Physical Sciences and Mathematics Commons^™

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …

Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across …

Establishing Topological Data Analysis: A Comparison Of Visualization Techniques, Tanmay J. Kotha

USF Tampa Graduate Theses and Dissertations

When visualizing data, we would like to convey both the data and the uncertainty associated with it. There are many incentives to do this, ranging from hurricane path projection to geographical surveys. Important decision making tasks rely upon humans perceiving a clear picture of the data and having confidence in their decisions. Topological Data Analysis has the potential to visualize the data as features or hierarchies in ways that are familiar to human intuition, and thus could help us convey the variation associated with uncertainty.

In this thesis, we evaluate four visualization techniques: color maps, isocontours, Reeb graphs, and persistence …

Color Face Image Recognition Based On Lbpt Method, Jihua Ye, Yahui Chen, Shimin Wang

Journal of System Simulation

Abstract: Aiming to the shortcomings of exiting algorithm to obtain better color face image information for color facial image recognition, the LBPT algorithm was proposed to realize the high efficiency recognition of color face image. LBPT algorithm reflected the texture features of gray image through adaptively obtaining neighborhood radius, ascertaining the relationship between neighborhood radius and neighborhood pixel number, setting threshold. The RGB color model was used to separate the color face image into the R,G,B three component diagrams. The LBPT algorithm was used to obtain the feature of the component diagrams. In order to realize further recognition, the method …

Towards Characterizing Adversarial Defects Of Deep Learning Software From The Lens Of Uncertainty, Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun

Research Collection School Of Computing and Information Systems

Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. …

Relationship Between Risk Identification, Risk Response, And Project Success, Marsha Marinich

Walden Dissertations and Doctoral Studies

AbstractProjects are used to implement the organization's strategic goals, but high failure rates reduce projects' effectiveness in successfully achieving goals. High failure rates reduce project managers’ effectiveness of projects in successfully achieving goals. Senior leaders and project managers are unable to deliver successful projects due to unmanaged risks. Grounded in expected utility theory, the purpose of this quantitative correlational study was to examine the relationship between risk identification, risk responses, and project success. A survey was created in SurveyMonkey® and distributed on LinkedIn. Survey responses were analyzed from 71 project managers with at least five years of experience in Washington, …

Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu

Legacy Theses & Dissertations (2009 - 2024)

Uncertainty caused by unreliable or insufficient data and vulnerable machine learning models