Computer Sciences Commons^™

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Protocol Transformations Across Osi Network Stack Layers For Attack, Evasion, And Defense, Nathan Tusing

All Dissertations

Network endpoints frequently contend with errors and deviations within protocols. Many factors account for these deviations including noise, tampering, and algorithm implementations. Intermediate nodes are expected to modify instantiated protocols and not guarantee correctness. This ability to modify traffic enables all sides of network security to alter security and performance properties of protocols, and we define this intermediary modification of an instantiated protocol as a transformation. Protocol transformations traverse layers of the OSI reference model and changes a protocol's time series byte sequence. Within this thesis, we show that this framework applies to multiple domains and protocols. Common examples of …

Concert Tickets, Party Matching, Sleeping Barbers, And Single Lane Bridges: Characterizing Student Reasoning About Concurrency, Aubrey Lawson

All Dissertations

Programming with concurrency is challenging both to learn and to teach. A concurrent program has multiple computations happening “at the same time” either simultaneously or in an interleaved manner. It is non-deterministic, imposing only a partial ordering on its decomposed parts. Advantages of concurrency include the potential for increased program throughput, high responsiveness and reduced complexity of program structure. But a concurrent program can be more complex to reason about than a sequential program, in part because the conditions of correctness must hold for all possible execution sequences and also because programmers must implement and reason about synchronization constructs that …

Comparing And Evaluating Models Of Tile-Assembly Using Intrinsic Simulation, Daniel Hader

Graduate Theses and Dissertations

Tile-assembly studies abstract models of computation inspired by advancements in the emerging field of DNA-nanotechnology, where synthetic strands of DNA are used as building blocks for microscopic structures. These DNA strands can be made to combine into structural units with selectively sticky sides that abstractly resemble Wang tiles. However, unlike Wang tiles, an assembly process is modeled where tiles combine one-by-one to form larger assemblies according to matching rules based on their sticky sides. While in practice, these tile-assembly models have seen use in designing DNA nano-structures, the mathematical study of their theory has revealed an exciting interplay between geometric …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Interpreting Neural Networks For Particle Tracing In Fluid Simulation Ensembles: An Interactive Visualization Framework, Maanav Choubey

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding the internal mechanisms of neural networks, particularly Multi-Layer Perceptrons (MLP), is essential for their effective application in a variety of scientific domains. In particular, in the scientific visualization domain their adoption has recently shown to be a promising tool to predict particle trajectories in fluid dynamics simulation and aid the interactive visualization of flows. This research addresses the critical challenge of interpretability of such models.

While interpretability has been extensively explored in fields like computer vision and natural language processing, its application to time series data, particularly for particle tracing (or prediction of trajectories), has not garnered sufficient attention. …

Optimizing Mobility On Demand Systems: Multiagent Reinforcement Learning Approaches To Order Assignment And Vehicle Guidance, Jiyao Li

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores ways to improve Mobility on Demand (MoD) systems, which are services like ride-sharing and autonomous taxi systems. The main goal is to make these services more efficient and reliable, benefiting both passengers and drivers by better matching the number of available vehicles with the number of people needing rides.

For ride-sharing services, a new method called T-Balance helps match riders with drivers and guides empty taxis to areas where more people need rides. This reduces wait times for passengers and increases earnings for drivers. Another method, called GRL-HM, looks at how riders and drivers behave to further …

Interpretable And Robust Deep Anomaly Detection, He Cheng

All Graduate Theses and Dissertations, Fall 2023 to Present

Anomaly detection is crucial in fields like cybersecurity, healthcare, and finance, as it helps identify unusual or potentially harmful events in data. With the rise of deep learning, advanced models have been developed for anomaly detection, but they often operate as "black boxes" that lack transparency and can be susceptible to malicious attacks. My research addresses these issues by creating methods that make deep learning-based anomaly detection more understandable and by investigating how such models can be compromised by backdoor attacks.

