GenAI4ELab/papercli-papers huggingface dataset:
HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation
Authors: Hao Sun; Xiaoli Tang; Chengyi Yang; Zhenpeng Yu; Xiuli Wang; Qijie Ding; Zengxiang Li; Han Yu
Venue: AAAI 2024 (main)

Abstract: Gas usage estimation plays a critical role in various aspects of the power generation and delivery business, including budgeting, resource planning, and environmental preservation. Federated Learning (FL) has demonstrated its potential in enhancing the accuracy and reliability of gas usage estimation by enabling distributedly owned data to be leveraged, while ensuring privacy and confidentiality. However, to effectively motivate stakeholders to contribute their high-quality local data and computational resources for this purpose, incentive mechanism design is key. In this paper, we report our experience designing and deploying the Hierarchical FL Incentive mechanism for Gas usage estimation (HiFi-Gas) system. It is designed to cater to the unique structure of gas companies and their affiliated heating stations. HiFi-Gas provides effective incentivization in a hierarchical federated learning framework that consists of a horizontal federated learning (HFL) component for effective collaboration among gas companies and multiple vertical federated learning (VFL) components for the gas company and its affiliated heating stations. To motivate active participation and ensure fairness among gas companies and heating stations, we incorporate a multi-dimensional contribution-aware reward distribution function that considers both data quality and model contributions. Since its deployment in the ENN Group in December 2022, HiFi-Gas has successfully provided incentives for gas companies and heating stations to actively participate in FL training, resulting in more than 12% higher average gas usage estimation accuracy and substantial gas procurement cost savings. This implementation marks the first successful deployment of a hierarchical FL incentive approach in the energy industry.
Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
Authors: Siva Likitha Valluru; Biplav Srivastava; Sai Teja Paladi; Siwen Yan; Sriraam Natarajan
Venue: AAAI 2024 (main)

Abstract: Building teams and promoting collaboration  are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel deployed system to recommend teams using a variety of AI methods, such that (1) each team achieves the highest possible  skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced amongst the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short- and long-term objectives. We validate the success of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams but higher goodness, and (2) qualitatively, by conducting a large-scale user study at a college-wide level, and demonstrate that users overall found the tool very useful and relevant. Lastly, we evaluate our system in two diverse settings in US and India (of researchers and proposal calls) to establish generality of our approach, and deploy it at a major US university for routine use.
Multi-Stage Prompting for Next Best Agent Recommendations in Adaptive Workflows
Authors: Prerna Agarwal; Harshit Dave; Jayachandu Bandlamudi; Renuka Sindhgatta; Kushal Mukherjee
Venue: AAAI 2024 (main)

Abstract: Traditional business processes such as loan processing, order processing, or procurement have a series of steps that are pre-defined at design and executed by enterprise systems. Recent advancements in new-age businesses, however, focus on having adaptive and ad-hoc processes by stitching together a set of functions or steps enabled through autonomous agents. Further, to enable business users to execute a flexible set of steps, there have been works on providing a conversational interface to interact and execute automation. Often, it is necessary to guide the user through the set of possible steps in the process (or workflow). Existing work on recommending the next agent to run relies on historical data. However, with changing workflows and new automation constantly getting added, it is important to provide recommendations without historical data. Additionally, hand-crafted recommendation rules do not scale. The adaptive workflow being a combination of structured and unstructured information, makes it harder to mine. Hence, in this work, we leverage Large Language Models (LLMs) to combine process knowledge with the meta-data of agents to discover NBAs specifically at cold-start. We propose a multi-stage approach that uses existing process knowledge and agent meta-data information to prompt LLM and recommend meaningful next best agent (NBA) based on user utterances.
A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill Level
Authors: J. Fredrik R. Bjørnland; Yrjar Gedde; Johannes Rehm; Irina Reshodko; Odd Erik Gundersen
Venue: AAAI 2024 (main)

