Physical Sciences and Mathematics Commons^™

Building A Kinder Super Highway: Online Group Behavior Driven By Platform Design And Social Policy, Milo Trujillo

Graduate College Dissertations and Theses

Much of human socialization occurs online, and is mediated by telecommunicationsplatforms, particularly social media. These platforms both facilitate and restrict interaction in two ways: first, through the technical affordances they offer, such as conversation trees or direct messages or community self-moderation and voting; and second, through social policy, particularly regarding what content is permissible on a platform and how infractions are penalized. My work engages with platform influence over group social behavior through a series of case studies and through introducing purpose-built methodology.

I begin by examining the influence GitHub exhibits over open-source software de-velopment by contrasting the development practices …

The Law Of The Iterated Logarithm For Lp-Norms Of Kernel Estimators Of Cumulative Distribution Functions, Fuxia Cheng

Faculty Publications – Mathematics

In this paper, we consider the strong convergence of Lp-norms (p ≥ 1) of a kernel estimator of a cumulative distribution function (CDF). Under some mild conditions, the law of the iterated logarithm (LIL) for the Lp-norms of empirical processes is extended to the kernel estimator of the CDF.

Coalescence: A Carnivore Coexistence Curriculum That Braids Indigenous & Western Ecological Knowledge Into A Relevant And Experiential Learning Opportunity For Youth, Stephanie Anne Barron

Graduate Student Theses, Dissertations, & Professional Papers

As grizzly bears (Ursus arctos horriblis) begin to reoccupy more of their historic range, and as humans and large carnivore populations continue to increase, incidences of human carnivore conflict are on the rise. A decolonial curriculum designed in collaboration with the Confederated Salish and Kootenai Tribe’s wildlife biologists stands to increase awareness of Indigenous ecological knowledge and teach youth about the importance of coexistence with carnivores. Additionally, this project could greatly influence youth perceptions of grizzly bears and other large carnivores. This research project examines the development and implementation of a carnivore coexistence curriculum for youth that is guided by …

Characterizing Mountainous Bedrock Groundwater Systems Across Gradients In Topography And Lithology In Western Montana, David L. Baude

Graduate Student Theses, Dissertations, & Professional Papers

Mountain aquifer groundwater systems remain one of the least understood hydrologic systems in upland hydrology. In this study, we aim to characterize bedrock groundwater system dynamics and bedrock-saprolite interactions across a variety of landscape positions and lithology. We investigate groundwater hydraulics, recharge temperature and elevation, residence times, and bedrock recharge rates across two mountainous headwater catchments in west central Montana. We used a suite of environmental tracers (³H, CFCs, SF₆, and ⁴He) and residence time distribution models to estimate groundwater mean residence times over a range of timescales from decades to millennia. Tracers were sampled …

A Tale Of Two Working Landscapes, Sage C. Sutcliffe

Graduate Student Theses, Dissertations, & Professional Papers

No abstract provided.

Hydrogeomorphic Response To Flooding In Northern Yellowstone National Park, Zachary P. Deluca

Graduate Student Theses, Dissertations, & Professional Papers

Understanding and predicting flood-induced geomorphic change, and the relative influences of fluvial forces and valley-bottom geometry on system response, are persistent quandaries in geomorphic process studies. We combine field surveys, remote sensing, and hydraulic modeling to assess the hydrogeomorphic effects of historic flooding in northern Yellowstone National Park (YNP) in a variety of channel configurations. We compare impulse, a metric that incorporates a flow duration threshold based on threshold channel theory, grain size, channel-bed slope, and flood depth and stream power estimates with hydrogeomorphic response. Measurements of pre- and post-flood active-channel width change in aerial photos captured geomorphic response associated …

Diagenesis And Quartz Grain Provenance Of The Turner Sandstone, Carson Lee Broaddus

Graduate Student Theses, Dissertations, & Professional Papers

Provenance analysis of sedimentary rocks is an essential tool for basin analysis and paleogeographic reconstructions. Numerous tools and analytical methods have been developed over the years to investigate the source of detrital constituents in sedimentary rocks. The Late Cretaceous (Turonian) Turner Sandstone in the eastern Powder River Basin (PRB), WY, and the time equivalent Frontier Formation in the western PRB are commonly described as the remnants of a marginal marine depositional system. However, contradictory evidence exists about the source of those deposits. Here, I present results from a comprehensive scanning electron microscope panchromatic cathodoluminescence (SEM-PCL) analysis of >1200 individual sand-sized …

A Soil-Plant-Atmosphere Continuum Model To Simulate Seedling Response To Water Stress, Michael Kurt Kendree Jr.

Graduate Student Theses, Dissertations, & Professional Papers

Most current ecohydrologic models simplify the hydraulics that are at play in plant-water relationships by assuming that the water content within the plant is static. In reality, plants have a relative water content that varies with time in response to environmental stresses. To some extent, variations in plant relative water content are regulated by changes in the osmotic potential of water within plant cells, which contributes to the resilience of plants during periods of water shortage. We present a model that simulates plant water transport and storage within the soil-plant-atmosphere continuum. The model incorporates the role of osmotic regulation in …

Better Understanding The Barred Owl, Adam Lee Potts

Graduate Student Theses, Dissertations, & Professional Papers

This thesis research presents an analysis of barred owl nest site selection in an understudied region of their expanded range. The barred owl has dramatically expanded its range westwards over the last 150 years, prompting multiple conflicting reinterpretations of their species account. Hypotheses of range expansion are reviewed to provide context for this study’s research. This study gathers empirical data on nest site selection in the mixed conifer forests of Montana’s Seeley-Swan and Mission Valleys. Eight barred owl nests were located following extensive territory surveys. Field measurements of tree height and diameter using USFS Forest Inventory and Analysis (FIA) plot …

Paragenesis Of The Austin Chalk Formation In Southwest Texas, Mitchell C. Sherry

Graduate Student Theses, Dissertations, & Professional Papers

Presented in this study is a detailed investigation of the paragenesis and diagenetic processes of the Austin Chalk Formation in southwest Texas, utilizing a combination of core-based, petrographic, mineralogic, SEM, and geochemical based analyses. This study is conducted on a single whole rock core from an oil and gas producing well in Webb County, Texas. At this distal shelf location within the Maverick Basin, the Austin Chalk Formation consists of foraminiferal wackestones, siliceous wackestones, foraminiferal mudstones, bentonites, and foraminiferal packstone facies. These facies were deposited below wave base through pelagic, hemipelagic, and gravity driven processes. Petrographic and SEM analyses reveal …

Temporal And Spatial Connectivity And The Impacts Of Hydropower In The Magdalena River Basin, Colombia, Serena Lynn Butler

Graduate Student Theses, Dissertations, & Professional Papers

Tropical regions hold untapped hydropower potential, but dam construction often adversely impacts multiple dimensions of connectivity in river systems, rigorous evaluation of which is often lacking. This study investigates temporal and spatial dimensions of longitudinal and lateral connectivity in the Magdalena River Basin, Colombia, a region with ongoing and planned hydropower projects. Limited in-situ monitoring data is a common challenge in tropical rivers. To address this, we utilized satellite imagery from Landsat (via Google Earth Engine) and Surface Water and Ocean Topography (SWOT) data. We examined alterations in the flow regime over time and relative to dam construction. We also …

Identifying And Predicting Patterns Of Snowpack Ripening With Machine Learning Methods, Clement Cherblanc

Graduate Student Theses, Dissertations, & Professional Papers

The timing of water release from the snowpack plays key roles in ecosystem services, groundwater recharge, and water resource management. However, two internal barriers in a standing snowpack must be overcome before runoff can outflow from the base: 1) the cold content must be exhausted, and 2) the interconnected network of snow grains must be filled with liquid water to residual saturation. Expressing the liquid water as latent heat allows the two barriers to be grouped as an energy (J/m²) to define a snowpack’s Runoff Energy Hurdle (REH). The growth and loss of REH is driven by evolution of pore …

Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used …

Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …

Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng

Research Collection School Of Computing and Information Systems

Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following a twin-server architecture based on partially homomorphic cryptosystems have received increasing attention. The Secure Outsourced Computation on Integers (SOCI) [1] toolkit is the state-of-the-art among these schemes which can perform secure computation on integers without requiring the costly bootstrapping operation as in fully homomorphic encryption; however, SOCI suffers from relatively large computation and communication overhead. In this paper, we propose SOCI+ which significantly improves the performance of SOCI. Specifically, SOCI+ employs a novel (2,2)-threshold Paillier cryptosystem with fast …

Demonstrating Canvas-Based Processing Of Multiple Camera Streams At The Edge, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We demonstrate criticality-aware canvas-based processing of multiple concurrent camera streams at the resource constrained edge to show substantial improvement in the accuracy-throughput trade-off. The proposed system focuses the available computation resources on select Regions of Interest (RoI) across all the camera streams by (i) extracting RoI from the input camera stream (ii) 2D bin packing the RoI on a canvas frame and (iii) batching and inferring upon these constructed composite canvas frames with a YOLOv5 object detection model. Our experiments show that such canvas-based processing can (i) sustain real-time processing throughput of 23 FPS per camera across 6 concurrent input …

Dl-Drl: A Double-Level Deep Reinforcement Learning Approach For Large-Scale Task Scheduling Of Multi-Uav, Xiao Mao, Guohua Wu, Mingfeng Fan, Zhiguang Cao, Witold Pedrycz

Research Collection School Of Computing and Information Systems

Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge, particularly when dealing with large-scale problems. The deep reinforcement learning (DRL) based methods that learn useful patterns from massive data demonstrate notable advantages. However, their decision space will become prohibitively huge as the problem scales up, thus deteriorating the computation efficiency. To alleviate this issue, we propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework …

Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Research Collection School Of Computing and Information Systems

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …

Clearspeech: Improving Voice Quality Of Earbuds Using Both In-Ear And Out-Ear Microphones, Dong Ma, Ting Dang, Ming Ding, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Wireless earbuds have been gaining increasing popularity and using them to make phone calls or issue voice commands requires the earbud microphones to pick up human speech. When the speaker is in a noisy environment, speech quality degrades significantly and requires speech enhancement (SE). In this paper, we present ClearSpeech, a novel deep-learningbased SE system designed for wireless earbuds. Specifically, by jointly using the earbud’s in-ear and out-ear microphones, we devised a suite of techniques to effectively fuse the two signals and enhance the magnitude and phase of the speech spectrogram. We built an earbud prototype to evaluate ClearSpeech under …

Remote Multi-Person Heart Rate Monitoring With Smart Speakers: Overcoming Separation Constraint, Ngoc Doan Thu Tran, Dong Ma, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Heart rate is a key vital sign that can be used to understand an individual’s health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present …

Active Code Learning: Benchmarking Sample-Efficient Training Of Code Models, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), especially for those with limited budgets. Therefore, efficiently training models of code with less human effort has become an emergent problem. Active learning is such a technique to address this issue that allows developers to train a model with reduced data while producing models with desired performance, which has been well studied in computer vision and natural language processing domains. Unfortunately, there is no such work that explores the effectiveness of active learning for code …

Learning An Interpretable Stylized Subspace For 3d-Aware Animatable Artforms, Chenxi Zheng, Bangzhen Liu, Xuemiao Xu, Huaidong Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the interpretability and 3D controllability of the latent space in a 3D-aware GAN to a stylized sub-space of a customized GAN, revitalizing the original artforms. To this end, the proposed two-stage optimization framework of 3DArtmator begins with discovering an anchor in the original latent space that accurately mimics the …

Stealthy Backdoor Attack For Code Models, Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with that activate the backdoors. Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with backdoor attacks. To this end, we propose A (dversarial eature as daptive Back). A achieves stealthiness …

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 …

Dilf: Differentiable Rendering-Based Multi-View Image-Language Fusion For Zero-Shot 3d Shape Understanding, Xin Ning, Zaiyang Yu, Lusi Li, Weijun Li, Prayag Tiwari

Computer Science Faculty Publications

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong

Computer Science Faculty Publications

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based …

Building Datasets To Support Information Extraction And Structure Parsing From Electronic Theses And Dissertations, William A. Ingram, Jian Wu, Sampanna Yashwant Kahu, Javaid Akbar Manzoor, Bipasha Banerjee, Aman Ahuja, Muntabir Hasan Choudhury, Lamia Salsabil, Winston Shields, Edward A. Fox

Computer Science Faculty Publications

Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …