Computer Sciences Commons^™

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Challenges In Scaling R-Tree Spatial Search On Processing-In-Memory, Tasmia Jannat, Michael Gowanlock, Satish Puri

Computer Science Faculty Research & Creative Works

Spatial query processing is important in scientific, geospatial, and data-intensive applications. R-trees are widely used to index spatial objects, but their query-dependent traversal creates irregular work across different regions. This poster studies the challenges of scaling R-tree spatial search on a commercial Processing-in-Memory (PIM) system. Although PIM reduces CPU to memory data movement by executing search near memory, it does not remove full-pipeline overheads: the host still manages data placement, query batching, kernel launches, result retrieval, and aggregation. Our results show strong DPU-side search acceleration, with PIM kernel speedup ranging from about 20 x to 73 x, but end-to-end speedup …

Learning Programming In Informal Spaces: Using Emotion As A Lens To Understand Novice Struggles On R/Learnprogramming, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Novice programmers experience emotional difficulties in informal online learning environments, where Confusion and Frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies causes of emotional struggle, and explores design opportunities for affect-aware support systems. We manually annotated 1,500 posts from r/learnprogramming using the Learning-Centered Emotions framework, applying clustering, and axial coding. Confusion, Curiosity, and Frustration dominated emotional experiences, sometimes co-occurring and linked to early learning stages. Positive emotions were infrequent. The primary emotional triggers included ambiguous errors, unclear learning pathways, and misaligned resources. We identify five key areas where novice programmers …

Performance Evaluation Of Approximate Nearest Neighbor Search On Nvidia Bluefield-3 Dpu, Sophia Bhoria, Nathan Tibbetts, Arjun Kirubakaran, Alima Subedi, Satish Puri

Computer Science Faculty Research & Creative Works

Advanced SmartNICs known as Data Processing Units (DPU) enable in-network data analytics, being equipped with standard processors and accelerators capable of doing custom computation on-NIC. These SmartNICs are advantageous because the host CPU can delegate simpler data analytics tasks, like filtering, to the NIC where the data first arrives. Only data needing further refinement must be passed on to the host CPU. Our benchmarks focus on NVIDIA's commercially available Bluefield-3 DPU. Similarity search, particularly Approximate Nearest Neighbor (ANN) search, is an important domain with wide usage across numerous applications. We explore ANN search on SmartNICs, providing insight into the performance …

Building The Next Cybersecurity Workforce: A Grades 7–12 Curriculum To Close The Cyber Talent Gap, Mohammed A. Salam, Iqbal Shareef, Rich P. Manprisio

Journal of Cybersecurity Education, Research and Practice

In today’s rapidly evolving technological landscape, cyberattacks pose increasing threats, yet a global shortage of cybersecurity and digital forensics professionals leaves industries vulnerable, similar to having too few law enforcement officers in a densely populated city. The judicial system faces rising digital crimes and fraud cases, further strained by the lack of experts to analyze and extract digital evidence. Despite high demand, millions of positions remain unfilled. This paper identifies the root causes of the cybersecurity workforce shortage and proposes a targeted solution: a curriculum for Grades 7–12 designed to foster cybersecurity awareness and interest. The methodology included a comprehensive …

Taxing Ai In The Wake Of The Emergence Of Agentic Ai, Reuven S. Avi-Yonah, Herbert Snitz

Articles

This article addresses taxation measures to regulate AI agents (i.e., AI that is not fully under the control of humans, including both ANI and AGI). In each case, the focus is on what the agent’s capabilities would be, not on how it becomes capable of successfully performing its tasks (though the latter would likely be relevant to important philosophical questions, e.g., might robots achieve sentience or consciousness, and if so, would they have rights?). Regarding mirroring what we human beings can do, the hallmark of AGI is that it will not be task-specific (like ANI), but will possess broad capabilities, …

Genedit: A Context-Aware Equity, Diversity And Inclusion Principles Integration Tool For Software Engineering Education, Chetan Arora, Ajanie Kodagoda Bammanna Arachchige, Jinchun Du, Muhammad Aamir Cheema, Aster Cosmos, Antonette Shibani, Vasudha Malhotra, Naeem Janjua, Afaq Shah

Research outputs 2022 to 2026

Equity, diversity and inclusion (EDI) is widely acknowledged as essential in software engineering (SE), yet day-to-day integration into teaching remains uncommon due to time pressures, low instructor confidence, and fragmented resources. We present GenEDIt, a purpose-built, LLM-backed chatbot that helps educators weave EDI into SE education artefacts without altering intended learning outcomes. Unlike generic chat interfaces, GenEDIt provides a tailored UI and workflow, and embeds retrieval over a vetted EDI knowledge base. The tool supports five educator-centred modes - (1) methods for integrating EDI into current activities; (2) examples/datasets; (3) EDI-integrated assessments and rubrics; (4) reflective prompts; and (5) rapid …

