Digital Commons Network^™

Obsolescence: Evaluating An Educational Serious Game On Artificial Intelligence Impacts To Military Strategic Goals, Timothy C. Kokotajlo

Theses and Dissertations

Artificial Intelligence (AI) threatens to bring significant disruption to all aspects of military operations. This research develops a Serious Game (SG) and assessment methodology to provide education on the mindsets required for engaging with disruptive AI technologies. The game, Obsolescence, teaches strategic-level concepts recommended to the Department of Defense (DoD) from a compilation of reports on the current and future state of AI and warfighting. The methodology for assessing the educational value of Obsolescence addresses common challenges such as subjective reporting, control groups, population sizes, and measuring abstract or high levels of learning. The games proposed educational value is tested …

Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire

Research Collection Lee Kong Chian School Of Business

Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n …

Smoothing Of Convolutional Neural Network Classifications, Glen R. Drumm

Theses and Dissertations

Smoothing convolutional neural networks is investigated. When intermittent and random false predictions happen, a technique of average smoothing is applied to smooth out the incorrect predictions. While a simple problem environment shows proof of concept, obstacles remain for applying such a technique to a more operationally complex problem.

Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice

Theses and Dissertations

We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …

State Of Industry 5.0—Analysis And Identification Of Current Research Trends, Aditya Akundi, Daniel Euresti, Sergio Luna, Wilma Ankobiah, Amit Lopes, Immanuel Edinbarough

Manufacturing & Industrial Engineering Faculty Publications

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing …

Artificial Intelligence In Dentistry, Orthodontics And Orthognathic Surgery: A Literature Review, Tania Arshad Siddiqui, Rashna Hoshang Sukhia, Dinaz Ghandhi

Section of Dental-Oral Maxillofacial Surgery

Artificial intelligence is the ability of machines to work like humans. The concept initially began with the advent of mathematical models which gave calculated outputs based on inputs fed into the system. This was later modified with the introduction of various algorithms which can either give output based on overall data analysis or by selection of information within previous data. It is steadily becoming a favoured mode of treatment due to its efficiency and ability to manage complex conditions in all specialities. In dentistry, artificial intelligence has also popularised over the past few decades. They have been found useful for …

Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher

Articles

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best …

Many-Objective Evolutionary Algorithms: Objective Reduction, Decomposition And Multi-Modality., Monalisa Pal Dr.

Doctoral Theses

Evolutionary Algorithms (EAs) for Many-Objective Optimization (MaOO) problems are challenging in nature due to the requirement of large population size, difficulty in maintaining the selection pressure towards global optima and inability of accurate visualization of high-dimensional Pareto-optimal Set (in decision space) and Pareto-Front (in objective space). The quality of the estimated set of Pareto-optimal solutions, resulting from the EAs for MaOO problems, is assessed in terms of proximity to the true surface (convergence) and uniformity and coverage of the estimated set over the true surface (diversity). With more number of objectives, the challenges become more profound. Thus, better strategies have …

Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil

Published and Grey Literature from PhD Candidates

Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of …

Multi-Agent Pathfinding In Mixed Discrete-Continuous Time And Space, Thayne T. Walker

Electronic Theses and Dissertations

In the multi-agent pathfinding (MAPF) problem, agents must move from their current locations to their individual destinations while avoiding collisions. Ideally, agents move to their destinations as quickly and efficiently as possible. MAPF has many real-world applications such as navigation, warehouse automation, package delivery and games. Coordination of agents is necessary in order to avoid conflicts, however, it can be very computationally expensive to find mutually conflict-free paths for multiple agents – especially as the number of agents is increased. Existing state-ofthe- art algorithms have been focused on simplified problems on grids where agents have no shape or volume, and …

Could Alexa Increase Your Social Worth?, Peter Tripp

Electronic Theses and Dissertations

People have historically used personal introductions to build social capital, which is the foundation of career networking and is perhaps the most effective way to advance a career (Lin, 2001). With societal changes, such as the pandemic (Venkatesh & Edirappuli, 2020), and the increasing capabilities of Artificial Intelligence (AI), new approaches may emerge that impact societal relationships. Social capital theory highlights the need for reciprocal agreements to establish the trust between parties (Gouldner, 1960). My theoretical prediction and focus of this research include two principles: The impact of reciprocity in evaluating trust of the source of the introduction and the …

Cardiovascular Applications Of Artificial Intelligence In Research, Diagnosis, And Disease Management, Viswanathan Rajagopalan, Houwei Cao

Center for No Boundary Thinking

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different …

Liability For Use Of Artificial Intelligence In Medicine, W. Nicholson Price, Sara Gerke, I. Glenn Cohen

Law & Economics Working Papers

While artificial intelligence has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties, but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving health-care providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability: individual health-care providers, focused on physicians; institutions, focused on hospitals; and developers.

The Incompatibility Of Artificial Intelligence And Citizens United, Michael R. Siebecker

Sturm College of Law: Faculty Scholarship

In Citizens United v. FEC, the Supreme Court granted corporations essentially the same political speech rights as human beings. But does the growing prevalence of artificial intelligence (“AI”) in directing the content and dissemination of political communications call into question the jurisprudential soundness of such a commitment? Would continuing to construe the corporation as a constitutional rights bearer make much sense if AI entities could wholly own and operate business entities without any human oversight? Those questions seem particularly important, because in the new era of AI, the nature and practices of the modern corporation are quickly evolving. The magnitude …

Overview Of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, And Their Open-Source Software, Richard S. Segall

Faculty Publications

This chapter first provides an overview with examples of what neural networks (NN), machine learning (ML), and artificial intelligence (AI) are and their applications in biomedical and business situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trail download for limited time use, and open-source software (OSS) for machine learning (ML) are provided. Characteristics of artificial intelligence (AI) technologies for machine learning available as open source are discussed. Illustrations of applications of neural networks, machine learning, and …

