Human-Machine Communication: Complete Volume 4,
2022
University of Central Florida
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,
2022
University of Central Florida
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,
2022
Arizona State University
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,
2022
The University of Iowa
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 …
Considering The Context To Build Theory In Hci, Hri, And Hmc: Explicating Differences In Processes Of Communication And Socialization With Social Technologies,
2022
Hong Kong Baptist University
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 …
Human, Hybrid, Or Machine? Exploring The Trustworthiness Of Voice-Based Assistants,
2022
Technical University Dresden
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 …
The Symptom Of Ethics: Rethinking Ethics In The Face Of The Machine,
2022
Northern Illinois University
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,
2022
Çanakkale Onsekiz Mart University
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 …
Testability Of Instrumental Variables In Linear Non-Gaussian Acyclic Causal Models,
2022
Peking University, Beijing, China & Beijing Technology and Business University, Beijing, China
Testability Of Instrumental Variables In Linear Non-Gaussian Acyclic Causal Models, Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang
Machine Learning Faculty Publications
This paper investigates the problem of selecting instrumental variables relative to a target causal influence X → Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria …
New Approaches To Convex Polygon Formations,
2022
University of Nebraska at Omaha
New Approaches To Convex Polygon Formations, Rui Yang
Computer Science Graduate Research Workshop
No abstract provided.
Deep Learning-Based Image Watermarking With Transform Invariant Representation Learning,
2022
University of Nebraska at Omaha
Deep Learning-Based Image Watermarking With Transform Invariant Representation Learning, Arjon Das
Computer Science Graduate Research Workshop
No abstract provided.
Industrial Digital Twins At The Nexus Of Nextg Wireless Networks And Computational Intelligence: A Survey,
2022
National University of Sciences and Technology, Pakistan
Industrial Digital Twins At The Nexus Of Nextg Wireless Networks And Computational Intelligence: A Survey, Shah Zeb, Aamir Mahmood, Syed Ali Hassan, Md. Jalil Piran, Mikael Gidlund, Mohsen Guizani
Machine Learning Faculty Publications
By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and …
A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics,
2022
Old Dominion University
A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides
College of Sciences Posters
With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …
Visual Homing For Robot Teams: Do You See What I See?,
2022
Fordham University
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Faculty Publications
Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used …
Unsupervised Automatic Speech Recognition: A Review,
2022
United Arab Emirates University & Mohamed bin Zayed University of Artificial Intelligence
Unsupervised Automatic Speech Recognition: A Review, Hanan Aldarmaki, Asad Ullah, Sreepratha Ram, Nazar Zaki
Natural Language Processing Faculty Publications
Automatic Speech Recognition (ASR) systems can be trained to achieve remarkable performance given large amounts of manually transcribed speech, but large labeled data sets can be difficult or expensive to acquire for all languages of interest. In this paper, we review the research literature to identify models and ideas that could lead to fully unsupervised ASR, including unsupervised sub-word and word modeling, unsupervised segmentation of the speech signal, and unsupervised mapping from speech segments to text. The objective of the study is to identify the limitations of what can be learned from speech data alone and to understand the minimum …
Lattice Optics Optimization For Recirculatory Energy Recovery Linacs With Multi-Objective Optimization,
2022
Old Dominion University
Lattice Optics Optimization For Recirculatory Energy Recovery Linacs With Multi-Objective Optimization, Isurumali Neththikumara, Todd Satogata, Alex Bogacz, Ryan Bodenstein, Arthur Vandenhoeke
College of Sciences Posters
Beamline optics design for recirculatory linear accelerators requires special attention to suppress beam instabilities arising due to collective effects. The impact of these collective effects becomes more pronounced with the addition of energy recovery (ER) capability. Jefferson Lab’s multi-pass, multi-GeV ER proposal for the CEBAF accelerator, ER@CEBAF, is a 10- pass ER demonstration with low beam current. Tighter control of the beam parameters at lower energies is necessary to avoid beam break-up (BBU) instabilities, even with a small beam current. Optics optimizations require balancing both beta excursions at high-energy passes and overfocusing at low-energy passes. Here, we discuss an optics …
Physics-Informed Neural Networks (Pinns) For Dvcs Cross Sections,
2022
Old Dominion University
Physics-Informed Neural Networks (Pinns) For Dvcs Cross Sections, Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten, Yaohang Li, Huey-Wen Lin, Simonetta Liuti, Sorawich Maichum
College of Sciences Posters
We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy and generalization. By incorporating physics knowledge to our deep learning model, our framework shows precise predictions on the DVCS cross sections and better extrapolation on …
Reinforcement Learning With Deep Q-Networks,
2022
Western Kentucky University
Reinforcement Learning With Deep Q-Networks, Caleb Cassady
Masters Theses & Specialist Projects
In the past decade, machine learning strategies centered on the use of Deep Neural Networks (DNNs) have caught the interest of researchers due to their success in complicated classification and prediction problems. More recently, these DNNs have been applied to reinforcement learning tasks with state of- the-art results using Deep Q-Networks (DQNs) based on the Q-Learning algorithm. However, the DQN training process is different from standard DNNs and poses significant challenges for certain reinforcement learning environments. This paper examines some of these challenges, compares proposed solutions, and offers novel solutions based on previous research. Experiment implementation available at https://github.com/caleb98/dqlearning.
A New Metaphor: How Artificial Intelligence Links Legal Reasoning And Mathematical Thinking,
2022
Marquette University Law School
A New Metaphor: How Artificial Intelligence Links Legal Reasoning And Mathematical Thinking, Melissa E. Love Koenig, Colleen Mandell
Marquette Law Review
Artificial intelligence’s (AI’s) impact on the legal community expands exponentially each year. As AI advances, lawyers have more powerful tools to enhance their ability to research and analyze the law, as well as to draft contracts and other legal documents. Lawyers are already using tools powered by AI and are learning to shift their methodologies to take advantage of these enhancements. To continue to grow into their shifting role, lawyers should understand the relationship between AI, mathematics, and legal reasoning.
Integrating Blockchains And Intelligent Agents In The Pursuit Of Artificial General Intelligence,
2022
Liberty University
Integrating Blockchains And Intelligent Agents In The Pursuit Of Artificial General Intelligence, Benjamin Fluharty
Senior Honors Theses
Artificial General Intelligence (AGI) is the next greatest technological milestone. AGI can be defined as a realized artificial intelligence (AI) with the ability to understand and solve problems of various scope within constantly changing environments. To take steps toward this goal, a baseline of information will be provided regarding surrounding topics and the current state of AGI, itself. Through the culmination of swarms of highly optimized narrow AI agents, a collaborative effort will be extended toward general intelligence. Blockchains have been selected to facilitate this connection. A software deliverable will accompany this thesis to illustrate how this idea might be …
