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检索条件"机构=Computer Modeling and Simulation"

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Broadening Access to simulations for End-Users via Large Language Models: Challenges and Opportunities

Broadening Access to Simulations for End-Users via Large Lan...

引用

simulation Winter Conference

作者： Philippe J. Giabbanelli Jose J. Padilla Ameeta Agrawal Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA USA Dept. of Computer Science Portland State University Portland OR USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Large Language Models (LLMs) are becoming ubiquitous to create intelligent virtual assistants that assist users in interacting with a system, as exemplified in marketing. Although LLMs have been discussed in modeling & simulation (M&S), the community has focused on generating code or explaining results. We examine the possibility of using LLMs to broaden access to simulations, by enabling non-simulation end-users to ask what-if questions in everyday language. Specifically, we discuss the opportunities and challenges in designing such an end-to-end system, divided into three broad phases. First, assuming the general case in which several simulation models are available, textual queries are mapped to the most relevant model. Second, if a mapping cannot be found, the query can be automatically reformulated and clarifying questions can be generated. Finally, simulation results are produced and contextualized for decision-making. Our vision for such system articulates long-term research opportunities spanning M&S, LLMs, information retrieval, and ethics.

关键词： Ethics Codes Large language models Virtual assistants simulation Machine vision Decision making Information retrieval

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Relationship graph learning network for visual relationship detection 2

Relationship graph learning network for visual relationship ...

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Li, Yanan Yu, Jun Zhan, Yibing Chen, Zhi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450383080

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN. © 2021 ACM.

关键词： Semantics

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Semiconductor Manufacturing Data Synthesis through GANs

Semiconductor Manufacturing Data Synthesis through GANs

引用

IEEE/SEMI Conference and Workshop on Advanced Semiconductor Manufacturing

作者： Brian Tse Tori Wright Emmanuel Nsiye Timothy Azinord David Medina Sean Mondesire College of Engineering and Computer Science University of Central Florida Orlando FL USA Institute for Simulation and Training University of Central Florida Orlando FL USA School of Modeling Simulation & Training University of Central Florida Orlando FL USA

ISBN: (数字)9798350384550

ISBN: (纸本)9798350384567

This paper introduces a novel application of Generative Adversarial Networks (GANs) in the creation of a digital twin of a semiconductor manufacturing plant, as well as the investigation of new evaluation metrics in synthetic data quality. Digital twins are the next technological step for long-term semiconductor manufacturing capacity planning, performance optimization, and job scheduling. However, real-time predictive analytics for this industry demands substantial historical data about equipment, operations, and performance metrics like Overall Equipment Effectiveness (OEE). In cases where such data is unavailable, GANs can fill these data gaps by providing sufficient synthetic data to enable predictive modeling and digital twin creation. This investigation successfully generated synthetic semiconductor manufacturing data through Conditional, Wasserstein, and Copula GANs, enabling the creation of the digital twin for predictive modeling and further research of manufacturing improvements. A new method of evaluating synthetic data through a metric based on the distance between distributions scaled by similarity is also investigated.

关键词： Semiconductor device modeling Semiconductor device manufacture Predictive models Generative adversarial networks Data models Loss measurement Digital twins

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Exploring Large Language Models for Analyzing Changes in Web Archive Content: A Retrieval-Augmented Generation Approach

Exploring Large Language Models for Analyzing Changes in Web...

引用

IEEE International Conference on Big Data

作者： Jhon G. Botello Lesley Frew Jose J. Padilla Michele C. Weigle Department of Computer Science Old Dominion University Norfolk Virginia USA Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk Virginia USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Websites typically display only their most recent content. However, the dynamic nature of the web leads to frequent updates and deletions. Web archives preserve snapshots of earlier versions for those interested in tracking changes over time. Analyzing these changes often requires a manual process that relies on traditional methods focused on terms or phrase-level differences. This study explores the capability of Large Language Models (LLMs), specifically GPT-4o, through a Retrieval-Augmented Generation (RAG) approach for detecting changes in archived web pages. Using WARC-GPT, a RAG pipeline to interact with Web ARChive (WARC) files, we identify and analyze changes across a small set of U.S. federal environmental web pages that changed between 2016 and 2020. Our findings show that GPT-4o can effectively be used to detect inconsistencies in web archive content, including consideration of the change and the semantic context upon which the changes occurred. Our exploration represents an initial step toward using Artificial Intelligence (AI) for deeper and scalable web change analysis.

关键词： Large language models Computational modeling Retrieval augmented generation Semantics Pipelines Web pages Focusing Manuals Big Data Data models

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Improved Mayfly Algorithm Based on Co-Evolution for Feature Selection

Improved Mayfly Algorithm Based on Co-Evolution for Feature ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Guo Tianyu Dong Hongbin Zhou Jing National Engineering Laboratory for E-government Modeling and Simulation School of Computer Science and Technology Harbin Engineering University Harbin China

Mayfly algorithm (MA) is a new type of intelligent optimization algorithm with excellent search ability and a broad application prospect in feature selection. However, as the dimension of the data increases, MA also has the problem of weak search ability and falling into the local optimal solution. To solve these problems, this paper combines the idea of collaborative evolution and proposes an improved Mayfly algorithm (CEIMA) suitable for feature selection problems. The CEIMA algorithm first divides the population into two sub-populations and initializes them in different ways to increase the diversity of the population. During evolution, information sharing is achieved through an information transmission mechanism between sub-populations, achieving collaborative evolution between sub-populations to enhance the global search ability of CEIMA. Finally, the mRMR-m strategy is proposed to mutate the global optimal individual based on feature importance, improving the ability of CEIMA to escape from local optimal solutions. Through a comparison with 7 feature selection algorithms on 12 UCI datasets, the effectiveness of CEIMA is proven.

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Composability Verification of Complex Systems Using Colored Petri Nets

Composability Verification of Complex Systems Using Colored ...

引用

2021 Winter simulation Conference, WSC 2021

作者： Mahmood, Imran Askari, Syed Hassan Sarjoughian, Hessam S. Center for Research in Modeling Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (纸本)9781665433112

The discipline of component-based modeling and simulation offers promising gains including reductions in development cost, time, and system complexity. This paradigm promotes the use and reuse of modular components for adequate development of complex simulations. Achieving effective and meaningful model reuse through the composition of components still remains a daunting challenge. 'Composability', an integral part of this challenge, is the capability to select and assemble model components in various combinations to satisfy specific user requirements. In this paper, we propose the use of Colored Petri Nets for component-oriented model development, model composition, and the verification of composed models using state-space analysis technique. We present a case study of an elevator model as a proof of concept. Our case study explains the proposed process of developing and composing CPN-based model components and verifying the composed model using state-space analysis. © 2021 IEEE.

关键词： Petri nets

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MemoryScan Environments: Creating Large-Scale Memory-Evocative Digital Twins 3

MemoryScan Environments: Creating Large-Scale Memory-Evocati...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Walters, Lori C. Michlowitz, Robert A. Martinez, Esteban Segarra McMahan, Ryan P. Kider, Joseph T. University of Central Florida IST School of Modeling Simulation and Training OrlandoFL32816 United States University of Central Florida Department of History OrlandoFL32816 United States University of Central Florida Department of Computer Science OrlandoFL32816 United States

ISBN: (纸本)9798350318470

The MemoryScan project seeks to create large-scale memory-evocative digital virtual environments designed to enhance and amplify the detail and granularity of captured memories and historical recollections. This project integrates diverse data sources such as large-scale point clouds, 3D models, virtual reality interfaces, and satellite imagery to build an interactive virtual environment accessible through head-mounted displays (HMDs) or web browsers. This approach transcends traditional collection methods facilitating an enriched and multisensory engagement with the past through these human-centric Digital Twins, fostering a deeper connection with the historical narratives and collective memories encapsulated within this interactive digital world. © 2023 IEEE.

关键词： Web browsers

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Enhancing Sentiment Analysis with Attention Mechanism and Bayesian-Lipschitz Recurrent Neural Networks

Enhancing Sentiment Analysis with Attention Mechanism and Ba...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Mahmudul Hasan Sachin Shetty Peter B. Foytik Department of Electrical and Computer Engineering Old Dominion University Norfolk Virginia USA Virginia Modeling Analysis and Simulation Center (VMASC) Old Dominion University Suffolk Virginia USA

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

In medical chatbots and other healthcare domains, government agencies and other essential industries use sentiment analysis for public safety, improving the service quality of products, and other necessary purposes. Sometimes, the data is noisy, ambiguous, and contains mixed sentiments, where different parts of the text may convey different emotions, creating a problem in adequately detecting the sentiment. We proposed a novel recurrent neural network called the Bayesian-Lipschitz RNN with an attention mechanism to address these challenges. The Bayesian approach manages noisy and ambiguous data by representing uncertainty and avoiding overfitting. At the same time, the attention mechanism focuses on the most relevant parts of the text, effectively capturing context and mixed sentiments. Then, the Lipschitz Recurrent Neural Networks (LRNN), based on hidden-to-hidden matrices, are employed and constructed using a symmetric-skew decomposition scheme. The hidden-to-hidden matrices are used to update the hidden state and this hidden state is used to predict the sentiment, mitigate the vanishing gradient and stability issues, and got accuracy with 92.45 %.

关键词： Sentiment analysis Recurrent neural networks Attention mechanisms Symmetric matrices Uncertainty Stability analysis Public security Bayes methods Noise measurement Matrix decomposition

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Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid 23

Trustworthy Artificial Intelligence Framework for Proactive ...

引用

Proceedings of the Winter simulation Conference

作者： Md. Shirajum Munir Sachin Shetty Danda B. Rawat Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Department of Electrical and Computer Science Howard University Washington D.C. USA

ISBN: (纸本)9798350369663

The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker's DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward's minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability.

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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