Browsing by Author "Batarseh, Feras A."

Now showing 1 - 9 of 9
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    Ajna: A Wearable Shared Perception System for Extreme Sensemaking
    Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
    This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
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    Enabling Artificial Intelligence Adoption through Assurance
    Freeman, Laura J.; Rahman, Abdul; Batarseh, Feras A. (MDPI, 2021-08-25)
    The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.
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    Explainable Neural Claim Verification Using Rationalization
    Gurrapu, Sai Charan (Virginia Tech, 2022-06-15)
    The dependence on Natural Language Processing (NLP) systems has grown significantly in the last decade. Recent advances in deep learning have enabled language models to generate high-quality text at the same level as human-written text. If this growth continues, it can potentially lead to increased misinformation, which is a significant challenge. Although claim verification techniques exist, they lack proper explainability. Numerical scores such as Attention and Lime and visualization techniques such as saliency heat maps are insufficient because they require specialized knowledge. It is inaccessible and challenging for the nonexpert to understand black-box NLP systems. We propose a novel approach called, ExClaim for explainable claim verification using NLP rationalization. We demonstrate that our approach can predict a verdict for the claim but also justify and rationalize its output as a natural language explanation (NLE). We extensively evaluate the system using statistical and Explainable AI (XAI) metrics to ensure the outcomes are valid, verified, and trustworthy to help reinforce the human-AI trust. We propose a new subfield in XAI called Rational AI (RAI) to improve research progress on rationalization and NLE-based explainability techniques. Ensuring that claim verification systems are assured and explainable is a step towards trustworthy AI systems and ultimately helps mitigate misinformation.
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    H2OGAN: A Deep Learning Approach for Detecting and Generating Cyber-Physical Anomalies
    Lin, Yen-Cheng (Virginia Tech, 2024-05-17)
    The integration of Artificial Intelligence (AI) into water supply systems (WSSs) has revolutionized real-time monitoring, automated operational control, and predictive decision-making analytics. However, AI also introduces security vulnerabilities, such as data poisoning. In this context, data poisoning could involve the malicious manipulation of critical data, including water quality parameters, flow rates, and chemical composition levels. The consequences of such threats are significant, potentially jeopardizing public safety and health due to decisions being made based on poisoned data. This thesis aims to exploit these vulnerabilities in data-driven applications within WSSs. Proposing Water Generative Adversarial Networks, H2OGAN, a time-series GAN-based model designed to synthesize water data. H2OGAN produces water data based on the characteristics within the expected constraints of water data cardinality. This generative model serves multiple purposes, including data augmentation, anomaly detection, risk assessment, cost-effectiveness, predictive model optimization, and understanding complex patterns within water systems. Experiments are conducted in AI and Cyber for Water and Agriculture (ACWA) Lab, a cyber-physical water testbed that generates datasets replicating both operational and adversarial scenarios in WSSs. Identifying adversarial scenarios is particularly importance due to their potential to compromise water security. The datasets consist of 10 physical incidents, including normal conditions, sensor anomalies, and malicious attacks. A recurrent neural network (RNN) model, i.e., gated recurrent unit (GRU), is used to classify and capture the temporal dynamics those events. Subsequently, experiments with real-world data from Alexandria Renew Enterprises (AlexRenew), a wastewater treatment plant in Alexandria, Virginia, are conducted to assess the effectiveness of H2OGAN in real-world applications.
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    Leadership for CyberBioSecurity: The Case of Oldsmar Water
    Kaufman, Eric K.; Adeoye, Samson; Batarseh, Feras A. (2023-02-01)
    Agriculture and life sciences are increasingly becoming cyber-driven, relying on artificial intelligence and the Internet of things (IoTs) for the automation of operational processes (Murch & Drape, 2022). The more leverage humans seek and obtain from industrial control systems (ICS) for the efficient treatment, distribution, and recycling of drinking water and wastewater, the more likely will be the convolution of CyberBioSecurity issues. To this end, this case study explores the cyberattack on the Oldsmar, Florida, water treatment plant on February 5, 2021. Through a forward-looking lens to synthesize the undisguised case, the case equips students with the requisite skills for the adaptive challenges of the contemporary world of wicked problems. Thus, this teaching case provides the opportunity to build learners’ capacity to apply adaptive leadership and leadership-as-practice to wicked problems, like CyberBioSecurity. Instead of attempting to solve adaptive challenges with a limited and insufficient set of technical tools, learners will recognize the potential of modern approaches to leadership for advancing holistic (re)solutions to complex, challenging situations.
