, we use this environment to evaluate standard RL algorithms in the context of heat alert issuance. Our analysis shows that policy constraints are needed to improve RL's initially poor performance. Third, a post-hoc contrastive analysis provides insight into scenarios where our modified heat alert-RL policies yield significant gains/losses over the current National Weather Service alert policy in the United States.
PrecipDiff: Leveraging Image Diffusion Models to Enhance Satellite-Based Precipitation Observations
Authors: Ting-Yu Dai; Hayato Ushijima-Mwesigwa
Venue: AAAI 2025 (main)

Abstract: A recent report from the World Meteorological Organization (WMO) highlights that water-related disasters have caused the highest human losses among natural disasters over the past 50 years, with over 91\% of deaths occurring in low-income countries. This disparity is largely due to the lack of adequate ground monitoring stations, such as weather surveillance radars (WSR), which are expensive to install. For example, while the US and Europe combined possess over 600 WSRs, Africa, despite having almost one and half times their landmass, has fewer than 40.
To address this issue, satellite-based observations offer a global, near-real-time monitoring solution. However, they face several challenges like accuracy, bias, and low spatial resolution. 
This study leverages the power of diffusion models and residual learning to address these limitations in a unified framework. We introduce the first diffusion model for correcting the inconsistency between different precipitation products. Our method demonstrates the effectiveness in downscaling satellite precipitation estimates from 10 km to 1 km resolution. Extensive experiments conducted in the Seattle region demonstrate significant improvements in accuracy, bias reduction, and spatial detail. Importantly, our approach achieves these results using only precipitation data, showcasing the potential of a purely computer vision-based approach for enhancing satellite precipitation products and paving the way for further advancements in this domain.
RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?
Authors: Adrian de Wynter; Ishaan Watts; Tua Wongsangaroonsri; Minghui Zhang; Noura Farra; Nektar Ege Altıntoprak; Lena Baur; Samantha Claudet; Pavel Gajdušek; Qilong Gu; Anna Kaminska; Tomasz Kaminski; Ruby Kuo; Akiko Kyuba; Jongho Lee; Kartik Mathur; Petter Merok; Ivana Milovanović; Nani Paananen; Vesa-Matti Paananen; Anna Pavlenko; Bruno Pereira Vidal; Luciano Ivan Strika; Yueh Tsao; Davide Turcato; Oleksandr Vakhno; Judit Velcsov; Anna Vickers; Stéphanie F. Visser; Herdyan Widarmanto; Andrey Zaikin; Si-Qing Chen
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) and small language models (SLMs) are being adopted at remarkable speed, although their safety still remains a serious concern. With the advent of multilingual S/LLMs, the question now becomes a matter of scale: can we expand multilingual safety evaluations of these models with the same velocity at which they are deployed? To this end, we introduce RTP-LX, a human-transcreated and human-annotated corpus of toxic prompts and outputs in 28 languages. RTP-LX follows participatory design practices, and a portion of the corpus is especially designed to detect culturally-specific toxic language. We evaluate 10 S/LLMs on their ability to detect toxic content in a culturally-sensitive, multilingual scenario. We find that, although they typically score acceptably in terms of accuracy, they have low agreement with human judges when scoring holistically the toxicity of a prompt; and have difficulty discerning harm in context-dependent scenarios, particularly with subtle-yet-harmful content (e.g. microaggressions, bias). We release this dataset to contribute to further reduce harmful uses of these models and improve their safe deployment.
Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition
Authors: Thao Do; Dinh Phu Tran; An Vo; Daeyoung Kim
Venue: AAAI 2025 (main)

Abstract: Extracting fine-grained OCR text from aged documents in diacritic languages remains challenging due to unexpected artifacts, time-induced degradation, and lack of datasets. While standalone spell correction approaches have been proposed, they show limited performance for historical documents due to numerous possible OCR error combinations and differences between modern and classical corpus distributions.
We propose a method utilizing available content-focused ebooks as a reference base to correct imperfect OCR-generated text, supported by large language models. This technique generates high-precision pseudo-page-to-page labels for diacritic languages, where small strokes pose significant challenges in historical conditions. The pipeline eliminates various types of noise from aged documents and addresses issues such as missing characters, words, and disordered sequences.
Our post-processing method, which generated a large OCR dataset of classical Vietnamese books, achieved a mean grading score of 8.72 on a 10-point scale. This outperformed the state-of-the-art transformer-based Vietnamese spell correction model, which scored 7.03, when evaluated on a sampled subset of the dataset. We also trained a baseline OCR model to assess and compare it with well-known engines. Experimental results demonstrate the strength of our baseline model compared to widely used open-source solutions. The resulting dataset will be released publicly to support future studies.