Abstract: Currently, students acquire driving skills by practicing in actual traffic conditions and through direct interactions with an instructor. While one-on-one interactions could be tailored to a student’s learning style and skill level, making them effective for learning, one-on-one interactions are also inefficient, potentially costly, and not standardized with limitations on which traffic situation can be safely taught. For these exact reasons Way AS has developed and commercially deployed a virtual driving instructor that educates students in high-fidelity simulators. In this paper, we present a module, the Lesson generator, that extends the virtual driving instructor to generate personalized lessons for individual students with the goal to practice in a focused and deliberately fashion the skills that need practice for the students to become proficient drivers. A case study is presented, and the path to deployment is discussed.
Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks
Authors: Marc W. Brittain; Luis E. Alvarez; Kara Breeden
Venue: AAAI 2024 (main)

Abstract: Advanced Air Mobility (AAM) introduces a new, efficient mode of transportation with the use of vehicle autonomy and electrified aircraft to provide increasingly autonomous transportation between previously underserved markets. Safe and efficient navigation of low altitude aircraft through highly dense environments requires the integration of a multitude of complex observations, such as surveillance, knowledge of vehicle dynamics, and weather. The processing and reasoning on these observations pose challenges due to the various sources of uncertainty in the information while ensuring cooperation with a variable number of aircraft in the airspace. These challenges coupled with the requirement to make safety-critical decisions in real-time rule out the use of conventional separation assurance techniques. We present a decentralized reinforcement learning framework to provide autonomous self-separation capabilities within AAM corridors with the use of speed and vertical maneuvers. The problem is formulated as a Markov Decision Process and solved by developing a novel extension to the sample-efficient, off-policy soft actor-critic (SAC) algorithm. We introduce the use of attention networks for variable-length observation processing and a distributed computing architecture to achieve high training sample throughput as compared to existing approaches. A comprehensive numerical study shows that the proposed framework can ensure safe and efficient separation of aircraft in high density, dynamic environments with various sources of uncertainty.
Neural Bookmarks: Information Retrieval with Deep Learning and EEG Data
Authors: Glenn Bruns; Michael Haidar
Venue: AAAI 2024 (main)

Abstract: In neural memory decoding, a concept being mentally recalled is identified using brain data. Recently, the feasibility of neural memory decoding with EEG data has been demonstrated. Here we propose a new application – neural information retrieval – that uses neural memory decoding to allow a document to be retrieved merely by thinking about it. In this paper we describe neural memory decoding, define the application of neural information retrieval, present experimental results related to the practicality of the application, and discuss issues of deployment and data privacy.
CHRONOS: A Schema-Based Event Understanding and Prediction System
Authors: Maria Chang; Achille Fokoue; Rosario Uceda-Sosa; Parul Awasthy; Ken Barker; Sadhana Kumaravel; Oktie Hassanzadeh; Elton Soares; Tian Gao; Debarun Bhattacharjya; Radu Florian; Salim Roukos
Venue: AAAI 2024 (main)

Abstract: Chronological and Hierarchical Reasoning Over Naturally Occurring Schemas (CHRONOS) is a system that combines language model-based natural language processing with symbolic knowledge representations to analyze and make predictions about newsworthy events. CHRONOS consists of an event-centric information extraction pipeline and a complex event schema instantiation and prediction system. Resulting predictions are detailed with arguments, event types from Wikidata, schema-based justifications, and source document provenance. We evaluate our system by its ability to capture the structure of unseen events described in news articles and make plausible predictions as judged by human annotators.
ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations
Authors: Muntabir Hasan Choudhury; Lamia Salsabil; William A. Ingram; Edward A. Fox; Jian Wu
Venue: AAAI 2024 (main)

Abstract: Electronic theses and dissertations (ETDs) have been proposed, advocated, and generated for more than 25 years. Although ETDs are hosted by commercial or institutional digital library repositories, they are still an understudied type of scholarly big data, partially because they are usually longer than conference and journal papers. Segmenting ETDs will allow researchers to study sectional content. Readers can navigate to particular pages of interest, to discover and explore the content buried in these long documents. Most existing frameworks on document page classification are designed for classifying general documents, and perform poorly on ETDs. In this paper, we propose ETDPC. Its backbone is a two-stream multimodal model with a cross-attention network to classify ETD pages into 13 categories. To overcome the challenge of imbalanced labeled samples, we augmented data for minority categories and employed a hierarchical classifier. ETDPC outperforms the state-of-the-art models in all categories, achieving an F1 of 0.84 -- 0.96 for 9 out of 13 categories. We also demonstrated its data efficiency. The code and data can be found on GitHub (https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation).
Data-Driven Structural Fire Risk Prediction for City Properties
Authors: Rupasree Dey; Alan Fern
Venue: AAAI 2024 (main)

Abstract: Fire Departments conduct inspections to prevent fires but it is unclear how to best allocate their limited inspection resources across the properties in a city. Currently, they use their intuition and experience to decide on which properties to inspect and lack a data-driven approach that could lead to a more principled use of inspection resources. The main contribution of this paper is to investigate such an approach, based on machine learning for predicting a fire risk score for properties in a city based on historical fire-incident data. These scores can then be used to help prioritize inspection resources toward higher-risk properties. We present a case study using data from a South Dakota fire department which contains information about properties in a city along with records of fire in- incidents. We use this data consisting of more than 72,000 properties to train a machine learning model to predict fire risk and evaluate its ability to rank the fire risk of properties in the city. We conduct and analyze experiments with variations of XG-Boost, which is an algorithm well-suited to the challenges in application, including missing data and a highly-skewed class distribution. Our evaluation of the model-generated rankings, based on ranking metrics, shows that the model significantly outperforms random rankings and other natural baselines. We also analyze the feature importance computed for the models, which provides further insight into the model behavior. This model has been integrated into an interface for displaying the rankings across a city and is ready for beta testing.
Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors
Authors: Bhanu Teja Gullapalli; Stephanie Carreiro; Brittany P Chapman; Eric L Garland; Tauhidur Rahman
Venue: AAAI 2024 (main)

Abstract: Long-term and high-dose prescription opioid use places individuals at risk for opioid misuse, opioid use disorder (OUD), and overdose. Existing methods for monitoring opioid use and detecting misuse rely on self-reports, which are prone to reporting bias, and toxicology testing, which may be infeasible in outpatient settings. Although wearable technologies for monitoring day-to-day health metrics have gained significant traction in recent years due to their ease of use, flexibility, and advancements in sensor technology, their application within the opioid use space remains underexplored. In the current work, we demonstrate that oral opioid administrations can be detected using physiological signals collected from a wrist sensor. More importantly, we show that models informed by opioid pharmacokinetics increase reliability in predicting the timing of opioid administrations. Forty-two individuals who were prescribed opioids as a part of their medical treatment in-hospital and after discharge were enrolled. Participants wore a wrist sensor throughout the study, while opioid administrations were tracked using electronic medical records and self-reports. We collected 1,983 hours of sensor data containing 187 opioid administrations from the inpatient setting and 927 hours of sensor data containing 40 opioid administrations from the outpatient setting. We demonstrate that a self-supervised pre-trained model, capable of learning the canonical time series of plasma concentration of the drug derived from opioid pharmacokinetics, can reliably detect opioid administration in both settings. Our work suggests the potential of pharmacokinetic-informed, data-driven models to objectively detect opioid use in daily life.
VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch
Authors: Sawinder Kaur; Yi Xiao; Asif Salekin
Venue: AAAI 2024 (main)

Abstract: AI's widespread integration has led to neural networks (NN) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that automates the search and training of compressed models with robustness guarantees. These models are well-suited for safety-critical applications and adhere to predefined architecture and size limitations, making them deployable on resource-restricted platforms. The method trains models 2-3 times faster than the state-of-the-art approaches, surpassing them by average accuracy and robustness gains of 15.1 and 9.8 percentage points, respectively. When deployed on a resource-restricted generic platform, these models require 5-8 times less memory and 2-4 times less inference time than models used in verified robustness literature. Our comprehensive evaluation across various model architectures and datasets, including MNIST, CIFAR, SVHN, and a relevant pedestrian detection dataset, showcases VeriCompress's capacity to identify compressed verified robust models with reduced computation overhead compared to current standards. This underscores its potential as a valuable tool for end users, such as developers of safety-critical applications on edge or Internet of Things platforms, empowering them to create suitable models for safety-critical, resource-constrained platforms in their respective domains.
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Authors: Harsh Kumar; Tong Li; Jiakai Shi; Ilya Musabirov; Rachel Kornfield; Jonah Meyerhoff; Ananya Bhattacharjee; Chris Karr; Theresa Nguyen; David Mohr; Anna Rafferty; Sofia Villar; Nina Deliu; Joseph Jay Williams
Venue: AAAI 2024 (main)