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    Mapping the Landscape of Cyberbiosecurity Education
    Adeoye, Samson; Kaufman, Eric K.; Brown, Anne M.; Batarseh, Feras A. (2023-04-18)
    As an emerging field at the nexus of digital technologies and life sciences, the integration of Cyberbiosecurity education into postsecondary and graduate programs remains unclear. Many of the educational practices and related workforce development skills associated with Cyberbiosecurity may be hidden in the shadows of more established programs such as in computer science, engineering, and agriculture and life sciences albeit in non-integrative forms. However, signature pedagogies in the professions emerge from these early, established practices, making it important to recognize their impact in ways that allow for intentionality with widespread adoption for workforce preparation. Using a map of the Cyberbiosecurity enterprise in the United States, our research team is surveying Cyberbiosecurity stakeholders to identify what trends with signature and shadow pedagogies exist that may influence future education program directions, including training methods, educational requirements, and credentialing. This study offers evidence-based insights into improving workforce readiness and interdisciplinary interactions in Cyberbiosecurity.
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    Pathways for Cyberbiosecurity Workforce Preparation: Integrating Insights from Both Cybersecurity and Biosecurity
    Adeoye, Samson; Bagby, B.; Batarseh, Feras A.; Brown, Anne M.; Kaufman, Eric K.; Lindberg, Heather (2023-04-18)
    As technological advances have improved operational processes and exposed them to vulnerabilities in the agricultural and life sciences, the convergence of life sciences and information technology has become the inevitable proposition among researchers and educators. Cyberbiosecurity, as an emerging field between life sciences and the digital world, needs broad stakeholder input to properly align the needs of both sectors. As the complexity and strain on essential biological pipelines increases, it is essential for life scientists to be supported and educated in concepts and mediums that promote computational, data, and risk management skills development related to cyberbiosecurity. Our survey research is helping to reveal stakeholder perceptions of priorities for guiding cyberbiosecurity workforce preparedness and serve as a foundation for creating educational initiatives in higher education for cyberbiosecurity.
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    A survey on artificial intelligence assurance
    Batarseh, Feras A.; Freeman, Laura J.; Huang, Chih-Hao (2021-04-26)
    Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied for different problems. One important notion for the adoption of AI algorithms into operational decision processes is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 and 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented, and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.
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    Trustworthy Soft Sensing in Water Supply Systems using Deep Learning
    Sreng, Chhayly (Virginia Tech, 2024-05-22)
    In many industrial and scientific applications, accurate sensor measurements are crucial. Instruments such as nitrate sensors are vulnerable to environmental conditions, calibration drift, high maintenance costs, and degrading. Researchers have turned to advanced computational methods, including mathematical modeling, statistical analysis, and machine learning, to overcome these limitations. Deep learning techniques have shown promise in outperforming traditional methods in many applications by achieving higher accuracy, but they are often criticized as 'black-box' models due to their lack of transparency. This thesis presents a framework for deep learning-based soft sensors that can quantify the robustness of soft sensors by estimating predictive uncertainty and evaluating performance across various scenarios. The framework facilitates comparisons between hard and soft sensors. To validate the framework, I conduct experiments using data generated by AI and Cyber for Water and Ag (ACWA), a cyber-physical system water-controlled environment testbed. Afterwards, the framework is tested on real-world environment data from Alexandria Renew Enterprise (AlexRenew), establishing its applicability and effectiveness in practical settings.