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Old Dominion University

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2021

Articles 1 - 15 of 15

Full-Text Articles in Physical Sciences and Mathematics

The Maritime Domain Awareness Center– A Human-Centered Design Approach, Gary Gomez Nov 2021

The Maritime Domain Awareness Center– A Human-Centered Design Approach, Gary Gomez

Political Science & Geography Faculty Publications

This paper contends that Maritime Domain Awareness Center (MDAC) design should be a holistic approach integrating established knowledge about human factors, decision making, cognitive tasks, complexity science, and human information interaction. The design effort should not be primarily a technology effort that focuses on computer screens, information feeds, display technologies, or user interfaces. The existence of a room with access to vast amounts of information and wall-to-wall video screens of ships, aircraft, weather data, and other regional information does not necessarily correlate to possessing situation awareness. Fundamental principles of human-centered information design should guide MDAC design and technology selection, and …

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Risk Communication In The Tourism Industry, Lindsay E. Usher, Ashley Schroeder Nov 2021

Risk Communication In The Tourism Industry, Lindsay E. Usher, Ashley Schroeder

Human Movement Sciences & Special Education Faculty Publications

This chapter focuses on risk communication in the tourism sector. Tourism organizations must communicate with a variety of stakeholders when conveying messages about impending severe weather or disasters, which are increasing due climate change and sea level rise. There is also an increased need to distribute information to tourism stakeholders about preparing for, continuing service during, and recovering from, disasters. Stakeholders involved with the tourism industry include business owners, government officials and tourists, all of whom have differing degrees of vulnerability in a destination when a threat occurs. Different messages regarding disaster preparation and recovery must be communicated to the …

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Supporting Renewable Energy Market Growth Through The Circular Integration Of End-Of-Use And End-Of-Life Photovoltaics, Erika Marsillac Oct 2021

Supporting Renewable Energy Market Growth Through The Circular Integration Of End-Of-Use And End-Of-Life Photovoltaics, Erika Marsillac

Information Technology & Decision Sciences Faculty Publications

Energy demand continues to grow with the world’s burgeoning population. Meeting energy needs through renewable sources allows for market growth with limited environmental impact, but sourcing constraints can limit production, creating industrial and environmental problems. The exploitation of end-of-use and end-of-life photovoltaic (PV) options that are traditionally treated as waste offers a valuable opportunity to support renewable energy market growth with fewer sourcing constraints and minimal environmental impacts, but this circular investment has not yet been broadly implemented, nor is broad guidance widely available to aid its implementation. From a business perspective, this paper discusses the technical issues, assesses the …

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Developing An Artificial Intelligence Framework To Assess Shipbuilding And Repair Sub-Tier Supply Chains Risk, Rafael Diaz, Katherine Smith, Beatriz Acero, Francesco Longo, Antonio Padovano Jan 2021

Developing An Artificial Intelligence Framework To Assess Shipbuilding And Repair Sub-Tier Supply Chains Risk, Rafael Diaz, Katherine Smith, Beatriz Acero, Francesco Longo, Antonio Padovano

VMASC Publications

The defense shipbuilding and repair industry is a labor-intensive sector that can be characterized by low-product volumes and high investments in which a large number of shared resources, technology, suppliers, and processes asynchronously converge into large construction projects. It is mainly organized by the execution of a complex combination of sequential and overlapping stages. While entities engaged in this large-scale endeavor are often knowledgeable about their first-tier suppliers, they usually do not have insight into the lower tiers suppliers. A sizable part of any supply chain disruption is attributable to instabilities in sub-tier suppliers. This research note conceptually delineates a …

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Hybrid Models As Transdisciplinary Research Enablers, Andreas Tolk, Alison Harper, Navonil Mustafee Jan 2021

Hybrid Models As Transdisciplinary Research Enablers, Andreas Tolk, Alison Harper, Navonil Mustafee

Computational Modeling & Simulation Engineering Faculty Publications

Modelling and simulation (M&S) techniques are frequently used in Operations Research (OR) to aid decision-making. With growing complexity of systems to be modelled, an increasing number of studies now apply multiple M&S techniques or hybrid simulation (HS) to represent the underlying system of interest. A parallel but related theme of research is extending the HS approach to include the development of hybrid models (HM). HM extends the M&S discipline by combining theories, methods and tools from across disciplines and applying multidisciplinary, interdisciplinary and transdisciplinary solutions to practice. In the broader OR literature, there are numerous examples of cross-disciplinary approaches in …

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Solutions To Fermi Questions, Feb. 2021, Larry Weinstein Jan 2021

Solutions To Fermi Questions, Feb. 2021, Larry Weinstein

Physics Faculty Publications

Solutions for Fermi Questions, Feb. 2021. How many visible photons per second does a light bulb emit? How much does the U.S. spend on residential lightbulbs (both the bulbs and the electricity) every year?

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Hidden Markov Model And Cyber Deception For The Prevention Of Adversarial Lateral Movement, Md Ali Reza Al Amin, Sachin Shetty, Laurent Njilla, Deepak K. Tosh, Charles Kamhoua Jan 2021

Hidden Markov Model And Cyber Deception For The Prevention Of Adversarial Lateral Movement, Md Ali Reza Al Amin, Sachin Shetty, Laurent Njilla, Deepak K. Tosh, Charles Kamhoua

Computational Modeling & Simulation Engineering Faculty Publications

Advanced persistent threats (APTs) have emerged as multi-stage attacks that have targeted nation-states and their associated entities, including private and corporate sectors. Cyber deception has emerged as a defense approach to secure our cyber infrastructure from APTs. Practical deployment of cyber deception relies on defenders' ability to place decoy nodes along the APT path optimally. This paper presents a cyber deception approach focused on predicting the most likely sequence of attack paths and deploying decoy nodes along the predicted path. Our proposed approach combines reactive (graph analysis) and proactive (cyber deception technology) defense to thwart the adversaries' lateral movement. The …

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Detecting Incentivized Review Groups With Co-Review Graph, Yubao Zhang, Shuai Hao, Haining Wang Jan 2021

Detecting Incentivized Review Groups With Co-Review Graph, Yubao Zhang, Shuai Hao, Haining Wang

Computer Science Faculty Publications

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who …

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Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu Jan 2021

Stock Trend Prediction Using Candlestick Charting And Ensemble Machine Learning Techniques With A Novelty Feature Engineering Scheme, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu

Information Technology & Decision Sciences Faculty Publications

Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment …

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Generic Design Methodology For Smart Manufacturing Systems From A Practical Perspective, Part I—Digital Triad Concept And Its Application As A System Reference Model, Zhuming Bi, Wen-Jun Zhang, Chong Wu, Chaomin Luo, Lida Xu Jan 2021

Generic Design Methodology For Smart Manufacturing Systems From A Practical Perspective, Part I—Digital Triad Concept And Its Application As A System Reference Model, Zhuming Bi, Wen-Jun Zhang, Chong Wu, Chaomin Luo, Lida Xu

Information Technology & Decision Sciences Faculty Publications

Rapidly developed information technologies (IT) have continuously empowered manufacturing systems and accelerated the evolution of manufacturing system paradigms, and smart manufacturing (SM) has become one of the most promising paradigms. The study of SM has attracted a great deal of attention for researchers in academia and practitioners in industry. However, an obvious fact is that people with different backgrounds have different expectations for SM, and this has led to high diversity, ambiguity, and inconsistency in terms of definitions, reference models, performance matrices, and system design methodologies. It has been found that the state of the art SM research is limited …

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Improving Stock Trading Decisions Based On Pattern Recognition Using Machine Learning Technology, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang Jan 2021

Improving Stock Trading Decisions Based On Pattern Recognition Using Machine Learning Technology, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang

Information Technology & Decision Sciences Faculty Publications

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from …

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Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen Jan 2021

Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen

Computational Modeling & Simulation Engineering Faculty Publications

While the minimum cost flow (MCF) problems have been well documented in many publications, due to its broad applications, little or no effort have been devoted to explaining the algorithms for identifying loop formation and computing the value needed to solve MCF network problems. This paper proposes efficient algorithms, and MATLAB computer implementation, for solving MCF problems. Several academic and real-life network problems have been solved to validate the proposed algorithms; the numerical results obtained by the developed MCF code have been compared and matched with the built-in MATLAB function Linprog() (Simplex algorithm) for further validation.

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Image Source Identification Using Convolutional Neural Networks In Iot Environment, Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Li Da Xu Jan 2021

Image Source Identification Using Convolutional Neural Networks In Iot Environment, Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Li Da Xu

Information Technology & Decision Sciences Faculty Publications

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results …

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Blockchain For A Resilient, Efficient, And Effective Supply Chain, Evidence From Cases, Adrian Gheorghe, Farinaz Sabz Ali Pour, Unal Tatar, Omer Faruk Keskin Jan 2021

Blockchain For A Resilient, Efficient, And Effective Supply Chain, Evidence From Cases, Adrian Gheorghe, Farinaz Sabz Ali Pour, Unal Tatar, Omer Faruk Keskin

Engineering Management & Systems Engineering Faculty Publications

In the modern acquisition, it is unrealistic to consider single entities as producing and delivering a product independently. Acquisitions usually take place through supply networks. Resiliency, efficiency, and effectiveness of supply networks directly contribute to the acquisition system's resiliency, efficiency, and effectiveness. All the involved firms form a part of a supply network essential to producing the product or service. The decision-makers have to look for new methodologies for supply chain management. Blockchain technology introduces new methods of decentralization and delegation of services, which can transform supply chains and result in a more resilient, efficient, and effective supply chain. This …

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A Monte-Carlo Analysis Of Monetary Impact Of Mega Data Breaches, Mustafa Canan, Omer Ilker Poyraz, Anthony Akil Jan 2021

A Monte-Carlo Analysis Of Monetary Impact Of Mega Data Breaches, Mustafa Canan, Omer Ilker Poyraz, Anthony Akil

Engineering Management & Systems Engineering Faculty Publications

The monetary impact of mega data breaches has been a significant concern for enterprises. The study of data breach risk assessment is a necessity for organizations to have effective cybersecurity risk management. Due to the lack of available data, it is not easy to obtain a comprehensive understanding of the interactions among factors that affect the cost of mega data breaches. The Monte Carlo analysis results were used to explicate the interactions among independent variables and emerging patterns in the variation of the total data breach cost. The findings of this study are as follows: The total data breach cost …

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