Artificial Intelligence and Robotics Commons^™

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …

Computational Analysis Of Drug Targets And Prediction Of Protein-Compound Interactions, Sina Ghadermarzi

Theses and Dissertations

Computational prediction of compound-protein interactions generated a substantial amount of interest in the recent years owing to the importance of the knowledge of these interaction for drug discovery and drug repurposing efforts. Research suggests that the currently known drug targets constitute only a fraction of a complete set of drug targets, limiting our ability to identify suitable targets to develop new drugs or to repurpose current drugs for new diseases. These efforts are further thwarted by our limited knowledge of protein-drug (and more generally protein-compound) interactions, where only a subset of drug targets is typically known for the currently used …

Interpretable Design Of Reservoir Computing Networks Using Realization Theory, Wei Miao, Vignesh Narayanan, Jr-Shin Li

Publications

The reservoir computing networks (RCNs) have been successfully employed as a tool in learning and complex decision-making tasks. Despite their efficiency and low training cost, practical applications of RCNs rely heavily on empirical design. In this article, we develop an algorithm to design RCNs using the realization theory of linear dynamical systems. In particular, we introduce the notion of α-stable realization and provide an efficient approach to prune the size of a linear RCN without deteriorating the training accuracy. Furthermore, we derive a necessary and sufficient condition on the irreducibility of the number of hidden nodes in linear RCNs based …

A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles

Computer Science Faculty Publications

[First paragraph] Concerns about the replicability, robustness and reproducibility of findings in scientific literature have gained widespread attention over the last decade in the social sciences and beyond. This attention has been catalyzed by and has likewise motivated a number of large-scale replication projects which have reported successful replication rates between 36% and 78%. Given the challenges and resources required to run high-powered replication studies, researchers have sought other approaches to assess confidence in published claims. Initial evidence has supported the promise of prediction markets in this context. However, they require the coordinated, sustained effort of collections of human experts …

Application Of Artificial Intelligence And Machine Learning For Hiv Prevention Interventions, John Schneider

Faculty, Staff and Student Publications

In 2019, the US Government announced its goal to end the HIV epidemic within 10 years, mirroring the initiatives set forth by UNAIDS. Public health prevention interventions are a crucial part of this ambitious goal. However, numerous challenges to this goal exist, including improving HIV awareness, increasing early HIV infection detection, ensuring rapid treatment, optimising resource distribution, and providing efficient prevention services for vulnerable populations. Artificial intelligence has had a pivotal role in revolutionising health care and has shown great potential in developing effective HIV prevention intervention strategies. Although artificial intelligence has been used in a few HIV prevention intervention …

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, And Beyond

Faculty, Staff and Student Publications

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

A Probabilistic Perspective Of Human-Machine Interaction, Mustafa Canan, Mustafa Demir, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-the-art Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The human-human interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decision-making than merely using classical probability theory (CPT). …

Highlights Generation For Tennis Matches Using Computer Vision, Natural Language Processing And Audio Analysis, Alon Liberman

Senior Independent Study Theses

This project uses computer vision, natural language processing and audio analysis to automatize the highlights generation task for tennis matches. Computer vision techniques such as camera shot detection, hough transform and neural networks are used to extract the time intervals of the points. To detect the best points, three approaches are used. Point length suggests which points correspond to rallies and aces. The audio waves are analyzed to search for the highest audio peaks, which indicate the moments where the crowd cheers the most. Sentiment analysis, a natural language processing technique, is used to look for points where the commentators …

Multi-User Eye-Tracking, Bhanuka Mahanama

Computer Science Faculty Publications

The human gaze characteristics provide informative cues on human behavior during various activities. Using traditional eye trackers, assessing gaze characteristics in the wild requires a dedicated device per participant and therefore is not feasible for large-scale experiments. In this study, we propose a commodity hardware-based multi-user eye-tracking system. We leverage the recent advancements in Deep Neural Networks and large-scale datasets for implementing our system. Our preliminary studies provide promising results for multi-user eye-tracking on commodity hardware, providing a cost-effective solution for large-scale studies.

Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu

Computer Science Faculty Publications

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …

Understanding The Effectivity And Increased Reliance Of Credit Risk Machine Learning Models In Banking, Grishma Baruah

Cybersecurity Undergraduate Research Showcase

Credit risk analysis and making accurate investment and lending decisions has been a challenge for the financial industry for many years, as can be seen with the 2008 financial crisis. However, with the rise of machine learning models and predictive analytics, there has been a shift to increased reliance on technology for determining credit risk. This transition to machine learning comes with both advantages, such as potentially eliminating human error and assumptions from lending decisions, and disadvantages, such as time constraints, data usage inabilities, and lack of understanding nuances in machine learning models. In this paper, I look at four …

Mitigation Of Algorithmic Bias To Improve Ai Fairness, Kathy Wang

Cybersecurity Undergraduate Research Showcase

As artificial intelligence continues to evolve rapidly with emerging innovations, mass-scale digitization could be disrupted due to unfair algorithms with historically biased data. With the rising concerns of algorithmic bias, detecting biases is essential in mitigating and implementing an algorithm that promotes inclusive representation. The spread of ubiquitous artificial intelligence means that improving modeling robustness is at its most crucial point. This paper examines the omnipotence of artificial intelligence and its resulting bias, examples of AI bias in different groups, and a potential framework and mitigation strategies to improve AI fairness and remove AI bias from modeling techniques.

Reinforcement Learning: Low Discrepancy Action Selection For Continuous States And Actions, Jedidiah Lindborg

College of Graduate Studies: Theses & Dissertations

In reinforcement learning the process of selecting an action during the exploration or exploitation stage is difficult to optimize. The purpose of this thesis is to create an action selection process for an agent by employing a low discrepancy action selection (LDAS) method. This should allow the agent to quickly determine the utility of its actions by prioritizing actions that are dissimilar to ones that it has already picked. In this way the learning process should be faster for the agent and result in more optimal policies.

Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron

College of Graduate Studies: Theses & Dissertations

The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.

We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing …

Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders

College of Graduate Studies: Theses & Dissertations

Academic instructors and institutions desire the ability to accurately and autonomously measure the attentiveness of students in the classroom. Generally, college departments use unreliable direct communication from students (i.e. emails, phone calls), distracting and Hawthorne effect-inducing observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. Young et al. discuss that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, …

License Plate Image Quality Enhancement Utilizing Super Resolution Generative Adversarial Networks, Mark Moelter

College of Graduate Studies: Theses & Dissertations

This thesis focuses primarily on enhancing the image quality of blurred license plates through the use of Super-Resolution Generative Adversarial Networks (SRGANs) [1]. We propose a synthetic dataset with SRGAN model to promote blurred image quality enhancement, and allow for model evaluation on a multitude of image input and output size combinations. SRGAN is mainly used for low-resolution image enhancement, but by heavily blurring the input images, the model is tested on its ability to blindly deblur and upsample images to the desired super-resolution (SR) size. The model enhances the image quality to nearly that of the reference images. The …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Synthetic Augmentation Methods For Object Detection In Overhead Imagery, Nicholas R. Hamilton

Dissertations, Master's Theses and Master's Reports

The multidisciplinary area of geospatial intelligence (GEOINT) is continually changing and becoming more complex. From efforts to automate portions of GEOINT using machine learning, which augment the analyst and improve exploitation, to optimizing the growing number of sources and variables, there is no denying that the strategies involved in this collection method are rapidly progressing. The unique and inherent complexities involved in imagery analysis from an overhead perspective--—e.g., target resolution, imaging band(s), and imaging angle--—test the ability of even the most developed and novel machine learning techniques. To support advancement in the application of object detection in overhead imagery, we …

A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. …

Healthcare 5.0 Security Framework: Applications, Issues And Future Research Directions, Mohammad Wazid, Ashok Kumar Das, Noor Mohd, Youngho Park

VMASC Publications

Healthcare 5.0 is a system that can be deployed to provide various healthcare services. It does these services by utilising a new generation of information technologies, such as Internet of Things (IoT), Artificial Intelligence (AI), Big data analytics, blockchain and cloud computing. Due to the introduction of healthcare 5.0, the paradigm has been now changed. It is disease-centered to patient-centered care where it provides healthcare services and supports to the people. However, there are several security issues and challenges in healthcare 5.0 which may cause the leakage or alteration of sensitive healthcare data. This demands that we need a robust …