Digital Commons Network^™

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-based objectives introduce inherent conflicts that yield distribution gaps across modalities. In this work, we identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases: (i) an alignment–uniformity conflict, whereby the repulsion of uniformity undermines pairwise alignment, and (ii) an intra-alignment conflict, where aligning multiple modalities induces competing alignment directions. To address these issues, we propose a principled decoupling of alignment and uniformity for multimodal representations, providing a conflict-free recipe for multimodal learning that …

Rendering Data Unlearnable By Exploiting Llm Alignment Mechanisms, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. In this work, we address the problem of data protection against unwanted model learning in a realistic blackbox setting. We propose Disclaimer Injection, a novel data-level defence that renders text unlearnable to LLMs. Rather than relying on model-side controls or explicit data removal, our approach exploits the models’ own alignment mechanisms: injecting carefully designed alignment-triggers to prevent effective learning. Through layer-wise analysis, we find that finetuning on such protected data induces persistent activation …

Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-only detection approaches often struggle with information insufficiency and high false-positive rates in complex environments. To address these limitations, we present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection. Capitalizing on the exceptional cross-modal representation learning capabilities of Contrastive Language-Image Pretraining (CLIP) across visual, audio, and textual domains, our framework introduces two major innovations: an efficient audio-visual fusion that enables adaptive cross-modal integration through lightweight parametric adaptation while maintaining the frozen CLIP backbone, and a novel audio-visual prompt that dynamically enhances …

Robust Graph Learning On The Web: Challenges, Methods, And Applications, Ao Xiang, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, Qing He

Research Collection School Of Computing and Information Systems

Graph learning is transforming web intelligence, powering applications from recommender systems to anomaly detection. However, most existing approaches implicitly assume ideal conditions where training and testing data are accurate, complete, and free from manipulation. In reality, web environments rarely exhibit such stability. Dynamic user behavior, incomplete or outdated content, adversarial interference, and sudden distribution shifts can all erode the reliability of even state-of-the-art models, leading to biased or unsafe outcomes. This tutorial provides a comprehensive survey of emerging strategies for robust graph learning on the web. We first present a structured taxonomy of the principal robustness threats specific to web …

Divide-And-Conquer: Generating Ui Code From Screenshots, Yuxuan Wan, Chaozheng Wang, Yi Dong, Wenxuan Wang, Shuqing Li, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Websites are critical in today’s digital world, with over 1.11 billion currently active and approximately 252,000 new sites launched daily. Converting website layout design into functional UI code is a time-consuming yet indispensable step of website development. Manual methods of converting visual designs into functional code present significant challenges, especially for non-experts. To explore automatic design-to-code solutions, we first conduct a motivating study on GPT-4o and identify three types of issues in generating UI code: element omission, element distortion, and element misarrangement. We further reveal that a focus on smaller visual segments can help multimodal large language models (MLLMs) mitigate …

Block Kinematics, Interseismic Coupling And Fault Slip Rates In The Aegean Region From Gps And Earthquake Slip Vector Data: 2. Hellenic Subduction System, Konstantinos Chousianitis, Volkan Özbey, Sotiris Sboras, Robert Mccaffrey, Semih Ergintav

Geology Faculty Publications and Presentations

The Hellenic subduction system is the primary locus of Nubia‐Eurasia convergence, yet thedistribution of strain accumulation along it is still debated. Here, we invert 832 geodetic velocities and 146earthquake slip vectors to assess the spatial variability of interseismic locking and provide new constraints onthe rate of slip deficit accumulation. We find that the subduction interface beneath southwestern Peloponneseand across the area from offshore south Peloponnese to offshore southwest of western Crete hosts three patchesof low to moderate locking (0%–40%) at 10–50 km depth whose slip rate deficits account for ∼15–30% of thetotal plate convergence. The offshore weakly locked patches show …

Constraints On Gravitational Waves From The 2024 Vela Pulsar Glitch, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang

Physics & Astronomy Faculty Publications

Among known neutron stars, the Vela pulsar is one of the best targets for gravitational-wave searches. It is also one of the most prolific in terms of glitches, which are sudden frequency changes in a pulsar’s rotation. Such glitches could cause a variety of transient gravitational-wave signals. Here, we search for signals associated with a Vela glitch on 2024 April 29 in data of the two LIGO detectors from the fourth LIGO–Virgo–KAGRA observing run. We search both for seconds-scale burst-like emission, primarily from fundamental (f-)mode oscillations, and for longer quasi-monochromatic transients up to 4 months in duration, primarily …

