Artificial Intelligence and Robotics Commons^™

Time Slot Allocation Method Of Data Link Based On Improved Difference Algorithm, Yuting Zhu, Huankun Su, Xiaodong Feng, Shijie Lei, Yanfang Fu

Journal of System Simulation

Abstract: Aiming at the problems of single algorithm, being prone to local optima, and weak generalization ability of the current strategies, based on an improved differential evolutionary algorithm, a chaos algorithm, an adaptive variational crossover algorithm, and a problem solution processing mechanism, a time slot allocation strategy is proposed. The chaos algorithm is used to initialize the population to increase the diversity and avoid the premature convergence. The selection probability parameter is then used to make the crossover and variation process more flexible, expanding the search range in early to increase the possibility of global optima in late. The experiment …

Research On Verification Method Of Missile Elastic Suppression Based On Frequency Compensation, Rixin Su, Ou Zhang

Journal of System Simulation

Abstract: For the elastic model of missile body in six degree of freedom mathematical simulation, the research on the verification method of elastic vibration suppression is carried out.. The notch filter used for elastic vibration suppression is introduced, the verification idea for filter design in boost-phase and passive-phase stages of missile flight is analyzed, and the problem currently existing in mathematical simulation verification is pointed out. Based on the frequency modulation phenomenon, an online verification method of frequency compensation for the notch filter is put forward, and it can be found that the function can be applied to any order …

Towards Machine Proficiency With Semantic Underspecification, Zachary S. Gottesman

Dartmouth College Master’s Theses

Human natural language communication frequently relies on extra-linguistic information to fill in gaps in the linguistic signal left by semantic underspecification, or the omission of details that can be inferred from prior knowledge or other modalities. Underspecification is particularly common in conversations between acquaintances, since these interlocutors share context. Underspecification is a key and beneficial feature of natural language that improves efficiency, although it can cause communication to fail if it is not resolved correctly. For language models to communicate effectively and in a human-like fashion, they must learn how to recognize and utilize underspecified language. This thesis argues that …

Gradient-Based Deep Reinforcement Learning Interpretation Methods, Yuan Wang, Lin Xu, Xiaoze Gong, Yongliang Zhang, Yongli Wang

Journal of System Simulation

Abstract: The learning process and working mechanism of deep reinforcement learning methods such as DQN are not transparent, and their decision basis and reliability cannot be perceived, which makes the decisions made by the model highly questionable and greatly limits the application scenarios of deep reinforcement learning. To explain the decision-making mechanism of intelligent agents, this paper proposes a gradient based saliency map generation algorithm SMGG. It uses the gradient information of feature maps generated by high-level convolutional layers to calculate the importance of different feature maps. With the known structure and internal parameters of the model, starting from the …

Prediction Of Converter Gas Generation Based On Intermission Production Improved Elman, Jiajie Fei, Dinghui Wu, Junyan Fan, Jing Wang

Journal of System Simulation

Abstract: Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaotic PSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction …

The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms, William H. Little

Symposium of Student Scholars

Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.

This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is …

Exploring Neural Networks For Breast Cancer Tissue Classification, Stephen Jacobs, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use …

Analysis And Computation Of Constrained Sparse Coding On Emerging Non-Von Neumann Devices, Kyle Henke

Mathematics & Statistics ETDs

This dissertation seeks to understand how different formulations of the neurally inspired Locally Competitive Algorithm (LCA) represent and solve optimization problems. By studying these networks mathematically through the lens of dynamical and gradient systems, the goal is to discern how neural computations converge and link this knowledge to theoretical neuroscience and artificial intelligence (AI). Both classical computers and advanced emerging hardware are employed in this study. The contributions of this work include:

1. Theoretical Work: A comprehensive convergence analysis for networks using both generic Rectified Linear Unit (ReLU) and Rectified Sigmoid activation functions. Exploration of techniques to address the binary …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Generative Ai In User-Generated Content, Yiqing Hua, Shuo Niu, Jie Cai, Lydia B. Chilton, Hendrick Heuer, Donghee Yvette Wohn

