GenAI4ELab/papercli-papers huggingface dataset:
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
Authors: Flemming Kondrup; Thomas Jiralerspong; Elaine Lau; Nathan de Lara; Jacob Shkrob; My Duc Tran; Doina Precup; Sumana Basu
Venue: AAAI 2023 (main)

Abstract: Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL. We find that DeepVent recommends ventilation parameters within safe ranges, as outlined in recent clinical trials. The CQL algorithm offers additional safety by mitigating the overestimation of the value estimates of out-of-distribution states/actions. We evaluate our agent using Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from the MIMIC-III dataset.
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
Authors: Julian Lee; Kamal Viswanath; Alisha Sharma; Jason Geder; Marius Pruessner; Brian Zhou
Venue: AAAI 2023 (main)

Abstract: Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide high maneuverability for naval tasks such as surveillance and terrain exploration. Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics. We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. Our inverse model finds a set of fin kinematics with the multi-objective goal of reaching a target thrust and creating a smooth kinematic transition between flapping cycles. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.
AnimateSVG: Autonomous Creation and Aesthetics Evaluation of Scalable Vector Graphics Animations for the Case of Brand Logos
Authors: Deborah Mateja; Rebecca Armbruster; Jonathan Baumert; Tim Bleil; Jakob Langenbahn; Jan Christian Schwedhelm; Sarah Sester; Armin Heinzl
Venue: AAAI 2023 (main)

Abstract: In the light of the constant battle for attention on digital media, animating digital content plays an increasing role in modern graphic design. In this study, we use artificial intelligence methods to create aesthetic animations along the case of brand logos. With scalable vector graphics as the standard format in modern graphic design, we develop an autonomous end-to-end method using complex machine learning techniques to create brand logo animations as scalable vector graphics from scratch. We acquire data and setup a comprehensive animation space to create novel animations and evaluate them based on their aesthetics. We propose and compare two alternative computational models for automated logo animation and carefully weigh up their idiosyncrasies: on the one hand, we set up an aesthetics evaluation model to train an animation generator and, on the other hand, we combine tree ensembles with global optimization. Indeed, our proposed methods are capable of creating aesthetic logo animations, receiving an average rating of ‘good’ from observers.
Grape Cold Hardiness Prediction via Multi-Task Learning
Authors: Aseem Saxena; Paola Pesantez-Cabrera; Rohan Ballapragada; Kin-Ho Lam; Markus Keller; Alan Fern
Venue: AAAI 2023 (main)

Abstract: Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures, such as, sprinklers, heaters, and wind machines, when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.
Reward Design for an Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Authors: Anna L. Trella; Kelly W. Zhang; Inbal Nahum-Shani; Vivek Shetty; Finale Doshi-Velez; Susan A. Murphy
Venue: AAAI 2023 (main)

Abstract: While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.
Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation
Authors: Dirk van Bokkem; Max van den Hemel; Sebastijan Dumančić; Neil Yorke-Smith
Venue: AAAI 2023 (main)

Abstract: Increasing global food demand, accompanied by the limited number of expert growers, brings the need for more sustainable and efficient horticulture. The controlled environment of greenhouses enable data collection and precise control. For optimally controlling the greenhouse climate, a grower not only looks at crop production, but rather aims at maximising the profit. However this is a complex, long term optimisation task. In this paper, Constraint Programming (CP) is applied to task of optimal greenhouse economic control, by leveraging a learned greenhouse climate model through a CP embedding. In collaboration with an industrial partner, we demonstrate how to model the greenhouse climate with an LSTM model, embed this LSTM into a CP optimisation framework, and optimise the expected profit of the grower. This data-to-decision pipeline is being integrated into a decision support system for multiple greenhouses in the Netherlands.
Fault Injection Based Interventional Causal Learning for Distributed Applications
Authors: Qing Wang; Jesus Rios; Saurabh Jha; Karthikeyan Shanmugam; Frank Bagehorn; Xi Yang; Robert Filepp; Naoki Abe; Larisa Shwartz
Venue: AAAI 2023 (main)