How Can Accessibility In Computing Education Be Improved Through Hci Research?, Dr David Santandreu Calonge, Linda Smail, Firuz Kamalov, Dima Yousef, Melody Sylvain

All Works

Accessibility - accommodation of diverse sensory, motor, cognitive, and linguistic needs - remains critically underrepresented in computing education despite broad societal acknowledgment of its importance. This position paper argues that systemic inequities and persistent barriers to integrating accessibility and inclusion - ranging from curricular inertia to limited faculty capacity - require new methodological approaches drawn from Human-Computer Interaction (HCI) research. HCI provides tested frameworks that pair participatory design with inclusive pedagogy, guided by empirical evaluation to drive systemic change. We identify three key areas where HCI can advance accessibility education: (1) embedding accessibility within core curricula through design-based pedagogies, (2) …

Decoding The Allosteric Grammar Of Protein Kinases: A Dual-Stream Framework Integrating Protein Language Models And Energy Landscape Frustration Analysis, Will Gatlin, Max Ludwick, Lucas Turano, Brandon Foley, Kamila Riedlova, Vít Škrhák, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The spatial and energetic encoding of allosteric regulatory sites remains a major challenge in structural biology, frequently representing a “blind spot” for sequence-based artificial intelligence (AI) models. We present a protein language model (PLM)-guided approach complemented by the energy landscape frustration analysis as a dual-stream framework to investigate the relationship between AI prediction of binding sites and biophysical organization of regulatory pockets across the human kinome. By probing a fine-tuned residue-level PLM classifier across 453 kinase structures, a clear performance gap is discovered between highly predictable orthosteric pockets (Types I, I.5, and II) and poorly resolved distal allosteric sites (Type …

Research On Image Feature Analysis Of Intangible Cultural Heritage Brocade Integrating Multi-Scale Visual Perception, Ruiyang Yuan, Hao Wang, Shu Zhou, Hui Zhu, Jingwen Qiu

Journal of Scientific Information Research

[Purpose/significance] Addressing the challenges posed by the complex semantic characteristics of intangible cultural heritage brocade imagery, the difficulty in extracting their profound connotations, and the inadequate utilisation of multi-scale features by traditional deep learning models, this paper aims to explore a method for analysing the characteristics of intangible cultural heritage brocade images that integrates multi-scale visual features. [Method/process] This paper constructs a multi-scale feature analysis framework for intangible cultural heritage brocade images (ICH_BC), integrating convolutional neural networks with Transformer architectures. The framework employs ResNet to extract local texture and detail features from brocade images, utilises VIT to capture global structural …

Expert Interview: "The Mirror Of Ai" In The Domain Of Scientifc Information Research, Taitian Mao, Yulai Bao, Jianxiang Wei, Peng Wu, Chuanming Yu, Gan Tang, Dongyan Wei, Yifei Ma, Wei Wang, Yu Ma

Journal of Scientific Information Research

Professor Mao Taitian and colleagues argues elucidates the adaptation logic, practical pathways, and prerequisites of intelligent agents to empower the high-quality development of scientific information. Professor Bao Yulai and colleagues advocate that integrating the perceptual elasticity of domain-specific large models with the cognitive rigidity of ontology can establish a new paradigm for intelligence services in complex scenarios. The synergy between the two can not only expand the theoretical boundaries of information science and serve national strategies, but also advance intelligence services from assisted analysis to intelligent decision-making. Professor Wei Jianxiang and colleagues point out that generative artificial intelligence has triggered …

Religious Bias In Llms Is Significantly Understudied, Sheryl Carty, Nancy Fulda, Walter Reade

Faculty Publications

In the earlier years of development of LLMs, it was relatively easy to prompt an LLM to respond with toxic or biased statements about religion. Subsequent improvements in frontier models addressed many of the issues of bias and toxicity in general, including against religion. At the same time, the adoption and usage of these models has grown exponentially. Small and implicit biases, therefore, have a magnified overall impact. In this paper, we (1) briefly review previous efforts to measure religious bias in LLMs, (2) show, by reviewing over 12,000 papers dealing with bias in LLMs, that religious bias has been …

Security Architecture Decision Framework For Endpoint Protection In Resource-Constrained K-12 Environments, Jason Folker

Journal of Cybersecurity Education, Research and Practice

K-12 educational institutions face an ongoing challenge in protecting endpoints when budgets and staffing prevent the implementation of standard security best practices. Technology directors routinely make difficult decisions about administrative rights, software controls, and security tooling, but they lack frameworks designed for the constraints and priorities specific to educational environments. This paper develops a security architecture decision framework tailored for K-12 endpoint protection. The framework integrates five weighting dimensions to help technology directors evaluate competing architectural choices. These dimensions include educational impact, security risk reduction, resource requirements, compliance obligations, and operational feasibility. The framework creates structured documentation that helps decision-makers …

Cooperative Evolution For Discovering Scalable Spiking Neural Network Architectures, Catherine C. Rodriquez

LSU Master's Theses

SNNs are a foundational model in neuromorphic computing, where efficient architectures must be discovered for a wide range of applications. Evolutionary methods, such as EONS, offer a flexible approach to this search but become increasingly difficult to scale as task complexity grows due to the rapidly expanding combined topology–parameter search space and associated memory demands. To address this challenge, we propose a co-evolutionary ensemble framework in which a population of candidate SNNs is evolved with fitness defined by each network’s marginal contribution to group performance. Grounded in cooperative game theory and difference evaluation functions from multiagent systems, this formulation provides …

Dynlp: Parallel Dynamic Batch Update For Label Propagation In Graph-Based Semi-Supervised Learning, S. M. Shovan, Arindam Khanda, S. M. Ferdous, Sajal K. Das, Mahantesh Halappanavar

Computer Science Faculty Research & Creative Works

Semi-supervised learning aims to infer class labels using only a small fraction of labeled data. In graph-based semi-supervised learning, this is typically achieved through label propagation to predict labels of unlabeled nodes. However, in real-world applications, new data often arrives in batches, and stale data often becomes irrelevant. Each time a new batch appears, reapplying the traditional label propagation algorithm to recompute all labels is redundant, computationally intensive, and inefficient. To address the absence of an efficient label propagation update method, we propose DynLP, a novel GPU-centric Dynamic Batched Parallel Label Propagation algorithm that performs only the necessary updates, propagating …

Real-Time Image Denoising And Reconstruction Using Generative Adversarial Networks (Gans), Roa'a M. Al-Airaji, Haider Th. Salim Alrikabi, Rula Kamil, Svyd Iryna

Karbala International Journal of Modern Science

This paper presents a real-time image denoising and reconstruction system based on a compact Generative Adversarial Network (GAN) designed for embedded systems and edge computing. The proposed model employs a [ generator and a PatchGAN discriminator, trained using hybrid loss function that combines L1, perceptual, and adversarial terms to balance pixel and perceptual realism. Evaluations were conducted on multiple datasets, including DIV2K, BSD68, SIDD, DND, RENOIR, and PolyU at noise levels (σ = 15, 25, 50). Quantitatively, the proposed model achieves an average PSNR of 32.8 dB and an SSIM of 0.88, which is higher by more than 3 dB …

Energy Efficiency Limits And Future Electricity Demand Of Computing Devices, Ricardo Pinto, Tiago Domingos, Paul E. Brockway, Matthew Kuperus Heun, Tânia Sousa

University Faculty Publications and Creative Works

• ICT (information and communication technologies) represented 4% of the world electricity consumption in 2020; 
• Computing devices represented 2% of the world electricity consumption in 2020; 
• In recent scenarios datacentre electricity demand reaches 3% of world electricity in 2030, and more than 4% in 2035

Toward Intelligent Machines: Conscious Learning And Hardware-Accelerated Ai, Pavia Bera

USF Tampa Graduate Theses and Dissertations

Artificial intelligence systems excel at narrow, well-defined tasks but remain brittle at theboundaries of their training distributions: they cannot quantify uncertainty, adapt continuously to non-stationary data, or operate efficiently on energy-constrained hardware. This dissertation addresses these limitations through four coordinated contributions spanning probabilistic learning algorithms, biologically inspired temporal memory, cross-entity warning propagation via distributed associative memory, and spintronic processing-in-memory.

The first contribution introduces Boosted Bayesian Neural Networks (BBNNs), which extend standard mean-field variational inference by iteratively constructing a mixture posterior through Boosting Variational Inference. On five clinical medical classification datasets, BBNNs achieve superior uncertainty calibration—lower Negative Log-Likelihood and Expected Calibration …

Heterogeneous Graph-Augmented Contrastive Learning For Extreme Multi-Class Fiqh Classification, Ali A. Jalil

Al-Bahir

• Background/Introduction: Fine-grained text classification in the field of Islamic Jurisprudence (Fiqh) is difficult because of the structural interdependence of the legal concepts and the extremely multi-class long-tail data distribution (667 classes with 5,979 samples, 52.2% of which contain less than 5 samples). The main problem with traditional flat classifiers is that they assume that target classes are independent and orthogonal output neurons which discards very important relational semantics.
• Objectives: This paper seeks to remediate this extreme imbalance and maintain structural taxonomy by modeling the structural space of classification label space itself as an object to be learned, while giving a …

Zero-Shot Transfer Of Foundation Time-Series Forecasters To Datacenter Operational Telemetry: Mapping A Domain Nobody Gets To Study, David Pace Jr

