Physical Sciences and Mathematics Commons^™

Pam3s: Progressive Two-Stage Auction-Based Multi-Platform Multi-User Mutual Selection Scheme In Mcs, Bin Luo, Xinghua Li, Yinbin Miao, Man Zhang, Ximeng Liu, Yanbing Ren, Xizhao Luo, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Mobile crowdsensing (MCS) has been applied in various fields to realize data sharing, where multiple platforms and multiple Mobile Users () have appeared recently. However, aiming at mutual selection, the existing works ignore making ' utilities with the limited resources and platforms' utilities while achieving the desired sensing data quality maximum as far as possible. Thus, they cannot motivate both and platforms to participate. To address this problem, standing on both sides of and platforms with conflicting interests, we propose a Progressive two-stage Auction-based Multi-platform Multi-user Mutual Selection scheme (). Specifically, in, we treat mutual selection as a two-stage auction …

Men Are From Mars And Women Are From Venus: Dyadic Collaboration In The Metaverse, Shu Schiller, Fiona Fui-Hoon Nah, Andy Luse, Keng Siau

Research Collection School Of Computing and Information Systems

Purpose: The gender composition of teams remains an important yet complex element in unlocking the success of collaboration and performance in the metaverse. In this study, the authors examined the collaborations of same- and mixed-gender dyads to investigate how gender composition influences perceptions of the dyadic collaboration process and outcomes at both the individual and team levels in the metaverse. Design/methodology/approach: Drawing on expectation states theory and social role theory, the authors hypothesized differences between dyads of different gender compositions. A blocked design was utilized where 432 subjects were randomly assigned to teams of different gender compositions: 101 male dyads, …

Amperometric Bio-Sensing Of Lactate And Oxygen Concurrently With Local Field Potentials During Status Epilepticus, Eliana Fernandes, Ana Ledo, Greg A. Gerhardt, Rui M. Barbosa

Neurology Faculty Publications

Epilepsy is a prevalent neurological disorder with a complex pathogenesis and unpredictable nature, presenting limited treatment options in >30 % of affected individuals. Neurometabolic abnormalities have been observed in epilepsy patients, suggesting a disruption in the coupling between neural activity and energy metabolism in the brain. In this study, we employed amperometric biosensors based on a modified carbon fiber microelectrode platform to directly and continuously measure lactate and oxygen dynamics in the brain extracellular space. These biosensors demonstrated high sensitivity, selectivity, and rapid response time, enabling in vivo measurements with high temporal and spatial resolution. In vivo recordings in the …

Characterizing Linearizable Qaps By The Level-1 Reformulation-Linearization Technique, Lucas Waddell, Warren Adams

Faculty Journal Articles

The quadratic assignment problem (QAP) is an extremely challenging NP-hard combinatorial optimization program. Due to its difficulty, a research emphasis has been to identify special cases that are polynomially solvable. Included within this emphasis are instances which are linearizable; that is, which can be rewritten as a linear assignment problem having the property that the objective function value is preserved at all feasible solutions. Various known sufficient conditions for identifying linearizable instances have been explained in terms of the continuous relaxation of a weakened version of the level-1 reformulation-linearization-technique (RLT) form that does not enforce nonnegativity on a subset …

X-Ray Detection Of The Most Extreme Star-Forming Galaxies At The Cosmic Noon Via Strong Lensing, Q. Daniel Wang, Carlos Garcia Diaz, Patrick S. Kamieneski, Kevin C. Harrington, Min S. Yun, Nicholas Foo, Brenda L. Frye, Eric F. Jimenez-Andrade, Daizhong Liu, James D. Lowenthal, Belén Alcalde Pampliega, Massimo Pascale, Amit Vishwas, Mark A. Gurwell

Astronomy: Faculty Publications

Hyperluminous infrared galaxies (HyLIRGs)ãre the most extreme star-forming systems observed in the early Universe,ãnd their properties still elude comprehensive understanding. We have undertakenã large XMM -Newton observing programme to probe the totalãccreting black hole population in three HyLIRGsãt z = 2.12, 3.25,ãnd 3.55, gravitationally lensed by foreground galaxies. Selected from the Planck All-Sky Survey to Analyse Gravitationally lensed Extreme Starbursts (PASSAGES), these HyLIRGs haveãpparent infrared luminosities > 10 14 L⊙. Our observãtions re vealed X-ray emission in each of them. PJ1336 + 49ãppears to be dominated by high-mass X-ray binaries (HMXBs). Remarkably, the luminosity of this non-AGN X-ray emission exceeds byã …

Unsupervised Training Sequence Design: Efficient And Generalizable Agent Training, Wenjun Li, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

To train generalizable Reinforcement Learning (RL) agents, researchers recently proposed the Unsupervised Environment Design (UED) framework, in which a teacher agent creates a very large number of training environments and a student agent trains on the experiences in these environments to be robust against unseen testing scenarios. For example, to train a student to master the “stepping over stumps” task, the teacher will create numerous training environments with varying stump heights and shapes. In this paper, we argue that UED neglects training efficiency and its need for very large number of environments (henceforth referred to as infinite horizon training) makes …

Reverse Multi-Choice Dialogue Commonsense Inference With Graph-Of-Thought, Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng

Research Collection School Of Computing and Information Systems

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons …

Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which …

Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Mai Anh Tien, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or underestimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based cost constraint …

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 …

Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li

Research Collection School Of Computing and Information Systems

The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance …

Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam

Research Collection School Of Computing and Information Systems

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were …

Leveraging Llms And Generative Models For Interactive Known-Item Video Search, Zhixin Ma, Jiaxin Wu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

While embedding techniques such as CLIP have considerably boosted search performance, user strategies in interactive video search still largely operate on a trial-and-error basis. Users are often required to manually adjust their queries and carefully inspect the search results, which greatly rely on the users’ capability and proficiency. Recent advancements in large language models (LLMs) and generative models offer promising avenues for enhancing interactivity in video retrieval and reducing the personal bias in query interpretation, particularly in the known-item search. Specifically, LLMs can expand and diversify the semantics of the queries while avoiding grammar mistakes or the language barrier. In …

Mitigating Fine-Grained Hallucination By Fine-Tuning Large Vision-Language Models With Caption Rewrites, Lei Wang, Jiabang He, Shenshen Li, Ning Liu, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have shown remarkable performance in natural language processing (NLP) tasks. To comprehend and execute diverse human instructions over image data, instruction-tuned large vision-language models (LVLMs) have been introduced. However, LVLMs may suffer from different types of object hallucinations. Nevertheless, LVLMs are evaluated for coarse-grained object hallucinations only (i.e., generated objects non-existent in the input image). The fine-grained object attributes and behaviors non-existent in the image may still be generated but not measured by the current evaluation methods. In this paper, we thus focus on reducing fine-grained hallucinations of LVLMs. We propose ReCaption, a framework that consists …

Detecting Outdated Code Element References In Software Repository Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Outdated documentation is a pervasive problem in software development, preventing effective use of software, and misleading users and developers alike. We posit that one possible reason why documentation becomes out of sync so easily is that developers are unaware of when their source code modifications render the documentation obsolete. Ensuring that the documentation is always in sync with the source code takes considerable effort, especially for large codebases. To address this situation, we propose an approach that can automatically detect code element references that survive in the documentation after all source code instances have been deleted. In this work, we …

Mutation Analysis For Evaluating Code Translation, Giovani Guizzo, Jie M. Zhang, Federica Sarro, Christoph Treude, Mark Harman

Research Collection School Of Computing and Information Systems

Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA (Mutation-based Code Translation Analysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS (Mutation-based Translation Score), a measure to compute the level of trustworthiness of a translator. If a mutant of …

Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual …

Examining The Health Risks Of Particulate Matter 2.5 In New York City: How It Affects Marginalized Groups And The Steps Needed To Reduce Air Pollution, Freddy Castro

Dissertations, Theses, and Capstone Projects

The following examines the impact of particulate matter 2.5 (PM2.5) on public health, focusing on its sources and effects on vulnerable populations in New York City. PM2.5 is a particle that is 2.5 micrometers or less in diameter and, because of its size, can enter the bloodstream affecting the respiratory and cardiovascular systems and further complicating the health of the immunocompromised. Recent studies have shown that PM2.5 can come from various sources, including transportation and industrial emissions, as well as indoor sources like cigarettes and gas-operated stoves. Despite reduced levels of PM2.5 due to recent policy changes and initiatives taken …

Trends In Catch Rates Of Sawfish On The Australian North West Shelf, C. Wakefield, S. J. Newman, M. Braccini, A. Harry

Fisheries Research Articles

Northwestern Australia is thought to have some of the world’s last remaining viable sawfish populations, although little quantitative data exists on their status or trends. This study examined 17 years of logbook bycatch records (n = 815) for green sawfish Pristis zijsron and narrow sawfish Anoxypristis cuspidata from a trawl fishery operating on the Australian North West Shelf. Incidental sawfish captures by the fishery are rare, occurring approximately once every 75 trawls (~199 trawl hours). To standardize catch rates and account for excess zeros in the data, we employed generalized additive models for location, scale, and shape (GAMLSSs) using a …

Cognitive Load Scale In Learning Formal Definition Of Limit: A Rasch Model Approach, Rina Oktaviyanthi, Ria Noviana Agus, Mark Lester B. Garcia, Kornkanok Lertdechapat

Mathematics Faculty Publications

Constructing proofs for the limit using the formal definition induces a high cognitive load. Common assessment tools, like cognitive load scales, lack specificity for the concept of limits. This research aims to validate an instrument tailored to assess cognitive load in students focused on the formal definition of limits, addressing the need for diverse strategies in education. The research employs a quantitative survey design with a Rasch model approach, utilizing a data collection instrument in the form of a questionnaire. Subsequently, the data are analyzed by focusing on three aspects: (1) item fit to the Rasch model, (2) unidimensionality, and …

Tight Bounds For The N2-Chromatic Number Of Graphs, Arnold Eniego, Ian June L. Garces, Jose Rosario

Mathematics Faculty Publications

Let G be a connected graph. A vertex coloring of G is an N₂-vertex coloring if, for every vertex v, the number of different colors assigned to the vertices adjacent to v is at most two. The N₂-chromatic number of G is the maximum number of colors that can be used in an N₂vertex coloring of G. In this paper, we establish tight bounds for the N₂-chromatic number of a graph in terms of its maximum degree and its diameter, and characterize those graphs that attain these bounds.

Parameterized Algorithm For The Poset Cover Problem, Ivy D. Ordanel, Proceso L. Fernandez, Richelle Ann B. Juayong, Jhoirene B. Clemente, Henry N. Adorna

Department of Information Systems & Computer Science Faculty Publications

It is already known that the 1-Poset and 2-Poset Cover Problems are in P. In this paper, we extended the previous results and devised an algorithm for the k-Poset Cover Problem, for any k number of posets that cover the input. The algorithm runs in O(m2k n2), where m and n are the input size. With this running time, we can say that the problem belongs to XP (slicewise polynomial). The algorithm runs efficiently for small fixed k but runs exponentially for large k. While the algorithm running time has yet not to be efficient for large k, we have …

Forests Are Chill: The Interplay Between Thermal Comfort And Mental Wellbeing, Loïc Gillerot, Kevin Rozario, Pieter De Frenne, Rachel Oh, Quentin Ponette, Aletta Bonn, Winston T. L. Chow, Douglas Godbold, Matthias Steinparzaer, Daniela Haluza, Dries Landuyt, Bart Muys, Kris Verheyen

Research Collection College of Integrative Studies

As global warming and urbanisation intensify unabated, a growing share of the human population is exposed to dangerous heat levels. Trees and forests can effectively mitigate such heat alongside numerous health co-benefits like improved mental wellbeing. Yet, which forest types are objectively and subjectively coolest to humans, and how thermal and mental wellbeing interact, remain understudied. We surveyed 223 participants in peri-urban forests with varying biodiversity levels in Austria, Belgium and Germany. Using microclimate sensors, questionnaires and saliva cortisol measures, we monitored intra-individual changes in thermal and mental states from non-forest baseline to forest conditions. Forests reduced daytime modified Physiologically …

Examining Sustainable Overseas Investment Information-Sharing Model For Automobile Enterprises: A Multi-Modal Weight Network Approach, Yuan Cheng, Xiaofang Chen, Changbo Lin, Sheqing Ma, Jie Feng

Research Collection School Of Accountancy

In an era of globalization, automotive companies are increasingly looking to make overseas investments to expand their production capacity and explore foreign markets. However, the outcomes of such investments are often influenced by a myriad of factors, including policy changes, social dynamics, and market conditions. To address the need for a comprehensive overseas investment information-sharing model, this research proposes an innovative approach based on a multi-modal weight network. This model aims to provide users with a global perspective on overseas investment opportunities, encompassing policy insights, and market dynamics. It integrates data from various sources, offering multi-dimensional information on investment regions, …

Exciton Dynamics, Interaction, And Transport In Monolayers Of Transition Metal Dichalcogenides, Saroj Chand

Dissertations, Theses, and Capstone Projects

Monolayers Transition metal dichalcogenides (TMDs) have attracted much attention in recent years due to their promising optical and electronic properties for applications in optoelectronic devices. The rich multivalley band structure and sizable spin-orbit coupling in monolayer TMDs result in several optically bright and dark excitonic states with different spin and valley configurations. In the proposed works, we have developed experimental techniques and theoretical models to study the dynamics, interactions, and transport of both dark and bright excitons.

In W-based monolayers of TMDs, the momentum dark exciton cannot typically recombine optically, but they represent the lowest excitonic state of the system …

Trees In Urban Environments: How Soil Quality Impacts Tree Performance, Saidan Qi

Dissertations, Theses, and Capstone Projects

Cities around the world are increasingly investing in reforestation and afforestation efforts to mitigate impacts from climate change and population growth. However, urban soil conditions can be unfavorable for tree growth. Street trees are widely known to suffer from poor soil quality, but there has been no comprehensive review of this topic so far. Clean soils can be transported from nonurban areas to support cities’ green projects, but this approach is not sustainable. Artificial (constructed) soils can be created from various materials and have been proposed as an alternative medium for urban tree growth, but no research has been done …

Synthesis And Characterization Of Donor-Acceptor Fluorescent Conjugated Polymers By Acyclic Diene Metathesis (Admet) And Suzuki Polycondensation (Spc), Hamid Ardolic Jr.

Dissertations, Theses, and Capstone Projects

Conjugated polymers are useful in a wide variety of applications including production of Light Emitting Diodes (LEDs), Field Effect Transistors (FETs) and organic photovoltaics. Our work in the lab focuses on the development of materials for use in Polymer Solar Cells (PSCs) using various electron donating and electron accepting units to individually tune the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) respectively and produce a small but controlled band gap. This is accomplished through the synthesis of several precursors and their subsequent polymerization using Acyclic Diene Metathesis (ADMET) and Suzuki Polycondensation (SPC). Moderately strong electron donors, …

Clustering Of Patients With Heart Disease, Mukadder Cinar

Dissertations, Theses, and Capstone Projects

Heart disease, a leading cause of mortality worldwide, presents complex challenges in public health due to its varied manifestations. Accurate diagnosis and patient stratification are essential for effective management and improved outcomes. In response, this study employed machine learning techniques to analyze heart disease data obtained from UCI Machine Learning Repository, aiming to enhance patient care through advanced data analysis.

The study began with the application of K-Nearest Neighbors (KNN) classification, which categorized patients into 'Disease' and 'No Disease' groups. This preliminary step provided initial insights into the structure of the dataset. Subsequently, K-means clustering was applied in two rounds, …

What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth, William Mahoney Luckman

Dissertations, Theses, and Capstone Projects

The word “billion” is a mathematical abstraction related to “big,” but it is difficult to understand the vast difference in value between one million and one billion; even harder to understand the vast difference in purchasing power between one billion dollars, and the average U.S. yearly income. Perhaps most difficult to conceive of is what that purchasing power and huge mass of capital translates to in terms of power. This project blends design, text, facts, and figures into an interactive narrative website that helps the user better understand their position in relation to extreme wealth: https://whatdoesonebilliondollarslooklike.website/

The site incorporates …

Towards Sociobiogeochemistry: Critical Perspectives On Anthropogenic Alterations To Soil Nitrogen Chemistry Via U.S. Urban And Suburban Development, Christopher D. Ryan

Dissertations, Theses, and Capstone Projects

The ecological impacts of changes to land use are relevant to concerns about climate change, eutrophication of waterbodies, and reductions in biodiversity. As a foundational component of ecosystem functioning, changes to soil biogeochemistry have significant effects on overall ecosystem health. With cities continuing to grow and develop in extent, the impacts of urbanization and suburbanization on soils are of particular concern. Despite a wide range of natural climatic and geologic conditions, several factors have driven similar patterns of land transformation and management across the United States. In particular, federal initiatives including the Home Owners Loan Corporation, the Federal Housing Administration, …