Abstract: Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.
Improving Health Information Access in the World’s Largest Maternal Mobile Health Program via Bandit Algorithms
Authors: Arshika Lalan; Shresth Verma; Paula Rodriguez Diaz; Panayiotis Danassis; Amrita Mahale; Kumar Madhu Sudan; Aparna Hegde; Milind Tambe; Aparna Taneja
Venue: AAAI 2024 (main)

Abstract: Harnessing the wide-spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in underserved communities, with maternal and infant health being a key area of such mHealth programs. Unfortunately, dwindling listenership is a major challenge, requiring targeted interventions using limited resources. This paper focuses on Kilkari, the world's largest mHealth program for maternal and child care -- with over 3 million active subscribers at a time --  launched by India's Ministry of Health and Family Welfare (MoHFW) and run by the non-profit ARMMAN. We present a system called CHAHAK that aims to reduce automated dropouts as well as boost engagement with the program through the strategic allocation of interventions to beneficiaries. Past work in a similar domain has focused on a much smaller scale mHealth program and used markovian restless multiarmed bandits to optimize a single limited intervention resource. However this paper demonstrates the challenges in adopting a markovian approach in Kilkari; therefore CHAHAK instead relies on non-markovian time-series restless bandits, and optimizes a layered set of multiple interventions to improve listenership. We use real Kilkari data from the Odisha state in India to show CHAHAK's effectiveness in harnessing multiple interventions to boost listenership, benefiting marginalized communities. When deployed CHAHAK will assist the largest maternal mHealth program to date.
Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management
Authors: Bingxuan Li; Antonio Castellanos; Pengyi Shi; Amy Ward
Venue: AAAI 2024 (main)

Abstract: Incarceration-diversion programs have proven effective in reducing recidivism. Accurate prediction of the number of individuals with different characteristics in the program and their program outcomes based on given eligibility criteria is crucial for successful implementation, because this prediction serves as the foundation for determining the appropriate program size and the consequent staffing requirements. However, this task poses challenges due to the complexities arising from varied outcomes and lengths-of-stay for the diverse individuals in incarceration-diversion programs. In collaboration with an Illinois government agency, we develop a framework to address these issues. Our framework combines ML and queueing model simulation, providing accurate predictions for the program census and interpretable insights into program dynamics and the impact of different decisions in counterfactual scenarios. Additionally, we deploy a user-friendly web app beta-version that allows program managers to visualize census data by counties and race groups. We showcase two decision support use cases: Changing program admission criteria and launching similar programs in new counties.
TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
Authors: ChungYi Lin; Shen-Lung Tung; Hung-Ting Su; Winston H. Hsu
Venue: AAAI 2024 (main)

Abstract: To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns. Our extensive analysis validates its potential for transportation. Focusing on vehicle-related GCT flow prediction, we propose a graph neural network that integrates multivariate, temporal, and spatial facets for improved accuracy. Experiments reveal our model's superiority over baselines, especially in long-term predictions. We also highlight the potential for GCT flow integration into transportation systems.
Symbol Description Reading
Authors: Karol Lynch; Bradley Eck; Joern Ploennigs
Venue: AAAI 2024 (main)

Abstract: Mathematical formulas give concise representations of a document's key ideas in many natural sciences and engineering domains. The symbols that make up formulas carry semantic meaning that may differ by document or equation. What does ? mean in a given paper? Interpreting the symbols that comprise formulas requires identifying descriptions from the surrounding text. We approach this task of symbol description reading as an application of current AI technologies targeting the tuning of large language models for particular domains and automation of machine learning. Our pipeline integrates AI question answering and natural language processing to read symbol descriptions. We consider extractive and generative AI model variations and apply our pipeline on two example tasks of symbol description reading. Promising results provide motivation for wider deployment for which we describe a microservice architecture and related challenges.