Computer Sciences Commons^™

Eliminating Adversarial Noise Via Information Discard And Robust Representation Restoration, Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu

Machine Learning Faculty Publications

Deep neural networks (DNNs) are vulnerable to adversarial noise. Denoising model-based defense is a major protection strategy. However, denoising models may fail and induce negative effects in fully white-box scenarios. In this work, we start from the latent inherent properties of adversarial samples to break the limitations. Unlike solely learning a mapping from adversarial samples to natural samples, we aim to achieve denoising by destroying the spatial characteristics of adversarial noise and preserving the robust features of natural information. Motivated by this, we propose a defense based on information discard and robust representation restoration. Our method utilize complementary masks to …

Which Is Better For Learning With Noisy Labels: The Semi-Supervised Method Or Modeling Label Noise?, Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu

Machine Learning Faculty Publications

In real life, accurately annotating large-scale datasets is sometimes difficult. Datasets used for training deep learning models are likely to contain label noise. To make use of the dataset containing label noise, two typical methods have been proposed. One is to employ the semi-supervised method by exploiting labeled confident examples and unlabeled unconfident examples. The other one is to model label noise and design statistically consistent classifiers. A natural question remains unsolved: which one should be used for a specific real-world application? In this paper, we answer the question from the perspective of causal data generative process. Specifically, the performance …

A Perspective Note On Μ_N Σ Baire’S Space, N. Raksha Ben, G. Hari Siva Annam, G. Helen Rajapushpam

Neutrosophic Systems with Applications

This paper presents an introduction to many novel types of sets, including μN strongly dense sets, μN strongly nowhere dense sets, μN strongly first category sets, and μN strongly nowhere residual sets. The features of these sets are briefly elucidated. In addition, by the use of these techniques, we have successfully obtained the highly Baire space μN, and it is imperative to elucidate its inherent features.

A Perspective Note On Μ_N Σ Baire’S Space, N. Raksha Ben, G. Hari Siva Annam, G. Helen Rajapushpam

Neutrosophic Systems with Applications

This paper presents an introduction to many novel types of sets, including μN strongly dense sets, μN strongly nowhere dense sets, μN strongly first category sets, and μN strongly nowhere residual sets. The features of these sets are briefly elucidated. In addition, by the use of these techniques, we have successfully obtained the highly Baire space μN, and it is imperative to elucidate its inherent features.

Sentiment Analysis Before And During The Covid-19 Pandemic, Emily Musgrove

Mathematics Summer Fellows

This study examines the change in connotative language use before and during the Covid-19 pandemic. By analyzing news articles from several major US newspapers, we found that there is a statistically significant correlation between the sentiment of the text and the publication period. Specifically, we document a large, systematic, and statistically significant decline in the overall sentiment of articles published in major news outlets. While our results do not directly gauge the sentiment of the population, our findings have important implications regarding the social responsibility of journalists and media outlets especially in times of crisis.

Enhanced Quantum Chemistry With Machine Learning, Brock Dyer

Physics and Astronomy Summer Fellows

This file is a catalogue of the relevant quantum mechanical and computer programming topics that I learned during the summer which will be helping me to generate an artificial intelligence that will be able to perform computational chemical calculations at a much faster rate and comparable or better accuracy than current methods.

On Training Neurons With Bounded Compilations, Lance Kennedy

Master of Science in Computer Science Theses

Knowledge compilation offers a formal approach to explaining and verifying the behavior of machine learning systems, such as neural networks. Unfortunately, compiling even an individual neuron into a tractable representation such as an Ordered Binary Decision Diagram (OBDD), is an NP-hard problem. In this thesis, we consider the problem of training a neuron from data, subject to the constraint that it has a compact representation as an OBDD. Our approach is based on the observation that a neuron can be compiled into an OBDD in polytime if (1) the neuron has integer weights, and (2) its aggregate weight is bounded. …

Optimizing Large-Capacity Key-Value Systems For Efficient Data Management, Kefei Wang

LSU Doctoral Dissertations

In today's Internet services, key-value system is playing a crucial and indispensable role, and has attracted extensive research in both academia and industry. During the past years, we have witnessed an unprecedented quick development of both memory-based and flash-based key-value systems. With the rapid evolution of memory and storage technologies, we are seeing both challenges and research opportunities.