Contextualizing Artificial Intelligence: The History, Values, And Epistemology Of Technology In The Philosophy Of Science, Christopher Grimsley

Theses and Dissertations--Philosophy

Artificial intelligence (AI) and other advanced technologies pose new questions for philosophers of science regarding epistemology, science and values, and the history of science. I will address these issues across three essays in this dissertation. The first essay concerns epistemic problems that emerge with existing accounts of scientific explanation when they are applied to deep neural networks (DNNs). Causal explanations in particular, which appear at first to be well suited to the task of explaining DNNs, fail to provide any such explanation. The second essay will explore bias in systems of automated decision-making, and the role of various conceptions of …

Precision Clinical Medicine Through Machine Learning: Using High And Low Quantile Ranges Of Vital Signs For Risk Stratification Of Icu Patients, Khalid Alghatani, Nariman Ammar, Abdelmounaam Rezgui

Faculty Publications - Information Technology

Remote monitoring of patients in the intensive care unit (ICU) is a crucial observation and assessment task that is necessary for precision medicine. We have recently built a cloud-based intelligent remote patient monitoring (IRPM) framework in which we follow the state-of-the-art in risk stratification through machine learning-based prediction, but with minimal features that rely on vital signs, the most commonly used physiological variables obtained inside and outside hospitals. In this work, we significantly improve the functionality of the initial IRPM framework by building three machine learning models for readmission, abnormality, and next-day vital sign measurements. We provide a formal representation …

Educating Robot-Proof Attorneys, Tammy Oltz

Faculty Scholarship

Technology, particularly that powered by Artificial Intelligence (AI), is disrupting the legal profession. In the coming years, lawyers who do not understand technology will face both ethical and professional risks. This year, the duty of technological competence marks its tenth anniversary. While lawyers are still learning what the duty means and what it will mean in the future, one thing is certain: those who ignore it could find themselves at risk of being on the wrong side of an ethics violation.

However, even without that regulatory obligation, lawyers who continue to ignore the current technological revolution put themselves at risk …

Moving From Harm Mitigation To Affirmative Discrimination Mitigation: The Untapped Potential Of Artificial Intelligence To Fight School Segregation And Other Forms Of Racial Discrimination, Andrew Gall

Catholic University Journal of Law and Technology

No abstract provided.

Why Do Family Members Reject Ai In Health Care? Competing Effects Of Emotions, Eun Hee Park, Karl Werder, Lan Cao, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find …

Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage

Legacy Theses & Dissertations (2009 - 2024)

In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that are most informative for each test instance individually may improve not only the quality of prediction but also the overall interpretability of the model. To this end, in this dissertation, we study the problem of optimizing the trade-off between instance-level sparsity and the quality of prediction using a dynamic instance-wise decision-making approach. Specifically, this approach sequentially reviews features one at a time for each data instance given …

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

Artificial Intelligence And Digital Twins In Sustainable Agriculture And Forestry: A Survey, Jing Nie, Yi Wang, Yang Li, Xuewei Chao

Turkish Journal of Agriculture and Forestry

Affected by global economic pressure and epidemics, sustainable agriculture has received widespread attention from farmers and agricultural engineers. Throughout history, agricultural technology has closely followed the pace of scientific and technological development and has followed the footsteps of mechanization, automation, and intelligence to progress continuously. At this stage, artificial intelligence (AI) is dominating the field of agriculture and advancing the progress of sustainable agriculture. However, the large amount of data required by AI technology and the high cost of data have ensued, while the rapid development of virtualization technology has made people gradually begin to consider the application of digital …

The Transforming Importance Of Social Skills In The Labor Market In The 2010s, Andrew Bradjan

CMC Senior Theses

The growing importance of social skills has led to an increase in returns to wages and employment for workers specializing in social skill-intensive occupations. Between 2012 and 2019, social skill-intensive occupations grew by 3 percentage points as a share of the U.S. labor force. Math-intensive occupations also grew by a similar amount during this time. To analyze these patterns, I utilize a model of team production where workers trade tasks to exploit their comparative advantage. Social skills reduce coordination costs between workers and allow them to specialize and cooperate with other workers more effectively by trading tasks. This model predicts …

Legal Liability For Artificially Intelligent "Robot Lawyers", Michael Loy

Lewis & Clark Law Review

This Comment discusses liability for the use of autonomous “robot lawyers” in the practice of law. A “robot lawyer” is an artificially intelligent (AI) software program that performs legal tasks traditionally handled by lawyers. Law firms currently use “non-autonomous” robot lawyers that operate under the supervision of a human attorney to assist in legal research, e-discovery, and contract drafting. In the future, “autonomous” robot lawyers that operate without human oversight may one day represent clients. However, courts and legislatures have not determined who will be liable for errors, omissions, malpractice, or other harms caused by autonomous robot lawyers. This Note …

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

Don't Give Me That Story! -- A Human-Centered Framework For Usable Narrative Planning, Rachelyn Farrell

Theses and Dissertations--Computer Science

Interactive or branching stories are engaging and can be embedded into digital systems for a variety of purposes, but their size and complexity makes it difficult and time-consuming for humans to author them. Narrative planning algorithms can automatically generate large branching stories with guaranteed causal consistency, using a hand-authored library of story content pieces. The usability of such a system depends on both the quality of the narrative model upon which it is built and the ability of the user to create the story content library.

Current narrative planning algorithms use either a limited or no model of character belief, …

Smart Decision-Making Via Edge Intelligence For Smart Cities, Nathaniel Hudson

Theses and Dissertations--Computer Science

Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and …

Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski

Electrical & Computer Engineering Faculty Publications

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues

VMASC Publications

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …