Digital Commons Network^™

Fast And Reliable Authentication Method For Indoor Constrained Drones, Fatima Ali Al Nuaimi

Theses

Over the last twenty years, the commercial sector of Unmanned Aerial Vehicles has been growing exponentially, owing to their rapid deployment, high mobility, and the number of applications in the industry field such as military, transportation, critical infrastructures, as well as in the academic field for research purposes. One of the main communication systems adopted by the UAVs relies on transmitting wireless signals.

In particular, UAVs are adopted for indoor use-case scenarios, such as warehouse inventory applications and indoor building inspections. They need to transmit control messages and sensitive data by leveraging an efficient, short-range, and secure communication channel to …

Higher Education Collaboration For Digital Transformation In Pandemic Panamá

Journal of Comparative & International Higher Education

Special Issue

Digital Turn In Higher Education: An Examination Of Enablers And Inhibitors In The Turkish Context, Oğuzhan Bozoğlu

Journal of Comparative & International Higher Education

No abstract provided.

Enhancing Sell-Type Home Reversion Products For Retirement Financing, Koon Shing Kwong, Jing Rong Goh, Ting Lin Collin Chua

Research Collection School Of Economics

Loan-type reverse mortgage plans and sell-type home reversion plans for retirement financing are two well-known equity release plans that entitle homeowners not only to release cash from their properties but also to allow them to age in place. Recently, a new hybrid equity release plan was proposed to incorporate the home reversion plan’s features with an option of staying in the property for a fixed period without being subject to survival. This additional option provides flexibility to homeowners to better meet their retirement financial and personal needs by reducing the financial uncertainty of home reversion products. In this article, we …

Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft

Research Collection School Of Computing and Information Systems

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …

Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has long been studied and deployed to several applications. Understanding a food image at the instance level, including recognition, counting and segmentation, is essential to quantifying nutrition and calorie consumption. Nevertheless, existing techniques are limited to either category-specific instance detection, which does not reflect precisely the instance size at the pixel level, or category-agnostic instance segmentation, which is insufficient for dish recognition. This paper presents a compact and fast multi-task network, namely FoodMask, for clustering-based food instance counting, segmentation and recognition. The network learns a semantic space simultaneously encoding food category distribution and instance height at pixel basis. …

Machine Learning For Refining Knowledge Graphs: A Survey, Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in knowledge graphs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this paper presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into …

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 …

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 …

Cfd Analysis Of Multi-Stage Phase Change Material For Latent Thermal Energy Storage In Solar Power Plants, Ahmad Wahbi Omari

Theses

The integration of latent thermal energy storage into concentrated solar power plants represents a promising solution for efficiently harnessing excessive solar energy during daylight hours and subsequently utilizing it during the nighttime when solar energy generation is limited or non-existent, all while catering to consistent energy demands. The core objective of enhancing latent thermal energy storage revolves around elevating its thermal capacity and minimizing the melting time, thus making it a more effective energy storage solution. This is achieved by employing three distinct organic phase change materials: Octadecanoic (PCM1), RT58 (PCM2) and Dodecanoic (PCM3), each characterized by variations in their …

Investigating The Mass Mortality Induced Factors Of Pearl Oyster (Pinctada Radiata) At Abu Dhabi Pearls Farm, Uae, Hamad Ahmed Almansoori

Theses

The aquaculture of shellfish plays a crucial role in enhancing maritime environments by mitigating algal blooms and improving water quality through the filtration of organic matter, nutrients, and bacteria. This study investigated the environmental factors influencing oyster (Pinctada radiata) mortalities through a comprehensive analysis of water quality in Al Mirfa and Al Mughirah farming sites of Abu Dhabi (UAE), from February 2021 to February 2022. To reach such understanding of the main factors led to mass mortalities of cultivated oysters, different monthly sampling and both chemical and biological analyses of the farm water, sediments as well as for both living …

Iowa Waste Reduction Center Newsletter, February 2024, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Conference Season in Full Swing
--- ISOSWO Student Conference Scholarship Deadline
--- Diane Albertson Memorial Scholarship
--- Keeping Tabs on PFAS
--- Coating Workshop Coming to Des Moines
--- Important Reminders

Remaining Useful Life Prediction Using Graph Convolutional Attention Networks With Temporal Convolution-Aware Nested Residual Connections, Yupeng Wei, Dazhong Wu, Janis Terpenny

Faculty Research, Scholarly, and Creative Activity

Degradation of engineered systems can result in poor performance and failure. Graph Convolutional Networks (GCNs) have been used to predict the remaining useful life (RUL) of engineered systems by analyzing condition monitoring data. Conventional GCNs typically stack multiple spectral graph convolutional layers, where each layer aggregates condition monitoring data and then projects the aggregated data into another feature space. However, conventional GCNs suffer from two issues. Firstly, repeated aggregation operations affect the temporal correlation of condition monitoring data. Secondly, repeated aggregation and projection operations may generate less significant features, resulting in poor prediction performance. To address these issues, we introduce …

Southern Maine, New Hampshire, And Northern Massachusetts Continental Shelf Geophysical Database: 2022 Field Campaign – Grain Size Data, Station Summaries, And Seafloor Photographs, Larry G. Ward, Rachel C. Morrison, Michael Bogonko

Data Catalog

Presented in this data report are the geophysical data collected during a major field campaign in 2022, with the purpose of obtaining ground truth for the expansion and improvement of high-resolution surficial geology maps of the western Gulf of Maine (WGOM) and for the description of reference sites developed for future evaluations of acoustic systems (Ward et al., 2021a; 2021b). Data from the UNH Ocean Engineering 972 Hydrographic Field Course classes in 2021 and 2022 are also included. This expansion of the geophysical database is being used to verify seafloor classifications in previously mapped areas that lack sufficient data, and …

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, …

Harnessing Holistic Discourse Features And Triadic Interaction For Sentiment Quadruple Extraction In Dialogues, Bobo Li, Hao Fei, Lizi Liao, Et Al

Research Collection School Of Computing and Information Systems

Dialogue Aspect-based Sentiment Quadruple (DiaASQ) is a newly-emergent task aiming to extract the sentiment quadruple (i.e., targets, aspects, opinions, and sentiments) from conversations. While showing promising performance, the prior DiaASQ approach unfortunately falls prey to the key crux of DiaASQ, including insufficient modeling of discourse features, and lacking quadruple extraction, which hinders furthertask improvement. To this end, we introduce a novel framework that not only capitalizes on comprehensive discourse feature modeling, but also captures the intrinsic interaction for optimal quadruple extraction. On the one hand, drawing upon multiple discourse features, our approach constructs a token-level heterogeneous graph and enhances token …

Vadclip: Adapting Vision-Language Models For Weakly Supervised Video Anomaly Detection, Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful visual representations with rich semantics. An open and worthwhile problem is efficiently adapting such a strong model to the video domain and designing a robust video anomaly detector. In this work, we propose VadCLIP, a new paradigm for weakly supervised video anomaly detection (WSVAD) by leveraging the frozen CLIP model directly without any pre-training and fine-tuning process. Unlike current works that directly feed extracted features into the weakly supervised classifier for frame-level binary classification, VadCLIP makes …

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 …

Simple Image-Level Classification Improves Open-Vocabulary Object Detection, Ruohuan Fang, Guansong Pang, Xiao Bai

Research Collection School Of Computing and Information Systems

Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, eg., region-level knowledge distillation, regional prompt learning, or region-text pre-training, to expand the detection vocabulary. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale image-level text descriptions. This limits their capability in detecting hard objects of …

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 …

Hackles: Simulating And Visually Representing The Anxiety Of Walking Alone, Sydney Pratte, Anthony Tang, Shannon Hoover, Lora Oehlberg

Research Collection School Of Computing and Information Systems

In this work, we compare the designs of two fashion-tech garments that communicate the anxiety felt when walking alone. While the two garments share a common vision, they are designed to be worn in two radically different settings and to communicate to different audiences: one directly communicates an empathetic experience to its wearer; the other a model wears at a runway show and must share its story to a general audience. We used Research Through Design (RtD) methods to design both fashion-tech garments. Then, we recorded and analyzed the design process for both garments via an annotated portfolio to compare …

Validating Indigenous Farmers’ Practice In The Management Of The Fall Armyworm Spodoptera Frugiperda (J. E. Smith) In Maize Cropping Systems In Africa, Saliou Niassy, Evanson Rigan Omuse, John Emanuel Khang’Ati, Ines Bächinger, David Mfuti Kupesa, Xavier Cheseto, Benjamin W. Mbatha, Robert S. Copeland, Samira Abuelgasim Mohamed, Mphatso Gama, Joyce Mulila Mitti, Yeneneh Belayneh, Nicolas Delabays, François Lefort, Sunday Ekesi, Sevgan Subramanian

All Peer-Reviewed Publications

Before the invasion of the fall armyworm (FAW) Spodoptera frugiperda into Africa, smallholder farmers had been using indigenous practices such as applying fish soup to plants to manage stemborer pests. Although farmers have since begun adapting this practice against FAW, no attempt has been made to scientifically evaluate this practice. Therefore, we assessed the efficacy of applying fish soup to maize plants that were artificially infested with FAW under semi-field conditions. Our results showed that foliar damage is inversely correlated with the concentration of a fish soup and sugar solution, with the highest (100%) concentration resulting in the lowest foliar …

A Road Map For Developing Novel Decision Support System (Dss) For Disseminating Integrated Pest Management (Ipm) Technologies, Franck B.N. Tonle, Saliou Niassy, Milliam M.Z. Ndadji, Maurice T. Tchendji, Armand Nzeukou, Bester T. Mudereri, Kennedy Senagi, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Integrated Pest Management (IPM) technologies offer effective solutions to reduce the negative effects of crop pests while considering human and environmental health. However, disseminating these technologies faces several barriers, with one of the most significant being the lack of farmer awareness regarding their availability, deployment, and uptake. Digital tools are perceived as a new form of leverage for overcoming these barriers. This study analyzes current IPM digital tools and their potential to boost farmers’ awareness of the deployment and adoption of IPM technologies. From a software engineering perspective, this study aims to emphasize the critical functionalities and limitations of various …

Accessing Reactive Metal Hydrides Through Designed Heterometallic Bridges, Fernando Gonzalez, Edgardo De Leon, Manar M. Shoshani

School of Integrative Biological & Chemical Sciences Faculty Publications

A methodology to access reactive hydride moieties is highly desirable, yet limited. Multimetallic hydride fragments are notable for their heightened reactivity and catalysis, but deliberate access to these species is lacking. In this highlight, we discuss recent developments by our group in the design of a new heterometallic complex that invokes an architecture designed to provide modular access to reactive hydride moieties by leveraging metal hydrides in combination with pendent donors to a model heterotrimetallic Ni–(Al–H)2 complex. An amplification of insertion-based reactivity has been examined in the hydrofunctionalization of quinolines, and our complex substantially outperformed the parent aluminum hydride LAlH …

Struvite-Phosphorus Effects On Greenhouse Gas Emissions And Plant And Soil Response In A Furrow-Irrigated Rice Production System In Eastern Arkansas, Diego Della Lunga, Kristofor R. Brye, Trenton L. Roberts, Jonathan Brye, Michelle Evans-White, Christopher G. Henry, Daniel J. Lessner, Chandler Arel

Crop, Soil and Environmental Sciences Faculty Publications and Presentations

Phosphorus (P) fertilizers with low water solubility, like struvite (MgNH₄PO₄·6H₂O), have been identified to possibly reduce nutrient losses in furrow-irrigated cropping systems. However, there is a lack of research on the impacts of P and nitrogen (N) fertilization on greenhouse gas [GHG; i.e., methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂)] production in furrow-irrigated rice (Oryza sativa). The objective of this study was to evaluate the effects of electrochemically precipitated struvite (ECST), chemically precipitated struvite (CPST), triple superphosphate (TSP), diammonium phosphate (DAP), environmentally smart nitrogen …

Vaccine Immunogenicity Versus Gastrointestinal Microbiome Status: Implications For Poultry Production, Chrysta N. Beck, Jiangchao Zhao, Gisela F. Erf

Poultry Science Faculty Publications and Presentations

At the turn of the 21st century, the importance of maintaining a balanced microbiome was brought to the forefront of the microbiology, immunology, and physiology research fields. Exploring the complex interactions between vaccine administration, mucosal microbiome, oral tolerance, and enteric inflammation in health and disease is challenging since environmental factors (such as diet and sanitation) have major influences on gut microbiota composition. High enteric pathogen load has been shown to contribute to dampened cell-mediated and humoral immune responses to vaccines in human case studies, either through elevated enteric inflammation or increased tolerance to environmental microbes. Although antibiotic and probiotic interventions …

Legislative Update, Katherine Albers, Lauren Bean, Dillon Dewey, Hannah Jackson, Victoria Mantel

Journal of Dispute Resolution

The Legislative Update is compiled and written annually by the Journal of Dispute Resolution’s Associate Members under the direction of the Associate Editor in Chief. It is designed to provide readers with a listing of pertinent legislation affecting the field of alternative dispute resolution (“ADR”) and a more detailed look at certain bills because of their importance or novelty within the field.

Self-Optimizing Feature Generation Via Categorical Hashing Representation And Hierarchical Reinforcement Crossing, Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, Yanjie Fu

Computer Science Faculty Publications and Presentations

Feature generation aims to generate new and meaningful features to create a discriminative representation space. A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities. We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, …

Integral Representations Over Finite Limits For Quantum Amplitudes, Jack C. Straton

Physics Faculty Publications and Presentations

We extend previous research to derive three additional M-1-dimensional integral representations over the interval [0,1]" The prior version covered the interval [0,∞]" role="presentation position: relative;">[0,∞][0,∞]. This extension applies to products of M Slater orbitals, since they (and wave functions derived from them) appear in quantum transition amplitudes. It enables the magnitudes of coordinate vector differences (square roots of polynomials) |x1−x2|=x12−2x1x2cosθ+_x22" to be shifted from disjoint products of functions into a single quadratic form, allowing for the completion of its square. The M-1-dimensional integral representations of M Slater orbitals that both this extension and the prior version introduce …

The Fourier–Legendre Series Of Bessel Functions Of The First Kind And The Summed Series Involving 1F2 Hypergeometric Functions That Arise From Them, Jack C. Straton

Physics Faculty Publications and Presentations

The Bessel function of the first kind JN^(kx) is expanded in a Fourier–Legendre series, as is the modified Bessel function of the first kind IN^(kx). The purpose of these expansions in Legendre polynomials was not an attempt to rival established numerical methods for calculating Bessel functions but to provide a form for JN^(kx) useful for analytical work in the area of strong laser fields, where analytical integration over scattering angles is essential. Despite their primary purpose, one can easily truncate the series at 21 terms to provide 33-digit accuracy that matches the IEEE extended precision in …