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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Mullick, Ankan Nandy, Abhilash Kapadnis, Manav Nitin Patnaik, Sohan Raghav, R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur India Department of Mechanical Engineering Indian Institute of Technology Kharagpur India Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur India

A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document that express an intent corresponding to the case be clearly understood. In this paper, we introduce a dataset of 93 legal documents, belonging to the case categories of either Murder, Land Dispute, Robbery, or Corruption, where phrases expressing intent same as the category of the document are annotated. Also, we annotate fine-grained intents for each such phrase to enable a deeper understanding of the case for a reader. Finally, we analyze the performance of several transformer-based models in automating the process of extracting intent phrases (both at a coarse and a fine-grained level), and classifying a document into one of the possible 4 categories, and observe that, our dataset is challenging, especially in the case of fine-grained intent classification. © 2021 Copyright for this paper by its authors.

关键词： Classification (of information)

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Transforming engineering Education Using Generative AI and Digital Twin Technologies

arXiv

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arXiv 2024年

作者： Lin, Yu-Zheng Alhamadah, Ahmed Hussain J. Redondo, Matthew William Patel, Karan Himanshu Ghimire, Sujan Latibari, Banafsheh Saber Salehi, Soheil Satam, Pratik Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States

Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom’s taxonomy in the cognitive domain. We align Bloom’s six cognitive stages with educational levels: undergraduate studies for"Remember" and"Understand," master’s level for"Apply" and"Analyze," and doctoral level for"Evaluate" and"Create." Low-fidelity DTs aid essential knowledge acquisition and skill training, providing a low-risk environment for grasping fundamental concepts. Medium-fidelity DTs offer more detailed and dynamic simulations, enhancing application skills and problem-solving. High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model’s fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives. © 2024, CC BY-NC-ND.

关键词： Knowledge acquisition

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Composite Distributed Learning and Synchronization of Nonlinear Multi-Agent systems with Complete Uncertain Dynamics

Composite Distributed Learning and Synchronization of Nonlin...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Emadodin Jandaghi Dalton L. Stein Adam Hoburg Paolo Stegagno Mingxi Zhou Chengzhi Yuan Department of Mechanical Industrial and Systems Engineering Kingston RI USA Department of Electrical Computer and Biomedical Engineering Kingston RI USA Graduate School of Oceanography Kingston RI USA

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

This paper addresses the problem of composite synchronization and learning control in a network of multiagent robotic manipulator systems with heterogeneous nonlinear uncertainties under a leader-follower framework. A novel two-layer distributed adaptive learning control strategy is introduced, comprising a first-layer distributed cooperative estimator and a second-layer decentralized deterministic learning controller. The first layer is to facilitate each robotic agent’s estimation of the leader’s information. The second layer is responsible for both controlling individual robot agents to track desired reference trajectories and accurately identifying/learning their nonlinear uncertain dynamics. The proposed distributed learning control scheme represents an advancement in the existing literature due to its ability to manage robotic agents with completely uncertain dynamics including uncertain mass matrices. This allows the robotic control to be environment-independent which can be used in various settings, from underwater to space where identifying system dynamics parameters is challenging. The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method. Numerical simulations validate the effectiveness of the proposed scheme.

关键词： Adaptive learning computer aided instruction Uncertainty System dynamics Distance learning Aerospace electronics Nonlinear dynamical systems

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Robo ludens: Game design techniques applied in HRI experiments

Robo ludens: Game design techniques applied in HRI experimen...

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2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021

作者： Muñoz Cardona, John Edison Dautenhahn, Kerstin Systems Design Engineering Department University of Waterloo WaterlooON Canada Electrical and Computer Engineering System Design Engineering Departments University of Waterloo WaterlooON Canada

ISBN: (纸本)9781450382908

Games have been used extensively to study human behavior. Researchers in the field of human-robot interaction (HRI) are becoming more aware of the importance of designing compelling and playful games to study interrelationships among players. Despite the growing interest, the use of game design techniques in the creation of playful experiences for HRI experiments is still in its infancy and more multidisciplinary activities should be promoted to foster the convergence between game research and HRI. This workshop aims at discussing the value of using iterative game design techniques to integrate playful experiences using social robots for HRI experiments. More concretely, we want to explore tools, approaches and methods used in previous experiences for appropriate design of interactive games in HRI. Furthermore, based on previous research, a taxonomy for game design using social robots will be presented and attendees will have access to hands-on material created to facilitate the design of interactive games considering important aspects of the robotic systems to maximize the fun experience. We hope this workshop will bring to HRI researchers, game designers, roboticists, and technology enthusiasts enlightening thoughts and ideas to confront the often complicated and time-demanding process of designing compelling games for HRI experiments. © 2021 Owner/Author.

