Digital Commons Network^™

Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang

Computer Science Faculty Publications

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate …

Retrogressive Document Manipulation Of Us Federal Environmental Websites, Lesley Frew, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

Changes made to webpages can affect their retrievability. Often this is done with the intention of increasing the page's search engine ranking to improve overall access to information on the page. The Environmental Data and Governance Initiative (EDGI) created a dataset that describes changes on US federal environmental webpages between 2016 and 2020. EDGI noted that many environmental terms were deleted from the pages, but without user data, claims that page retrievability and public information access were lowered are only anecdotal. The Open Resource for Click Analysis in Search (ORCAS) dataset was created during the same time frame, from 2017 …

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

Computer Science Faculty Publications

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

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

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

Computer Science Faculty Publications

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

Autonomous Strike Uavs For Counterterrorism Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

UAVs are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and potential solutions for successful implementation of an autonomous UAV mission.

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

Computer Science Faculty Publications

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

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

Computer Science Faculty Publications

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

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

Computer Science Faculty Publications

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

Learning Optimal Inter-Class Margin Adaptively For Few-Shot Class-Incremental Learning Via Neural Collapse-Based Meta-Learning, Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot …

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

Computer Science Faculty Publications

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

Osfs-Vague: Online Streaming Feature Selection Algorithm Based On A Vague Set, Jie Yang, Zhijun Wang, Guoyin Wang, Yanmin Liu, Yi He, Di Wu

Computer Science Faculty Publications

Online streaming feature selection (OSFS), as an online learning manner to handle streaming features, is critical in addressing high-dimensional data. In real big data-related applications, the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments. However, existing OSFS methods rely on presented and fixed hyperparameters, which undoubtedly lead to poor selection performance when encountering dynamic features. To make up for the existing shortcomings, the authors propose a novel OSFS algorithm based on vague set, named OSFS-Vague. Its main idea is to combine uncertainty and three-way decision theories to improve feature selection from the …

A-Disetrac Advanced Analytic Dashboard For Distributed Eye Tracking, Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Yasith Jayawardena, Mohan Sunkara, Andrew T. Duchowski, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K …

Quantification Of Landside Congestion In Ports: An Analysis Based On Gps Data, Kumushini Thennakoon, Namal Bandaranayake, Senevi Kiridena, Asela K. Kulatunga

Computer Science Faculty Publications

Hinterland transport is a critical segment in maritime cross-border logistics, which links the end-users of global supply chains to the maritime segment. Truck-based hinterland transport is known to cause congestion in and around ports. This study aimed to quantify the congestion caused by trucks at the Port of Colombo, which has not been a subject of a systematic study. To this end, the study makes use of GPS data. In addition to revealing heavy congestion within the port, the study also reveals significant variations in congestion during different times of the day with the duration of journeys peaking from 1200hrs …

Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

Mosaic: A Prune-And-Assemble Approach For Efficient Model Pruning In Privacy-Preserving Deep Learning, Yifei Cai, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu

Computer Science Faculty Publications

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic …

Speculative Anisotropic Mesh Adaptation On Shared Memory For Cfd Applications, Christos Tsolakis, Nikos Chrisochoides

Computer Science Faculty Publications

Efficient and robust anisotropic mesh adaptation is crucial for Computational Fluid Dynamics (CFD) simulations. The CFD Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

Vulnerable, Recalcitrant And Resilient: A Foucauldian Discourse Analysis Of Risk And Older People Within The Context Of Covid-19 News Media, Marjorie Skoss, Rachel Batten, Patricia Cain, Mandy Stanley

Research outputs 2022 to 2026

Risk is an innate and integral part of everyday life and is present in simple, everyday occupations and complex actions. Age-related stereotypes can mean older people have little opportunity to engage in activities that present some degree of risk. The present study explores the discourse around risk and older people in the context of the COVID-19 pandemic. We investigated news media as a reflection of the dominant public discourse around older people's behaviour to identify how risk is represented in relation to occupational engagement. Texts relating to older people and COVID-19 were sourced from the West Australian newspaper for a …

Ecu-Mss-2 Dataset, A New Multi-Species Seagrass, Md K. Noman, Syed M. S. Islam, Seyed M. Jalali, Jumana Abu-Khalaf, Paul Lavery

Research Datasets

The "ECU-MSS-2' dataset contains four different habitats, 'Amphibolis' spp (hereafter 'Amphibolis"), 'Halophila' spp (hereafter 'Halophila'), 'Posidonia' spp (hereafter 'Posidonia') and 'Background'. We compiled this image dataset from different sources. The 'Halophila' images were collected by the Centre for Marine Ecosystems Research, Edith Cowan University, Western Australia.

The 'Amphibolis' 'Background' and 'Posidonia' images were collected by the Department of Biodiversity, Conservation and Attractions (DBCA), Australia. The 'Amphibolis' class includes the seagrass species Amphibolis griffithii and Amphibolis Antarctica. The 'Halophila' class includes Halophila ovalis, while the 'Posidonia' class includes Posidonia sinuosa, Posidonia coriacea, and Posidonia Australis. The 'Background' class includes coral, sand, …

Measuring The Purpose In Life In The Adult Population: A Scoping Review, Somrudee Arunjit, Karnsunaphat Balthip, Jos M. Latour

School of Nursing and Midwifery

Background: The purpose in life can motivate individuals to realize that life is essential for existence and well-being. Adults might experience crises that can lead to a lack of purpose in life. Consequently, promoting purpose in life is necessary, but it requires a suitable measurement scale. Objective: This scoping review aimed to identify and map the content, psychometric properties, and answer option scales of instruments intended to measure purpose in life in adult populations. Design: A scoping review was employed. Data Sources: The database used was PubMed. The libraries were APA PsycNet, Wiley Online Library, and Cochrane Library. The search …

Data-Driven Communicative Behaviour Generation: A Survey, Nurziya Oralbayeva, Amir Aly, Anara Sandygulova, Tony Belpaeme

School of Engineering, Computing and Mathematics

The development of data-driven behaviour generating systems has recently become the focus of considerable attention in the fields of human–agent interaction and human–robot interaction. Although rule-based approaches were dominant for years, these proved inflexible and expensive to develop. The difficulty of developing production rules, as well as the need for manual configuration to generate artificial behaviours, places a limit on how complex and diverse rule-based behaviours can be. In contrast, actual human–human interaction data collected using tracking and recording devices makes humanlike multimodal co-speech behaviour generation possible using machine learning and specifically, in recent years, deep learning. This survey provides …

Reflections On The Iheart Programme By Different Stakeholders: : Self-Confidence, Mental Wellbeing And Resilience, Gulsah Selin Tumkaya

School of Law, Humanities and Social Sciences Theses

To promote social justice and educational equity for all students, teachers are given a significant place in international policy and literature as essential agents of change. Training teachers for inclusive practices necessitates building their self-confidence to increase their understanding of how they may use their resources to support children and adolescents, as well as boost their self-confidence. Teachers’ resilience to overcome difficulties may also have a crucial role in increasing the effectiveness of inclusive practices. This is because, despite agreement about the benefits of inclusive education, the number of teachers leaving their jobs increases daily. The aim of the project, …

The Influence Of Higher Education Study On Practitioners In The Field Of Outdoor And Adventure Education, Katy Hensby

School of Law, Humanities and Social Sciences Theses

This thesis sets out to answer whether higher education (HE) study specific to outdoor and adventure education (OAE) influences the practice of OAE practitioners in the field of OAE. From my own experience in the field of OAE and the field of HE, the reasons why a practitioner should study an OAE degree were under-researched. The current work is set in the context of the current body of knowledge surrounding the field of OAE. It recognises the limited evidence base that acknowledges the impact of OAE HE on practitioners in the field. The theoretical framework used in this research is …

Is There A Relationship Between Parents' Screen Usage And Young Children’S Development?, Delphine Nguyen

School of Psychology Theses

There has been growing concern over the links between children's screen time use and cognitive development (Halton, 2020). However, researchers have generally overlooked the possible impact of parental screen time, which might decrease the opportunities of learning and social interactions for young children. To address this gap, we investigated the relationship between parental screen use and toddlers’ development. However, the start of this thesis coincided with the Covid-19 pandemic, and a few experimental tasks had to be adapted online. Thus, this thesis examined first whether online paradigms can provide valid data (word recognition, word learning and language assessment). Second, the …

Landscapes Of Play: An Exploration And Illumination Of Children’S Unsupervised Play Close To Home And A Researcher’S Journey To Becoming Posthuman., Mandy Andrews

School of Law, Humanities and Social Sciences Theses

Title: Landscapes of Play: An exploration and illumination of children’s unsupervised play close to home and a researcher’s journey to becoming posthuman. Author: Mandy Jo Andrews This thesis explores children’s play experiences and engagements out of doors close to home. Adopting a Deleuzian informed posthumanism the researcher’s journey towards immanence prompts re-thinking about play, childhood-nature entanglements, and play-space-place-player generation. Children’s unsupervised play outside of organised settings such as school or adventure playgrounds is less researched than that of organised settings. This thesis puts to work Deleuze’s process philosophy and Barad’s agential realism, together with Haraway’s biology informed multispecies manifesto, Bennett’s …

Imagine The Unimaginable, Denis Kaiser

Lake Union Herald

No abstract provided.

Evaluating The Integration Of A Mathematics Enhancement Programme Into Jamaican Primary Mathematics Classes, Shandelene Khadine Kedisha Binns-Thompson

School of Law, Humanities and Social Sciences Theses

This embedded quasi-experimental research design examined the impact of an enrichment initiative entitled the Mathematics Enhancement Programme (MEP) on Jamaican students’ performance and attitude towards mathematics. It identified teaching strategies for integrating the MEP into Jamaican primary mathematics classes. It investigated the impact of the MEP on teachers’ pedagogical practices, and it identified the barriers to integrating the MEP. A sample of 331 students and 12 teachers were conveniently selected from three schools from three parishes in Jamaica for the intervention group. The comparison group consisted of 180 students and seven teachers conveniently selected from two schools in central Jamaica. …

A Novel Approach To Expedite Emergency Investigation For Suspected Cauda Equina Syndrome Referrals From Community And Primary Care Services: A Service Evaluation, Jonathon Gill, Sue Greenhalgh, Jos M. Latour, Stephen Pickup, Gillian Yeowell

School of Nursing and Midwifery

Introduction: Although a rare spinal emergency, cauda equina syndrome (CES) can result in significant physical, emotional, and psychological sequalae. Introducing a CES pathway enhances diagnosis but may increase Radiology and Orthopaedic workload. To address this, one NHS hospital in England introduced a novel CES pathway. Utilising a criteria-led pathway, patients were referred directly from community/primary care, via the Emergency Department, for an emergency MRI scan. Objective: To compare the outcomes of patients referred via an original and redesigned Community and Primary Care CES pathway. Design: A retrospective service evaluation was undertaken of all emergency MRI scans investigating suspected CES via …

Doing The Dishes: A Comprehensive Analysis Of The Late Bronze Age Ceramics Excavated From Hut 12 At Mokarta, Sicily, Justin Lee Singleton

Dissertations

Problem

This dissertation is presented as a fulfillment of the work that Sebastiano Tusa, the site director of Mokarta, had requested before his passing, that is, a complete catalogue of all Hut 12 ceramics, their functions, and how these may have fit into the site as a whole. The site itself, Mokarta, is a Late Bronze Age site in western Sicily that was destroyed by fire ca. 950 B.C. The site has not yet been fully excavated, and only a fraction of information that has been gathered has been published. The research for this dissertation is the first comprehensive analysis …