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A Summoned Life: Vocation In The Analects, Teng-Kuan Ng

Research Collection College of Integrative Studies

This article explores the concept of vocation in the Analects. While most treatmentsof vocation focus on the Christian tradition, and while Confucian thought is oftenviewed as categorically nontheistic, Robert Merrihew Adams’s Finite and InfiniteGoods (1999) presents an illuminating comparative framework for tracing outKongzi’s religious ethical vision of how persons think, act, and feel when graspedby a sense of divine calling. I argue that in the Analects a vocation is compositelyconceived as: (1) a special divine imperative to an individual or a community (2) tolove particular goods within one’s actual circumstances (3) with nonconsequentialist,noncompetitive, and potentially counter-cultural commitment (4) such that …

Hospitality At The End Of The World: An Ideological Rhetorical Criticism Of Tactical Technical Communication In The Prepper Journal, Ryan Cheek

English and Technical Communication Faculty Research & Creative Works

No abstract provided.

Adam Smith: Social Imaginary And Religion, Jan Van Vliet

Pro Rege

No abstract provided.

By Wisdom, God Laid An Intricate Foundation: A Christian Approach To Ecological Restoration, Michelle E. Howell

Pro Rege

No abstract provided.

In The Beginning: Crucial Lessons For Our World From The First Three Chapters Of Genesis (Book Review), Eduardo Echeverria

Pro Rege

Reviewed Title: In the Beginning: Crucial Lessons for Our World from the First Three Chapters of Genesis. Catholic Answers Press, May 2, 2024. 272 pp. BOD392K526

Why Should We Give Thanks?, Calvin Vandermeyden

Pro Rege

No abstract provided.

A Reformed Psychology Of Human Relations In Business: Part Ii, John Van Til

Pro Rege

No abstract provided.

A Climatological Analysis Of Drought And Flood In California, Kristen Faith Coston

Masters Theses

This research is investigating the climate states that produce droughts and floods and seeks to explain how a sudden shift from persistent drought to drought-alleviating flood is possible. Objectives include investigating how drought and flood in California are influenced by temperature and climate patterns, whether there are any correlation between climate/ocean indices and the Standardized Precipitation Index (SPI), and whether Atmospheric Rivers (ARS) are influenced by certain atmospheric/oceanic states.

All data is collected for 41 years, between 1983 and 2024. The SPI dataset is obtained by coding to filter for the mean value of all pixels in Sacramento County. Monthly …

Enhancing Autonomous Vehicle Resilience Through Engineering Requirements And Snow-Adaptive Lane Detection, Alexandra Marie Masterson

Masters Theses

This thesis investigates two distinct but interrelated challenges in the development of resilient autonomous vehicle (AV) systems: the formalization of engineering requirements for AV perception subsystems and the enhancement of visual lane detection under snow-covered road conditions. In the first study, field experiments were conducted using a campus-deployed autonomous research vehicle to evaluate the impacts of perception related failures including GPS outages, HD map inconsistencies, and weather interference—on vehicle operation. These findings were used to develop a set of qualitative engineering requirements that promote AV resilience through proactive design. In the second study, a custom snow-focused lane detection dataset was …

Pfas In Naturally Occurring Surface Water Foams, Donovan S. Vitale

Masters Theses

Surface Water Foam (SWF) and underlying Surface Water (SW) were collected from six distinct sites across Michigan using a novel sampling device. PFAS were detected in all SWF samples with total PFAS concentrations (Σ41 compounds, n=14) varying widely from 2,693 to 402,172 ng/L .SWF samples were enriched in high and intermediate molecular volume PFAS relative to SW samples (n=10) which were dominated by low molecular volume, short-chain PFAS with total PFAS concentrations ranging from 4.4 to 31 ng/L. Wet and dry SWFs were distinguished by appearance, liquid content, and PFAS composition. Dry SWFs had higher total PFAS concentrations than wet …

Lost And Found: The Forgotten Origins Of The “Cruel And Unusual Punishments” Prohibition, John D. Bessler

Articles

The U.S. Supreme Court and legal scholars have long traced the origins of the Eighth Amendment’s prohibition against “cruel and unusual punishments” to the English Declaration of Rights, codified as the English Bill of Rights (1689). The English Declaration of Rights recited that, in King James II’s reign, “illegal and cruel punishments” had been “inflicted,” with its tenth clause then declaring in hortatory fashion: “That excessive bail ought not to be required, nor excessive fines imposed, nor cruel and unusual punishments inflicted.” The prohibitions against excessive bail and excessive fines and the final phrase—“nor cruel and unusual punishments inflicted”—were later …

De-Identified And Unregulated: How Data Brokers Outpace State Privacy Laws, Hannah Moore

Vanderbilt Journal of Entertainment & Technology Law

State consumer privacy laws, though increasingly important in the absence of a comprehensive federal privacy framework, fail to effectively regulate the practices of data brokers who exploit de-identified data. Laws like the Tennessee Information Protection Act (TIPA) exempt de-identified data from key protections, leaving significant gaps in oversight.

While the Health Insurance Portability and Accountability Act (HIPAA) establishes standards for de-identification, advanced analytics and linkage techniques employed by data brokers render this data increasingly susceptible to re-identification. The Federal Trade Commission (FTC) has taken steps to address these risks, but its limited authority highlights the need for comprehensive solutions.

This …

Third-Party Funding Of Patent Litigation: Problems And Solutions, Korok Ray, Adam Olson

Utah Law Review

This Article documents the rise of third-party funding in patent litigation. We track funding sources for patent lawsuits in the United States from 2002 to 2021, which show an increase in cases and percentage funded by third parties. While we cannot say third-party funding increased patent litigation, we can observe the correlation. The use of the champerty doctrine by patent trolls distorts the marketplace and causes frivolous litigation.

The secondary patent market created a robust litigation market in which non-practicing entities (NPEs) can buy from innovators and sue defendants. Capital investments bet on litigation, hoping for a return without interest …

The Role Of Swarm Intelligence Systems In Shaping Urban Development Policies, Sudaff Mohammed, Wahda Shuker Al-Hinkawi, Nada Abdulmueen Hasan

Iraqi Journal of Architecture and Planning

Swarm intelligence is a nature-inspired complex system that draws from the behaviours of social creatures such as ants and birds. This system functions through simple behavioural rules enacted by autonomous, intelligent agents. Existing literature indicates that swarm intelligence possesses a wide range of principles and characteristics derived from the theories of Biomimicry, complex adaptive systems, and parametric and generative design. While the previous studies have intensively addressed the computational aspects of intelligence, a comprehensive conceptual framework is essential for analysing complex urban forms and structures. Therefore, this research develops and applies a conceptual model of swarm intelligence by examining several …

Advancing Fake News Detection With Graph Neural Network And Deep Learning, Haji Gul, Feras Al-Obeidat, Muhammad Wasim, Adnan Amin, Fernando Moreira

All Works

In the modern era of digital technology, the rapid distribution of news via social media platforms substantially contributes to the propagation of false information, presenting challenges in upholding the accuracy and reliability of information. This study presents an updated approach that utilizes graph neural networks (GNNs) alongside with advanced deep learning techniques to improve the identification of false information. In contrast to traditional approaches that primarily rely on analyzing text and assessing the credibility of sources, our methodology utilizes the structural information of news propagation networks. This allows for a detailed comprehension of the interconnections and patterns that are indicative …

Adapting Teaching And Learning With Existing Generative Ai By Higher Education Students: Comparative Study Of Zayed University And King Abdulaziz University, Dina Tbaishat, Ghada Amoudi, Maha Elfadel

All Works

This study examines the role of higher education students’ perceptions in adapting Generative AI (GenAI) tools for teaching and learning, with a particular focus on the factors that influence student satisfaction and engagement. A comparative approach is adopted, exploring student experiences at Zayed University (ZU) in the UAE and King Abdulaziz University (KAU) in Saudi Arabia. The principal variables of interest, including Expected Benefits (EB), University Support (US), Ethical Awareness (EA), and Technology Self-Efficacy (TSE), are examined, with particular attention to their direct and mediated influences through Behavioral Intention (BI) on student satisfaction (SS). Data were collected through surveys and …

A Novel Fractional Order Model For Analyzing Counterterrorism Operations And Mitigating Extremism, Mutaz Mohammad, Isa Abdullahi Baba, Evren Hincal, Fathalla A. Rihan

All Works

This study examines the profound impact of terrorism on individuals and society by developing a fractional-order mathematical model to analyze and enhance counterterrorism efforts. The model accounts for the persistent and complex nature of extremist behavior, particularly emphasizing the importance of preventing violent extremism before it escalates into terrorism. Real-world data on terrorist activities in Nigeria – specifically from the Boko Haram insurgency – was used to calibrate and validate the model, ensuring its relevance and accuracy. The model reveals that the basic reproduction number (R0) plays a decisive role in determining the long-term success of counterterrorism strategies. Numerical simulations …

Sequestrichnia In Complex Ophiomorpha Isp. From Late Pleistocene Grainstones In The Bahamas, With Modern Analogue Comparisons, H. Allen Curran, Koji Seike, Bosiljka Glumac

Geosciences: Faculty Publications

An important scientific contribution of ichnological research is to expand knowledge of animal behavior through the study of trace fossils. Ophiomorpha isp. is the dominant trace fossil preserved in Late Pleistocene shallow-marine grainstones throughout the Bahama Archipelago. These large, distinctive, and complex burrows commonly display elements reflecting specialized activities. Pouchlike structures of Ophiomorpha from two Exuma sites are interpreted as sequestrichnia—in this case, chambers constructed by callichirid shrimp specifically for storage of seagrasses and algae as food resources. Two morphologies are recognized: structures with radiating arms and blunt endings, and rectangular forms with pairs of arms separated by a short …

The Benefits And Pitfalls Of Public Private Partnerships: Case Study Of The Suez Canal Container Terminal, Egypt, Magda Elswesy

Theses and Dissertations

The study explores the public-private partnerships (PPPs) in constructing, managing, and operating container terminals in Egypt. It examines the benefits the public sector gains from such collaborations and the pitfalls that can be mitigated to foster more effective institutional cooperation. The study specifically focuses on enhancing Egypt's marine container terminals through these partnerships, emphasizing the potential use of growing technology to enable equipment automation. It also highlights concerns regarding the risk of mismanagement of Egyptian terminals, arising from insufficient expected volume to ensure appropriate profitability, especially with the opening of new terminals. The study adopts a qualitative methodology and presents …

Load Balancing In Mobile Networks Using Deep Reinforcement Learning And Traffic Prediction, Shorouk Raafat Mokhtar Abouamasha

Theses and Dissertations

Wireless communication networks are advancing at a rapid pace, driven by various challenges and ambitious goals. This rapid growth is driven by a range of applications, including technologies like the Internet of Things (IoT), as well as innovations in smart cities, autonomous vehicles, and more. Different applications demand specific performance criteria such as high data throughput, low latency, robust reliability, and efficient energy usage. In this thesis, we investigate two enhancements that can be adopted in wireless networks to tackle the challenges of resource optimization and network management. The motivation behind this is the fact that future networks will face …

Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …

On-Demand Scenario Generation For Testing Automated Driving Systems, Songyang Yan, Xiaodong Zhang, Kunkun Hao, Haojie Xin, Yonggang Luo, Jucheng Yang, Ming Fan, Chao Yang, Jun Sun, Zijiang Yang

Research Collection School Of Computing and Information Systems

The safety and reliability of Automated Driving Systems (ADS) are paramount, necessitating rigorous testing methodologies to uncover potential failures before deployment. Traditional testing approaches often prioritize either natural scenario sampling or safety-critical scenario generation, resulting in overly simplistic or unrealistic hazardous tests. In practice, the demand for natural scenarios (e.g., when evaluating the ADS's reliability in real-world conditions), critical scenarios (e.g., when evaluating safety in critical situations), or somewhere in between (e.g., when testing the ADS in regions with less civilized drivers) varies depending on the testing objectives. To address this issue, we propose the On-demand Scenario Generation (OSG) Framework, …

Cashift: Benchmarking Log-Based Cloud Attack Detection Under Normality Shift, Jiongchi Yu, Xiaofei Xie, Qiang Hu, Bowen Zhang, Ziming Zhao, Yun Lin, Lei Ma, Ruitao Feng, Frank Liau

Research Collection School Of Computing and Information Systems

With the rapid advancement of cloud-native computing, securing cloud environments has become an important task. Log-based Anomaly Detection (LAD) is the most representative technique used in different systems for attack detection and safety guarantee, where multiple LAD methods and relevant datasets have been proposed. However, even though some of these datasets are specifically prepared for cloud systems, they only cover limited cloud behaviors and lack information from a whole-system perspective. Another critical issue to consider is normality shift, which implies that the test distribution could differ from the training distribution and highly affect the performance of LAD. Unfortunately, existing works …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …

Unsupervised Recognition Of Unknown Objects For Open-World Object Detection, Ruohuan Fang, Guansong Pang, Wenjun Miao, Xiao Bai, Jin Zheng, Xin Ning

Research Collection School Of Computing and Information Systems

Open-world object detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models detect the unknowns that exhibit similar features to the known objects, but they suffer from a severe label bias problem, i.e., they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this article proposes a novel module, namely reconstruction error-based Weibull (REW) model, that …

Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

Research Collection School Of Computing and Information Systems

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …

Collaborative Tree Search For Enhancing Embodied Multi-Agent Collaboration, Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou

Research Collection School Of Computing and Information Systems

Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLMdriven reward functions to provide a more thoughtful and promising approach to …

Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable inten sity. The former enforces …

Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting …

Large Language Models For Logical Fallacy Detection, Nicole Anne Hui-Ying Teo, Donghao Huang, Erik Cambria, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Identifying logical fallacies is essential for maintaining log-ical reasoning and reducing false information in a variety of domains, such as the media, law, and education. We present an extensive study on the use of large language models (LLMs) for logical fallacy detection and provide a comparative overview of model performance across various fallacy classes. We evaluate the logical fallacy detection capabilities of multiple state-of-the-art models (LLaMA, Qwen, Gemma, Phi) utilizing accuracy, precision, recall, and F1-score as assessment measures. Accord-ing to our findings, our models do well on simple fallacies like “circular reasoning,” but they have trouble with more interpretive reasoning …