Physical Sciences and Mathematics Commons^™

Leveraging Targeted Regions Of Interest By Analyzing Code Comprehension With Ai-Enabled Eye-Tracking, Md Shakil Hossain

Electronic Theses and Dissertations

Code comprehension studies techniques for extracting information that give insights on how code is understood. For educators teaching programming courses, this is an important but often difficult task, especially given the challenges of large class sizes, limited time, and grading resources. By analyzing where a student looks during a code comprehension task, instructors can gain insights into what information the student deems important and assess whether they are looking in the right areas of the code. The proportion of time spent viewing a part of the code is also a useful indicator of the student's decision-making process. The goal of …

Explicit Rule Learning: A Cognitive Tutorial Method To Train Users Of Artificial Intelligence/Machine Learning Systems, Anne Linja

Dissertations, Master's Theses and Master's Reports

Today’s intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system’s underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems’ output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used …

Metaenhance: Metadata Quality Improvement For Electronic Theses And Dissertations, Muntabir H. Choudhury, Lamia Salsabil, Himarsha R. Jayanetti, Jian Wu

College of Sciences Posters

Metadata quality is crucial for digital objects to be discovered through digital library interfaces. Although DL systems have adopted Dublin Core to standardize metadata formats (e.g., ETD-MS v1.1¹), the metadata of digital objects may contain incomplete, inconsistent, and incorrect values [1]. Most existing frameworks to improve metadata quality rely on crowdsourced correction approaches, e.g., [2]. Such methods are usually slow and biased toward documents that are more discoverable by users. Artificial intelligence (AI) based methods can be adopted to overcome this limit by automatically detecting, correcting, and canonicalizing the metadata, featuring quick and unbiased responses to document metadata. …

Thinking Local With Original Data In Ai And Machine Learning Research, David G. Taylor, Robert Mccloud

WCBT Working Papers

Sacred Heart University spent significant funds to establish an AI lab. Initially there is no ongoing research and no real plan for a research agenda. This paper details how the Jack Welch College of Business and Technology created and implemented an active meaningful research plan. It involves two key elements: thinking local and using business connections to foster active, impactful research. Surrounding communities, business connections, area environment, and other Sacred Heart University departments all played a part. The research plan also identifies a specific issue in working with local and business contact sources: the AI researcher almost never gets data …

Differentially Private Stochastic Convex Optimization In (Non)-Euclidean Space Revisited, Jinyan Su, Changhong Zhao, Di Wang

Machine Learning Faculty Publications

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general `dp spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space; (2) unconstrained DP-SCO in `dp space; (3) DP-SCO with heavy-tailed data over a constrained and bounded set in `dp space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set, rather …

Enhancing Neuromorphic Computing With Advanced Spiking Neural Network Architectures, Paolo Gabriel Alejandro Cachi Delgado

Theses and Dissertations

This dissertation proposes ways to address current limitations of neuromorphic computing to create energy-efficient and adaptable systems for AI applications. It does so by designing novel spiking neural networks architectures that improve their performance. Specifically, the two proposed architectures address the issues of training complexity, hyperparameter selection, computational flexibility, and scarcity of neuromorphic training data. The first architecture uses auxiliary learning to improve training performance and data usage, while the second architecture leverages neuromodulation capability of spiking neurons to improve multitasking classification performance. The proposed architectures are tested on Intel's Loihi2 neuromorphic chip using several neuromorphic datasets, such as NMIST, …

Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Publications

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …

A Multistage Framework For Detection Of Very Small Objects, Duleep Rathgamage Don, Ramazan Aygun, Mahmut Karakaya

Published and Grey Literature from PhD Candidates

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our …

Structured Attention For Image Analysis, Xin Xing

Theses and Dissertations--Computer Science

Attention mechanism, an approach to maintain the local and global features over the input, is the crucial element of the Transformer. This dissertation explores structured attention for image analysis, proposing attention-based methods for multi-label learning and Alzheimer’s Disease (AD) diagnosis.
For the multi-label learning task, I present two works under the Vision Transformer (ViT) framework. The first work focuses on supervised learning of multi-label classification. I address the problems of the multi-label classification and propose a model named AssocFormer, which adopts the association module to access the objects’ association relationship to improve the model performance. The second work addresses the …

Directional Speaker Poster, Eugene Ng, Bryan Wong, Ruhaan Das

Student Works

Changi Airport is set to expand with a new terminal, Terminal 5. Currently, many of the airport's processes are manual, requiring a high dependence on staff. This proposal aims to incorporate automation and AI for a smoother passenger experience.

Determining Child Sexual Abuse Posts Based On Artificial Intelligence, Susan Mckeever, Christina Thorpe, Vuong Ngo

Conference papers

The volume of child sexual abuse materials (CSAM) created and shared daily both surface web platforms such as Twitter and dark web forums is very high. Based on volume, it is not viable for human experts to intercept or identify CSAM manually. However, automatically detecting and analysing child sexual abusive language in online text is challenging and time-intensive, mostly due to the variety of data formats and privacy constraints of hosting platforms. We propose a CSAM detection intelligence algorithm based on natural language processing and machine learning techniques. Our CSAM detection model is not only used to remove CSAM on …

Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei

Walden Dissertations and Doctoral Studies

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …

Artificial Intelligence-Based Medical Device Technologies Implementation Strategies In The Nigerian Health Care Industry, Oliver Chikaodinaka Iheme

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI)-based medical device technologies can aid medical professionals in delivering faster and more accurate treatment, but health care leaders are concerned with eliminating challenges that impede implementation. Grounded in the technology-organization-environment and technology acceptance models, the purpose of this qualitative multi-case study was to explore strategies health care leaders in Nigeria use to obtain, adopt, and implement AI-based medical device technologies. The participants were 11 health care leaders in Nigeria who successfully implemented AI-based medical device technologies in their hospitals. Data were collected using semi-structured interviews and the review of organizational documents. Through thematic analysis, five themes were …

Artificial Intelligence-Based Medical Device Technologies Implementation Strategies In The Nigerian Health Care Industry, Oliver Chikaodinaka Iheme

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI)-based medical device technologies can aid medical professionals in delivering faster and more accurate treatment, but health care leaders are concerned with eliminating challenges that impede implementation. Grounded in the technology-organization-environment and technology acceptance models, the purpose of this qualitative multi-case study was to explore strategies health care leaders in Nigeria use to obtain, adopt, and implement AI-based medical device technologies. The participants were 11 health care leaders in Nigeria who successfully implemented AI-based medical device technologies in their hospitals. Data were collected using semi-structured interviews and the review of organizational documents. Through thematic analysis, five themes were …

Liquid Tab, Nathan Hulet

Williams Honors College, Honors Research Projects

Guitar transcription is a complex task requiring significant time, skill, and musical knowledge to achieve accurate results. Since most music is recorded and processed digitally, it would seem like many tools to digitally analyze and transcribe the audio would be available. However, the problem of automatic transcription presents many more difficulties than are initially evident. There are multiple ways to play a guitar, many diverse styles of playing, and every guitar sounds different. These problems become even more difficult considering the varying qualities of recordings and levels of background noise.

Machine learning has proven itself to be a flexible tool …

Rage Against The Machine: Who Is Responsible For Regulating Generative Artificial Intelligence In Domestic And Cross-Border Litigation?, S. I. Strong

Faculty Articles

In 2023, ChatGPT—an early form of generative artificial intelligence (AI) capable of creating entirely new content—took the world by storm. The first shock came when ChatGPT demonstrated its ability to pass the U.S. bar exam. Soon thereafter, the world learned that ChatGPT was being used by both lawyers and judges in actual litigation.

Some within the legal community find the use of generative AI in civil and criminal litigation entirely unproblematic. Others find generative AI troubling as a matter of due process and procedural fairness due to its propensity not only to misinterpret legitimate legal authorities but to create fictitious …

Finer Details Of Language Modeling: Text Segmentation, Working Within Resource Limits, And Watermarking, Evan Gordon Lucas

Dissertations, Master's Theses and Master's Reports

Language modeling is a vast sub-field of natural language processing and this work focuses on solving some specific problems within that field. Technically, the work falls into a number of sub-categories within natural language processing; how to segment texts, improving sparse transformer performance for summarization tasks, character level models for dialect determination, watermarking of large language models, and a general method of incorporating minimal human feedback for continual or online learning. Despite touching on many small areas, they all connect as being related to the very general problem of handling sequential data. Language and text can be thought of as …

Prediction Of Sumoylation Sites In Proteins From Language Model Representations, Evgenii Sidorov

Dissertations, Master's Theses and Master's Reports

Sumoylation is an essential post-translational modification intimately involved in a diverse range of eukaryotic cellular mechanisms and plays a significant role in DNA repair. Some researchers hypothesize that a high level of SUMOylation events in cancer cells improves cells' chances for survival under stress conditions by regulating tumor-related proteins.

This study belongs to a booming field of harnessing computational power to the domain of life. Prediction of protein structure, its molecular function, and the design of new drugs are just a few examples of the applications within this exciting area of research. By leveraging computational power, researchers can analyze vast …

General-Purpose Planning Algorithms In Partially-Observable Stochastic Games, Bryan Mckenney

Honors Theses and Capstones

Partially observable stochastic games (POSGs) are difficult domains to plan in because they feature multiple agents with potentially opposing goals, parts of the world are hidden from the agents, and some actions have random outcomes. It is infeasible to solve a large POSG optimally. While it may be tempting to design a specialized algorithm for finding suboptimal solutions to a particular POSG, general-purpose planning algorithms can work just as well, but with less complexity and domain knowledge required. I explore this idea in two different POSGs: Navy Defense and Duelyst.

In Navy Defense, I show that a specialized algorithm framework, …

Humans In The Loop, Nicholson Price Ii, Rebecca Crootof, Margot Kaminski

Articles

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human in the loop” systems. We make four contributions to the discourse.

First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into decision making process. Regardless …

From Human Behavior To Machine Behavior, Zerong Xi

Electronic Theses and Dissertations, 2020-2023

A core pursuit of artificial intelligence is the comprehension of human behavior. Imbuing intelligent agents with a good human behavior model can help them understand how to behave intelligently and interactively in complex situations. Due to the increase in data availability and computational resources, the development of machine learning algorithms for duplicating human cognitive abilities has made rapid progress. To solve difficult scenarios, learning-based methods must search for solutions in a predefined but large space. Along with implementing a smart exploration strategy, the right representation for a task can help narrow the search process during learning. This dissertation tackles three …

Science, Technology, Society, And Law, Paolo Davide Farah, Justo Corti Varela

Book Chapters

Traditionally, science and technology have been granted as sources of knowledge and objective truth. However, much more recently, they are also seen as human activities, conducted in a social environment. This new approach focuses on the intersections between science, technology and society, and particularly their regulation by the law. Concerns on how to best regulate the interaction come up in modern societies, and when either their use or their impacts are global, international law and international organizations become involved. The impact of the fourfold relation is so high that science and technology are seen as one of the reasons for …

The Intersections Among Science, Technology, Policy And Law: In Between Truth And Justice, Paolo Davide Farah, Justo Corti Varela

Book Chapters

Different visions on the interaction between science, technology, policy and law have been presented. As common axe, we can detect the continuous search for truth and justice. Science and Law as social constructs, the distinction between truths and opinions through procedural method based on evidence and rationality, or how natural science “things” became facts, and consequently “truth”, are examples of this search. The evidence-gathering process that integrates scientific evidence into trial (sometimes by procedure and other times by a more substantive approach) is another possible approach. Of course, that the game of mutual influence among the four elements creates contradictions …

The Interlinkages Science-Technology-Law: Information And Communication Society, Knowledge-Based Economy And The Rule Of Law, Giovanni Bombelli, Paolo Davide Farah

Book Chapters

This chapter focuses on the circular and complex relationship between science, technology, society, and law. The technology/society connection focuses on the democratic deficit issue. The democratic deficit would be a consequence of the lack of adaptability of western democracy to complex (information) societies, where technology (and the increasing access to data that it permits) is separating the connection between information and knowledge (as well as the classical legitimacy couple of democracy-truth) moving these societies towards a technocracy. On one hand, the technology-law circle deals with the progressive reduction of law to a normative technique (since the law is always late …

Artificial Intelligence And Contract Formation: Back To Contract As Bargain?, John Linarelli

Book Chapters

Some say AI is advancing quickly. ChatGPT, Bard, Bing’s AI, LaMDA, and other recent advances are remarkable, but they are talkers not doers. Advances toward some kind of robust agency for AI is, however, coming. Humans and their law must prepare for it. This chapter addresses this preparation from the standpoint of contract law and contract practices. An AI agent that can participate as a contracting agent, in a philosophical or psychological sense, with humans in the formation of a con-tract will have to have the following properties: (1) AI will need the cognitive functions to act with intention and …

Exploration Of Robotics Need In The Medical Field And Robotic Arm Operation Via Glove Control, Aditi Vijayvergia

Master’s Theses

This thesis project is an exercise in getting hands-on experience in redesigning and modifying a robotic system. It also involves understanding the current need for robotic applications in hospital settings. To achieve the above, a thorough literature review of the current state of robotics in a hospital setting was conducted. Moreover, a number of interviews with medical care professionals were completed. Three main themes were obtained from the literature review and five main themes were obtained from the interviews which will be presented in this thesis report. The next phase of the project involved redesigning a system that is composed …

Comparative Study Of Generative Models For Text-To-Image Generation, Nazia Siddiqui

Electronic Theses and Dissertations

The development of deep learning algorithms has tremendously helped computer vision applications, image processing methods, Artificial Intelligence, and Natural Language Processing. One such application is image synthesis, which is the creation of new images from text. Recent techniques for text-to-image synthesis offer an intriguing yet straight forward conversion capability from text to image and have become a popular research topic. Synthesis of images from text descriptors has practical and creative applications in computer-aided design, multimodal learning, digital art creation, etc. Non-Fungible Tokens (NFTs) are a form of digital art that is being used as tokens for trading across the globe. …

Online Sexual Predator Detection, Muhammad Khalid

Electronic Theses and Dissertations

Online sexual abuse is a concerning yet severely overlooked vice of modern society. With more children being on the Internet and with the ever-increasing advent of web-applications such as online chatrooms and multiplayer games, preying on vulnerable users has become more accessible for predators. In recent years, there has been work on detecting online sexual predators using Machine Learning and deep learning techniques. Such work has trained on severely imbalanced datasets, and imbalance is handled via manual trimming of over-represented labels. In this work, we propose an approach that first tackles the problem of imbalance and then improves the effectiveness …

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Practical Ai Value Alignment Using Stories, Md Sultan Al Nahian

Theses and Dissertations--Computer Science

As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally - using only a measure of task performance as feedback--can violate societal norms for acceptable behavior or cause harm. Consequently, it becomes necessary to prioritize task performance and ensure that AI actions do not have detrimental effects. Value alignment is a property of intelligent agents, wherein they solely pursue goals and activities that are non-harmful and beneficial to humans. Current approaches to value alignment largely depend on imitation learning or learning from demonstration methods. However, the dynamic nature …