Centering Culture, Language, And Identity In A History Of Mathematics Course For Preservice Teachers, Luis Miguel Fernández, Tenchita Alzaga Elizondo

School of Mathematical & Statistical Sciences Faculty Publications

This article presents the redesign of an undergraduate History of Mathematics course for preservice teachers at a Hispanic-Serving Institution. Grounded in Community- and Family-Centered Pedagogy (CFCP) and Marshall’s framework for culturally sustaining mathematics teaching, the course intentionally moved beyond Eurocentric narratives to center students’ linguistic, familial, and cultural resources as integral to mathematical meaning-making. Two core assignments anchored the redesign: (1) evolving Padlet reflections that connected global mathematical histories to students’ family and community practices, and (2) bilingual Cuentos Matemáticos that wove mathematical concepts with personal and cultural narratives in English and Spanish. Selected student reflections illustrate how these experiences …

Statistical Evaluation Of A New Orleans Historic Rainfall Dataset (1840–1893), Arielle C. Filostrat

LSU Master's Theses

This study evaluates the climatic credibility of a newly identified historical rainfall dataset for New Orleans, Louisiana, spanning 1840–1893. The New Orleans Historical Dataset (NOHD) is compared to a modern observational record from the Audubon rain gauge (1893–2024) to determine whether nineteenth-century rainfall observations are consistent with known precipitation behavior in the region. Establishing the reliability of early records is critical for extending climatological baselines and improving understanding of long-term rainfall variability.

Historical rainfall observations were first evaluated through archival review using the New Orleans Medical and Surgical Journal and other nineteenth-century meteorological records to verify notable rainfall events. Daily …

Stuck In The Heat: A Case Study Looking At Green Infrastructure And Economic Mobility In San Antonio, Texas, Maria Fernanda Guerrero

Public Administration Institute

This case study explores how environmental conditions structure economic mobility in San Antonio, Texas. Three ZIP codes from urbanized areas within the city were used to conduct this study: 1)Near Northwest Area [78228]  2)Alamo Heights Area [78209]  3)Inwood Area [78248] While economic mobility is often associated with income, education, and employment, this study argues that place-based environmental conditions also influence whether communities have access to economic opportunity and advancement. Using a comparative case study approach, this research analyzes differences in poverty, household median income, educational attainment, heat vulnerability, environmental burden, and access to green infrastructure across the three ZIP codes. …

X-Ray Made Simple: Lay Radiology Report Generation And Robust Evaluation, Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin

Computer Science Faculty Publications

While multimodal generative models have advanced radiology report generation (RRG), challenges remain in making reports accessible to patients and ensuring reliable evaluation. The technical language and templated nature of professional reports hinder patient comprehension and enable models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. To address these limitations, we propose the Layman’s RRG framework, which leverages layperson-friendly language to enhance patient accessibility and promote more robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. Our approach also introduces and releases two refined layman-style datasets (at the sentence …

Does Poor Regulation Lead To Pollution? Key Takeaways From A Systematic Review, Vincent Emenyonu, Kat O. Mara, Mark Lund

Research outputs 2022 to 2026

Formal recycling of used lead acid batteries (ULAB) is a multibillion-dollar industry, valued at US$12.12 billion in 2024, and expected to grow to more than $26 billion within eight years. Demand for recycled lead (Pb) for new batteries grows in developing countries where costs limit the uptake of lithium-ion batteries. ULAB recycling in these regions poses significant danger to human health and the environment. Stringent regulatory approaches for ULAB recycling in developing countries, intended to reduce pollution, have thus far been ineffective as they typically do not adequately cover small-scale informal recycling in rural and remote communities. We examine what …

Semantic Context Improvisational Retrieval-Augmented Generation For Empathic Conversational Ai, Sharjeel Tahir, Judith Johnson, Jumana Abu-Khalaf, Syed Afaq Ali Shah

Research outputs 2022 to 2026

A fundamental limitation of modern conversational AI is its limited capacity to demonstrate sustained empathy in long-form interactions. We propose SCIRAG (Semantic Context Improvisational Retrieval-Augmented Generation), a feedback-driven retrieval framework for adaptive empathic dialogue. It employs a dual-loop retrieval framework, iteratively optimizing a static counseling dataset through user metadata and feedback memory refinement. To enhance contextual alignment, we deploy retrieval adaptation, enabling the model to retain and leverage past conversational cues based on user preferences. When integrated with Mixtral-8x7B, SCIRAG improves human-rated empathic understanding by +1.26 points and empathic response by +1.00 point on the RoPE scale, while increasing acceptability …

A Robust Hybrid Security Framework: Integrating Multi-Layered Text Encryption With Barcode-Based Steganography, Mohamed Sayed, Talaat M. Wahbi, Farooq Abdalwahab Haboub

BAU Journal - Science and Technology

The widespread use of the Internet is causing increasing security concerns regarding online communications. One method for achieving secure communication between authorized parties is steganography. We herein employ multilevel technologies, including compression, encryption, barcoding, and steganography to secure a secret text message. Type I multilevel steganography is used with a two-level setup. The first level uses enhanced least significant bit (secure LSB-L1) image steganography; the output is a stego-image file, the cover is an image file, and the secret data in this level is English text. The output from the first level is encrypted using the RSA algorithm, and the …

Enhanced E-Commerce Recommendation Experience With Collaborative Sentiment Analysis And Ranked Content-Based Filtering, Sandi El Zein, Farah Hamze, Amani Merhi, Lynn Noureddine, Lama Affara

BAU Journal - Science and Technology

The rise of online shopping has made informed purchasing decisions increasingly difficult, as consumers face an overwhelming number of product choices and struggle to manually evaluate specifications and user reviews. This paper presents an AI-powered, sentiment-aware product recommendation tool that effectively aligns user preferences with real-world customer feedback from online reviews. The proposed system utilized Instruct ABSA deep learning models for feature extraction and DeBERTa-v3 for sentiment analysis to turn reviews into interpretable scores that would nominate optimal products. An interactive Rasa-based chatbot interface, TopPickAI, was developed to give a seamless user experience, educate users on product features, and conversationally …

Analysis Of Normalization, Resampling, And Weighted Voting To Improve K-Nn Performance In Diabetes Disease Detection, Agustiyar, R. Rizal Isnanto, Budi Warsito, Adi Wibowo, Ferry Jie

Research outputs 2022 to 2026

Diabetes is a global health issue, and machine learning has become crucial for early detection. The k-Nearest Neighbors (k-NN) machine learning algorithm is widely used because it is simple and easy to implement; however, its performance is sensitive to feature scale differences, class imbalance, and the assumption of uniform decision-making, as commonly found in clinical datasets such as the Pima Indians Diabetes (PID) dataset. Many studies have been conducted to address these issues; however, very few have systematically evaluated the combined effects of preprocessing and distance-based decision-making methods on k-NN performance. This study evaluates the combined effects of preprocessing strategies …

Principal Leadership As A Predictor Of Teacher Performance: Evidence From An Indonesian Elementary School, Irminapinem Pinem, Antonius Remigius Abi, Hamela Sari Sitompul

Jurnal Pendidikan Sains

Teacher performance is a critical determinant of educational quality and student achievement, making the identification of factors that influence teacher effectiveness an important area of educational research. Among these factors, principal leadership has been recognized as a key organizational variable that shapes teachers’ professional practices and instructional performance. This study aimed to examine the relationship between principal leadership and teacher performance at State Elementary School Raya Kahean, Simalungun Regency, Indonesia. The study employed a quantitative correlational research design involving 52 teachers selected through total sampling. Data were collected using two structured questionnaires measuring principal leadership and teacher performance. The principal …

The Effect Of Emotional Intelligence And Logical-Mathematical Intelligence On Students’ Mathematical Problem-Solving Ability, Zulkyfli Siduppa, Sutji Rochaminah, Muh Hasbi, Dasa Ismaimuza

Jurnal Pendidikan Sains

Mathematical problem-solving ability is an essential competency in mathematics education because it supports students’ reasoning, critical thinking, and decision-making skills. This study aimed to examine the influence of emotional intelligence and logical-mathematical intelligence on the mathematical problem-solving ability of Grade XII students at SMA Negeri 1 Tolitoli. The study employed a quantitative survey design involving 143 students from the Mathematics and Natural Sciences (MIPA) program during the 2023/2024 academic year. Data were collected using an emotional intelligence questionnaire, a logical-mathematical intelligence questionnaire, and a mathematical problem-solving test on algebraic function limits. All instruments were validated and demonstrated satisfactory reliability, with …

Corkscrew Motion Of Trypanosoma Brucei Is Driven By Helical Beating Of The Flagellum And Facilitated By Its Bent Shape, Sizhe Cheng, Devadyouti Das, Mykhaylo Barchuk, Raveen Armstrong, Michele M. Klingbeil, Becca Thomases, Shuang Zhou

Mathematics Sciences: Faculty Publications

Significance:

Trypanosoma brucei is a single-cell parasite causing sleeping sickness across 37 sub-Saharan countries, threatening ~60 million people. Revealing its locomotion mechanism is critical to understand its lifecycle in fly vectors and mammalian hosts; however, progress has been hindered by the rapid and complex cell deformation driven by a laterally attached flagellum. By tracking the 3D motion of fluorescent particles attached to cell surface, we demonstrate that tip-to-base helical waves generate self-propulsion like an active corkscrew, with reactive torque inducing counterrotation of the cell body. These findings inform future investigations of the underlying biomechanics, suggest potential routes for therapeutic intervention, …

Interval-Valued Neutrosophic Dombi Bonferroni Mean Aggregation Operators In Medical Diagnosis And Sustainable Energy, Maryam Faisal, Muhammad Nadeem, Muhammad Kamran

Neutrosophic Systems with Applications

Medical diagnosis is one of the most difficult fields in which decisions must be made due to the fact that medical information often has characteristics of uncertainty, incompleteness, imprecision and even contradiction. Traditional aggregation and decision-making methods are often not well suited to such complexities, and may result in less reliable diagnostic outcomes. In order to overcome these drawbacks, the authors propose a new approach using a novel representation of Interval-Valued Neutrosophic Sets (IVNSs), the Dombi operational laws, and Bonferroni Mean (BM) aggregation operators. The proposed framework is specifically aimed at coping with uncertainty, indeterminacy and falsity all at once …

An Interactive Multi-Objective Programming Approach For Optimizing Fully Trapezoidal Spherical Fuzzy Linear Programming Problem With Application, Sultan S. Alodhaibi, Hissah Ibrahim Almuzini, Hamiden Abd El-Wahed Khalifa

Neutrosophic Systems with Applications

In this paper, a linear programming framework with completely uncertain parameters is investigated by employing trapezoidal spherical fuzzy numbers (TrSFNs). The proposed formulation incorporates a spherical fuzzy (SF) decision environment in which the optimization process simultaneously maximizes the degree of positive membership while minimizing the corresponding neutral and negative membership degrees. By utilizing the concept of the α -cut associated with TrSFNs, the original fully fuzzy linear programming problem is transformed into an interval-valued linear programming model with confidence levels. To rank and compare the resulting interval objective values, an interval ordering approach based on the decision maker's preferences—considering the …

Conditionals And Modalities In Constructive Quantum Logics, Juan P. Aguilera, Guillaume Massas

Mathematics, Physics, and Computer Science Faculty Articles and Research

We investigate logics that generalize both intuitionistic logic and quantum logic. In earlier work, we introduced Ex-logic, an extension of Holliday's fundamental logic that coincides with the intersection of orthologic and the implication-free fragment of intuitionistic logic. In this paper, we add an implication connective to Ex-logic and axiomatize iEx-logic, the intersection of full intuitionistic logic and orthomodular logic with the implication connective interpreted as the Sasaki hook. As a consequence, we obtain a characterization of the lattice of logics extending iEx-logic as the product of the lattice of intermediate logics and the lattice of orthomodular logics. We also explore …

Exploiting Uncertainty Of Computational Methodology In Optimizing User Interface In Human-Computer Interaction, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Human-computer interaction (HCI) evaluation and optimization of user interfaces (UIs) constitute a complex multi-criteria decision-making challenge, marked by conflicting evaluation dimensions, subjective expert judgments, and inherent uncertainty in user experience assessment. Traditional evaluation approaches, such as heuristic expert reviews and user satisfaction surveys, rely on sharp, binary classifications that fail to capture the gradual and overlapping nature of human cognitive and affective states. This limitation necessitates a more robust uncertainty-aware methodology that can model the true complexity of HCI evaluation. This paper proposes a hybrid mathematical model that integrates various Multi-Criteria Decision Making (MCDM) techniques of Entropy, and Simple Additive …

Generative Endurance Logic: An Axiomatic Framework For Reasoning About Outcome-Generating Objects Under Constraints, Hafiz Burhan Ul Haq, Muhammad Nauman Irshad

Neutrosophic Systems with Applications

This paper introduces Generative Endurance Logic (GEL), a formal framework for studying objects through the outcomes they can produce. In many cases, an object cannot be judged only by a fixed truth value, score, or utility value. A rule, model, action, or strategy may behave well in one situation but fail when the context changes or when small perturbations occur. GEL addresses this issue by treating each object as a generator of outcomes. Each object a is linked to a generation map Ga:X×Ω→Y, where X is the context space, Ω is the …