LSU New Orleans Theses and Dissertations

This thesis asks how best to forecast real datacenter operational telemetry. Can pretrained foundation time-series forecasters do it zero-shot, or do trained classical baselines perform best? On a single-site benchmark of eight audited targets and four horizons (32 slices), the zero-shot foundation forecasters Moirai, Chronos, and TimesFM win 26 of 32 slices against multivariate classical baselines. However, the foundation wrappers operate per-channel on the target history, while classical baselines consume the full feature tensor. When the same classical models are retrained univariate, the foundation lead persists (27 of 32 slices) and a 3-seed rerun of a classical challenger reproduces it; …

Pharmed. System Manual: Hololens Application, Eden Woldeselassie

Systems Manuals - 2026

This document provides the full life cycle of PharmEd HoloLens. PharmEd stands for Pharmacy Education. The overall document will go through the complete system manual by going over requirements, followed by design, how to install the application, how to upload the application to Microsoft store, troubleshooting and source code. The first section will explain the background, the existing problems and why the need to develop PharmEd.  Here techniques of requirement gathering and some statistics will elaborated. The following section discusses about functional requirements. The requirement section includes subtopics: functional and nonfunctional requirements for both -programmer and end user- and the …

Robot Remote Control Utility, Jeremy Cantu, Paul Uhlig

Systems Manuals - 2026

The purpose of this document is to describe the Robot Control Management Utility developed in the course of this graduate project. Information included in this document will include the background of the developed product, as well as the functional requirements, data structures, software modules, interface design, installation guide, and sample and troubleshooting guides.

E Classroom+, Ami B. Jariwala

Systems Manuals - 2026

A software platform that enhances many processes correlated with learning is called a learning management system (LMS). It is also known as a management software package that enables the delivery of learning content, resources and activities and also that handles the correlated administration operations. In classrooms, today, teachers, are being commenced with convenience to remodel learning from a traditional transmission model to a student-centered model where students are mostly responsible for their learning. The research conveys that in order to remodel learning, schools require to support a student-centered approach where students can be supported in finding, analyzing, organizing, evaluating, internalizing, …

Kta System Manual, Cayden Garcia

Systems Manuals - 2026

The purpose of this document is to explain the ins and outs of KTA. This includes but not limited to:

- The problem that colleges face when it comes to data analysis and collection

- The history of data analytics at a collegiate level

- Software and hardware requirements for KTA – Developer and User

- The overall design of KTA, how the software is structured and how the stack interacts with one another

- How to install KTA on your machine

- Sample Sessions – including issues that the user may run into and how to fix them

Smart Atm, Majid Hakeem

Systems Manuals - 2026

The Smart ATM is a new system that goal is to create a new experience for ATMs users by creating a different way of using the ATM. It would protect users from germs and bacteria. Instead of using buttons and touch screens, the main input for this system will be the motion sensor. To specify, the system uses Microsoft Kinect, which is the motion sensor for the Xbox One.

This document is a user guide, which is intended to give assistance to users for using the Smart ATM. This document contains an application overview, list of user requirements, the design …

Idioma Tutor, Oralia Mijares

Systems Manuals - 2026

The Idioma Tutor was developed specifically for individuals who desire to learn English through a non-traditional approach, eLearning. Idioma Tutor is an interactive game designed to strengthen individual’s English language adaptation, using an individual-learner approach. The game uses immediate feedback to provide self-analysis for learners. It captivates users and applies best-practices in eLearning and game development to retain and fully engage learners. This system manual outlines provides detailed information for using the Idioma Tutor. This system will be used as a catalyst to support the success of individuals who desire to learn the English language in a fun and interactive …

Solving Rubik's Cube By Using Artificial Intelligence, Polat Coban, Seth Reed

Systems Manuals - 2026

This document will detail a proposal to build a Rubik’s cube simulator, and a Rubik’s cube solver. It is broken into several sections which in turn are broken into subsections.

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation. We evaluate four prominent SOLMs using various prompt strategies and parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation …

Activity Transition Graph Generation: How Far Are We?, Jiakun Liu, Peixin Zhang, Han Hu, Yonghui Liu, Wei Minn, Ferdian Thung, Shahar Maoz, Eran Toch, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android applications (i.e., apps) are indispensable nowadays and are getting bigger and bigger with an increasing number offunctionalities. To understand how to access functionalities in an app, prior studies proposed tools to model the transitionsbetween functionalities with the activity transition graph (ATG). ATG is an important data structure and has been used forvarious Android app analyses, including app design, understanding, and testing. However, there is no benchmarking work onATG generation. It is still unclear whether the transitions identified by tools are correct and how many transitions are missed.To fill this gap, we manually identified all transitions in 98 applications to …

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …