Physical Sciences and Mathematics Commons^™

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 …

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 …

How People Prompt Generative Ai To Create Interactive Vr Scenes, Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Generative AI tools can provide people with the ability to create virtual environments and scenes with natural language prompts. Yet, how people will formulate such prompts is unclear---particularly when they inhabit the environment that they are designing. For instance, it is likely that a person might say, "Put a chair here,'' while pointing at a location. If such linguistic and embodied features are common to people's prompts, we need to tune models to accommodate them. In this work, we present a Wizard of Oz elicitation study with 22 participants, where we studied people's implicit expectations when verbally prompting such programming …

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

Large Language Model Powered Agents For Information Retrieval, An Zhang, Yang Deng, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like …

Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …

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 …

Final 2024 Residential Metals Abatement Program (Rmap) Park Soil Sampling Field Sampling Plan (Fsp) Submittal #14 [Silver Bow Homes Park, Butte Koa, Fairmont Rv Park, And Cccs Gymnasium], Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

On Angles In Higher Order Brillouin Tessellations And Related Tilings In The Plane, Herbert Edelsbrunner, Alexey Garber, Mohadese Ghafari, Teresa Heiss, Morteza Saghafian

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

For a locally finite set in R 2 , the order-k Brillouin tessellations form an infinite sequence of convex face-to-face tilings of the plane. If the set is coarsely dense and generic, then the corresponding infinite sequences of minimum and maximum angles are both monotonic in k. As an example, a stationary Poisson point process in R 2 is locally finite, coarsely dense, and generic with probability one. For such a set, the distributions of angles in the Voronoi tessellations, Delaunay mosaics, and Brillouin tessellations are independent of the order and can be derived from the formula for angles in …

Development Of The Roadway Pothole Management Program, Dingxin Cheng

Mineta Transportation Institute

Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pothole Management Program (PMP), as it enhances the program's efficiency, effectiveness, …

Mapping Of Pavement Conditions Using Smartphone/Tablet Lidar Case Study: Sensor Performance Comparison, Calvin Beavers, Chad Day, Austin Krietemeyer, Scott M. Peterson, Yushin Ahn, Xiaojun Li

Mineta Transportation Institute

Poor road conditions affect millions of drivers, and assessing the condition of paved surfaces is a critical step towards repairing them. This project explores the feasibility of using the Apple iPad Pro LiDAR sensor as a cost-effective tool for assessing the damage and condition of paved surfaces. Our research aims to provide accurate and precise measurements using readily available consumer devices and compare the results to state-of-the-art equipment. This investigation involved visual inspection, identification, and classification of pavement distresses, followed by a comparison of the iPad and iPhone LiDAR data with a survey-grade terrestrial laser scanner. The project revealed several …

Measurement Of The Photoproduction Cross-Section Of F1(1285) In The Exclusive Reactions Γp → P′K∓KSΠ± At 7.5 < Eγ < 11.5 Gev With Gluex At Jefferson Lab, Tyler Viducic

Physics Theses & Dissertations

The f₁(1285) meson is commonly understood to belong to the axial-vector nonet as the low-mass isoscalar partner to the f₁(1420) but has been suggested as a candidate for a KK^∗+c.c molecule. A nearly mass-degenerate 0 ⁻⁺ state has been observed in πp scattering that calls into question the established branching ratios of the f₁(1285) decays. Recently, the differential cross-section for the photoproduction of f₁(1285) was measured by the CLAS experiment and the results disagreed with theoretical predictions. Additionally, the CLAS experiment did not observe a mass-degenerate 0⁻⁺ state. …

Investigation Of Cooperative Subradiance In Dense Ultracold Rubidium Ensembles, Brent Michael Jones

Physics Theses & Dissertations

This dissertation presents results of an experimental investigation of the time resolved fluorescence from a cold and dense ensemble of ⁸⁷Rb atoms after the sample is illuminated by a short probe pulse. The goal of the experiment was to investigate cooperative subradiant behavior, characterized by a much longer decay time compared to the natural lifetime, induced in an off resonant probe regime. Such long lived states, if controllable, would provide a means to store quantum information. The samples were initially created in a magnetooptical trap before atoms were loaded into a far off resonance trap to achieve atomic densities …

Coherent Backscattering Under Conditions Of Electromagnetically Induced Transparency In Ultracold Rubidium, Joshua D. Carter

Physics Theses & Dissertations

This dissertation presents experimental results of coherent backscattering of light in an ultracold ensemble of rubidium atoms confined in a magneto optical trap under conditions of electromagnetically induced transparency (EIT) in a cascade-type system. Electromagnetically induced transparency was investigated experimentally in both a counterpropagating and orthogonal laser geometry and compared to theory. The experimental results were largely in good agreement with theory. Coherent backscattering was then measured with and without an EIT control field present to investigate the modification, if any, that EIT has on the enhancement of the coherent backscattering cone. The results indicated that the electromagnetically induced transparency …

Mesostructure Reconstruction Of Prepreg Platelet Molded Composite With Artificial Intelligence, Richard Larson

Mechanical & Aerospace Engineering Theses & Dissertations

Prepreg platelet molded composites (PPMC) are long, discontinuous fiber reinforced polymer materials. PPMC are an important subcategory of composite materials as they are processible into geometrically complex structures and can be produced via high-throughput manufacturing processes, however they have higher stiffness and strength as compared to traditional discontinuous fiber reinforced polymers. However, there is inherent randomness in the structure of PPMCs and as such, PPMC parts frequently require per part testing that is cost prohibitive.

