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Full-Text Articles in Physical Sciences and Mathematics
Towards Explainable Ai Using Attribution Methods And Image Segmentation, Garrett J. Rocks
Towards Explainable Ai Using Attribution Methods And Image Segmentation, Garrett J. Rocks
Honors Undergraduate Theses
With artificial intelligence (AI) becoming ubiquitous in a broad range of application domains, the opacity of deep learning models remains an obstacle to adaptation within safety-critical systems. Explainable AI (XAI) aims to build trust in AI systems by revealing important inner mechanisms of what has been treated as a black box by human users. This thesis specifically aims to improve the transparency and trustworthiness of deep learning algorithms by combining attribution methods with image segmentation methods. This thesis has the potential to improve the trust and acceptance of AI systems, leading to more responsible and ethical AI applications. An exploratory …
Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud
Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud
Honors Undergraduate Theses
This paper investigates the impact the LASSO, mRMR, SHAP, and Reinforcement Feature Selection techniques on random forest models for the breast cancer subtypes markers ER, HER2, PR, and TN as well as identifying a small subset of biomarkers that could potentially cause the disease and explain them using explainable AI techniques. This is important because in areas such as healthcare understanding why the model makes a specific decision is important it is a diagnostic of an individual which requires reliable AI. Another contribution is using feature selection methods to identify a small subset of biomarkers capable of predicting if a …
From Human Behavior To Machine Behavior, Zerong Xi
From Human Behavior To Machine Behavior, Zerong Xi
Electronic Theses and Dissertations, 2020-
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 …
Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi
Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi
Electronic Theses and Dissertations, 2020-
With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention. Artificial intelligence is observed to have caused unanticipated negative consequences. In this dissertation, we address two critical aspects regarding human-centered artificial intelligence (HCAI), a new paradigm for developing artificial intelligence that is ethical, fair, and helps to improve the human condition. In the first part of this dissertation, we investigate the effect that AI curation of contents by social media platforms has on an online discussions, by studying a polarized discussion in the Twitter network. We then develop a network communication model that …
The Social And Behavioral Influences Of Interactions With Virtual Dogs As Embodied Agents In Augmented And Virtual Reality, Nahal Norouzi
The Social And Behavioral Influences Of Interactions With Virtual Dogs As Embodied Agents In Augmented And Virtual Reality, Nahal Norouzi
Electronic Theses and Dissertations, 2020-
Intelligent virtual agents (IVAs) have been researched for years and recently many of these IVAs have become commercialized and widely used by many individuals as intelligent personal assistants. The majority of these IVAs are anthropomorphic, and many are developed to resemble real humans entirely. However, real humans do not interact only with other humans in the real world, and many benefit from interactions with non-human entities. A prime example is human interactions with animals, such as dogs. Humans and dogs share a historical bond that goes back thousands of years. In the past 30 years, there has been a great …
Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri
Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri
Electronic Theses and Dissertations, 2020-
The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them …
Visual Learning Beyond Human Curated Datasets, Muhammad Abdullah Jamal
Visual Learning Beyond Human Curated Datasets, Muhammad Abdullah Jamal
Electronic Theses and Dissertations, 2020-
The success of deep neural networks in a variety of computer vision tasks heavily relies on large- scale datasets. However, it is expensive to manually acquire labels for large datasets. Given the human annotation cost and scarcity of data, the challenge is to learn efficiently with insufficiently labeled data. In this dissertation, we propose several approaches towards data-efficient learning in the context of few-shot learning, long-tailed visual recognition, and unsupervised and semi-supervised learning. In the first part, we propose a novel paradigm of Task-Agnostic Meta- Learning (TAML) algorithms to improve few-shot learning. Furthermore, in the second part, we analyze the …
Unsupervised Meta-Learning, Siavash Khodadadeh
Unsupervised Meta-Learning, Siavash Khodadadeh
Electronic Theses and Dissertations, 2020-
Deep learning has achieved classification performance matching or exceeding the human one, as long as plentiful labeled training samples are available. However, the performance on few-shot learning, where the classifier had seen only several or possibly only one sample of the class is still significantly below human performance. Recently, a type of algorithm called meta-learning achieved impressive performance for few-shot learning. However, meta-learning requires a large dataset of labeled tasks closely related to the test task. The work described in this dissertation outlines techniques that significantly reduce the need for expensive and scarce labeled data in the meta-learning phase. Our …
Reviving Mozart With Intelligence Duplication, Jacob E. Galajda
Reviving Mozart With Intelligence Duplication, Jacob E. Galajda
Honors Undergraduate Theses
Deep learning has been applied to many problems that are too complex to solve through an algorithm. Most of these problems have not required the specific expertise of a certain individual or group; most applied networks learn information that is shared across humans intuitively. Deep learning has encountered very few problems that would require the expertise of a certain individual or group to solve, and there has yet to be a defined class of networks capable of achieving this. Such networks could duplicate the intelligence of a person relative to a specific task, such as their writing style or music …
Implication Of Manifold Assumption In Deep Learning Models For Computer Vision Applications, Marzieh Edraki
