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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Sayyed Farid Ahamed Priyanka Aggarwal Sachin Shetty Erin Lanus Laura J. Freeman Virginia Modeling Analysis and Simulation Center Old Dominion University Virginia USA Hume Center Virginia Tech Virginia USA Virginia Tech Virginia USA

ISBN: (纸本)9781728181059

Success of machine learning algorithms hinges on access to labeled dataset. Obtaining a labeled dataset is an expensive, challenging and time-consuming process, leading to the development of transfer learning (TL) methodology. TL incorporates gained knowledge from a previously trained source model into specific yet similar task models with limited data domain coverage. In this paper, we propose an automated targeted transfer learning (ATTL) method to resolve the transferability between source and target with minimal data requirements. The ATTL method decides how much target data is essential for model training, along with selected source data, to obtain the skateholder's specified performance metrics. The ATTL framework optimizes the system to select minimal target data based on two approaches: combinatorial coverage and adaptive selection methodology, along with specific source data for fine-tuning given a pre-trained source model. We evaluated the ATTL method on the Kaggle's ‘planes in satellite imagery’ dataset and the results identified that acquiring a small number of intentionally well-chosen samples from the target environment can achieve model performance of 97% in comparison to the baseline transfer learning accuracy of 92%.

关键词： Training Measurement Adaptation models Satellites Machine learning algorithms Transfer learning Neural networks

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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simulation Winter Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz Modeling and Analysis Innovation Center The MITRE Corporation Charlottesville VA USA Mission Analysis Group L3Harris Corporation Herndon VA USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA University of Central Florida School of Modeling Simulation and Training Orlando FL USA Department of Systems and Information Engineering University of Virgina Charlottesville VA USA Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn University Auburn AL USA

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within modeling and simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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Machine Learning Empowered Radio Frequency Signal Classification for UAS Detection

Machine Learning Empowered Radio Frequency Signal Classifica...

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作者： Nilsen, M Shetty, S Gold, K Kamhoua, C Virginia Modeling Analysis and Simulation Center Suffolk VA ARMY RESEARCH LAB ADELPHI MD U.S. Army Research Laboratory Adelphi MD

Rapid developments in the unmanned aerial systems (UAS) have made its usage in a variety of offensive as well as defensive applications especially in military, high priority and sensitive government sites. The ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization,device fingerprinting and protocol identification. These insights can aid estimating the UAS transmitters capabilities without their knowledge. In this paper, we present a Radio Frequency Signal Classification (RFx2;Class) toolbox that can monitor, detect, and classify wireless signals emitted by UAS. The advantage of the RF-Class toolbox is extracting information about transmitters and providing receivers information about certain transmitted signals. The classification of RF signals will be done based on the modulation scheme recognition, exploitation of cyclostationary features and leveraging RF band allocation information. The modulation recognition capability can also be used for cyber offensive strategies. Once the modulation scheme is recognized, we can demodulate, decode and extract packets. Once the packets are extracted, we can accurately detect the protocol. The final step involves crafting a malicious packet and injecting the packet in the adversarial communication environment with intent to launch offensive operations. To demo the feasibility and accuracy of our approach, we have evaluated the performance on a real environment with an UAS (Drone DJI Phantom 4). Our initial experimental result showed that we were able to detect presence of drone signal successfully in presence of varying SNR regimes.

关键词： Modulation Orthogonal frequency division multiplexing Radio frequency Algorithms Signal processing Software defined radio Unmanned aerial systems Frequency Learning Radio equipment Surface warfare Frequency bands Recognition Signal detection Classification Data processing Detection

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Human factors, ergonomics and Industry 4.0 in the Oil&Gas industry: a bibliometric analysis

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Procedia Computer Science 2021年 180卷 1049-1058页

作者： Francesco Longo Antonio Padovano Lucia Gazzaneo Jessica Frangella Rafael Diaz University of Calabria Via Pietro Bucci Cubo 45C Arcavacata di Rende (CS) 87036 Italy Virginia Modeling Analysis & Simulation Center Old Dominion University Suffolk VA 23435 USA