Computer Science

Generative AI (Gen-AI) is rapidly changing the landscape of User-Generated Content (UGC) on social media. AI tools for generating text, images, and videos, such as Large-Language Models (LLM), image generation AI, AI-powered video material tools, and deep fake technologies, are accelerating creators in obtaining content ideas, drafting outlines, and streamlining creative workflows. The capabilities of Gen-AI could introduce new opportunities to lower the bar and accelerate the pace of content creation for grassroots creators, thereby expanding the volume of AI-generated UGC on social media. However, we lack the necessary understanding of how the wide deployment of such technologies will impact …

An Examination Of Behavior Of Youtube Commenters, John E. Leonard

Computer Science ETDs

YouTube comments are a unique form of social media, as viewers mainly interact with other viewers through comments sections, and cannot choose to interact with specific people. This lack of control over which comments are presented to them forces users to view spam.

Multiple general patterns of behavior were found in the dataset of YouTube comments. For example, most users posted just after a video’s publication. In addition, users tended to watch videos in the evening over the early morning.

A novel method was found for quantifying the likelihood of coordinated accounts being controlled by one person using time sharing. …

Online Temporal Data Mining And Learning: Pursuing Enhanced Efficiency And Robust Algorithms, Sheng Zhong

Computer Science ETDs

Time series data mining and learning serve as a cornerstone across various domains, including finance, healthcare, and science. Recent advancements in network and sensor technologies have ignited an increasing interest in real-time temporal data mining and learning techniques. Various tasks benefit from these techniques, such as environmental monitoring, event detection, anomaly identification, and forecasting. However, these techniques still face significant challenges in the online environment settings, encompassing aspects like efficiency, accuracy, robustness, and scarcity of labeled data. This dissertation presents four innovative solutions: FilCorr, DCT-MASS, FewSig, and BitLINK to overcome these challenges. We evaluate each method and showcase their practical …

Companionship, Romance, And Self-Perception With Conversational Chatbots, Jonathan Windsor

Departmental Honors & Graduate Capstone Projects

Serving as a metaphorical gateway transcending the communicative barriers of physical relationships in interpersonal dialogues, artificial imators of human behavior and speech, also known as conversational chatbots; a simulation of human knowledge and existence in a bi-directional conversation, functions as a rhetor of expression. Spanning from contexts of professional to romantic, I serve to dissect and critically analyze the nuances of human-machine relationships based on pre-established literature, inviting ethical considerations and biases in their design and marketing. Corporate influences spark pre-established servitude-esque relationships with conversational agents. Professional applications, both task-oriented and emotionally based alike, paint a mixed picture of …

Machine Learning: Face Recognition, Mohammed E. Amin

Publications and Research

This project explores the cutting-edge intersection of machine learning (ML) and face recognition (FR) technology, utilizing the OpenCV library to pioneer innovative applications in real-time security and user interface enhancement. By processing live video feeds, our system encodes visual inputs and employs advanced face recognition algorithms to accurately identify individuals from a database of photos. This integration of machine learning with OpenCV not only showcases the potential for bolstering security systems but also enriches user experiences across various technological platforms. Through a meticulous examination of unique facial features and the application of sophisticated ML algorithms and neural networks, our project …

Behavioral Intention For Ai Usage In Higher Education, Isaac A. Odai, Elliot Wiley

Student Research Symposium

This study sought to further understand the cognitive factors that influence undergraduate students' behavioral intention to use generative AI. Generative AI's presence in academic spaces opens the door for ethical and pedagogical questions. This study surveyed 51 undergraduate communication students to measure their attitudes, subjective norms, self efficacy and their behavioral intention to use GenAI for school work. The results of this study showed behavioral intent had a positive relationship with attitudes and subjective norms. The implications of these findings show that personal beliefs and the perceived beliefs of others are correlated to undergraduate students’ intent to use GenAI for …

