Digital Commons Network^™

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 …

Software Reliability Model Estimation For An Indeterministic Crime Cluster Through Reinforcement Learning, Dileep Kumar Kadali, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indeterministic in Neutrosophic Logic, using a software reliability model. The study utilizes reinforcement learning, Neutrosophic logic, and non-homogeneous Poisson process crime data to estimate indeterministic cluster data in crime. The "Non-homogeneous Poisson Process with Neutrosophic Logic" technique performs well in evaluating and deterring crime based on crime data analysis. The crime cluster involving offenders correctly classified as failure to accomplish does better than uncertain cluster reliability estimation with least squares and logistic …

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 …

Software Reliability Model Estimation For An Indeterministic Crime Cluster Through Reinforcement Learning, Dileep Kumar Kadali, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indeterministic in Neutrosophic Logic, using a software reliability model. The study utilizes reinforcement learning, Neutrosophic logic, and non-homogeneous Poisson process crime data to estimate indeterministic cluster data in crime. The "Non-homogeneous Poisson Process with Neutrosophic Logic" technique performs well in evaluating and deterring crime based on crime data analysis. The crime cluster involving offenders correctly classified as failure to accomplish does better than uncertain cluster reliability estimation with least squares and logistic …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

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.

Topics In The Study Of The Pragmatic Functions Of Phonetic Reduction In Dialog, Nigel G. Ward, Carlos A. Ortega

Departmental Technical Reports (CS)

Reduced articulatory precision is common in speech, but for dialog its acoustic properties and pragmatic functions have been little studied. We here try to remedy this gap. This technical report contains content that was omitted from the journal article (Ward et. al, submitted). Specifically, we here report 1) lessons learned about annotating for perceived reduction, 2) the finding that, unlike in read speech, the correlates of reduction in dialog include high pitch, wide pitch range, and intensity, and 3) a baseline model for predicting reduction in dialog, using simple acoustic/prosodic features, that achieves correlations with human perceptions of 0.24 for …

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.

Monero: Powering Anonymous Digital Currency Transactions, Jake Braddy

Theses/Capstones/Creative Projects

Cryptocurrencies rely on a distributed public ledger (record of transactions) in order to perform their intended functions. However, the public’s ability to audit the network is both its greatest strength and greatest weakness: Anyone can see what address sent currency, and to whom the currency was sent. If cryptocurrency is ever going to take some of the responsibility of fiat currency, then there needs to be a certain level of confidentiality. Thus far, Monero has come out on top as the preferred currency for embodying the ideas of privacy and confidentiality. Through numerous cryptographic procedures, Monero is able to obfuscate …

Navigate The World Of Rfid: Diversity, Capabilities, And Constraints Of Readers And Tags, Rachana Pandey

2024 Spring Honors Capstone Projects - Archive

Radio-Frequency Identification (RFID) technology, a method for storing and retrieving data through electromagnetic transmission to an RFID tag, is revolutionizing inventory and asset management in various sectors, including healthcare. This research explores the applications of RFID in a medical setting. It assesses various RFID readers and tags, focusing on their functional capabilities, ranges, and limitations within a medical environment. Employing a comprehensive approach, the study integrates an extensive literature review, comparative analysis, and empirical data from both experimental simulations and real-world healthcare scenarios. The aim is to identify RFID solutions that optimize surgical equipment management, thereby enhancing both operational efficiency …

Make Revocation Cheaper: Hardware-Based Revocable Attribute-Based Encryption, Xiaoguo Li, Guomin Yang, Tao Xiang, Shengmin Xu, Bowen Zhao, Robert H. Deng, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

As an advanced one-to-many public key encryption system, attribute-based encryption (ABE) is widely believed to be a promising technology for achieving flexible and fine-grained access control of encrypted data on untrusted storage servers (e.g., public cloud servers). However, user revocation in ABE is a critical but challenging problem, and designing efficient revocable ABE has been an active research topic in the past decade. Almost all the existing revocable ABE schemes incorporate a timestamp in the encryption algorithm such that revoked users cannot decrypt ciphertexts generated in future time intervals. To prevent revoked users from decrypting past ciphertexts, the storage server …

Achieving Responsible Anomaly Detection, Xiao Han

All Graduate Theses and Dissertations, Fall 2023 to Present

In the digital transformation era, safeguarding online systems against anomalies – unusual patterns indicating potential threats or malfunctions – has become crucial. This dissertation embarks on enhancing the accuracy, explainability, and ethical integrity of anomaly detection systems. By integrating advanced machine learning techniques, it improves anomaly detection performance and incorporates fairness and explainability at its core.

The research tackles performance enhancement in anomaly detection by leveraging few-shot learning, demonstrating how systems can effectively identify anomalies with minimal training data. This approach overcomes data scarcity challenges. Reinforcement learning is employed to iteratively refine models, enhancing decision-making processes. Transfer learning enables the …

A Framework That Explores The Cognitive Load Of Cs1 Assignments Using Pausing Behavior, Joshua O. Urry

All Graduate Theses and Dissertations, Fall 2023 to Present

Pausing behavior in introductory Computer Science (CS1) courses has been related to a student’s performance in the course and could be linked to a student’s cognitive load, or assignment difficulty. Having an objective measure of the cognitive load would be beneficial to course instructors as it would help them design assignments that are not too difficult. Two studies are presented in this work. The first study uses Cognitive Load Theory and Vygotsky’s Zone of Proximal Development as a theoretical framework to analyze pause times between keystrokes to better understand what types of assignments need more educational support than others. The …

Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi

LSU New Orleans Theses and Dissertations

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …

Choreographing The Rhythms Of Observation: Dynamics For Ranged Observer Bipartite-Unipartite Spatiotemporal (Robust) Networks, Edward A. Holmberg Iv

LSU New Orleans Theses and Dissertations

Existing network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics.

This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness …

The Pawn System: How Procedurally Adaptive Webbed Narratives Create Stories, Steven T. Bordelon

LSU New Orleans Theses and Dissertations

This thesis describes the design, implementation, and testing of a novel procedural narrative system called the Procedurally Adaptive Webbed Narrative (PAWN) system. PAWN procedurally generates characters and, responding to choices made by the player, produces more responsive characters and relationships involving the player and these narrative agents. Initially, this thesis discusses other interactive narrative types that exist, such as emergent or event-driven narratives, along with their strengths and weaknesses. It then examines each aspect of PAWN, starting with initial actor generation, then moving to the capturing of game events and translating them into logical objects called Occurrences. These Occurrences are …

Analysis And Numerical Simulation Of Tumor Growth Models, Daniel Acosta Soba

Masters Theses and Doctoral Dissertations

In this dissertation we focus on the numerical analysis of tumor growth models. Due to the difficulty of developing physically meaningful approximations of such models, we divide the main problem into more simple pieces of work that are addressed in the different chapters. First, in Chapter 2 we present a new upwind discontinuous Galerkin (DG) scheme for the convective Cahn–Hilliard model with degenerate mobility which preserves the pointwise bounds and prevents non-physical spurious oscillations. These ideas are based on a well-suited piecewise constant approximation of convection equations. The proposed numerical scheme is contrasted with other approaches in several numerical experiments. …

Advancing Learning Models For High-Dimensional Data: From Molecular Modeling To Motion Planning, Tuan Nguyen Anh Tran

Legacy Theses & Dissertations (2009 - 2024)

Processing and analyzing high-dimensional data, particularly in domains like protein research and robotics, introduces unique challenges. In protein analysis, high-dimensional data often arises from complex molecular structures and their interactions. Analyzing protein data involves intricate computational models and algorithms that must deal with the large size of molecular datasets and the need to extract biologically relevant information. This makes it challenging to discern significant protein structure-activity relationships, understand complex biological interactions, or predict protein behavior accurately. Similarly, in robotics, high-dimensional data is prevalent when dealing with sensory inputs, the dimensionality of the robot, or the complexity of the operational environments. …

Reviving The Past: Enhancing Language Models With Historical Text Optimization, Heather D. Broome

Honors Theses

Recent advancements in Natural Language Processing (NLP) have brought attention to the significant potential that exists for widespread applications of Large Language Models (LLMs). As demands and expectations for LLMs rise, ensuring efficiency and accuracy becomes paramount. Addressing these challenges requires more than just optimizing current techniques; it urges novel approaches to NLP as a whole. This study investigates novel data preprocessing methods designed to enhance LLM performance by mitigating inefficiencies rooted in natural language, particularly by simplifying the complexities presented by historical texts. Utilizing the classical text The Odyssey by Homer, two preprocessing techniques are introduced: tokenization of names …

Many Direct-To-Consumer Canine Genetic Tests Can Identify The Breed Of Purebred Dogs, Halie M. Rando, Kiley Graim, Greg Hampikian, Casey S. Greene

Computer Science: Faculty Publications

OBJECTIVE To compare pedigree documentation and genetic test results to evaluate whether user-provided photographs influence the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs.