Implication Of Manifold Assumption In Deep Learning Models For Computer Vision Applications, Marzieh Edraki
Electronic Theses and Dissertations, 2020-
The Deep Neural Networks (DNN) have become the main contributor in the field of machine learning (ML). Specifically in the computer vision (CV), there are applications like image and video classification, object detection and tracking, instance segmentation and visual question answering, image and video generation are some of the applications from many that DNNs have demonstrated magnificent progress. To achieve the best performance, the DNNs usually require a large number of labeled samples, and finding the optimal solution for such complex models with millions of parameters is a challenging task. It is known that, the data are not uniformly distributed …
Towards Robust Artificial Intelligence Systems, Sunny Raj
Towards Robust Artificial Intelligence Systems, Sunny Raj
Electronic Theses and Dissertations, 2020-
Adoption of deep neural networks (DNNs) into safety-critical and high-assurance systems has been hindered by the inability of DNNs to handle adversarial and out-of-distribution input. State-of-the-art DNNs misclassify adversarial input and give high confidence output for out-of-distribution input. We attempt to solve this problem by employing two approaches, first, by detecting adversarial input and, second, by developing a confidence metric that can indicate when a DNN system has reached its limits and is not performing to the desired specifications. The effectiveness of our method at detecting adversarial input is demonstrated against the popular DeepFool adversarial image generation method. On a …
Transparency And Communication Patterns In Human-Robot Teaming, Shan Lakhmani
Transparency And Communication Patterns In Human-Robot Teaming, Shan Lakhmani
Electronic Theses and Dissertations
In anticipation of the complex, dynamic battlefields of the future, military operations are increasingly demanding robots with increased autonomous capabilities to support soldiers. Effective communication is necessary to establish a common ground on which human-robot teamwork can be established across the continuum of military operations. However, the types and format of communication for mixed-initiative collaboration is still not fully understood. This study explores two approaches to communication in human-robot interaction, transparency and communication pattern, and examines how manipulating these elements with a robot teammate affects its human counterpart in a collaborative exercise. Participants were coupled with a computer-simulated robot to …
Collaborative Artificial Intelligence Algorithms For Medical Imaging Applications, Naji Khosravan
Collaborative Artificial Intelligence Algorithms For Medical Imaging Applications, Naji Khosravan
Electronic Theses and Dissertations
In this dissertation, we propose novel machine learning algorithms for high-risk medical imaging applications. Specifically, we tackle current challenges in radiology screening process and introduce cutting-edge methods for image-based diagnosis, detection and segmentation. We incorporate expert knowledge through eye-tracking, making the whole process human-centered. This dissertation contributes to machine learning, computer vision, and medical imaging research by: 1) introducing a mathematical formulation of radiologists level of attention, and sparsifying their gaze data for a better extraction and comparison of search patterns. 2) proposing novel, local and global, image analysis algorithms. Imaging based diagnosis and pattern analysis are "high-risk" Artificial Intelligence …
Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery, Senglee Koh
Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery, Senglee Koh
Electronic Theses and Dissertations
State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task …
Environmental Physical-Virtual Interaction To Improve Social Presence With A Virtual Human In Mixed Reality, Kangsoo Kim
Environmental Physical-Virtual Interaction To Improve Social Presence With A Virtual Human In Mixed Reality, Kangsoo Kim
Electronic Theses and Dissertations
Interactive Virtual Humans (VHs) are increasingly used to replace or assist real humans in various applications, e.g., military and medical training, education, or entertainment. In most VH research, the perceived social presence with a VH, which denotes the user's sense of being socially connected or co-located with the VH, is the decisive factor in evaluating the social influence of the VH—a phenomenon where human users' emotions, opinions, or behaviors are affected by the VH. The purpose of this dissertation is to develop new knowledge about how characteristics and behaviors of a VH in a Mixed Reality (MR) environment can affect …
Complex Affect Recognition In The Wild, Behnaz Nojavanasghari
Complex Affect Recognition In The Wild, Behnaz Nojavanasghari
Electronic Theses and Dissertations
Artificial social intelligence is a step towards human-like human-computer interaction. One important milestone towards building socially intelligent systems is enabling computers with the ability to process and interpret the social signals of humans in the real world. Social signals include a wide range of emotional responses from a simple smile to expressions of complex affects. This dissertation revolves around computational models for social signal processing in the wild, using multimodal signals with an emphasis on the visual modality. We primarily focus on complex affect recognition with a strong interest in curiosity. In this dissertation,we ?rst present our collected dataset, EmoReact. …
Online Path Planning And Control Solution For A Coordinated Attack Of Multiple Unmanned Aerial Vehicles In A Dynamic Environment, Juan Vega-Nevarez
Online Path Planning And Control Solution For A Coordinated Attack Of Multiple Unmanned Aerial Vehicles In A Dynamic Environment, Juan Vega-Nevarez
Electronic Theses and Dissertations
The role of the unmanned aerial vehicle (UAV) has significantly expanded in the military sector during the last decades mainly due to their cost effectiveness and their ability to eliminate the human life risk. Current UAV technology supports a variety of missions and extensive research and development is being performed to further expand its capabilities. One particular field of interest is the area of the low cost expendable UAV since its small price tag makes it an attractive solution for target suppression. A swarm of these low cost UAVs can be utilized as guided munitions or kamikaze UAVs to attack …