Toward The Integration Of Behavioral Sensing And Artificial Intelligence, Subigya K. Nepal

Dartmouth College Ph.D Dissertations

The integration of behavioral sensing and Artificial Intelligence (AI) has increasingly proven invaluable across various domains, offering profound insights into human behavior, enhancing mental health monitoring, and optimizing workplace productivity. This thesis presents five pivotal studies that employ smartphone, wearable, and laptop-based sensing to explore and push the boundaries of what these technologies can achieve in real-world settings. This body of work explores the innovative and practical applications of AI and behavioral sensing to capture and analyze data for diverse purposes. The first part of the thesis comprises longitudinal studies on behavioral sensing, providing a detailed, long-term view of how …

Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin

Military Cyber Affairs

Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.

Accuracy Of Machine Learning To Predict The Outcomes Of Shoulder Arthroplasty: A Systematic Review, Amir H. Karimi, Joshua Langberg, Ajith Malige, Omar Rahman, Joseph A. Abboud, Michael A. Stone

Department of Orthopaedic Surgery Faculty Papers

BACKGROUND: Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS: A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to …

Towards Scalable Autonomous Underwater Construction With Free-Floating Robots, Samuel Eric Lensgraf

Dartmouth College Ph.D Dissertations

This thesis presents the first free-floating autonomous underwater construction system. Our system built structures weighing up to 100Kg (75Kg in water). Our robot builds structures made of standard cinder blocks and custom designed interlocking cement blocks. It is the first construction robot that uses active buoyancy compensation to efficiently transport building materials. It is also the first construction robot that can reconfigure visual fiducial markers on a foundation during the construction process to expand its working area.

Underwater construction is a challenging problem for free-floating robots. Currents can buffet the robot, and visibility conditions can change. We focus on achieving …

Engineering Education In The Age Of Ai: Analysis Of The Impact Of Chatbots On Learning In Engineering, Flor A. Bravo, Juan M. Cruz Bohorquez

Henry M. Rowan College of Engineering Departmental Research

The purpose of this paper is to explore the influence of using AI chatbots on learning within the context of engineering education. We framed this study on the principles of how learning works in order to describe the contributions and challenges of AI chatbots in five categories: (1) facilitating the acquisition, completion, or activation of prior knowledge and helping organize knowledge and making connections; (2) enhancing student motivation to learn; (3) fostering self-directed learning and the acquisition, practice, and application of the skills and knowledge they acquire; (4) supporting goal-directed practice and feedback; and (5) addressing student diversity and creating …

Simulacra And Historical Fidelity In Digital Recreation Of Lost Cultural Heritage: Reconstituting Period Materialities For The Period Eye, Trent Olsen, James Hutson, Charles O'Brien, Jeremiah Ratican

Faculty Scholarship

The advancement of digital technologies in art history has opened avenues for reconstructing lost or damaged cultural heritage, a need highlighted by the deteriorated state of many artworks from the 1785 Salon. Grounded in the concept of the “Period Eye” by art historian Michael Baxandall, which emphasizes understanding artworks within their original historical and cultural contexts, this study proposes a subfield focused on Reconstituting Period Materialities for the Period Eye. This methodology bridges comprehensive historical research with generative visual artificial intelligence (AI) technologies, facilitating the creation and immersive virtual reality viewing of artworks. Beyond mere visual replication, the approach aims …

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining In Large Language Models (Llms), Jay Ratican, James Hutson

Faculty Scholarship

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth …

Academic Literature Review In Age Of Ai And Large Language Models​, Aaron Tay

Research Collection Library

Explore the evolving landscape of academic research with a focus on open data and AI advancements, particularly in natural language processing. Join us for a practical presentation on leveraging emerging tools for literature review. Discover platforms like Connected Papers, ResearchRabbit, and Litmaps, offering paper exploration and recommendations based on initial 'seed papers.' Dive into AI-enhanced search engines like Elicit, Scispace, Semantic Scholar, and Scite.ai, powered by Large Language Models such as BERT and GPT. Learn about the latest developments, strengths, and weaknesses of these tools, and how they reshape literature review methods, from tool selection to query input techniques.

