Physical Sciences and Mathematics Commons^™

Verifiable Outsourced Attribute-Based Encryption Scheme For Cloud-Assisted Mobile E-Health System, Yinbin Miao, Feng Li, Xinghua Li, Jianting Ning, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

The cloud-assisted mobile electronic health (e-health) system facilitates e-health data sharing between healthcare providers and patients, but also raises the security and privacy concerns of e-health data. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been a promising technique to achieve fine-grained access control over encrypted e-health data, it still incurs high encryption and decryption burdens on mobile users such as smartphones and sensors. In addition, malicious cloud servers may conduct incorrect operations due to various interest incentives (e.g., leaking sensitive information to illegal users, saving computation and storage costs). To solve the above issues, in this paper we first propose an …

Privacy-Preserved Data Trading Via Verifiable Data Disturbance, Man Zhang, Xinghua Li, Yanbing Ren, Bin Luo, Yinbin Miao, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

To motivate data owner (DO) to trade data, the existing data trading allows DO to sell the disturbed data to the data consumer (DC), where the disturbance parameter and the data price are negotiated by them, and DO independently adds the disturbance noise to data (usually continuous type) following the negotiation result. However, DOs may violate the negotiated parameter and add more noise to data while obtaining the negotiated price, which damages DC's disturbed data availability. This deficiency is rooted in the absence of supervision and verifiability on DOs’ independent disturbances. Aiming at the above problem, we devise a privacy-preserved …

Towards Automated Slide Augmentation To Discover Credible And Relevant Links, Dilan Dinushka Senarath Arachchige, Christopher M. Poskitt, Kwan Chin (Xu Guangjin) Koh, Heng Ngee Mok, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning from concise educational materials, such as lecture notes and presentation slides, often prompts students to seek additional resources. Newcomers to a subject may struggle to find the best keywords or lack confidence in the credibility of the supplementary materials they discover. To address these problems, we introduce Slide++, an automated tool that identifies keywords from lecture slides, and uses them to search for relevant links, videos, and Q&As. This interactive website integrates the original slides with recommended resources, and further allows instructors to 'pin' the most important ones. To evaluate the effectiveness of the tool, we trialled the system …

Formalizing The Santa Fe Water Bank, Walker Williamson

Water Resources Professional Project Reports

Water users depend on institutional flexibility to efficiently allocate water in legally constrained and overallocated basins. Adaptive market-based mechanisms such as water banking proffer flexibility and fairness, but often face practical challenges fueled by empirical and institutional uncertainties. The Santa Fe Water Bank is a case in point. The City of Santa Fe transfers agricultural water rights from the Middle Rio Grande to its municipal Water Bank for sustainable development. Combined uncertainties in this process can obscure latent changes in water balance and foster misunderstanding, potentially provoking transfer protests. This study develops a formal model clarifying the interrelated institutional and …

Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang

Research Collection School Of Computing and Information Systems

Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based …

Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 …

Using Chatgpt As A Thought Partner In Writing Relevant Proportional Reasoning Word Problems, Andrea Berryhill, Lendon Chandler, Liza Bondurant, Bima Sapkota

School of Mathematical & Statistical Sciences Faculty Publications

In this article, we, two preservice mathematics teachers (PSTs) and two mathematics teacher educators (MTEs), explore the potential of using ChatGPT, a large language model (LLM), as a thought partner to create relevant proportional reasoning word problems. We detail our iterative process of refining our inputs and critically evaluating ChatGPT's outputs. Our takeaways from this journey offer valuable insights for educators seeking to leverage the capabilities of LLMs in their lesson-planning endeavors.

Carbon-Based Nanocomposite Membranes For Membrane Distillation: Progress, Problems And Future Prospects, Chhabilal Regmi, Yuwaraj K. Kshetri, S. Ranil Wickramasinghe

Chemical Engineering Faculty Publications and Presentations

The development of an ideal membrane for membrane distillation (MD) is of the utmost importance. Enhancing the efficiency of MD by adding nanoparticles to or onto a membrane’s surface has drawn considerable attention from the scientific community. It is crucial to thoroughly examine state-of-the-art nanomaterials-enabled MD membranes with desirable properties, as they greatly enhance the efficiency and reliability of the MD process. This, in turn, opens up opportunities for achieving a sustainable water–energy–environment nexus. By introducing carbon-based nanomaterials into the membrane’s structure, the membrane gains excellent separation abilities, resistance to various feed waters, and a longer lifespan. Additionally, the use …

On The Nature Of Low-Frequency Power Spectra In Solar Wind Turbulence, Mason Alden Dorseth

Theses and Dissertations

An important challenge in the accurate estimation of power spectra of plasma fluctuations in the solar wind at very low frequencies is that it requires extremely long signals, which will necessarily contain a mixture of qualitatively different solar wind streams. This unavoidably affects the structure of the power spectra by conflating all these different properties into a single power spectrum. This work develops a conditional statistical analysis to accurately estimate the power spectrum at arbitrarily low frequencies for ``pure'' slow solar wind stream, based on estimated autocorrelation functions of signals excluding portions that do not satisfy the required conditions. The …

Assessing The Gut Content Of Styela Plicata: A Benthic Filter Feeder In The Indian River Lagoon, Honor Lily Triumph Edmands

Theses and Dissertations

The Indian River Lagoon (IRL) has experienced declining water quality due to human activities involving nutrient input, leading to Harmful Algal Blooms (HABs). The Living Docks program affixes oyster mats to dock pilings to promote the settlement and recruitment of benthic filter feeders for water quality improvement. However, questions remain about filtering efficiencies of organisms that make up the mat communities, and whether organisms remove harmful species from the IRL. The pleated sea squirt, Styela plicata, is a common oyster mat resident. The goal of this thesis was to investigate what phytoplankton and bacteria S. plicata consumes, and if …

Establishing Metrics To Encourage Broader Use Of Atomic Requirements – A Call For Exchange And Experimentation, William L. Honig

Computer Science: Faculty Publications and Other Works

There are seemingly many advantages to being able to identify, document, test, and trace single or “atomic” requirements during system development and maintenance. Ongoing work with Agile development has focused on “user stories” that can capture individual features for implementation. However, it is still difficult to evaluate the quality of such requirements and teaching their creation is difficult.

Based on a working definition of atomic requirement, this paper proposes a set of metrics for their evaluation. Ten metrics designed to measure atomic requirements are presented here: five used on individual requirements statements and five applied to a requirements document or …

Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats And Defenses, Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

Deep learning methods have gained increasing attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge + …

Cloud Computation & Beyond, Yassine Chahid, Patrick Slattery

Publications and Research

This research aims to explore the advancements in cloud computing and their potential influences on other technologies, and which advancements in computer networking facilitate them. These technologies are rapidly progressing in power and complexity, thereby altering how institutions and the public access information. Cloud computing provides a service that enables hardware with limited capabilities to overcome the limitations of on-device components. Due to its reliance on device connections, networking technology serves as the backbone of its operation. The research method encompasses reviewing various publications to understand how networking infrastructure facilitates cloud solutions, and how improvements in relevant hardware can expand …

Efficient Machine Learning On Scientific Data Using Bayesian Optimization, Rui Xin

Theses and Dissertations

Deep Learning is pivotal in advancing data analysis across various scientific fields, from genomics to materials discovery. Despite its widespread use, efficiently learning from limited data and operating under resource constraints remains a significant challenge, often limiting its full potential in environments where data is scarce or resources are restricted. This dissertation explores Active Learning and Automated Machine Learning (AutoML) powered by Bayesian Optimization to enhance the efficiency of machine learning across multiple disciplines. It focuses on algorithm optimization and data management through three interconnected studies. In the first study, we investigate how data management technique - active learning helps …

Calibration And Use Of The Transparency Tube As An Estimate Of Turbidity: An Analysis Of Factors Controlling Light Attenuation In Estuarine Waters, Makenna Leigh Thompson

Theses and Dissertations

The transparency tube is low-cost, portable, and easy to use to measure the transparency or clarity of the water. Filling the tube with water creates a small-scale water column where water will be slowly drained out of the bottom until the Secchi disk at the bottom of the tube is visible from the top opening, and the depth of the remaining water in the tube is recorded in centimeters. Unlike traditional Secchi disk measurements, the advantage of this instrument is that sampling depth in highly turbid waters is possible with a centimeter scale resolution. Transparency tube measurement, through careful calibrations, …

Preregistration And Registration As A New Method For Transparency Of External Validation In Artificial Intelligence And Machine Learning Applications To Address Overfitting, Underspecification, And Shortcut Learning, Marilyn Elaine Gartley

Theses and Dissertations

Statistics has been defined as the study of how information should be employed to reflect on and give guidance for action in a practical situation involving uncertainty. The essence of uncertainty is that there is more than one possible outcome, and the actual outcome is unknown in advance; it is indeterminate. The goal of statistical methods is inference: namely, statistical inference—reaching conclusions about populations or deriving scientific insights from data which are collected from a representative sample of that population through providing a mathematical understanding of inference, quantifying the degree of support that data offer for assertions of knowledge, as …

Multi-Scale Deep Representation Learning In Synthetic Biology, Xiaoyi Liu

Theses and Dissertations

Synthetic biology advances and combines the expertise of engineers and biologists, bridging the gap between engineering and natural life. Synthetic biology has been generally categorized into two broad branches by developing new biological components, networks, and systems to reprogram organisms. The first branch involves using synthetic molecules to mimic natural biological functions. The second branch focuses on assembling natural biological components in novel ways, aiming to produce systems with unique, practical functions. Thus, the de novo engineering of biological modules and synthetic pathways is used in related practical bioengineering applications, such as drug-targeting strategies and microbial product manufacturing. Therefore, synthetic …

Existence Of Smooth Solutions For The Landau Equation With Hard Potentials, Shelly Ann Taylor

Theses and Dissertations

This dissertation is concerned with the Landau equation, an integro-differential equation that models the particle density of a plasma as it evolves in phase space. The main topic is the (large-data) local existence of classical solutions to the Landau equation in the case of hard potentials (γ ∈ (0, 1]). Solutions have previously been constructed by Chaturvedi [SIAM. J. Math. Anal., 55(5), 5345–5385, 2023] for initial data in an exponentially-weighted Sobolev space of order 10, but it is not a priori clear whether these solutions have more regularity than the initial data. We improve Chaturvedi’s existence result in two ways: …

An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen K. Tullino, Andrew S. Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques

Faculty Publications

The proposed Integrated Space Test Lexicon is intended to amalgamate the numerous definitions of integrated (IT or IT&E), development test (DT or DT&E), and operational test (OT or OT&E) into unified, service-wide definitions, aligned with the Space Test Enterprise Vision. Refining such definitions will help distill the core characteristics of these fundamental test types to first identify space system activities composing what is traditionally known as DT and OT, then to provide a means of how these activities fit into the IT paradigm and support space system development. In forging a common understanding of how DT and OT support space …

Optimization Of A Plate Beam System For Energy Harvesting Using A Piezoelectric Material, Jose Manuel Almendros Espantaleon

Doctoral Dissertations and Master's Theses

With a continuously growing demand for power, driven by the need to reduce our environmental footprint, this research provides an examination of the potential of energy harvesting with smart materials technology and its practical applications. The energy harvesting system considered here works on generating energy through vibrations of a piezoelectric material beam which will undergo sustained vibrations due to flow of air over its surface. It is assumed that sustained limit cycle oscillations of this system will occur at the flutter velocity. This research creates an optimization framework to obtain the best values of parameters that will result in the …

Balloon Borne Gps-Enabled Radiosondes That Enable Simultaneous Multi-Point Atmospheric Sensing With A Single Ground Station, Peter A. Ribbens

Doctoral Dissertations and Master's Theses

Radiosondes are balloon borne atmospheric instruments that are a critical tool for understanding dynamics in the lower layers of the atmosphere. The low-cost radiosondes developed in the Space and Atmospheric Instrumentation Lab have been further developed to improve the system's use as a science-quality atmospheric instrument that is unique in its ability to simultaneously track multiple sondes with a single ground station. Sensors to measure temperature and pressure were added to improve measurements of the atmospheric state. A printed circuit board shield and 3D-printed shell were designed to make mass manufacturing possible. A thermistor-based temperature sensor was developed and tested …

Radio Propagation Through Density Irregularities In The Auroral Ionosphere, Pralay Raj Vaggu

Doctoral Dissertations and Master's Theses

A radio wave propagating through a structured or turbulent ionosphere undergoes multiple effects, such as refraction, diffraction, etc., that distort the incident radio wave by inducing phase and amplitude fluctuations. These fluctuations are called ionospheric scintillation. Scintillation effects can be detrimental to Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS), but the observed effects can be used as a tool to study the underlying plasma process that causes scintillation. Scintillation is commonly seen in equatorial and high-latitude regions. This study centers around the scintillation and its causative plasma processes that dominantly happen in the high-latitude ionosphere …

Exploring The Effects Of An Alfalfa Leaf-Derived Adsorbent On Microbial Community, Ileal Morphology, Barrier Function, And Immunity In Turkey Poults During Chronic Aflatoxin B1 Exposure, Maria De Jesus Nava-Ramirez, Jing Liu, Juan Omar Hernandez-Ramirez, Xochitl Hernandez-Velasco, Juan D. Latorre, Alma Vazquez-Duran, Guolong Zhang, Roberto Señas-Cuesta, Sergio Gomez-Rosales, Andressa Stein, Billy M. Hargis, Guillermo Tellez-Isaias, Abraham Mendez-Albores, Jesus A. Maguey-Gonzalez

Poultry Science Faculty Publications and Presentations

This article follows-up on our recently published work, which evaluated the impact of the addition of an alfalfa leaf-derived adsorbent in the aflatoxin B₁ (AFB₁)-contaminated diet in regard to the production parameters, blood cell count, serum biochemistry, liver enzymes, and liver histology of turkey poults. This paper presents complementary results on microbial community, ileal morphology, barrier function, and immunity. For this purpose, 350 1-day-old female turkey poults were randomly distributed into five groups: (1) Control, AFB₁-free diet; (2) AF, AFB₁-contaminated diet at 250 ng/g; (3) alfalfa, AFB₁-free diet + 0.5% ( …

Deciphering Water Quality And Algal Dynamics In Clear Lake Through Hyperspectral Analysis Using Emit Data, Wenzhao Li, Shahryar Fazli, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study evaluates the potential application of hyperspectral Earth Surface Mineral Dust Source Investigation (EMIT) remote sensing for monitoring harmful algal blooms (HABs) and water quality in Clear Lake, California. The research focuses on correlating the chlorophyll-a (Chl-a) concentrations with EMIT spectral signatures, using waterbody-wide statistical analysis of Chl-a and EMIT data sampling at various lake locations. Results demonstrate distinct spectral signatures associated with varying Chl-a levels, highlighting the potential of hyperspectral imaging in differentiating algae levels and assessing water quality variables. It also indicates the EMIT’s utility in filling data gaps and offering high-resolution monitoring. This study underscores the …

Assessing Rice Phenological Features With Hyperspectral Imaging Insights From Earth Surface Mineral Dust Source Investigation (Emit), Shahryar Fazli, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

In California's Central Valley, water management and crop health, particularly in rice cultivation, are critical. This paper details the application of Earth surface Mineral dust source InvesTigation (EMIT) hyperspectral imaging, specifically employing Spectral Correlation Mapper (SCM) and Spectral Information Divergence (SID), for precise phenological analysis. By aligning EMIT data with Hyperion satellite references, we address spectral and geographical discrepancies. Our methodology includes seasonal sampling of spectral curves to capture the phenological stages of rice. Results show a strong correlation (R ² = 0.86) between August EMIT and Reference dataset, emphasizing EMIT's utility in enhancing agricultural practices and water efficiency in …

Application And Analysis Of Machine Learning And Deep Learning Algorithms In Detection Of Ddos Cyberattacks, Dipok Deb

Theses and Dissertations

A Distributed Denial-of-Service (DDoS) attack involves overwhelming a target system's data bandwidth or computational resources, often using multiple attack systems, aiming to slow down or disable the targeted system. Detecting and mitigating DDoS attacks effectively remains challenging due to their varying characteristics. One of the promising approaches involves developing an AI based Intrusion Detection System (IDS) against cyberattacks. In this study, we aim to develop an AI based Intrusion Detection System (IDS) for DDoS threat detection using Machine Learning, Deep Learning, or hybrid techniques. Different Machine Learning (ML) and Deep Learning (DL) algorithms like Random Forest (RF), Naïve Bayes (NB), …

Greenhouse Gas Emissions From Urban Lawn Soil Amended With Biochar And Compost Under Contrasting Redox Conditions, Angel Salinas

Theses and Dissertations

Green fields around the world are being converted to urban land uses to cope with population growth and urbanization. The objective of this study was to investigate how urban lawn soils can contribute to greenhouse gas (GHG) emissions in relation to soil saturation-drainage cycles. We measured carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions as well as redox potential (Eh) from garden soils amended with biochar, compost, or biochar + compost under a mesocosm scale. Bermuda turfgrass seed was seeded in each column (5-gallon bucket) and three water saturation conditions (half-saturated, full-saturated, and unsaturated soils) were maintained for …

Strong Marine Heatwaves Trigger Flowering In Seagrass, Catalina A. García-Escudero, Victoria Litsi-Mizan, Pavlos T. Efthymiadis, Vasilis Gerakaris, Oscar Serrano, Eugenia T. Apostolaki

Research outputs 2022 to 2026

In recent decades, the global intensification of marine heatwaves has impacted several ecosystems and species, including the endemic Mediterranean seagrass Posidonia oceanica. However, the scarcity of research in Eastern Mediterranean meadows, where historical and present thermal conditions differ from those of the Western Mediterranean, hampers our ability to draw comprehensive conclusions regarding the species' response to elevated sea temperatures. Here, we studied flowering patterns of P. oceanica meadows (3–15 m depth) of the Greek seas and assessed their potential association with marine heatwaves, while also examining the effects on plant growth associated with the transition from vegetative to sexual reproduction. …

Testing Multiple Environmental Dna Substrates For Detection Of The Cryptic And Critically Endangered Burrowing Freshwater Crayfish Engaewa Pseudoreducta, Kathryn L. Dawkins, Paul Nevill, Brian Chambers, Shane Herbert, Quinton F. Burnham

Research outputs 2022 to 2026

Effective conservation of endangered species depends on knowledge of their distributions, but species detection can often be challenging. An example of this is provided by the Critically Endangered Margaret River burrowing crayfish (Engaewa pseudoreducta), which is highly cryptic. Due to the burrowing habit of this crayfish, detection of this species currently requires a great deal of effort, the results are often non-conclusive, and, as it involves manual excavation of their burrows, the habitat of this and other species is destroyed in the detection process. In response to these challenges, this study developed and optimized a species-specific probe-based qPCR assay targeting …

Unveiling The Dynamics Of Ai Applications: A Review Of Reviews Using Scientometrics And Bertopic Modeling, Raghu Raman, Debidutta Pattnaik, Laurie Hughes, Prema Nedungadi

Research outputs 2022 to 2026

In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The …