Digital Commons Network^™

From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang

Research Collection School Of Computing and Information Systems

This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where …

Adan: Adaptive Nesterov Momentum Algorithm For Faster Optimizing Deep Models, Xingyu Xie, Pan Zhou, Huan Li, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that …

Towards Privacy-Aware Iot Communications: Delegable, Revocable, And Efficient, Pengfei Wu, Jianfei Sun, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is widely recognized for its potential to enhance efficiency and productivity across various industries. However, its increasing prevalence has also made it a more attractive target for cybercriminals. While many advanced cryptographic solutions have been developed to secure IoT, some practical security and privacy issues such as self-sovereign delegation, flexible revocation, and lightweight access remain inadequately addressed in existing solutions. In this paper, we propose PLIC, a Privacy-aware Lightweight IoT Communication scheme, which not only enables any authorized user to flexibly delegate their lightweight access privileges to other delegatees, such that they can also access …

Ohss: Optimizing Homomorphic Secret Sharing To Support Fast Matrix Multiplication, Shuguang Zhang, Jianli Bai, Kun Tu, Ziyue Yin, Chan Liu

Research Collection School Of Computing and Information Systems

Homomorphic Secret Sharing (HSS) has evolved as a state-of-the-art methodology for achieving secure two-party computation, synthesizing the advantages of secret sharing and homomorphic encryption. This amalgamation ensures minimal computational and communicational overhead, making it particularly adept at arithmetic operations. However, HSS faces challenges in scalability and efficiency when confronted with extensive matrix operations, including both matrix-vector and matrix-matrix multiplications, which are fundamental in numerous privacy-preserving computations, notably within the realm of privacy-preserving machine learning. In this research, we introduce Optimized Homomorphic Secret Sharing (OHSS), a refined version of HSS, crafted to address these limitations. Our contributions include enhancements to the …

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 …

Towards General Conceptual Model Editing Via Adversarial Representation Engineering, Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun

Research Collection School Of Computing and Information Systems

Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and efficient methods for applying these representations for general and flexible model editing remains challenging. In this work, we explore how to leverage insights from representation engineering to guide the editing of LLMs by deploying a representation sensor as an editing oracle. We first identify the importance of a robust and reliable sensor during editing, then propose an …

Ali-Agent: Assessing Llms’ Alignment With Human Values Via Agent-Based Evaluation, Jingnan Zheng, Han Wang, Tai D. Nguyen, An Zhang, Jun Sun, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) can elicit unintended and even harmful content when misaligned with human values, posing severe risks to users and society. To mitigate these risks, current evaluation benchmarks predominantly employ expertdesigned contextual scenarios to assess how well LLMs align with human values. However, the labor-intensive nature of these benchmarks limits their test scope, hindering their ability to generalize to the extensive variety of open-world use cases and identify rare but crucial long-tail risks. Additionally, these static tests fail to adapt to the rapid evolution of LLMs, making it hard to evaluate timely alignment issues. To address these challenges, …

Replay-And-Forget-Free Graph Class-Incremental Learning: A Task Profiling And Prompting Approach, Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID …

Safety Through Feedback In Constrained Rl, Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri

Research Collection School Of Computing and Information Systems

In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe behaviour. However, designing or evaluating such a cost function can be prohibitively expensive. For instance, in the domain of self-driving, designing a cost function that encompasses all unsafe behaviours (e.g., aggressive lane changes, risky overtakes) is inherently complex, it must also consider all the actors present in the scene making it expensive to evaluate. In such scenarios, the cost function can be learned from feedback collected offline in between training rounds. This …

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

A Comparative Study Of Patterns, Causes, And Impacts Of Data Breaches Across Geographical Regions And Time Frames, Bhavish Balsara

Electronic Theses, Projects, and Dissertations

The rise of digital technologies and interconnected systems has made data breaches a growing global concern. This culmination project explores the patterns, causes, and impacts of data breaches across various countries with varying levels of economic development and cybersecurity infrastructure from 2020 to 2023. This research aims to provide insights into the global landscape of data breaches and how they have evolved in recent years. The research questions are: (Q1) How do data breach patterns differ between countries with different levels of economic development and cybersecurity infrastructure? (Q2) What patterns and trends can be identified in data breaches when analyzing …

Real-Time Network Simulations For Ml/Dl Ddos Detection Using Docker, Luis D. Garcia

Master's Theses

As the integration of artificial intelligence (AI) within cybersecurity continues to

grow, machine learning (ML) and deep learning (DL) models are increasingly used to

detect cyber attacks. However, these models are rarely evaluated in real-time attack

scenarios to see how subtle changes from the real networking environment can affect

their predictions. To address this issue, we propose a scalable, platform-independent

Docker testbed specifically designed for simulating real-time Distributed Denial of

Service (DDoS) attack scenarios that allows researchers to deploy and evaluate their

pre-trained, ML and DL detection models. Our framework is simple to configure

and can run across Intel and …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Classifying Supersonic Frequencies For Active Acoustic Side-Channel Exploitation, Destin Hinkel

Graduate Theses and Dissertations (2019 - present)

Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing [1]. Acoustic side-channel attacks (SCAs) are typically performed passively: a listening device captures aural frequencies from a machine via a microphone that are transmitted to the attacker for analysis [1]–[3]. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency [4]. Furthermore, the SonarSnoop framework presents a novel active approach that involves both …

Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai

Graduate Theses and Dissertations (2019 - present)

An eclipse attack is a significant cyber threat targeting the network layer of blockchain platforms. Detecting eclipse attacks is challenging for several reasons. First, there are no available datasets for training and testing models. Second, comprehensive studies identifying features to detect eclipse attacks are lacking. Additionally, the amount of eclipse network traffic is much smaller than that of normal network traffic, which leads to imbalanced samples. Moreover, the characteristics of eclipse network traffic closely resemble those of normal traffic, causing overlapping samples, which makes it challenging for traditional classifiers to learn how to identify eclipse attacks. To address these challenges, …

Towards Robust And Fair Vision Learning In Open-World Environments, Thanh-Dat Truong

Graduate Theses and Dissertations

The rapid increase of large-scale data and high-performance computational hardware has promoted the development of data-driven machine vision approaches. Advanced deep learning approaches have achieved remarkable performance in various vision problems and are closing the capability gap between artificial intelligence (AI) and humans. However, towards the ultimate goal of AI, which replicates human ability in visual perception tasks, the machine vision learning methods still need to address several ill-posed challenges. First, while the current vision learning methods often rely on large-scale annotated data, the data annotation process is a costly and time-consuming process. Second, the unfaired predictions produced by vision …

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 …

Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira

Theses and Dissertations

Embedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial …

Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill

Theses and Dissertations

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …

Reevo: Large Language Models As Hyper-Heuristics With Reflective Evolution, Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, Guojie Song

Research Collection School Of Computing and Information Systems

The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design process. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present Reflective Evolution (ReEvo), a generic searching framework that emulates the reflective design approach of human experts while far surpassing human capabilities with its scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. Evaluations …

Causality Analysis For Neural Network Security, Bing Sun

Dissertations and Theses Collection (Open Access)

While neural networks are demonstrating excellent performance in a wide range of applications, there has been a growing concern on their reliability and dependability.Similar to traditional decision-making programs, neural networks inevitably have defects that need to be identified and mitigated at times. Neural networks are usually inherently black-boxes and do not provide explanations on how and why decisions are made. As a result, these defects are more ``hidden" and more challenging to eliminate. It is thus crucial to develop systematic approaches to identify and mitigate defects in a neural network in a rigorous way.

In this dissertation, we focus on …

Robust Learning With Probabilistic Relaxation Using Hypothesis-Test-Based Sampling, Zilin Wang

Dissertations and Theses Collection (Open Access)

In recent years, deep learning has been a vital tool in various tasks. The performance of a neural network is usually evaluated by empirical risk minimization. However, robustness issues have gained great concern which can be fatal in safety-critical applications. Adversarial training can mitigate the issue by minimizing the loss of worst-case perturbations of data. It is effective in improving the robustness of the model, but it is too conservative, and the plain performance of the model can be unsatisfying. Probabilistic Robust Learning (PRL) empirically balances the average- and worst-case performance while the robustness of the model is not provable …

Towards Robust, Secure, And Privacy-Aware Large Language Models Of Code, Zhou Yang

Dissertations and Theses Collection (Open Access)

The field of software engineering has witnessed a surge in large language models specifically tailored to understand and process code, which we call large language models for code (LLM4Code). The increasing popularity of LLM4Code is inseparable from three key factors: the availability of extensive datasets compiled from diverse data sources, the advancements in deep learning algorithms and computational power that facilitate the training of these powerful models, and the active engagement and collaboration within the research community fostering innovation and the rapid exchange of ideas and methodologies. As evidenced by a series of studies, LLM4Code has been experiencing rapid development …

Gtree: Gpu-Friendly Privacy-Preserving Decision Tree Training And Inference, Qifan Wang, Shujie Cui, Lei Zhou, Ye Dong, Jianli Bai, Yun Sing Koh, Giovanni Russello

Research Collection School Of Computing and Information Systems

Outsourcing Decision tree (DT) training and inference to cloud platforms raises privacy concerns. Recent Secure Multi-Party Computation (MPC)-based methods are hindered by heavy overhead. Few recent studies explored GPUs to improve MPC-protected deep learning, yet integrating GPUs into MPC-protected DT with massive data-dependent operations remains challenging, raising question: can MPC-protected DT training and inference fully leverage GPUs for optimal performance?We present GTree, the first scheme that exploits GPU to accelerate MPC-protected secure DT training and inference. GTree is built across 3 parties who jointly perform DT training and inference with GPUs. GTree is secure against semi-honest adversaries, ensuring that no …

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 …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

Deep Learning Vision-Based Bridge Inspection With Resource-Constrained Unmanned Aircraft Systems, Ji Young Lee

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Bridge inspection is critical for ensuring structural integrity, extending the service life of infrastructure, and minimizing maintenance costs. As bridges age and endure increasing loads, regular inspections help detect early signs of wear, such as cracks or corrosion, that could impact safety and performance. However, traditional inspection methods are labor-intensive, requiring significant time, specialized equipment, and manual access to challenging areas, which can lead to costly disruptions. Additionally, reliance on human inspectors introduces subjectivity, with assessments varying by individual expertise. These factors highlight the inefficiencies and safety risks in current inspection practices, underscoring the need for more objective, efficient solutions. …

Prevalence Of Autism Spectrum Characteristics In Students Taking Undergraduate Computing Courses, Rachel Michaela Mettenbrink

School of Computing: Dissertations, Theses, and Student Research

The incidence rate of autism spectrum condition (ASC) has increased significantly in recent decades, as awareness of the condition and its impacts increases amongst clinicians, parents, and the general population. Medical literature has proposed that there may be a relationship between ASC and participation in the computing field. This study tests for the prevalence of autism spectrum condition traits measured by delivering the Autism Spectrum Quotient (AQ) to a population of undergraduate computer science students. We examine the relationships between AQ scores and students taking undergraduate computer science classes, sex, socioeconomic status, and parents in the computing industry. Additionally, we …

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. …

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, …