Artificial Intelligence and Robotics Commons^™

Image-Data-Driven Deep Learning For Slope Stability Analysis, Behnam Azmoon

Dissertations, Master's Theses and Master's Reports

Landslides cause major infrastructural issues, damage the environment, and cause socio-economic disruptions. Therefore, various slope stability analysis methods have been developed to evaluate the stability of slopes and the probability of their failure. This dissertation attempts to take advantage of the recent advancements in remote sensing and computer technology to implement a deep-learning-based landslide prediction method.

Considering the novelty of this approach, this dissertation leads with proof-of-concept studies to evaluate and establish the suitability of deep learning models for slope stability analysis. To achieve this, a simulated 2D dataset of slope images was created with different geometries and soil properties. …

A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit

Engineering Technology Faculty Publications

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show …

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

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 …

Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco

Computer Science Faculty Publications

We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof of concept to mitigate theory bias in inferring vertex-level event …

Customer Gaze Estimation In Retail Using Deep Learning, Shashimal Senarath, Primesh Pathirana, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data …

Perceptions And Needs Of Artificial Intelligence In Health Care To Increase Adoption: Scoping Review, Han Shi Jocelyn Chew, Palakorn Achananuparp

Research Collection School Of Computing and Information Systems

Background: Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective: This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, …

Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss, Cheng Yan, Guansong Pang, Xiao Bai, Changhong Liu, Xin Ning, Jun Zhou

Research Collection School Of Computing and Information Systems

Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these fine-grained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail to effectively learn the fine-grained features as they are focused more on differentiating large appearance differences. To address this issue, we introduce a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences …

Cross-Modal Food Retrieval: Learning A Joint Embedding Of Food Images And Recipes With Semantic Consistency And Attention Mechanism, Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Palakorn Achananuparp, Ee-Peng Lim, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these …

Progress In Protein Structure Prediction: An Xai Perspective, Yosef E. Granillo, Badri Adhikari

Undergraduate Research Symposium

The full extent of the impact of deep learning models on structural biology will depend on their ability to provide novel biological insights. The field of structure prediction, where deep learning has produced miraculously accurate results, is at a critical stage of benefiting from the methods in interpretable deep learning. The exact mechanisms by which advanced computational models learn to interpret protein shapes and functions during their training remain largely unclear. These questions underscore the need for further research, as understanding these mechanisms is crucial for researchers to trust the predictions of these models. However, interpretable machine learning is ripening …

Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko

Doctoral Dissertations

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …

Man-In-The-Middle Attacks On Mqtt Based Iot Networks, Henry C. Wong

Masters Theses

“The use of Internet-of-Things (IoT) devices has increased a considerable amount in recent years due to decreasing cost and increasing availability of transistors, semiconductor, and other components. Examples can be found in daily life through smart cities, consumer security cameras, agriculture sensors, and more. However, Cyber Security in these IoT devices are often an afterthought making these devices susceptible to easy attacks. This can be due to multiple factors. An IoT device is often in a smaller form factor and must be affordable to buy in large quantities; as a result, IoT devices have less resources than a typical computer. …

Application Of Artificial Intelligence For Co2 Storage In Saline Aquifer (Smart Proxy For Snap-Shot In Time), Marwan Mohammed Alnuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent years, artificial intelligence (AI) and machine learning (ML) technology have grown in popularity. Smart Proxy Models (SPM) are AI/ML based data-driven models which have proven to be quite crucial in petroleum engineering domain with abundant data, or operations in which large surface/ subsurface volume of data is generated. Climate change mitigation is one application of such technology to simulate and monitor CO2 injection into underground formations.

The goal of the SPM developed in this study is to replicate the results (in terms of pressure and saturation outputs) of the numerical reservoir simulation model (CMG) for CO2 injection into …

Trust In Human-Robot Interaction Within Healthcare Services: A Review Study, Dedra Townsend, Amirhossein Majidirad

Mechanical & Aerospace Engineering Faculty Publications

There has always been a dilemma of the extent to which human can rely on machines in different activities of daily living. Ranging from riding on a self-driving car to having an iRobot vacuum clean the living room. However, when it comes to healthcare settings where robots are intended to work next to human, making decision gets difficult because repercussions may jeopardize people’s life. That has led scientists and engineers to take one step back and think out of the box. Having concept of trust under scrutiny, this study helps deciphering complex human-robot interaction (HRI) attributes. Screening essential constituents of …

Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues

VMASC Publications

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …

Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos

VMASC Publications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …

The Performance Optimization Of Asp Solving Based On Encoding Rewriting And Encoding Selection, Liu Liu

Theses and Dissertations--Computer Science

Answer set programming (ASP) has long been used for modeling and solving hard search problems. These problems are modeled in ASP as encodings, a collection of rules that declaratively describe the logic of the problem without explicitly listing how to solve it. It is common that the same problem has several different but equivalent encodings in ASP. Experience shows that the performance of these ASP encodings may vary greatly from instance to instance when processed by current state-of-the-art ASP grounder/solver systems. In particular, it is rarely the case that one encoding outperforms all others. Moreover, running an ASP system on …

Don't Give Me That Story! -- A Human-Centered Framework For Usable Narrative Planning, Rachelyn Farrell

Theses and Dissertations--Computer Science

Interactive or branching stories are engaging and can be embedded into digital systems for a variety of purposes, but their size and complexity makes it difficult and time-consuming for humans to author them. Narrative planning algorithms can automatically generate large branching stories with guaranteed causal consistency, using a hand-authored library of story content pieces. The usability of such a system depends on both the quality of the narrative model upon which it is built and the ability of the user to create the story content library.

Current narrative planning algorithms use either a limited or no model of character belief, …

Development Of Accurate And Efficient Computational Methodologies For Predicting Protein-Ligand And Protein-Protein Binding Free Energies, Alexander Hamilton Williams

Theses and Dissertations--Pharmacy

Computational modeling is an invaluable tool in the drug discovery process either for small ligand or protein therapeutics. The widespread availability of protein X-Ray Crystal and Cryo-Electron Microscopy (Cryo-EM) structures has allowed for more accurate molecular dynamics (MD) simulations that are not reliant on methods such as homology modeling, which may produce structures that require significant computational time to demonstrate their stability. In this thesis we describe several novel methodologies for the computationally efficient modeling of protein/ligand and protein/protein complexes that may be employed within both large-scale virtual screenings and lead compound optimization. These methodologies may also be utilized in …

Enhanced Convolutional Neural Network For Image-Based Steganalysis In Spatial Domain Using Spatial Rich Model And 2d Gabor Filters, Alaaldin Dwaik

Graduate Theses/Dissertations

During the past decade, many methods have been introduced to handle the image-based steganalysis problem. Traditional steganalysis methods are based on the two-step machine learning mechanism that consists of extracting and classifying phases. Most recent solutions are based on deep convolution neural networks (CNNs), which combine feature extraction and classification in one step. CNN-based steganalysis methods provide superior performance. These CNNs are designed to improve the detection rate by using a set of predefined filters for the pre-processing phase. In this thesis, I propose a CNN model that consists of two convolution layers for pre-processing and features extraction, and four …

Robot Guided Exercise Training: The Role Of The Human Model, Selena R. Richards

Honors Theses and Capstones

Physical therapy after an injury can be difficult for patients to access, whether it be due to location, finances, or other factors. To make physical therapy more accessible, robot-guided exercise training can be used. Commercial anthropomorphic robots have been created and have human-like movement, but may still lack qualities that make it easy for a human to understand its intentions intuitively. A human model that mimics the movement of a robot performing human exercises can be used to complement the robot and increase human understanding. The ideal model would be viewable from different angles and could visibly show the difference …

On-Ice Detection, Classification, Localization And Tracking Of Anthropogenic Acoustic Sources With Machine Learning, Steven J. Whitaker

Dissertations, Master's Theses and Master's Reports

Arctic acoustics have been of concern in recent years for the US navy. First-year ice is now the prevalent factor in ice coverage in the Arctic, which changes the previously understood acoustic properties. Due to the ice melting each year, anthropogenic sources in the Arctic region are more common: military exercises, shipping, and tourism. For the navy, it is of interest to detect, classify, localize, and track these sources to have situational awareness of these surroundings. Because the sources are on-water or on-ice, acoustic radiation propagates at a longer distance and so acoustics are the method by which the sources …

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 …

Supporting Stylized Language Models Using Multi-Modality Features, Chengxi Li

Theses and Dissertations--Computer Science

As AI and machine learning systems become more common in our everyday lives, there is an increased desire to construct systems that are able to seamlessly interact and communicate with humans. This typically means creating systems that are able to communicate with humans via natural language. Given the variance of natural language, this can be a very challenging task. In this thesis, I explored the topic of humanlike language generation in the context of stylized language generation. Stylized language generation involves producing some text that exhibits a specific, desired style. In this dissertation, I specifically explored the use of multi-modality …

Smart Decision-Making Via Edge Intelligence For Smart Cities, Nathaniel Hudson

Theses and Dissertations--Computer Science

Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and …

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 …

A Framework Of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines, Qi Wang, Zhaoying Liu, Ting Zhang, Shanshan Tu, Yujian Li, Muhammad Waqas

Research outputs 2022 to 2026

Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification. However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters. To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM). The framework consists of a feature extraction module …

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification At Jefferson Laboratory, Lasitha Vidyaratne, Adam Carpenter, Tom Powers, Chris Tennant, Khan M. Iftekharuddin, Md. Monibor Rahman, Anna S. Shabalina

Electrical & Computer Engineering Faculty Publications

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of …

Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski

Electrical & Computer Engineering Faculty Publications

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.