In this dissertation, we focus on understanding and optimizing the efficiency of large-capacity, high-speed key-value systems from the perspective of both software and hardware to meet the ever-growing performance expectations. We first propose a multi-tier mapping structure, called Cascade Mapping, exploring the …

Age-Related Changes In Circadian Regulation Of The Human Plasma Lipidome, Shadab A. Rahman, Rose M. Gathungu, Vasant R. Marur, Melissa St. Hilaire, Karine Scheuermaier, Marina Belenky, Jackson S. Struble, Charles A. Czeisler, Steven W. Lockley, Elizabeth B. Klerman, Jeanne F. Duffy, Bruce S. Kristal

Computer and Data Science Faculty Publications

Aging alters the amplitude and phase of centrally regulated circadian rhythms. Here we evaluate whether peripheral circadian rhythmicity in the plasma lipidome is altered by aging through retrospective lipidomics analysis on plasma samples collected in 24 healthy individuals (9 females; mean ± SD age: 40.9 ± 18.2 years) including 12 younger (4 females, 23.5 ± 3.9 years) and 12 middle-aged older, (5 females, 58.3 ± 4.2 years) individuals every 3 h throughout a 27-h constant routine (CR) protocol, which allows separating evoked changes from endogenously generated oscillations in physiology. Cosinor regression shows circadian rhythmicity in 25% of lipids in both …

Interposition Based Container Optimization For Data Intensive Applications, Rohan Tikmany

College of Computing and Digital Media Dissertations

Reproducibility of applications is paramount in several scenarios such as collaborative work and software testing. Containers provide an easy way of addressing reproducibility by packaging the application's software and data dependencies into one executable unit, which can be executed multiple times in different environments. With the increased use of containers in industry as well as academia, current research has examined the provisioning and storage cost of containers and has shown that container deployments often include unnecessary software packages. Current methods to optimize the container size prune unnecessary data at the granularity of files and thus make binary decisions. We show …

Survey Of Personalized Learning Software Systems: A Taxonomy Of Environments, Learning Content, And User Models, Heba Ismail, Nada Hussein, Saad Harous, Ashraf Khalil

All Works

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the …

International Soft Law Governance Of Artificial Intelligence Ethics: Current Situation, Challenges And Countermeasures, Mingting Zhu, Chongli Xu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) technology not only rapidly empowers economic and social development, but may also trigger many ethical issues highly related to the characteristics and development of AI technology itself. The rise of international soft law in the field of AI ethical governance is almost inevitable due to its flexibility, efficiency, low application cost, ability to fill the gap in hard law, and convenience in distinguishing governance and layered response to ethical issues. Under the current situation of developed international soft law and outdated hard law in this field, faced with the governance challenge of unstable cooperation among subjects of …

A Hybrid Metaheuristic And Computer Vision Approach To Closed-Loop Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano, Shay V. Wallace, Benji Maruyama, Gilbert L. Peterson

Faculty Publications

Fused deposition modeling (FDM) is one of the most popular additive manufacturing (AM) technologies for reasons including its low cost and versatility. However, like many AM technologies, the FDM process is sensitive to changes in the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters. The experience required to efficiently calibrate a printer to a new feedstock acts as a barrier to entry. To enable greater accessibility to non-expert users, this paper presents the first system for autonomous calibration of low-cost FDM 3D printers that demonstrates optimizing …

Neuro-Symbolic Representations For Information Retrieval, Laura Dietz, Hannah Bast, Shubham Chatterjee, Jeff Dalton, Jian Yun Nie, Rodrigo Nogueira

Computer Science Faculty Research & Creative Works

This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are …

Generative Relevance Feedback With Large Language Models, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton

Computer Science Faculty Research & Creative Works

Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of building a language model from retrieved results, we propose Generative Relevance Feedback (GRF) that builds probabilistic feedback models from long-form text generated from Large Language Models. We study the effective methods for generating text by varying the zero-shot generation subtasks: queries, entities, facts, news articles, documents, and essays. We evaluate GRF on document retrieval benchmarks covering a diverse set of queries and document collections, and the results show that GRF methods significantly outperform previous PRF methods. …

Interpretable Timbre Synthesis Using Variational Autoencoders Regularized On Timbre Descriptors, Anastasia Natsiou, Luca Longo, Sean O'Leary

Conference papers

Controllable timbre synthesis has been a subject of research for several decades, and deep neural networks have been the most successful in this area. Deep generative models such as Variational Autoencoders (VAEs) have the ability to generate a high-level representation of audio while providing a structured latent space. Despite their advantages, the interpretability of these latent spaces in terms of human perception is often limited. To address this limitation and enhance the control over timbre generation, we propose a regularized VAE-based latent space that incorporates timbre descriptors. Moreover, we suggest a more concise representation of sound by utilizing its harmonic …

A Newfangled Interpretation On Fermatean Neutrosophic Dombi Fuzzy Graphs, D. Sasikala, B. Divya

Neutrosophic Systems with Applications

Neutrosophic Dombi fuzzy graph is an advancement of the Dombi fuzzy graph and intuitionistic Dombi fuzzy graph. In this paper, we have initiated a new concept of the Fermatean neutrosophic Dombi fuzzy graph. Further, we identified a few products of the direct, cartesian, composition of Fermatean neutrosophic Dombi fuzzy graphs. Also, we examined the related proposition with suitable illustrations with graphs.

A Newfangled Interpretation On Fermatean Neutrosophic Dombi Fuzzy Graphs, D. Sasikala, B. Divya

Neutrosophic Systems with Applications

Neutrosophic Dombi fuzzy graph is an advancement of the Dombi fuzzy graph and intuitionistic Dombi fuzzy graph. In this paper, we have initiated a new concept of the Fermatean neutrosophic Dombi fuzzy graph. Further, we identified a few products of the direct, cartesian, composition of Fermatean neutrosophic Dombi fuzzy graphs. Also, we examined the related proposition with suitable illustrations with graphs.

External Behavior Of A Logic Program And Verification Of Refactoring, Jorge Fandinno, Zachary Hansen, Yuliya Lierler, Vladimir Lifschitz, Nathan Temple

Computer Science Faculty Publications

Refactoring is modifying a program without changing its external behavior. In this paper, we make the concept of external behavior precise for a simple answer set programming language. Then we describe a proof assistant for the task of verifying that refactoring a program in that language is performed correctly.

Fault Aware Task Scheduling In Cloud Using Min-Min And Dbscan, S. M.F.D.Syed Mustapha, Punit Gupta

All Works

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and …

How Technology May Be Used For Future Disease Predictions, Rich P. Manprisio

Journal of Applied Disciplines

Exasperated by the ongoing global pandemic, the healthcare system is grappling with the formidable challenges posed by proper and effective disease treatments. Nevertheless, amidst these growing difficulties, the healthcare field has witnessed significant technological advancements, offering promising avenues for disease prediction. Notably, a positive correlation exists between the utilization of technologies and their potential to serve as valuable tools for disease prediction. As our reliance on technological sophistication continues progressing, current research highlights numerous viable options to augment the healthcare sector. This review explores the current state of utilizing technologies and their potential to enhance healthcare, shedding light on their …

Detecting Mental Distresses Using Social Behavior Analysis In The Context Of Covid-19: A Survey, Sahraoui Dhelim, Liming Chen, Sajal K. Das, Huansheng Ning, Chris Nugent, Gerard Leavey, Dirk Pesch, Eleanor Bantry-White, Devin Michael Burns

Computer Science Faculty Research & Creative Works

Online social media provides a channel for monitoring people's social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published …

Using Machine Learning Techniques To Model Encoder/Decoder Pair For Non-Invasive Electroencephalographic Wireless Signal Transmission, Ernst Fanfan

Master of Science in Computer Science Theses

This study investigated the application and enhancement of Non-Invasive Brain-Computer Interfaces (NI-BCIs), focused on enhancing the efficiency and effectiveness of this technology for individuals with severe physical limitations. The core research goal was to improve current limitations associated with wires, noise, and invasive procedures often associated with BCI technology. The key discussed solution involves developing an optimized Encoder/Decoder (E/D) pair using machine learning techniques, particularly those borrowed from Generative Adversarial Networks (GAN) and other Deep Neural Networks, to minimize data transmission and ensure robustness against data degradation. The study highlighted the crucial role of machine learning in self-adjusting and isolating …

An Interactional Account Of Empathy In Human-Machine Communication, Shauna Concannon, Ian Roberts, Marcus Tomalin

Human-Machine Communication

Efforts to develop empathetic agents, or systems capable of responding appropriately to emotional content, have increased as the deployment of such systems in socially complex scenarios becomes more commonplace. In the context of human-machine communication (HMC), the ability to create the perception of empathy is achieved in large part through linguistic behavior. However, studies of how language is used to display and respond to emotion in ways deemed empathetic are limited. This article aims to address this gap, demonstrating how an interactional linguistics informed methodological approach can be applied to the study of empathy in HMC. We present an analysis …

Dbscan Inspired Task Scheduling Algorithm For Cloud Infrastructure, S. M.F.D.Syed Mustapha, Punit Gupta

All Works

Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task …

Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which …

Enhancing Video-Based Learning Using Knowledge Tracing: Personalizing Students’ Learning Experience With Orbits, Shady Shehata, David Santandreu, Philip Purnell, Mark Thompson

Natural Language Processing Faculty Publications

As the world regains its footing following the COVID-19 pandemic, academia is striving to consolidate the gains made in students’ education experience. New technologies such as video-based learning have shown some early improvement in student learning and engagement. In this paper, we present ORBITS predictive engine at YOURIKA company, a video-based student support platform powered by knowledge tracing. In an exploratory case study of one master’s level Speech Processing course at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi, half the students used the system while the other half did not. Student qualitative feedback was universally …

Climatic Clustering And Longitudinal Analysis With Impacts On Food, Bioenergy, And Pandemics, John Lagergren, Mikaela Cashman, Verónica G.Melesse Vergara, Paul R. Eller, Joao Gabriel Felipe Machado Gazolla, Hari B. Chhetri, Jared Streich, Sharlee Climer, Peter Thornton, Wayne Joubert, Daniel Jacobson

Computer Science Faculty Works

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on Earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply …

Ways To Participate In Ongoing Regulation Around Artificial Intelligence Ethics In The United States, Wilhelmina Randtke

University Libraries: Faculty Presentations

In January 2021, the US passed the National Artificial Intelligence Initiative Act of 2020. The goal is a cohesive federal AI initiative, and part of that is safety, ethics, and transparency. The act includes funding appropriations for 2021-2025, and roll out takes place over that time. In implementing this law, there is recent and ongoing activity to regulate AI in the US. Regular calls for public participation go out to the public on www.federalregister.gov in the form of open ended questions on which input is requested, and feedback on reports or action plans.

The linked data community is uniquely positioned …

Machine Learning Approach For Static Ransomware Analysis, Aldin Vehabovic

USF Tampa Graduate Theses and Dissertations

Ransomware is a form of malware which uses encryption methods to make data inaccessible to legitimate users. This cyberthreat has emerged as one of the most serious security challenges today, and a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As this malware type continues to expand, governments across the world are taking serious steps to limit its reach. In addition, researchers have proposed a range of ransomware detection and attribution schemes, most of which use advanced machine learning (ML) techniques to process and analyze real-world empirical data from …