Abstract: We apply the machinery of interventional causal learning with programmable interventions to the domain of applications management. Modern applications are modularized into interdependent components or services (e.g. microservices) for ease of development and management. The communication graph among such components is a function of application code and is not always known to the platform provider. In our solution we learn this unknown communication graph solely using application logs observed during the execution of the application by using fault injections in a staging environment. Specifically, we have developed an active (or interventional) causal learning algorithm that uses the observations obtained during fault injections to learn a model of error propagation in the communication among the components. The “power of intervention” additionally allows us to address the presence of confounders in unobserved user interactions. We demonstrate the effectiveness of our solution in learning the communication graph of well-known microservice application benchmarks. We also show the efficacy of the solution on a downstream task of fault localization in which the learned graph indeed helps to localize faults at runtime in a production environment (in which the location of the fault is unknown). Additionally, we briefly discuss the implementation and deployment status of a fault injection framework which incorporates the developed technology.
High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-World Manufacturing Environments
Authors: Carol Xu; Mahmoud Famouri; Gautam Bathla; Mohammad Javad Shafiee; Alexander Wong
Venue: AAAI 2023 (main)

Abstract: Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. An essential step in the manufacturing of light guide plates is the quality inspection of defects such as scratches, bright/dark spots, and impurities. This is mainly done in industry through manual visual inspection for plate pattern irregularities, which is time-consuming and prone to human error and thus act as a significant barrier to high-throughput production. Advances in deep learning-driven computer vision has led to the exploration of automated visual quality inspection of light guide plates to improve inspection consistency, accuracy, and efficiency. However, given the computational constraints and high-throughput nature of real-world manufacturing environments, the widespread adoption of deep learning-driven visual inspection systems for inspecting light guide plates in real-world manufacturing environments has been greatly limited due to high computational requirements and integration challenges of existing deep learning approaches in research literature. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality inspection (VQI) tailored for real-world manufacturing environments. To enable automated VQI on the edge computing within the fully-integrated VQI system, a highly compact deep anti-aliased attention condenser neural network (which we name Light-DefectNet) tailored specifically for light guide plate surface defect detection in resource-constrained scenarios was created via machine-driven design exploration with computational and “best-practices” constraints as well as L1 paired classification discrepancy loss. Experiments show that Light-DetectNet achieves a detection accuracy of ∼98.2% on the LGPSDD benchmark while having just 770K parameters
(∼33× and ∼6.9× lower than ResNet-50 and EfficientNet-B0, respectively) and ∼93M FLOPs (∼88× and ∼8.4× lower than ResNet-50 and EfficientNet-B0, respectively) and ∼8.8× faster inference speed than EfficientNet-B0 on an embedded ARM processor. As such, the proposed deep learning-driven workflow, integrated with the aforementioned LightDefectNet neural network, is highly suited for high-throughput, high-performance light plate surface VQI within real-world manufacturing environments.
End-to-End Pipeline for Trigger Detection on Hit and Track Graphs
Authors: Tingting Xuan; Yimin Zhu; Giorgian Borca-Tasciuc; Ming Xiong Liu; Yu Sun; Cameron Dean; Yasser Corrales Morales; Zhaozhong Shi; Dantong Yu
Venue: AAAI 2023 (main)

Abstract: There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. The triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.
OPRADI: Applying Security Game to Fight Drive under the Influence in Real-World
Authors: Luzhan Yuan; Wei Wang; Gaowei Zhang; Yi Wang
Venue: AAAI 2023 (main)

Abstract: Driving under the influence (DUI) is one of the main causes of traffic accidents, often leading to severe life and property losses. Setting up sobriety checkpoints on certain roads is the most commonly used practice to identify DUI-drivers in many countries worldwide. However, setting up checkpoints according to the police's experiences may not be effective for ignoring the strategic interactions between the police and DUI-drivers, particularly when inspecting resources are limited. To remedy this situation, we adapt the classic Stackelberg security game (SSG) to a new SSG-DUI game to describe the strategic interactions in catching DUI-drivers. SSG-DUI features drivers' bounded rationality and social knowledge sharing among them, thus realizing improved real-world fidelity. With SSG-DUI, we propose OPRADI, a systematic approach for advising better strategies in setting up checkpoints. We perform extensive experiments to evaluate it in both simulated environments and real-world contexts, in collaborating with a Chinese city's police bureau. The results reveal its effectiveness in improving police's real-world operations, thus having significant practical potentials.
Xaitk-Saliency: An Open Source Explainable AI Toolkit for Saliency
Authors: Brian Hu; Paul Tunison; Brandon RichardWebster; Anthony Hoogs
Venue: AAAI 2023 (main)

Abstract: Advances in artificial intelligence (AI) using techniques such as deep learning have fueled the recent progress in fields such as computer vision. However, these algorithms are still often viewed as "black boxes", which cannot easily explain how they arrived at their final output decisions. Saliency maps are one commonly used form of explainable AI (XAI), which indicate the input features an algorithm paid attention to during its decision process. Here, we introduce the open source xaitk-saliency package, an XAI framework and toolkit for saliency. We demonstrate its modular and flexible nature by highlighting two example use cases for saliency maps: (1) object detection model comparison and (2) doppelganger saliency for person re-identification. We also show how the xaitk-saliency package can be paired with visualization tools to support the interactive exploration of saliency maps. Our results suggest that saliency maps may play a critical role in the verification and validation of AI models, ensuring their trusted use and deployment. The code is publicly available at: https://github.com/xaitk/xaitk-saliency.
DetAIL: A Tool to Automatically Detect and Analyze Drift in Language
Authors: Nishtha Madaan; Adithya Manjunatha; Hrithik Nambiar; Aviral Goel; Harivansh Kumar; Diptikalyan Saha; Srikanta Bedathur
Venue: AAAI 2023 (main)

Abstract: Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software. Traditional software is made dependable by following rigorous practice like static analysis, testing, debugging, verifying, and repairing throughout the development and maintenance life-cycle. Similarly for machine learning systems, we need to keep these models up to date so that their performance is not compromised. For this, current systems rely on scheduled re-training of these models as new data kicks in. In this work, we propose DetAIL, a tool to measure the data drift that takes place when new data kicks in so that one can adaptively re-train the models whenever re-training is actually required irrespective of schedules. In addition to that, we generate various explanations at sentence level and dataset level to capture why a given payload text has drifted.
PARCS: A Deployment-Oriented AI System for Robust Parcel-Level Cropland Segmentation of Satellite Images
Authors: Chen Du; Yiwei Wang; Zhicheng Yang; Hang Zhou; Mei Han; Jui-Hsin Lai
Venue: AAAI 2023 (main)

Abstract: Cropland segmentation of satellite images is an essential basis for crop area and yield estimation tasks in the remote sensing and computer vision interdisciplinary community. Instead of common pixel-level segmentation results with salt-and-pepper effects, a parcel-level output conforming to human recognition is required according to the clients' needs during the model deployment. However, leveraging CNN-based models requires fine-grained parcel-level labels, which is an unacceptable annotation burden. To cure these practical pain points, in this paper, we present PARCS, a holistic deployment-oriented AI system for PARcel-level Cropland Segmentation. By consolidating multi-disciplinary knowledge, PARCS has two algorithm branches. The first branch performs pixel-level crop segmentation by learning from limited labeled pixel samples with an active learning strategy to avoid parcel-level annotation costs. The second branch aims at generating the parcel regions without a learning procedure. The final parcel-level segmentation result is achieved by integrating the outputs of these two branches in tandem. The robust effectiveness of PARCS is demonstrated by its outstanding performance on public and in-house datasets (an overall accuracy of 85.3% and an mIoU of 61.7% on the public PASTIS dataset, and an mIoU of 65.16% on the in-house dataset). We also include subjective feedback from clients and discuss the lessons learned from deployment.
Adaptive Temporal Planning for Multi-Robot Systems in Operations and Maintenance of Offshore Wind Farms
Authors: Ferdian Jovan; Sara Bernardini
Venue: AAAI 2023 (main)

Abstract: With the fast development of offshore wind farms as renewable energy sources, maintaining them efficiently and safely becomes necessary. The high costs of operation and maintenance (O&M) are due to the length of turbine downtime and the logistics for human technician transfer. To reduce such costs, we propose a comprehensive multi-robot system that includes unmanned aerial vehicles (UAV), autonomous surface vessels (ASV), and inspection-and-repair robots (IRR). Our system, which is capable of co-managing the farms with human operators located onshore, brings down costs and significantly reduces the Health and Safety (H&S) risks of O&M by assisting human operators in performing dangerous tasks. In this paper, we focus on using AI temporal planning to coordinate the actions of the different autonomous robots that form the multi-robot system. We devise a new, adaptive planning approach that reduces failures and replanning by performing data-driven goal and domain refinement. Our experiments in both simulated and real-world scenarios prove the effectiveness and robustness of our technique. The success of our system marks the first-step towards a large-scale, multirobot solution for wind farm O&M.
A Study of Students’ Learning of Computing through an LP-Based Integrated Curriculum for Middle Schools
Authors: Joshua Archer; Rory Eckel; Joshua Hawkins; Jianlan Wang; Darrel Musslewhite; Yuanlin Zhang
Venue: AAAI 2023 (main)

Abstract: There has been a consensus on integrating Computing into the teaching and learning of STEM (Science, Technology, Engineering and Math) subjects in K-12 (Kindergarten to 12th grade in the US education system). However, rigorous study on the impact of an integrated curriculum on students' learning in computing and/or the STEM subject(s) is still rare. In this paper, we report our research on how well an integrated curriculum helps middle school students learn Computing through the microgenetic analysis methods.
AI and Parallelism in CS1: Experiences and Analysis
Authors: Steven Bogaerts
Venue: AAAI 2023 (main)

Abstract: This work considers the use of AI and parallelism as a context for learning typical programming concepts in an introductory programming course (CS1). The course includes exercises in decision trees, a novel game called Find the Gnomes to introduce supervised learning, the construction and application of a vectorized neural network unit class, and obtaining speedup in training through parallelism. The exercises are designed to teach students typical introductory programming concepts while also providing a preview and motivating example of advanced CS topics. Students' understanding and motivation are considered through a detailed analysis of pre- and post-survey data gathered in several sections of the course each taught by one of four instructors across five semesters.
Shared Tasks as Tutorials: A Methodical Approach
Authors: Theresa Elstner; Frank Loebe; Yamen Ajjour; Christopher Akiki; Alexander Bondarenko; Maik Fröbe; Lukas Gienapp; Nikolay Kolyada; Janis Mohr; Stephan Sandfuchs; Matti Wiegmann; Jörg Frochte; Nicola Ferro; Sven Hofmann; Benno Stein; Matthias Hagen; Martin Potthast
Venue: AAAI 2023 (main)

Abstract: In this paper, we discuss the benefits and challenges of shared tasks as a teaching method. A shared task is a scientific event and a friendly competition to solve a research problem, the task. In terms of linking research and teaching, shared-task-based tutorials fulfill several faculty desires: they leverage students' interdisciplinary and heterogeneous skills, foster teamwork, and engage them in creative work that has the potential to produce original research contributions. Based on ten information retrieval (IR) courses at two universities since 2019 with shared tasks as tutorials, we derive a domain-neutral process model to capture the respective tutorial structure. Meanwhile, our teaching method has been adopted by other universities in IR courses, but also in other areas of AI such as natural language processing and robotics.