To improve transparency, I propose three methods that explain how these models detect anomalies. The first method, called Anomalous …

Feature Selection In Multivariate Time Series Data For Enhanced Solar Flare Classification, Yagnashree Velanki

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are powerful eruptions of energy from the Sun that can cause disruptions to technology here on Earth, like communication systems, GPS, and power grids. To help manage these risks, it’s important to accurately identify and classify these solar flares before they cause problems. In our research, we focused on improving how we classify solar flares by looking at large sets of complex data collected over time. We used several techniques to find the most important factors that help us tell different types of solar flares apart. Each method has its strengths, so instead of relying on just one, …

Supervised Generative Adversarial Networks For Time Series Generation In Embedding Space, Mohammadreza Eskandarinasab

All Graduate Theses and Dissertations, Fall 2023 to Present

Time series data, such as weather forecasts, stock market trends, or heart rate monitors, plays a vital role in many areas of our lives. However, creating realistic synthetic time series data for research and testing purposes has been a significant challenge due to limitations in existing methods, which often struggle with accuracy and consistency. In this study, we developed two new approaches to generate high-quality time series data more effectively. The first method introduces a dual-feedback system that helps the model learn and replicate real data patterns more accurately by providing guidance at different stages of the learning process. The …

Phypo: Priority-Based Hybrid Task Partitioning And Offloading In Mobile Computing Using Automated Machine Learning, Shehr Bano, Ghulam Abbas, Muhammad Bilal, Ziaul Haq Abbas, Zaiwar Ali, Muhammad Waqas

Research outputs 2022 to 2026

With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of …

Sustainable Energysense: A Predictive Machine Learning Framework For Optimizing Residential Electricity Consumption, Murad Al-Rajab, Samia Loucif

All Works

In a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application …

A Novel Approach To Sustainable Behavior Enhancement Through Ai-Driven Carbon Footprint Assessment And Real-Time Analytics, Ahmad Jasim Jasmy, Heba Ismail, Noof Aljneibi

All Works

This research introduces an Artificial Intelligence-driven mobile application designed to help users calculate and reduce their Carbon Footprint (CFP). The proposed system employs an Intelligent Sustainable Behavior Tracking and Recommendation System, analyzing users' carbon emissions from daily activities and suggesting eco-friendly alternatives. It facilitates sustainability discussions through its chat community and educates users on sustainable practices via an intelligent chatbot powered by a sustainability knowledge base. To promote social engagement around sustainability, the application incorporates a competition and reward system. Additionally, it aggregates behavioral data to inform government sustainability policies and address challenges. Emphasizing individual responsibility, the proposed system stands …

Asthma Prevalence Among United States Population Insights From Nhanes Data Analysis, Sarya Swed, Bisher Sawaf, Feras Al-Obeidat, Wael Hafez, Amine Rakab, Hidar Alibrahim, Mohamad Nour Nasif, Baraa Alghalyini, Abdul Rehman Zia Zaidi, Lamees Alshareef, Fadel Alqatati, Fathima Zamrath Zahir, Ashraf I. Ahmed, Mulham Alom, Anas Sultan, Abdullah Almahmoud, Agyad Bakkour, Ivan Cherrez-Ojeda

All Works

Asthma is a prevalent respiratory condition that poses a substantial burden on public health in the United States. Understanding its prevalence and associated risk factors is vital for informed policymaking and public health interventions. This study aims to examine asthma prevalence and identify major risk factors in the U.S. population. Our study utilized NHANES data between 1999 and 2020 to investigate asthma prevalence and associated risk factors within the U.S. population. We analyzed a dataset of 64,222 participants, excluding those under 20 years old. We performed binary regression analysis to examine the relationship of demographic and health related covariates with …

Neutrosophic Cox Proportional Hazards Model For Robust Variable Selection In Survival Analysis, Ibrahim Yasser, Aya A. Abd El-Khalek, A. A. Salama, Doaa A. Abdo

Neutrosophic Systems with Applications

This paper introduces a novel approach for variable selection in survival analysis by integrating neutrosophic logic into the Cox Proportional Hazards (Cox PH) model to address the limitations of recent studies related to high dimensionality. Neutrosophic logic, is a mathematical framework that allows for uncertainty, indeterminacy, and inconsistency, and particularly well suited for handling the complexity and often-ambiguous nature of biological data. By incorporating neutrosophic sets into the Cox PH model, we aim to enhance model robustness, improve variable selection, and address the curse of dimensionality. We compare the performance of the neutrosophic-enhanced Cox PH model with traditional variable selection …

Multi-Criteria Decision-Making Approach Based On Correlation Coefficient For Multi-Polar Interval-Valued Neutrosophic Soft Set, Hamza Naveed, Saalam Ali

Neutrosophic Systems with Applications

The correlation coefficient between two factors is crucial in statistical computation, indicating the extent and evolution of the appropriate link. The precision of applicability evaluations frequently relies on the thoroughness and caliber of data obtained from a certain dataset. Statistical research sometimes entails data marked by intrinsic trade-offs and uncertainty. This study seeks to present m-polar interval-valued neutrosophic soft sets (mPIVNSSs) through the integration of m-polar fuzzy sets with interval-valued neutrosophic soft sets. The suggested mPIVNSS structure is a significantly generalized version of m-polar neutrosophic soft sets and serves as a substantial extension of interval-valued neutrosophic soft sets. In this …

Analysis Of Bck/Bci-Algebras Based On Bipolar Complex Intuitionistic Fuzzy Soft Ideals, Zeeshan Ali

Neutrosophic Systems with Applications

In this article, we design an informative and reliable technique of bipolar complex intuitionistic fuzzy soft sets with numerous operational laws by merging the model of soft sets, complex fuzzy sets, and bipolar intuitionistic fuzzy sets to handle imprecise data. In addition, an ideal in a BCK-algebra is derived based on bipolar complex intuitionistic fuzzy soft set theory are proposed which can capture the information of hesitancy, vagueness, and non-membership information within the circumstance of BCK-algebra. Moreover, we design union, intersection, AND, and OR based on bipolar complex intuitionistic fuzzy soft ideal and simplify it with the help of numerous …

Addressing Inference Time Of Machine Learning Models In Embedded Systems, Samuel Black

UNLV Theses, Dissertations, Professional Papers, and Capstones

Embedded Systems are used for a wide range of specialized computing purposes including surveyal, safety, security, and quality of life. Many areas that embedded systems are used in require the use of machine learning models. Constraints can be placed on embedded systems. Timeliness of execution, user satisfaction, security, power, and resource limitations must be considered when designing for embedded systems. Neural networks excel at complex tasks that are otherwise intractable, but their relatively high computational cost poses a challenge for inclusion in embedded systems. Neural network architectures should be optimized to reduce the total number of operations performed while maintaining …

A Shared Mechanism For Tnp-Atp Recognition By Members Of The P2x Receptor Family, Xiao-Bo Ma, Chen-Xi Yue, Yan Liu, Yang Yang, Jin Wang, Xiao-Na Yang, Li-Dong Huang, Michael X Zhu, Motoyuki Hattori, Chang-Zhu Li, Ye Yu, Chang-Run Guo

Faculty, Staff and Student Publications

P2X receptors (P2X1-7) are non-selective cation channels involved in many physiological activities such as synaptic transmission, immunological modulation, and cardiovascular function. These receptors share a conserved mechanism to sense extracellular ATP. TNP-ATP is an ATP derivative acting as a nonselective competitive P2X antagonist. Understanding how it occupies the orthosteric site in the absence of agonism may help reveal the key allostery during P2X gating. However, TNP-ATP/P2X complexes (TNP-ATP/human P2X3 (hP2X3) and TNP-ATP/chicken P2X7 (ckP2X7)) with distinct conformations and different mechanisms of action have been proposed. Whether these represent species and subtype variations or experimental differences remains unclear. Here, we show …