Sustainable Accounting: The Benefits And Challenges Of Its Implementation, Collin Eckstein

University Honors College

This thesis is centered around the idea that sustainability is a necessary component within the world. Environmental, social, and governance (ESG) factors are key components of analyzing sustainable practices. Although accountants have historically focused on financial reporting, they now have the added function of sustainable accounting as environmental implications are an area of concern for companies. A substantial component of this thesis centers around analyzing how ESG factors contribute to an organization’s financial performance. It also observes how comprehensive sustainability reports impact investor decisions and examines the challenges that auditors face when providing assurance for sustainability initiatives. Furthermore, it examines …

Ai: Is The Future Robots?, Ruthanne Fischer

University Honors College

The goal of this thesis is to analyze how Artificial Intelligence is actively changing. The newly developed technology that is known as Artificial Intelligence can potentially have implications that are not yet known. Artificial Intelligence impacts the business world in many ways. Things such as ChatGPT and Deepfakes are some of the biggest forms of Artificial Intelligence today. It is important to know how Artificial Intelligence is changing every day because no one knows the true implications of a technology like this. It is crucial for companies to understand how this technology can potentially be harmful to their employees. Artificial …

No Sword, No Shield, No Problem: Ai In Pro Se Section 1983 Suits, Michaela Calhoun

University of Colorado Law Review Forum

Originating during the Reconstruction era, 42 U.S.C. 1983 emerged as a legislative tool to safeguard individuals’ constitutional rights and liberties. Initially designed to combat state-sanctioned violence, its efficacy has been eroded over time by subsequent judicial and legislative action. Unfortunately, the current state of Section 1983 falls short of this envisioned role, particularly for incarcerated individuals who find themselves navigating the complexities of the federal court system as pro se litigants.

Faced with a landscape devoid of resources, incarcerated individuals struggle to realize their constitutional rights, further perpetuating their collective status as a second-class citizenry—a status imposed by their own …

Ai And Advocacy: Maximizing Potential, Minimizing Risk, Matthew Salzano, Nicholas Fung, Ada Lin, Sofia Marchetta, Faith Colombo, Kaylah Davis, John Flynn, Carlos Fuentes, Fion Li, Malar Paavi Muthukumaran, Angelica Paramoshin, Chrisanne Pearce, Vianney Ramos, Charles St. Hilaire, Xi Zheng, Wei Zhuang

School of Communication and Journalism Faculty Publications

New Generative AI tools are revolutionizing writing and communication. This report focuses on AI and advocacy, the act of influencing public policy and resource allocation decisions within political, economic, and social systems and institutions. This report identifies three major opportunities and accompanying risks, plus one strong recommendation for advocates considering using AI. We argue that AI can be useful for advocates, but they must be careful to center human judgment and avoid risks that could distract from their important work or even contribute to societal harms.

Forya.Ai Business Plan, Landen Usher

Finance Undergraduate Honors Theses

This thesis presents a comprehensive business plan for Forya.AI, an innovative artificial intelligence-powered platform designed to streamline social media content creation for churches. The platform distinguishes itself by automating the generation of engaging content, leveraging church-provided images to produce visually appealing posts accompanied by contextually relevant captions. By integrating advanced AI algorithms, Forya.AI analyzes uploaded images, identifies thematic elements, and crafts captions that resonate with the spiritual and community values of the church. This process not only enhances the digital presence of religious organizations but also significantly reduces the time and financial resources traditionally required for content development. The business …

The Role Of Student Motivation In Integrating Ai Into Web Design Education: A Longitudinal Study, Jason Lively, James Hutson

Faculty Scholarship

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …