George Fox University
Southern Methodist University
Bowling Green State University
San Jose State University
University of Pennsylvania
James Madison University
Northern Illinois University
The College of Wooster
Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance,
2023
The Texas Medical Center Library
Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance, Kareem Wahid
The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access)
Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information …
Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis,
2023
Singapore Management University
Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra- and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a …
Using Azure Automl To Analyze The Effect Of Attendance And Seat Choice On University Student Grades,
2023
Southern Adventist University
Using Azure Automl To Analyze The Effect Of Attendance And Seat Choice On University Student Grades, Ac Hýbl
Campus Research Day
Teachers often claim that class attendance and sitting at the front of a classroom improves student grades. This study employs machine learning on a private University's attendance data to analyze this claim. We perform a correlation analysis in Azure by training regression models. No correlation is found. Next we use the K-means clustering algorithm in Azure. At k=2 clusters, a cluster with perfect attendance shows a higher average grade than a cluster with a late attendance average. Seat choice within the classroom does not prove important to the clustering models.
Rigorous Experimentation For Reinforcement Learning,
2023
University of Massachusetts Amherst
Rigorous Experimentation For Reinforcement Learning, Scott M. Jordan
Doctoral Dissertations
Scientific fields make advancements by leveraging the knowledge created by others to push the boundary of understanding. The primary tool in many fields for generating knowledge is empirical experimentation. Although common, generating accurate knowledge from empirical experiments is often challenging due to inherent randomness in execution and confounding variables that can obscure the correct interpretation of the results. As such, researchers must hold themselves and others to a high degree of rigor when designing experiments. Unfortunately, most reinforcement learning (RL) experiments lack this rigor, making the knowledge generated from experiments dubious. This dissertation proposes methods to address central issues in …
Learning From Sequential User Data: Models And Sample-Efficient Algorithms,
2023
University of Massachusetts Amherst
Learning From Sequential User Data: Models And Sample-Efficient Algorithms, Aritra Ghosh
Doctoral Dissertations
Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited' datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study …
Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects,
2023
Belmont University
Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe
Belmont University Research Symposium (BURS)
Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …
Attenuated Skeletal Muscle Metabolism Explains Blunted Reactive Hyperemia After Prolonged Sitting,
2023
University of Nebraska at Omaha
Attenuated Skeletal Muscle Metabolism Explains Blunted Reactive Hyperemia After Prolonged Sitting, Cody Anderson, Elizabeth Pekas, Michael Allen, Song-Young Park
UNO Student Research and Creative Activity Fair
Introduction: Although reduced post-occlusive reactive hyperemia (PORH) after prolonged sitting (PS) has been reported as impaired microvascular function, no specific mechanism(s) have been elucidated. One potential mechanism, independent of microvascular function, is that an altered muscle metabolic rate (MMR) may change the magnitude of PORH by modifying the oxygen deficit achieved during cuff-induced arterial occlusions. We speculated that if MMR changes during PS, this may invalidate current inferences about microvascular function during PS. Objective: Therefore, the objective of this study was to examine if peripheral leg MMR changes during PS and to ascertain whether the change in the oxygen deficit …
Fraud Pattern Detection For Nft Markets,
2023
Southern Methodist University
Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba
SMU Data Science Review
Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps),
2023
Southern Methodist University
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Impacts Of Cutting-Edge Artificial Intelligence On Economic Research Paradigm,
2023
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences, Beijing 100190, China
Impacts Of Cutting-Edge Artificial Intelligence On Economic Research Paradigm, Yongmiao Hong, Shouyang Wang
Bulletin of Chinese Academy of Sciences (Chinese Version)
No abstract provided.
Source-Free Domain Adaptation For Sleep Stage Classification,
2023
The University of Western Ontario
Source-Free Domain Adaptation For Sleep Stage Classification, Yasmin Niknam
Electronic Thesis and Dissertation Repository
The popularity of machine learning algorithms has increased in recent years as data volumes have risen, algorithms have advanced, and computational power and storage have improved. EEG-based sleep staging has become one of the most active research areas over the last decade. Labeling each sleep stage manually is a labor-intensive and time-consuming process that requires expertise, making it susceptible to human error. In the meantime, training models on an unseen dataset remains challenging due to physiological differences between subjects and electrode sensor configurations. Unsupervised domain adaptation approaches may provide a solution to this problem by borrowing knowledge from a labeled …
Ai Applications On Planetary Rovers,
2023
The University of Western Ontario
Ai Applications On Planetary Rovers, Alexis David Pascual
Electronic Thesis and Dissertation Repository
The rise in the number of robotic missions to space is paving the way for the use of artificial intelligence and machine learning in the autonomy and augmentation of rover operations. For one, more rovers mean more images, and more images mean more data bandwidth required for downlinking as well as more mental bandwidth for analyzing the images. On the other hand, light-weight, low-powered microrover platforms are being developed to accommodate the drive for planetary exploration. As a result of the mass and power constraints, these microrover platforms will not carry typical navigational instruments like a stereocamera or a laser …
Applications Of Generative Adversarial Networks In Single Image Datasets,
2023
University of Minnesota, Morris
Applications Of Generative Adversarial Networks In Single Image Datasets, Dylan E. Cramer
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal
One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.
Proactive Conversational Agents,
2023
Singapore Management University
Proactive Conversational Agents, Lizi Liao, Lizi Liao, Chirag Shah
Research Collection School Of Computing and Information Systems
Conversational agents, or commonly known as dialogue systems, have gained escalating popularity in recent years. Their widespread applications support conversational interactions with users and accomplishing various tasks as personal assistants. However, one key weakness in existing conversational agents is that they only learn to passively answer user queries via training on pre-collected and manually-labeled data. Such passiveness makes the interaction modeling and system-building process relatively easier, but it largely hinders the possibility of being human-like hence lowering the user engagement level. In this tutorial, we introduce and discuss methods to equip conversational agents with the ability to interact with end …
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment,
2023
Singapore Management University
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …
Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises,
2023
Singapore Management University
Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia
Research Collection School Of Computing and Information Systems
Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive …
The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers,
2023
Singapore Management University
The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang
Research Collection School Of Computing and Information Systems
This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total …
Deep Vulman: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework,
2023
Army Cyber Institute, U.S. Military Academy
Deep Vulman: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework, Soumyadeep Hore, Ankit Shah, Nathaniel D. Bastian
ACI Journal Articles
Cyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as the number of deficiencies in these systems is increasing at a significantly higher rate compared to the expansion rate of the security teams to mitigate them. The current approaches in cyber vulnerability management are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecting vulnerabilities for mitigation. These approaches are also constrained by the sub-optimal distribution of resources, providing no flexibility …
Modeling Daily Fantasy Basketball,
2023
California Polytechnic State University, San Luis Obispo
Modeling Daily Fantasy Basketball, Martin Jiang
Master's Theses
Daily fantasy basketball presents interesting problems to researchers due to the extensive amounts of data that needs to be explored when trying to predict player performance. A large amount of this data can be noisy due to the variance within the sport of basketball. Because of this, a high degree of skill is required to consistently win in daily fantasy basketball contests. On any given day, users are challenged to predict how players will perform and create a lineup of the eight best players under fixed salary and positional requirements. In this thesis, we present a tool to assist daily …
Wifi-Based Human Activity Recognition Using Attention-Based Bilstm,
2023
TU Dublin
Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert Ross, Susan Mckeever
Conference papers
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the …