ANIMALS 12 registered purebred pet dogs representing 12 different breeds. METHODS Each dog owner submitted 6 buccal swabs, 1 to each of 6 DTC genetic testing companies. Experimenters registered each sample per manufacturer instructions. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. DNA analysis and breed ancestry prediction were conducted by each company. The effect of …

Deep Reinforcement Learning Guided Improvement Heuristic For Job Shop Scheduling, Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving JSSP, where graph representation is employed to encode complete solutions. We design a Graph-Neural-Network-based representation scheme, consisting of two modules to effectively capture the information of dynamic topology and different types of nodes in graphs encountered during the improvement process. To speed up solution evaluation during improvement, …

Breathpro: Monitoring Breathing Mode During Running With Earables, Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma

Research Collection School Of Computing and Information Systems

Running is a popular and accessible form of aerobic exercise, significantly benefiting our health and wellness. By monitoring a range of running parameters with wearable devices, runners can gain a deep understanding of their running behavior, facilitating performance improvement in future runs. Among these parameters, breathing, which fuels our bodies with oxygen and expels carbon dioxide, is crucial to improving the efficiency of running. While previous studies have made substantial progress in measuring breathing rate, exploration of additional breathing monitoring during running is still lacking. In this work, we fill this gap by presenting BreathPro, the first breathing mode monitoring …

Swapvid: Integrating Video Viewing And Document Exploration With Direct Manipulation, Taichi Murakami, Kazuyuki Fujita, Kotaro Hara, Kazuki Takashima, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Videos accompanied by documents—document-based videos—enable presenters to share contents beyond videos and audience to use them for detailed content comprehension. However, concurrently exploring multiple channels of information could be taxing. We propose SwapVid, a novel interface for viewing and exploring document-based videos. SwapVid seamlessly integrates a video and a document into a single view and lets the content behaves as both video and a document; it adaptively switches a document-based video to act as a video or a document upon direct manipulation (e.g., scrolling the document, manipulating the video timeline). We conducted a user study with twenty participants, comparing SwapVid …

Evaluation Of Orca 2 Against Other Llms For Retrieval Augmented Generation, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

This study presents a comprehensive evaluation of Microsoft Research’s Orca 2, a small yet potent language model, in the context of Retrieval Augmented Generation (RAG). The research involved comparing Orca 2 with other significant models such as Llama-2, GPT-3.5-Turbo, and GPT-4, particularly focusing on its application in RAG. Key metrics, included faithfulness, answer relevance, overall score, and inference speed, were assessed. Experiments conducted on high-specification PCs revealed Orca 2’s exceptional performance in generating high quality responses and its efficiency on consumer-grade GPUs, underscoring its potential for scalable RAG applications. This study highlights the pivotal role of smaller, efficient models like …

Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi

School of Computing: Dissertations, Theses, and Student Research

The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …

Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

Research Collection School Of Computing and Information Systems

The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detection, various state-of-the-art (SOTA) deep learning (DL) techniques (e.g., self-supervised learning) are introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is …

Plug-And-Play Policy Planner For Large Language Model Powered Dialogue Agents, Yang Deng, Wenxuan Zhang, Wai Lam, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs. Most existing studies enable the dialogue policy planning of LLMs using various prompting schemes or iteratively enhance this capability in handling the given case with verbal AI feedback. However, these approaches are either bounded by the policy planning capability of the frozen LLMs or hard to be transferred to new cases. In this work, we introduce a new dialogue policy planning paradigm to strategize LLMs for proactive dialogue …

Reinforcement Retrieval Leveraging Fine-Grained Feedback For Fact Checking News Claims With Black-Box Llm, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

Retrieval-augmented language models have exhibited promising performance across various areas of natural language processing (NLP), including fact-critical tasks. However, due to the black-box nature of advanced large language models (LLMs) and the non-retrieval-oriented supervision signal of specific tasks, the training of retrieval model faces significant challenges under the setting of black-box LLM. We propose an approach leveraging Fine-grained Feedback with Reinforcement Retrieval (FFRR) to enhance fact-checking on news claims by using black-box LLM. FFRR adopts a two-level strategy to gather fine-grained feedback from the LLM, which serves as a reward for optimizing the retrieval policy, by rating the retrieved documents …