Computer Sciences Commons^™

Removal Of Phenol From Oilfield Produced Water Using Non-Conventional Adsorbent Medium By An Eco-Friendly Approach, Salem Jawad Alhamd, Mohammed Nsaif Abbas, Hassan Jameel Jawad Al-Fatlawy, Thekra Atta Ibrahim, Zaid Nsaif Abbas

Karbala International Journal of Modern Science

Petroleum extraction generates substantial quantities of produced water, a challenge compounded by water scarcity in oil-producing regions, notably the Middle East. Leveraging produced water effectively, adhering to environmental standards, can offer a viable solution to the issue of water scarcity. This study explores the potential of mandarin peels as an available, cost-effective adsorbent for treating synthetic aqueous solution simulated to oil-field produced water, specifically targeting phenol, a dangerous pollutant. Employing a batch-mode adsorption unit, six operational factors—phenol concentration, acidity, agitation speed, contact time, adsorbent dose, and temperature—were investigated. Results revealed an inverse relationship between phenol removal and pH, concentration, and …

Software Development And Market Research Process Of Plasma Software Distribution, Connor Moore

Honors College Theses

When trying to find the right software for scientific research, one may have to comb through the internet to acquire a suitable tool. Much of the scientific software online is in mostly unknown web pages where the only way to find the software is to already know about it, be told about it, or find the software by pure chance. Even worse, with few verification systems in place they may try a new program only for it to turn out to be malware. The search for the right software takes time away from the scientists, slowing the overall pace 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 …

Theoretical Spectroscopic Predictions Of Electronically Excited States, Noah R. Garrett

Honors Theses

The quest for faster computation of anharmonic vibrational frequencies of both ground and excited electronic states has led to combining coupled cluster theory harmonic force constants with density functional theory (DFT) cubic and quartic force constants for defining a quartic force field (QFF) utilized in conjunction with vibrational perturbation theory at second order (VPT2). This work shows that explicitly correlated coupled cluster theory at the singles, doubles, and perturbative triples level [CCSD(T)-F12] provides accurate anharmonic vibrational frequencies and rotational constants when conjoined with any of B3LYP, CAM-B3LYP, BHandHLYP, PBE0, and ωB97XD for roughly one-quarter of the computational time of the …

A Nlp Approach To Automating The Generation Of Surveys For Market Research, Anav Chug

Honors College Theses

Market Research is vital but includes activities that are often laborious and time consuming. Survey questionnaires are one possible output of the process and market researchers spend a lot of time manually developing questions for focus groups. The proposed research aims to develop a software prototype that utilizes Natural Language Processing (NLP) to automate the process of generating survey questions for market research. The software uses a pre-trained Open AI language model to generate multiple choice survey questions based on a given product prompt, send it to a targeted email list, and also provides a real-time analysis of the responses …

Improving Tattle-Tale K-Deniability, Nicholas G.E. Morales

Student Research Symposium

Ensuring privacy for databases is an ongoing struggle. While the majority of work has focused on using access control lists to protect sensitive data these methods are vulnerable to inference attacks. A set of algorithms, referred to as Tattle-Tale, was developed that could protect sensitive data from being inferred however its runtime performance wasn’t suitable for production code. This set of algorithms contained two main subsets, Full Deniability and K-Deniability. My research focused on improving the runtime or utility of the K-Deniability algorithms. I investigated the runtime of the K-Deniability algorithms to identify what was slowing the process down. Aside …

Integration Of Agent Models And Meta Reinforcement Learning (Meta-Rl) Algorithms For Car Racing Experiment, Vidyavarshini Holenarasipur Jayashankar

Student Research Symposium

Introduction: Achieving optimal performance in 2D racing games presents unique challenges, requiring adaptive strategies and advanced learning algorithms. This research explores the integration of sophisticated agent models with Meta Reinforcement Learning (Meta-RL) techniques, specifically Model-Agnostic Meta-Learning (MAML) and Proximal Policy Optimization (PPO), to enhance decision-making and adaptability within these simulated environments. We hypothesize that this innovative approach will lead to marked improvements in game performance and learning efficiency.

Methods: In our experimental setup, we applied MAML for its rapid adaptation capabilities and PPO for optimizing the agents' policy decisions within a 2D racing game simulator. The objective was …

A Novel Caching Algorithm For Efficient Fine-Grained Access Control In Database Management Systems, Anadi Shakya

Student Research Symposium

Fine-grained access Control (FGAC) in DBMS is vital for restricting user access to authorized data and enhancing security. FGAC policies govern how users are granted access to specific resources based on detailed criteria, ensuring security and privacy measures. Traditional methods struggle with scaling policies to thousands, causing delays in query responses. This paper introduces a novel caching algorithm designed to address this challenge by accelerating query processing and ensuring compliance with FGAC policies. In our approach, we create a circular hashmap and employ different replacement techniques to efficiently manage the cache, prioritizing entries that are visited more frequently. To evaluate …

Securing The Internet Of Things At Scale, Steven L. Willoughby

Student Research Symposium

The world of the connected “Internet of Things” (IoT), including the "Industrial Internet of Things" (IIoT) is expanding to include more devices which observe and influence our daily lives, routines, locations, and even our state of health. But have the underlying protocols by which they communicate this data kept pace with the need to protect our privacy and security?

My talk will introduce my research into an approach to better secure this information flow using appropriate access controls without sacrificing performance. I will assess the historical challenges and simple access controls applied to IoT networking protocols and how they can …

Story Of Your Lazy Function’S Life: A Bidirectional Demand Semantics For Mechanized Cost Analysis Of Lazy Programs, Laura Israel, Nicholas Coltharp

Student Research Symposium

Lazy evaluation is a powerful tool that enables better compositionality and potentially better performance in functional programming, but it is challenging to analyze its computation cost. Existing works either require manually annotating sharing, or rely on separation logic to reason about heaps of mutable cells. In this paper, we propose a bidirectional demand semantics that allows for reasoning about the computation cost of lazy programs without relying on special program logics. To show the effectiveness of our approach, we apply the demand semantics to a variety of case studies including insertion sort, selection sort, Okasaki's banker's queue, and the push …

Systematic Comparison Of Reservoir Computing Frameworks, Nihar S. Koppolu, Christof Teuscher

Student Research Symposium

In this poster, we present a systematic evaluation and comparison of five Reservoir computing (RC) software simulation frameworks, namely reservoirpy, RcTorch, pyRCN, pytorch-esn, and ReservoirComputing.jl. RC is a specific machine learning approach that leverages fixed, nonlinear systems to map signals into higher dimensions. Its unique strength lies in training only the readout layer, which reduces the training complexity. RC excels in temporal signal processing and is also well suited for various physical implementations. The increasing interest in RC has led to the proliferation of various RC simulation frameworks. Our RC simulation framework evaluation focuses on a feature comparison, documentation quality, …

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 …

Simulating Cross-Scale Solid-Fluid Interaction Phenomena, Jinyuan Liu

Dartmouth College Ph.D Dissertations

Solid-fluid interactions are ubiquitous in nature, and accurate simulation methods are essential for realistic animation, industrial design, and engineering analysis. Com- pared to large-scale coupling phenomena, simulating fine-scale interactions poses extra challenges due to factors such as surface tension, material wettability, and geometric complexity. In this thesis, we pursue novel methodologies to accurately model in- terfacial dynamics between surface-tension fluids and codimensional solids, involving capillary interactions, controllable wettability, and robust contact behaviors. Our ini- tial approach involves developing a novel three-way coupling method, which utilizes a thin liquid membrane, modelled as a simplicial mesh, to facilitate accurate momen- tum transfer, …

We Need A “Building Inspector For Iot” When Smart Homes Are Sold, Timothy J. Pierson, Cesar Arguello, Beatrice Perez, Wondimu Zegeye, Kevin Kornegay, Carl A. Gunter

Dartmouth Scholarship

Internet of Things (IoT) devices left behind when a home is sold create security and privacy concerns for both prior and new residents. We envision a specialized “building inspector for IoT” to help securely facilitate transfer of the home.

Hls Taking Flight: Toward Using High-Level Synthesis Techniques In A Space-Borne Instrument, Marion Sudvarg, Chenfeng Zhao, Ye Htet, Meagan Konst, Thomas Lang, Nick Song, Roger D. Chamberlain, Jeremy Buhler, James H. Buckley

Computer Science Faculty Research & Creative Works

FPGAs are widely deployed on high-energy astrophysics telescopes to preprocess and reduce sensor data read out by front-end electronics. Across instruments, these computational pipelines have similar semantics, sharing common stages such as pedestal subtraction, signal integration, zero-suppression, island detection, and centroiding. However, diverse telescope designs require unique implementations of these algorithms, and the logic is often rewritten from scratch for a new instrument. As an alternative, High-Level Synthesis (HLS) tools enable these algorithms to be implemented in a high-level language, which eases modifications and enables fast prototyping and deployment. Nonetheless, writing performant HLS code requires augmentation of the code with …

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 …

Surmounting Challenges In Aggregating Results From Static Analysis Tools, Dr. Ann Marie Reinhold, Brittany Boles, A. Redempta Manzi Muneza, Thomas Mcelroy, Dr. Clemente Izurieta

Military Cyber Affairs

Aggregation poses a significant challenge for software practitioners because it requires a comprehensive and nuanced understanding of raw data from diverse sources. Suites of static-analysis tools (SATs) are commonly used to assess organizational security but simultaneously introduce significant challenges. Challenges include unique results, scales, configuration environments for each SAT execution, and incompatible formats between SAT outputs. Here, we document our experiences addressing these issues. We highlight the problem of relying on a single vendor's SAT version and offer a solution for aggregating findings across multiple SATs, aiming to enhance software security practices and deter threats early with robust defensive operations.

