Artificial Intelligence and Robotics Commons^™

Singapore Public Sector Ai Applications Emphasizing Public Engagement: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

This article provides an overview of six examples of public sector AI applications in Singapore that illustrate different ways of enhancing engagement with the public. These applications demonstrate ways of enhancing engagement with the public by providing greater accessibility to government services (access anywhere, anytime) and speedier responses to public processes and feedback. Some applications make it substantially easier for members of the public to do things or make choices, while others reduce waiting time, either across an entire public infrastructure, or for an individual transaction. Some provide highly individualized coaching to guide a person through the process of doing …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Fair, Equitable, And Just: A Socio-Technical Approach To Online Safety, Daricia Wilkinson

All Dissertations

Socio-technical systems have been revolutionary in reshaping how people maintain relationships, learn about new opportunities, engage in meaningful discourse, and even express grief and frustrations. At the same time, these systems have been central in the proliferation of harmful behaviors online as internet users are confronted with serious and pervasive threats at alarming rates. Although researchers and companies have attempted to develop tools to mitigate threats, the perception of dominant (often Western) frameworks as the standard for the implementation of safety mechanisms fails to account for imbalances, inequalities, and injustices in non-Western civilizations like the Caribbean. Therefore, in this dissertation …

Machine Learning With Kay, Lasith Niroshan, James Carswell

Conference Papers

Computational power is very important when training Deep Learning (DL) models with large amounts of data (Wooldridge, 2021). Hence, High-Performance Computing (HPC) can be leveraged to reduce computational cost, and the Irish Centre for High-End Computing (ICHEC) provides significant infrastructure and services for research and development to both academia and industry. A portion of ICHEC's HPC system has been allocated for institutional access, and this paper presents a case study of how to use Kay (Ireland's national supercomputer) in the remote sensing domain. Specifically, this study uses clusters of Kay Graphics Processing Units (GPUs) for training DL models to extract …

Reinforcement In The Information Revolution, Phillip M. Baker

SPU Works

This chapter will outline what it means to be a behaving human and how AI makes sense of these concepts. It will then explore possible near-future implications of our remarkable progress in understanding how human behavior works with the assistance of AI from a neurobiological basis. A focus on understanding the reinforcement mechanisms of the brain will reveal the consequences of ceding control of so much of our brain-environment interactions to AI. It will conclude by offering a potential Christian response to this digital reality from a uniquely Anabaptist perspective.

Automation And Apocalypse: Imagining The Future Of Work, Michael Paulus

SPU Works

This chapter provides an orientation to the history of technology, work, and the theology of work and then explores three visions of the future of work—a literary dystopia, a philosophical utopia, and a theological apocalypse—as resources for understanding the significance of work and imagining its future. In the first vision, found in Kurt Vonnegut’s speculative novel Player Piano, automation leads to the end of meaningful work and nearly renders humans obsolete. This dystopic vision reveals the value of human work but remains skeptical about our ability to preserve it against the advances of automation. The second vision comes from …

Introduction, Michael Paulus

SPU Works

Artificial intelligence is rapidly and radically changing our lives and world. This book is a multidisciplinary engagement with the present and future impacts of AI from the standpoint of Christian faith. It provides technological, philosophical, and theological foundations for thinking about AI, as well as a series of reflections on the impact of AI on relationships, behavior, education, work, and moral action. The book serves as an accessible introduction to AI as well as a guide to wise consideration, design, and use of AI by examining foundational understandings and beliefs from a Christian perspective.

Imagining New Futures Beyond Predictive Systems In Child Welfare: A Qualitative Study With Impacted Stakeholders, Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu

Research Collection School Of Computing and Information Systems

Child welfare agencies across the United States are turning to datadriven predictive technologies (commonly called predictive analytics) which use government administrative data to assist workers’ decision-making. While some prior work has explored impacted stakeholders’ concerns with current uses of data-driven predictive risk models (PRMs), less work has asked stakeholders whether such tools ought to be used in the first place. In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them …

Effect Of Monetary Policy Rate On Market Interest Rates In Nigeria: A Threshold And Nardl Approach, Oluwafemi E. Awopegba, Joseph O. Afolabi, Lydia T. Adeoye, Godwin O. Akpokodje

CBN Journal of Applied Statistics (JAS)

This study examines the effect of monetary policy rate (MPR) on market interest rates in Nigeria. For parsimony, we develop two indexes called the short-term interest rate (SINT) and Lending interest rate (LINT) to represent deposit and lending rates respectively. The nonlinear autoregressive distributed lag (NARDL) and threshold regression models are adopted. The study uses monthly data from 2002:M1 to 2019:M12. The results of the threshold regression model indicate that the degree of the effect of MPR on SINT and LINT above the estimated threshold of 11 and 13 percent respectively is greater and significant than if MPR were to …

Social Dimension Of Inclusive Growth In Ecowas: Implication For Poverty Reduction, Toriola K. Anu, Goerge O. Emmanuel, Ajayi O. Felix

CBN Journal of Applied Statistics (JAS)

This study investigates the implication of the social dimension of inclusive growth on poverty reduction in Economic Community of West African States (ECOWAS) countries. It specifically examines how social indices of inclusive growth comprising of income inequality, education, and health outcomes affect poverty reduction. The study uses a panel dataset of the six (6) lower-middle income countries in ECOWAS which was analysed via panel Difference Generalised Method of Moment (D-GMM). The results show that GDP per capita exerts significant negative effect on poverty while inequality, education and health outcomes do not show significant effect on poverty. Although, the estimates of …

Effect Of Fdi Inflows On Employment Generation In Selected Ecowas Countries: Heterogeneous Panel Analysis, Timothy A. Aderemi, Olawunmi Omitogun, Bukonla G. Osisanwo

CBN Journal of Applied Statistics (JAS)

The aim of this study is to examine the effect of FDI on employment in ECOWAS sub region between 1990 and 2019. The study utilizes a panel autoregressive distributed lag model to analyse the short run and long run relationship between FDI and employment across ECOWAS sub region. In the short run, the impact of FDI on employment is negative and statistically not significant. Meanwhile, in the long run FDI has a positive and statistically significant impact on employment rate. This implies that FDI has the capacity to generate employment in countries in ECOWAS sub region. Therefore, this study recommends …

Impact Of Covid-19 Pandemic On The Nigeria Stock Market: A Sectoral Stock Prices Analysis, Peter A. Adekunle, Yakubu A. Bello, Udochukwu G. Nwachukwu

CBN Journal of Applied Statistics (JAS)

This study examines the impact of the COVID-19 pandemic on sectoral stock prices in Nigeria stock market using daily data covering from February 28, 2020 to June 26, 2020. Applying the autoregressive distributed lag (ARDL) bounds test, the study finds that COVID-19 pandemic had adverse impact on the stock market indices in the short run. Furthermore, the study documents negative response of sectoral stock prices to the pandemic while the stock prices of the banking sub-sector are the worst hit. Compared to the consumer goods, and industrial subsector indices, the speed of adjustment to long run equilibrium is faster for …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …

The Significance Of Sonic Branding To Strategically Stimulate Consumer Behavior: Content Analysis Of Four Interviews From Jeanna Isham’S “Sound In Marketing” Podcast, Ina Beilina

Student Theses and Dissertations

Purpose:
Sonic branding is not just about composing jingles like McDonald’s “I’m Lovin’ It.” Sonic branding is an industry that strategically designs a cohesive auditory component of a brand’s corporate identity. This paper examines the psychological impact of music and sound on consumer behavior reviewing studies from the past 40 years and investigates the significance of stimulating auditory perception by infusing sound in consumer experience in the modern 2020s.

Design/methodology/approach:
Qualitative content analysis of audio media was used to test two hypotheses. Four archival oral interview recordings from Jeanna Isham’s podcast “Sound in Marketing” featuring the sonic branding experts …

Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Showcase.

Using Ai To Examine Nocturnal Moth Pollination, Conor Moriarty

Biological Sciences Undergraduate Honors Theses

The importance of insect-mediated agriculture cannot be understated especially in the wake of pollinator population declines. Most research of insect-mediated pollination is focused on diurnal pollinators like the honeybee (Apis mellifera). Deepening our understanding of how other pollinators, such as nocturnal pollinators, can benefit agriculture and ecosystems will be very important as honeybee populations decline. I explored nocturnal moth interactions with flowering apple flowers during their pollination season to better understand how these nocturnal pollinators interact with the plants. To accomplish this, I used a University of Arkansas farm area with a dedicated apple orchard. I gathered my raw data …

Neighborhood Cooperative Multiagent Reinforcement Learning For Adaptive Traffic Signal Control In Epidemic Regions, Chengwei Zhang, Yu Tian, Zhibin Zhang, Wanli Xue, Xiaofei Xie, Tianpei Yang, Xin Ge, Rong Chen

Research Collection School Of Computing and Information Systems

Nowadays, multiagent reinforcement learning (MARL) have shared significant advances in the adaptive traffic signal control (ATSC) problems. For most of the researches, agents are all isomorphic, which disregards the situation in which isomerous intersections cooperative together in a real ATSC scenario, especially in epidemic regions where different intersections have quite different levels of importance. To this end, this paper models the ATSC problem as a networked Markov game (NMG), in which agents take into account information, including traffic conditions of it and its connected neighbors. A cooperative MARL framework named neighborhood cooperative hysteretic DQN (NC-HDQN) is proposed. Specifically, for each …

Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on the roads. Since …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Creating A Virtual Reality Experience In Service To A Non-Profit Agency, Frank Deese, Susan Lakin, Isabelle Anderson

Frameless

In the summer of 2018, RIT Professors Susan Lakin and Frank Deese discussed with the principal officers of the Society for the Protection and Care of Children (SPCC) in Rochester how the new technology of Virtual Reality might be used to not only impart information to viewers, but generate empathy for those receiving services from the organization as well as those performing those services. Their ultimate goal was to create an experience that could be viewed with VR headsets at fundraising events and on a website using low-cost Google Cardboard.

Human-Machine Communication: Complete Volume 4

Human-Machine Communication

This is the complete volume of HMC Volume 4.

Embracing Ai-Based Education: Perceived Social Presence Of Human Teachers And Expectations About Machine Teachers In Online Education, Jihyun Kim, Kelly Merrill Jr., Kun Xu, Deanna D. Sellnow

Human-Machine Communication

Technological advancements in education have turned the idea of machines as teachers into a reality. To better understand this phenomenon, the present study explores how college students develop expectations (or anticipations) about a machine teacher, particularly an AI teaching assistant. Specifically, the study examines whether students’ previous experiences with online courses taught by a human teacher would influence their expectations about AI teaching assistants in future online courses. An online survey was conducted to collect data from college students in the United States. Findings indicate that positively experienced social presence of a human teacher helps develop positive expectations about an …

Sex With Robots And Human-Machine Sexualities: Encounters Between Human-Machine Communication And Sexuality Studies, Marco Dehnert

Human-Machine Communication

Sex robots are a controversial topic. Understood as artificial-intelligence enhanced humanoid robots designed for use in partnered and solo sex, sex robots offer ample opportunities for theorizing from a Human-Machine Communication (HMC) perspective. This comparative literature review conjoins the seemingly disconnected literatures of HMC and sexuality studies (SeS) to explore questions surrounding intimacy, love, desire, sex, and sexuality among humans and machines. In particular, I argue for understanding human-machine sexualities as communicative sexuotechnical-assemblages, extending previous efforts in both HMC and SeS for more-than-human, ecological, and more fluid approaches to humans and machines, as well as to sex and sexuality. This …

I Get By With A Little Help From My Bots: Implications Of Machine Agents In The Context Of Social Support, Austin Beattie, Andrew C. High

Human-Machine Communication

In this manuscript we discuss the increasing use of machine agents as potential sources of support for humans. Continued examination of the use of machine agents, particularly chatbots (or “bots”) for support is crucial as more supportive interactions occur with these technologies. Building off extant research on supportive communication, this manuscript reviews research that has implications for bots as support providers. At the culmination of the literature review, several propositions regarding how factors of technological efficacy, problem severity, perceived stigma, and humanness affect the process of support are proposed. By reviewing relevant studies, we integrate research on human-machine and supportive …

Human, Hybrid, Or Machine? Exploring The Trustworthiness Of Voice-Based Assistants, Lisa Weidmüller

Human-Machine Communication

This study investigates how people assess the trustworthiness of perceptually hybrid communicative technologies such as voice-based assistants (VBAs). VBAs are often perceived as hybrids between human and machine, which challenges previously distinct definitions of human and machine trustworthiness. Thus, this study explores how the two trustworthiness models can be combined in a hybrid trustworthiness model, which model (human, hybrid, or machine) is most applicable to examine VBA trustworthiness, and whether this differs between respondents with different levels of prior experience with VBAs. Results from two surveys revealed that, overall, the human model exhibited the best model fit; however, the hybrid …

Considering The Context To Build Theory In Hci, Hri, And Hmc: Explicating Differences In Processes Of Communication And Socialization With Social Technologies, Andrew Gambino, Bingjie Liu

Human-Machine Communication

The proliferation and integration of social technologies has occurred quickly, and the specific technologies with which we engage are ever-changing. The dynamic nature of the development and use of social technologies is often acknowledged by researchers as a limitation. In this manuscript, however, we present a discussion on the implications of our modern technological context by focusing on processes of socialization and communication that are fundamentally different from their interpersonal corollary. These are presented and discussed with the goal of providing theoretical building blocks toward a more robust understanding of phenomena of human-computer interaction, human-robot interaction, human-machine communication, and interpersonal …

The Symptom Of Ethics: Rethinking Ethics In The Face Of The Machine, David J. Gunkel

Human-Machine Communication

This essay argues that it is the machine that constitutes the symptom of ethics— “symptom” understood as that excluded “part that has no part” in the system of moral consideration. Ethics, which has been historically organized around a human or at least biological subject, needs the machine to define the proper limits of the moral community even if it simultaneously excludes such mechanisms from any serious claim on moral consideration. The argument will proceed in five steps or movements. The first part will define and characterize “the symptom” as it has been operationalized in the work of Slovenian philosopher Slavoj …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna

Undergraduate Research Symposium

Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term …