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen

Research Collection School Of Computing and Information Systems

With the popularity of on-demand ride services worldwide, ride-sourcing platforms must maintain an adequate fleet size and cope with growing travel demand. Recently, platforms have attempted to provide vehicle rental services to drivers who do not own cars, then recruited them to provide on demand ride services. This helps lower the entry barrier for drivers and offers another profitable business for platforms. From the government's perspective, however, it is challenging to coordinately regulate a ride-sourcing business and vehicle rental business. This paper proposes a bi-level optimization model to investigate how the government regulates the ride-sourcing market integrated with vehicle rental …

Towards Unified Multimodal Editing With Enhanced Knowledge Collaboration, Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic …

Long-Tailed Out-Of-Distribution Detection Via Normalized Outlier Distribution Adaptation, Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in LongTailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, i.e., the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier …

Revisiting Masked Auto-Encoders For Ecg-Language Representation Learning, Hung Manh Pham, Aaqib Saeed, Dong Ma

Research Collection School Of Computing and Information Systems

We propose C-MELT, a novel framework for multimodal self-supervised learning of Electrocardiogram (ECG) and text encoders. C-MELT pre-trains a contrastive-enhanced masked auto-encoder architecture using ECG-text paired data. It exploits the generative strengths with improved discriminative capabilities to enable robust cross-modal alignment. This is accomplished through a carefully designed model, loss functions, and a novel negative sampling strategy. Our preliminary experiments demonstrate significant performance improvements with up to 12% in downstream cardiac arrhythmia classification and patient identification tasks. Our findings demonstrate C-MELT's capacity to extract rich, clinically relevant features from ECG-text pairs, paving the way for more accurate and efficient cardiac …

Lilac: Log Parsing Using Llms With Adaptive Parsing Cache, Zhihan Jiang, Jinyang Liu, Zhuangbin Chen, Yichen Li, Junjie Huang, Yintong Huo, Pinjia He, Jiazhen Gu, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised due to the use of human-crafted rules or learning-based models with limited training data. The recent emergence of powerful large language models (LLMs) demonstrates their vast pre-trained knowledge related to code and logging, making it promising to apply LLMs for log parsing. However, their lack of specialized log parsing capabilities currently hinders their parsing accuracy. Moreover, the inherent inconsistent answers, as well as the …

Divlog: Log Parsing With Prompt Enhanced In-Context Learning, Junjielong Xu, Ruichun Yang, Yintong Huo, Chengyu Zhang, Pinjia He

Research Collection School Of Computing and Information Systems

Log parsing, which involves log template extraction from semistructured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose DivLog, an effective log parsing …

4-Bit Shampoo For Memory-Efficient Network Training, Sike Wang, Pan Zhou, Jia Li, Hua Huang

Research Collection School Of Computing and Information Systems

Second-order optimizers, maintaining a matrix termed a preconditioner, are superior to first-order optimizers in both theory and practice. The states forming the preconditioner and its inverse root restrict the maximum size of models trained by second-order optimizers. To address this, compressing 32-bit optimizer states to lower bitwidths has shown promise in reducing memory usage. However, current approaches only pertain to first-order optimizers. In this paper, we propose the first 4-bit second-order optimizers, exemplified by 4-bit Shampoo, maintaining performance similar to that of 32-bit ones. We show that quantizing the eigenvector matrix of the preconditioner in 4-bit Shampoo is remarkably better …

3d Snapshot: Invertible Embedding Of 3d Neural Representations In A Single Image, Yuqin Lu, Bailin Deng, Zhixuan Zhong, Tianle Zhang, Yuhui Quan, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method …

Collaboration! Towards Robust Neural Methods For Routing Problems, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen

Research Collection School Of Computing and Information Systems

Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues – their performance significantly deteriorates on clean instances with crafted perturbations. To enhance robustness, we propose an ensemble-based Collaborative Neural Framework (CNF) w.r.t. the defense of neural VRP methods, which is crucial yet underexplored in the literature. Given a neural VRP method, we adversarially train multiple models in a collaborative manner to synergistically promote robustness against attacks, while boosting standard generalization on clean instances. A neural router is designed to adeptly distribute training instances among models, enhancing …