关键词： Human robot interaction

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Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection

Adaptive Ensembles of Fine-Tuned Transformers for LLM-Genera...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhixin Lai Xuesheng Zhang Suiyao Chen Electrical and Computer Engineering Cornell University Ithaca NY USA Recommendation Meituan Beijing China Industrial and Management Systems Engineering University of South Florida Tampa FL USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potential risks such as fake news in social media. Previous research has mostly tested single models on in-distribution datasets, limiting our understanding of how these models perform on different types of data for LLM-generated text detection tasks. We researched this by testing five specialized transformer-based models on both in-distribution and out-of-distribution datasets to better assess their performance and generalizability. Our results revealed that single transformer-based classifiers achieved decent performance on the in-distribution dataset but limited generalization ability on the out-of-distribution dataset. To improve it, we combined the individual classifier models using adaptive ensemble algorithms, which improved the average accuracy significantly from 91.8% to 99.2% on an in-distribution test set and from 62.9% to 72.5% on an out-of-distribution test set. The results indicate the effectiveness, good generalization ability, and great potential of adaptive ensemble algorithms in LLM-generated text detection.

关键词： Adaptation models Accuracy Large language models Text categorization Text detection Transformers Data models

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A Game-Theoretic Framework for Distributed Load Balancing: Static and Dynamic Game Models

arXiv

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arXiv 2025年

作者： Fardno, Fatemeh Rasoul Etesami, S. The Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States The Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynamic settings. In these applications, jobs/tasks are often held by selfish entities that do not want to coordinate with each other, yet the goal is to balance the load among servers in a distributed manner. First, we provide a static game formulation in which each player holds a job with a certain processing requirement and wants to schedule it fractionally among a set of heterogeneous servers to minimize its average processing time. We show that this static game is a potential game and admits a pure Nash equilibrium (NE). In particular, the best-response dynamics converge to such an NE after n iterations, where n is the number of players. We then extend our results to a dynamic game setting, where jobs arrive and get processed in the system, and players observe the load (state) on the servers to decide how to schedule their jobs among the servers in order to minimize their averaged cumulative processing time. In this setting, we show that if the players update their strategies using dynamic best-response strategies, the system eventually becomes fully load-balanced and the players' strategies converge to the pure NE of the static game. In particular, we show that the convergence time scales only polynomially with respect to the game parameters. Finally, we provide numerical results to evaluate the performance of our proposed algorithms under both static and dynamic settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Queueing networks

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Federated Learning for Lidar Super Resolution on Automotive Scenes

Federated Learning for Lidar Super Resolution on Automotive ...

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International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Gerasimos Arvanitis Aris S. Lalos Konstantinos Moustakas Industrial Systems Institute Athena Research Center Patras Greece Electrical and Computer Engineering University of Patras Greece

In this paper, the problem of lidar super-resolution is explored under a federated learning perspective. The high cost of high-resolution lidar sensors is a major obstacle to the widespread adoption of connected and autonomous vehicles (CAVs). To reduce the cost, this study investigates the use of low-cost sensors in conjunction with super-resolution algorithms. Unlike previous studies that approach this problem with centralized solutions, this work employs federated learning to leverage private lidar data from different autonomous vehicles under different environmental conditions, resulting in more robust and diverse deep learning model. Extensive experiments on a real-world lidar odometry dataset highlight the merits and applicability of the proposed lidar super-resolution method with federated learning.

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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International Conference on Intelligent Transportation

作者： Zhengming Zhang Zhengming Ding Yaobin Chen Stanley Chien Lingxi Li Rini Sheroy Joshua Domeyer Renran Tian School of Industrial Engineering Purdue University USA Department of Computer Science Tulane University USA School of Electrical and Computer Engineering Purdue University USA Toyota Collaborative Safety Research Center (CSRC) USA Department of Industrial and Systems Engineering North Carolina State University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users.

关键词： Deep learning Accuracy Urban areas Predictive models Traffic control Birds Trajectory Forecasting Vehicle dynamics Micromobility

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CDDIP: Constrained Diffusion-Driven Deep Image Prior for Seismic Data Reconstruction

arXiv

引用

arXiv 2024年

作者： Goyes-Peñafiel, Paul Kamilov, Ulugbek S. Arguello, Henry Department of Systems Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia Department of Computer Science and Engineering Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States

Seismic data frequently exhibits missing traces, substantially affecting subsequent seismic processing and interpretation. Deep learning-based approaches have demonstrated significant advancements in reconstructing irregularly missing seismic data through supervised and unsupervised methods. Nonetheless, substantial challenges remain, such as generalization capacity and computation time cost during the inference. Our work introduces a reconstruction method that uses a pre-trained generative diffusion model for image synthesis and incorporates Deep Image Prior to enforce data consistency when reconstructing missing traces in seismic data. The proposed method has demonstrated strong robustness and high reconstruction capability of post-stack and pre-stack data with different levels of structural complexity, even in field and synthetic scenarios where test data were outside the training domain. This indicates that our method can handle the high geological variability of different exploration targets. Additionally, compared to other state-of-the-art seismic reconstruction methods using diffusion models. During inference, our approach reduces the number of sampling timesteps by up to 4x. Our implementation is available at https://***/PAULGOYES/*** © 2024, CC BY.

关键词： Seismic response

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