Herein, a method using artificial intelligence is proposed as a more cost-effective method of inspecting PPMC parts. Different artificial intelligence (AI) architectures are explored to …

Explainable Artificial Intelligence: Methods And Evaluation, Gayane Grigoryan

Engineering Management & Systems Engineering Theses & Dissertations

A wide array of techniques within explainable artificial intelligence (XAI) have been developed to measure the importance of features in machine learning models. A notable portion of these methods draws upon principles of cooperative game theory (CGT), with the Shapley value emerging as a widely used solution concept. Despite the rising prominence of the Shapley value, other promising solutions from cooperative game theory—such as the Nucleolus, Banzhaf power index, Shapley-Shubik power index, and solutions to conflicting claims problems—have been comparatively overlooked, even though they hold significant potential. In this dissertation, multiple XAI methods based on these other CGT solutions are …

Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli

Engineering Management & Systems Engineering Theses & Dissertations

Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …

Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós

Computer Science Theses & Dissertations

Large Language Models (LLMs) have rapidly advanced the field of Natural Language Processing and become powerful tools for generating and evaluating scientific text. Although LLMs have demonstrated promising as evaluators for certain text generation tasks, there is still a gap until they are used as reliable text evaluators for general purposes. In this thesis project, I attempted to fill this gap by examining the discernibility of LLMs from human-written and LLM-generated scientific news. This research demonstrated that although it was relatively straightforward for humans to discern scientific news written by humans from scientific news generated by GPT-3.5 using basic prompts, …

Privacy-Preserving Deep Learning Framework For Iot Malware Detection, Sabbir Ahmed Khan

Computer Science Theses & Dissertations

Cyberattacks on IoT devices are accelerating at an unprecedented rate, largely driven by IoT malware activities. The IoT malware attacks typically comprise three stages: intrusion, infection, and monetization. Existing IoT malware detection methods fail to identify malicious activities at the intrusion and infection stages and thus cannot stop potential attacks timely. In our research, we have leveraged power side-channel information as input to our deep learning model to identify malware at early stages of intrusion on IoT devices. But, deploying a resource-intensive deep learning model on highly resource-constrained IoT devices is a significant challenge. Consequently, utilizing a Machine Learning as …

Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …

Hierarchical Damage Correlations For Old Photo Restoration, Weiwei Cai, Xuemiao Xu, Jiajia Xu, Huaidong Zhang, Haoxin Yang, Kun Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Restoring old photographs can preserve cherished memories. Previous methods handled diverse damages within the same network structure, which proved impractical. In addition, these methods cannot exploit correlations among artifacts, especially in scratches versus patch-misses issues. Hence, a tailored network is particularly crucial. In light of this, we propose a unified framework consisting of two key components: ScratchNet and PatchNet. In detail, ScratchNet employs the parallel Multi-scale Partial Convolution Module to effectively repair scratches, learning from multi-scale local receptive fields. In contrast, the patch-misses necessitate the network to emphasize global information. To this end, we incorporate a transformer-based encoder and decoder …

Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …

Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …

Toward Effective Secure Code Reviews: An Empirical Study Of Security-Related Coding Weaknesses, Wachiraphan Charoenwet, Patanamon Thongtanunam, Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Identifying security issues early is encouraged to reduce the latent negative impacts on software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code …

Esem: To Harden Process Synchronization For Servers, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Process synchronization primitives lubricate server computing involving a group of processes as they ensure those processes to properly coordinate their executions for a common purpose such as provisioning a web service. A malfunctioned synchronization due to attacks causes friction among processes and leads to unexpected, and often hard-to-detect, application transaction errors. Unfortunately, synchronization primitives are not naturally protected by existing hardware-assisted isolation techniques e.g., SGX, because their process-oriented isolation conflicts with the primitive's demand for cross-process operations.This paper introduces the Enclave-Semaphore service (ESem) which shelters application semaphores and their operations against kernel-privileged attacks. ESem encapsulates all semaphores in the platform …

Generalization Analysis Of Deep Nonlinear Matrix Completion, Antoine Ledent, Rodrigo Alves

Research Collection School Of Computing and Information Systems

We provide generalization bounds for matrix completion with Schatten $p$ quasi-norm constraints, which is equivalent to deep matrix factorization with Frobenius constraints. In the uniform sampling regime, the sample complexity scales like $\widetilde{O}\left( rn\right)$ where $n$ is the size of the matrix and $r$ is a constraint of the same order as the ground truth rank in the isotropic case. In the distribution-free setting, the bounds scale as $\widetilde{O}\left(r^{1-\frac{p}{2}}n^{1+\frac{p}{2}}\right)$, which reduces to the familiar $\sqrt{r}n^{\frac{3}{2}}$ for $p=1$. Furthermore, we provide an analogue of the weighted trace norm for this setting which brings the sample complexity down to $\widetilde{O}(nr)$ in all …

Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem, Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun

Research Collection School Of Computing and Information Systems

Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, where the messages are propagated by following the different topologies of the views and aggregated via graph attention. Then, we propose a novel operator based …

Adaptive Stabilization Based On Machine Learning For Column Generation, Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Eberhard Andrew, Guangquan Zhang

Research Collection School Of Computing and Information Systems

Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem. A natural phenomenon in CG is the heavy oscillation of the dual values during iterations, which can lead to a substantial slowdown in the convergence rate. Stabilization techniques are devised to accelerate the convergence of dual values by using information beyond the state of the current subproblem. …

Mvmoe: Multi-Task Vehicle Routing Solver With Mixture-Of-Experts, Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu

Research Collection School Of Computing and Information Systems

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants simultaneously. Specifically, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in computation. We further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational complexity. Experimentally, our method significantly promotes …