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Proceedings of the Winter simulation 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: (纸本)9798331534202

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.

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Categorizing Social Media Screenshots for Identifying Author Misattribution

arXiv

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arXiv 2024年

作者： Farris, Ashlyn M. Nelson, Michael L. Mathematics Department Harding University United States Department of Computer Science Virginia Modeling Analysis and Simulation Center Old Dominion University United States

Mis/disinformation is a common and dangerous occurrence on social media. Misattribution is a form of mis/disinformation that deals with a false claim of authorship, which means a user is claiming someone said (posted) something they never did. We discuss the difference between misinformation and disinformation and how screenshots are used to spread author misattribution on social media platforms. It is important to be able to find the original post of a screenshot to determine if the screenshot is being correctly attributed. To do this we have built several tools to aid in automating this search process. The first is a Python script that aims to categorize Twitter posts based on their structure, extract the metadata from a screenshot, and use this data to group all the posts within a screenshot together. We tested this process on 75 Twitter posts containing screenshots collected by hand to determine how well the script extracted metadata and grouped the individual posts, F1 = 0.80. The second is a series of scrapers being used to collect a dataset that can train and test a model to differentiate between various social media platforms. We collected 16,620 screenshots have been collected from Facebook, Instagram, Truth Social, and Twitter. Screenshots were taken by the scrapers of the web version and mobile version of each platform in both light and dark mode. Copyright © 2024, The Authors. All rights reserved.

关键词： Tweets

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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...

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simulation Winter Conference

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

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

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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...

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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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Composability Verification of Complex Systems Using Colored Petri Nets

Composability Verification of Complex Systems Using Colored ...

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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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"Why not looking backward?" a robust two-step method to automatically terminate Bayesian optimization 23

"Why not looking backward?" a robust two-step method to auto...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Shuang Li Ke Li Wei Li Control and Simulation Center Harbin Institute of Technology China and National Key Laboratory of Modeling and Simulation for Complex Systems China Department of Computer Science University of Exeter Exeter UK

Bayesian Optimization (BO) is a powerful method for tackling expensive blackbox optimization problems. As a sequential model-based optimization strategy, BO iteratively explores promising solutions until a predetermined budget, either iterations or time, is exhausted. The decision on when to terminate BO significantly influences both the quality of solutions and its computational efficiency. In this paper, we propose a simple, yet theoretically grounded, two-step method for automatically terminating BO. Our core concept is to proactively identify if the search is within a convex region by examining previously observed samples. BO is halted once the local regret within this convex region falls below a predetermined threshold. To enhance numerical stability, we propose an approximation method for calculating the termination indicator by solving a bilevel optimization problem. We conduct extensive empirical studies on diverse benchmark problems, including synthetic functions, reinforcement learning, and hyperparameter optimization. Experimental results demonstrate that our proposed method saves up to ≈ 80% computational budget yet is with an order of magnitude smaller performance degradation, comparing against the other peer methods. In addition, our proposed termination method is robust in terms of the setting of its termination criterion.

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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 ...

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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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Segment Anything Model Aided Beam Prediction for the Millimeter Wave Communication 24

Segment Anything Model Aided Beam Prediction for the Millime...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Raha, Avi Deb Adhikary, Apurba Munir, Md. Shirajum Qiao, Yu Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States Department of Artificial Intelligence Kyung Hee University Yongin-si17104 Korea Republic of

ISBN: (纸本)9788995004395

Millimeter wave (mmWave) and terahertz (THz) communication protocols employ extensive antenna arrays to ensure reliable signal reception. However, aligning these narrow beams incurs a significant cost in beam training, which increases proportionally with the number of antennas. While beam prediction methods based on semantics from the RGB images demonstrate initial feasibility, they are still low on accuracy. Motivated by the fact that the recently introduced Segment Anything Model (SAM) can generate very accurate masks, which can be considered semantic information in this domain, this study proposes a beam prediction solution based on the masks of SAM. The SAM can extract more accurate semantic data from visual sources. Then instead of using the RGB images directly, the semantics images have been used to predict the beamforming vector using a lightweight LeNet5 model. The experimental results show that the SAM-based proposed method can perform significantly better than the two state-of-the-art deep learning models. The proposed solution method can achieve near 100% in top-5 beam prediction accuracy in real-world communication scenarios. Copyright 2023 KICS.

关键词： Semantics

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BROADENING ACCESS TO simulationS FOR END-USERS VIA LARGE LANGUAGE MODELS: CHALLENGES AND OPPORTUNITIES

arXiv

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arXiv 2024年

作者： Giabbanelli, Philippe J. Padilla, Jose J. Agrawal, Ameeta Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Dept. of Computer Science Portland State University PortlandOR United States

关键词： Structured Query Language

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