Over the last few years, the Human Factors and Ergonomics (HF/E) discipline has significantly benefited from new human-centric engineered digital solutions of the 4.0 industrial age. Technologies are creating new socio-technical interactions between human and machine that minimize the risk of design-induced human errors and have largely contributed to remarkable improvements in terms of process safety, productivity, quality, and workers’ well-being. However, despite the Oil&Gas (O&G) sector is one of the most hazardous environments where human error can have severe consequences, Industry 4.0 aspects are still scarcely integrated with HF/E. This paper calls for a holistic understanding of the changing role and responsibilities of workers in the O&G industry and aims at investigating to what extent, what type of, and how academic publications in the O&G field integrate HF/E and Industry 4.0 in their research. Bibliometric analysis has been conducted to provide useful insights to researchers and practitioners and to assess the status quo. Our findings show that academic publications have mainly focused on simulation-based training to increase process safety whereas revealed the lack of specific studies on the application of cognitive solutions, such as Augmented Reality-enabled tools or Intelligent Fault Detection and Alarm Management solutions.

关键词： Human factor engineering Oil gas industry Bibliometric analysis Industry 4.0 Ergonomics

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A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network

A Novel Smartphone-Based Human Activity Recognition Approach...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Dipanwita Thakur Sandip Roy Suparna Biswas Edmond S. L. Ho Samiran Chattopadhyay Sachin Shetty Department of Computer Science Banasthali Vidyapith Jaipur Rajasthan India Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Kolkata India School of Computing Science University of Glasgow Scotland United Kingdom Department of Information Technology Jadavpur University Kolkata India

In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, Autoencoder and LSTM architectures for efficient smartphone-based HAR operation. There is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. The simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.

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On the Effectiveness of Intrusion Response Systems against Persistent Threats

On the Effectiveness of Intrusion Response Systems against P...

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2020 International Conference on Computing, Networking and Communications, ICNC 2020

作者： Ullah, Sharif Shelly, Sachin Hassanzadeh, Amin Nayak, Anup Hasan, Kamrul Old Dominion University NorfolkVA United States Virginia Modeling Analysis and Simulation Center SuffolkVA United States Accenlure Cyber Lab Cyber Fusion Cenler ArlinglonVA United States

ISBN: (纸本)9781728149059

Persistent threats are the most sophisticated type of cyber attacks that involves multi-step attack sequences. There is a need to develop timely, efficient and appropriate intrusion response to mitigate the risk from persistent threats. In this paper, we model an intrusion response system by considering the diverse strategy an attacker could employ in his lateral movement within the target environment. Our model investigates attackers' opportunity corresponding to each strategy in their potential penetration path. We design a response mechanism by, first, analyzing prior steps of the attack sequence and their impacts, next, extracting attacker's characteristics from them, and finally, incorporating their influence on the next adversarial options, to efficiently restricts the follow up opportunities. The objective of our method is to deter attack occurrence by increasing the cost and uncertainty in attack planning and execution. Simultaneously, it is designed to ensure real-time situational awareness and operational efficiency. The proposed approach has been implemented with a case study on Industrial Control Systems. The empirical results show the significant improvement of impeding active threat propagation in the network. © 2020 IEEE.

关键词： Network security

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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Resilience to hazards overlapping a pandemic: A shelter resource stabilization model

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International Journal of Mass Emergencies & Disasters 2023年第1期41卷 164 - 173页

作者： Nicole S Hutton Jennifer L Whytlaw Joshua Behr Juita-Elena (Wie) Yusuf Taiwo C Olanrewaju Lasisi Jennifer Marshall Vicky Seiler Rimer Department of Political Science and Geography Old Dominion University Norfolk VA USA Virginia Modeling Analysis & Simulation Center Old Dominion University Norfolk VA USA School of Public Service and Institute for Coastal Adaptation and Resilience Old Dominion University Norfolk VA USA CLIR Postdoctoral Fellow in Community Data Literacy at Carnegie Mellon University Libraries PA USA College of Public Health University of South Florida FL USA Department of Marketing Old Dominion University Norfolk VA USA

COVID-19 and the resulting financial impacts had budget and workforce implications for organizations involved in emergency shelter provision. To address distancing and sanitation protocols as well as virus transmission and vaccination rates, shelter supplies, facility modifications, and staffing adjustments were needed. As funding and authorizations to implement public health guidance and stabilize the workforce expired, emergency managers had to determine whether to continue pandemic protocols. In order to understand these relationships, we conducted a workshop in September 2021 with 137 emergency managers, public health leaders, and other government and nonprofit practitioners from 20 states, the U.S. Virgin Islands, and Canada to identify resource reallocation strategies utilized for sheltering from post-lockdown to post-vaccine periods of the COVID-19 pandemic. We applied a fiscal recovery framework to explore how these operational changes were influenced by and have implications for fiscal and policy support as well as operational adaptability. Results show that as fiscal and policy support waned, some pandemic protocols were suspended, thereby shifting and reducing human and facility resource needs. Distancing protocols benefited from improvements in masking, vaccine, and testing availability without consistent mandates, but non-congregate shelter provision was reduced as authorizations and funding expired. Modified service contracts and increased utilization of special needs registries can realign resources with health and safety needs. Based on participant responses, we developed a Retractable Stabilization Model to indicate how shelter resources can be reallocated and supported by future policy interventions to provide a range of emergency sheltering options during pandemics and overlapping hazards.

关键词： Pandemic hazard resilience emergency shelter

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Scalability of Sensor simulation in ROS-Gazebo Platform with and without Using GPU

Scalability of Sensor Simulation in ROS-Gazebo Platform with...

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Spring simulation Conference (SpringSim)

作者： Ahmet Saglam Yiannis Papelis Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk VA USA Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

Autonomous mobile robots depend heavily on sensors for interpreting the external environment and planning their movement. Given the complexity of even simple environments, the use of simulation is necessary to test sensory perception and movement strategies efficiently. Accurate and efficient sensor simulation is key to the successful development and fielding of autonomous mobile robots. In this paper, we investigate the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator. Specifically, the performance and scalability of the simulator are assessed with and without support from GPU in two virtual worlds, one highly-complex in terms of the number of collision targets and one not-complex. In both cases, we increase the number of sensors and evaluate system performance. The results show that the use of GPU helps improve performance, providing better speedups in the complex scene. However, the number of sensors that can be used is limited when using GPU.

关键词： Robot sensing systems Graphics processing units Computational modeling Complexity theory Scalability Analytical models

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A network agent-based model of ethnocentrism and intergroup cooperation

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Quality and Quantity 2020年第2期54卷 463-489页

作者： Lemos, Carlos M. Gore, Ross J. Lessard-Phillips, Laurence Shults, F. LeRon Department of Religion Philosophy and History University of Agder Kristiansand Norway Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA United States Institute for Research into Superdiversity (IRiS) School of Social Policy University of Birmingham Birmingham United Kingdom Institute for Global Development and Social Planning University of Agder Kristiansand Norway Center for Modeling Social Systems at NORCE Kristiansand Norway

We present a network agent-based model of ethnocentrism and intergroup cooperation in which agents from two groups (majority and minority) change their communality (feeling of group solidarity), cooperation strategy and social ties, depending on a barrier of “likeness” (affinity). Our purpose was to study the model’s capability for describing how the mechanisms of preexisting markers (or “tags”) that can work as cues for inducing in-group bias, imitation, and reaction to non-cooperating agents, lead to ethnocentrism or intergroup cooperation and influence the formation of the network of mixed ties between agents of different groups. We explored the model’s behavior via four experiments in which we studied the combined effects of “likeness,” relative size of the minority group, degree of connectivity of the social network, game difficulty (strength) and relative frequencies of strategy revision and structural adaptation. The parameters that have a stronger influence on the emerging dominant strategies and the formation of mixed ties in the social network are the group-tag barrier, the frequency with which agents react to adverse partners, and the game difficulty. The relative size of the minority group also plays a role in increasing the percentage of mixed ties in the social network. This is consistent with the intergroup ties being dependent on the “arena” of contact (with progressively stronger barriers from e.g. workmates to close relatives), and with measures that hinder intergroup contact also hindering mutual cooperation. © 2019, The Author(s).

关键词： Agent-based model Cooperation in networks Ethnocentrism

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