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.

Static Reflective Surfaces For Improved Terahertz Coverage, Thanh Le, Suresh Singh

Computer Science Faculty Publications and Presentations

LoS (Line of Sight) MIMO (Multiple Input Multiple Output) is considered the best way to deliver high capacity channels for terahertz communications due to the severe attenuation suffered by reflected components. Unfortunately, terahertz links are easily blocked by any obstruction resulting in link breakage. Therefore, it is necessary to provide alternative paths via reflectors. A problem shared by LoS paths and reflected paths (via polished reflectors) is that the channel matrix is rank 1 in the far-field. As a result, the achieved capacity is lower than what can theoretically be achieved in a rich multi-path environment. In this work, we …

Research Project Review: Human-Computer Interactions, Kylie E. Garcia

The Agora

A review of a body of research conducted by Dr. Gain Park, an assistant professor in the Department of Journalism and Media Studies at New Mexico State University. This review contains a summary of Dr. Park's research on human-computer interactions, commentary on its contributions and significance, as well as insights from Dr. Park.

The Impact Of Heterogeneous Voting Strategies And Candidate Issue Adaptation On Elections: An Agent-Based Model, Harmony Peura

Departmental Honors & Graduate Capstone Projects

Political candidates in a democracy articulate positions on the issues of the day, but they are also highly aware of voter sentiment on those issues, and tailor their campaigns accordingly as they seek to win elections. Voters, too, adjust their political opinions based on (among other things) interactions with others in their social network. I present an agent-based simulation that models this dynamic interplay between candidates and voters, in order to shed light on what outcomes candidates can expect to result from a policy of “chasing” votes. The voters in the simulation differ from one another in the decision procedure …

Virtual Pair Programming And Online Oral Exams: Effects On Social Interaction, Performance, And Academic Integrity In A Remote Computer Programming Course, M. V. Lubarda, A. M. Phan, C. Schurgers, N. Delson, M. Ghazinejad, S. Baghdadchi, M. Minnes, Minju Kim, C. Pilegard, J. Relaford-Doyle, C. L. Sandoval, H. Qi

Psychology Faculty Articles and Research

Background and context

Pair programming and oral exams were deployed in tandem in a remote undergraduate computer programming course to promote social interaction and enhance learning.

Objectives

We investigate their impact on social interactions, sense of connection, academic performance, and academic integrity within a virtual learning environment, and explore the dynamics of student collaboration in the context of voluntary pair programming.

Method

Students’ coding activities, pairing preferences, and performance were survey responses were recorded and analyized.

Findings

First and second year students were more likely to participate in virtual pair programming than their more senior classmates. Willingness to pair program …

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 …

Domination On Bipolar Fuzzy Graph Operations: Principles, Proofs, And Examples, Haifa Ahmed, Mohammed Alsharafi

Neutrosophic Systems with Applications

Bipolar fuzzy graphs, capable of capturing situations with both positive and negative memberships, have found diverse applications in various disciplines, including decision-making, computer science, and social network analysis. This study investigates the domain of domination and global domination numbers within bipolar fuzzy graphs, owing to their relevance in these aforementioned practical fields. In this study, we introduce certain operations on bipolar fuzzy graphs, such as intersection, join, and union of two graphs. Furthermore, we analyze the domination number and the global domination number for various operations on bipolar fuzzy graphs, including intersection, join, and union of fuzzy graphs and their …

Fixed Point Results In Complex Valued Neutrosophic B-Metric Spaces With Application, M. Pandiselvi, M. Jeyaraman

Neutrosophic Systems with Applications

In this manuscript, we introduce the idea of complex-valued Neutrosophic b-metric spaces along with numerous significant illustrations. We provide fixed-point results for contraction maps. To support the main result, we establish the existence and uniqueness of solutions for nonlinear integral equations after the work.

On Heptagonal Neutrosophic Semi-Open Sets In Heptagonal Neutrosophic Topological Spaces: Testing Proofs By Examples, Subasree R, Basarikodi K

Neutrosophic Systems with Applications

In terms of heptagonal neutrosophic topological spaces, the purpose of this paper is to present the idea of heptagonal neutrosophic semi-open sets. Additionally, we examine a few of its characterizations and heptagonal neutrosophic semi-interior and heptagonal neutrosophic semi-closure operators.

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 …

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 …