Recursive Neutrosophic Superhypergraphs With Illustrative Applications, Takaaki Fujita, Ajoy Kanti Das, Suman Das, Sankar Prasad Mondal, Volkan Duran

Neutrosophic Systems with Applications

Finite hypergraphs generalize ordinary graphs by permitting each hyperedge to join any nonempty set of vertices, and thus provide a natural model for truly multiway interactions. To represent hierarchical and multi-layer structure, SuperHyperGraphs iterate the powerset operation so that set-valued entities created at one level can be treated as vertices at higher levels. Independently, recursive hypergraphs allow edge recursion: an edge may contain not only vertices but also lower-level edges, yielding nested (and possibly self-referential) incidence controlled by a specified recursion depth. In this work we introduce and axiomatize Recursive Neutrosophic SuperHyperGraphs, a unified framework that combines vertex …

Tumor-Immune Dynamics With Memory And Time Delay: A Fractional-Order Model With Ctla-4 Regulation, Mutaz Mohammad, Mohyeedden Sweidan, Alexander Trounev, Fathalla Rihan

All Works

This study develops a fractional-order tumor-immune interaction model incorporating Caputo memory effects, delayed immune activation, and CTLA-4 checkpoint regulation. The model describes the coupled dynamics of tumor cells, CD4^+ T cells, IFN-γ, and CTLA-4, and extends classical integer-order tumor-immune models by accounting for hereditary immune responses and biologically motivated latency effects. Theoretical properties, including positivity, boundedness, equilibrium structure, and fractional-order stability, are examined to establish the biological and mathematical consistency of the model. The delayed fractional system is then investigated computationally by comparing several numerical methods, including finite difference discretization, Daubechies wavelet collocation, Euler wavelet collocation, and a predictor-corrector scheme. …

Monte Carlo Analysis Of Dose Distribution In Tumour And Adjacent Healthy Tissues During 192Ir Hdr Brachytherapy In The Presence Of Gold Nanoparticles, Damian P. Tippe, Innocent J. Lugendo

Tanzania Journal of Science

High dose rate (HDR) brachytherapy using ¹⁹²Ir is an effective modality for cancer treatment; however, its therapeutic efficiency is often constrained by unintended radiation exposure to healthy tissues surrounding the tumour. Fortunately, the incorporation of high atomic number (Z) nanoparticles as radiosensitizers has emerged as a promising strategy to enhance tumour dose while sparing adjacent normal tissues. In this study, the dose distribution in tumour and nearby healthy tissues during 192Ir HDR brachytherapy was investigated in the presence of gold (Au) nanoparticles using Monte Carlo simulations based on the Geant4 toolkit. Radial dose distributions and anisotropy characteristics were analysed …

The Role Of Zinc Precursor Concentration In Modulating The Structural, Optical, And Electrical Properties Of Sol-Gel Derived Czts Thin Films, Justine John Tibaijuka

Tanzania Journal of Science

This study reports on CZTS thin films fabricated by sol–gel spin coating using zinc acetate dihydrate, copper acetate dihydrate, tin chloride, and thiourea dissolved in methanol with monoethylamine as a stabilizer. The films were annealed rapidly at 540 °C by using an RTP furnace. Results show the significant influence of zinc concentration on films’ properties with band gap evolution between 1.46 and 1.7 eV, along with high absorption coefficients exceeding 10⁴ cm⁻¹ in the visible range. Structurally, the films evolved from poorly crystalline at low Zn content to well- crystallized, (112)-oriented kesterite near stoichiometry. However, excessive Zn caused slight degradation …

Hydrogeochemical Characterization Of Groundwater In Fractured Crystalline Basement Aquifers In Dodoma, Central Tanzania: Implications On Water Quality For Domestic And Irrigation Applications, George J. Ishabairu, Simon R. Melchioly, Charles H. Kasanzu, Gabriel D. Mulibo

Tanzania Journal of Science

Assessment of groundwater hydrogeochemistry characteristics of aquifers plays a vital role in establishing water quality status and its suitability for human and industrial use. The main objective of this research was to carry out hydrogeochemical characterization of groundwater and assess its impacts on water quality and spatial variability of groundwater geochemistry in Dodoma, Tanzania. The methods used include groundwater sampling, laboratory analysis to determine physico- chemical parameters and data processing to classify the groundwater hydrochemical facies. The results show that the average value for pH is 7.2, for electrical conductivity (EC) is 1108 µS/cm and for total dissolved solids (TDS) …