Computer Sciences Commons^™

Ecu-Malnett, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

Static Malware Analysis For Incident Response: Developing A Tactical Aid With Ember, Joel Meoak, Shengjie Xu

Journal of Cybersecurity Education, Research and Practice

Incident responders face a variety of challenges when identifying malware using existing solutions, particularly when rapid tactical decisions are needed. Traditional malware detection methods are often signature-based, limiting their effectiveness to previously known threats detected by anti-virus (AV) engines. Online analysis tools introduce confidentiality risks, potentially alerting adversaries that their actions are under scrutiny. While free sandbox environments offer useful capabilities, they often require substantial setup time and hardware resources that may not be available in the field. This research leverages the Elastic Malware Benchmark for Empowering Researchers (EMBER) dataset to develop a lightweight, portable tactical decision aid that enables …

A Review Of Research And Practices On Teaching Data Visualizations For Blind And Visually Impaired Students, Shiya Cao

Statistical and Data Sciences: Faculty Publications

Around 36 million people in the world are blind and an additional 217 million have moderate to severe vision impairment. In higher education, four percent of 54,204 undergraduates who participated in the 2022 American College Health Association survey reported to be blind or have low vision. Those students frequently do not have access to data visualizations we generally teach and use in postsecondary statistics and data science classes. The design of those visualizations is premised on implicit assumptions about the user’s visual ability. Making data visualizations accessible to blind and visually impaired (BVI) people would help improve equity in higher …

Interdisciplinary Narratives On Artificial Intelligence & Personnel Selection Systems, John Hunter, Melissa Intindola, Neil Boyd, Thiago Serra Azevedo Silva

Faculty Journal Articles

Artificial intelligence (AI) has quickly and persistently become a daily presence in our lives, and its omnipresence has eclipsed the speed with which scholars can fully assess its efficacy and pitfalls. AI’s ubiquity and the lack of a clear understanding of its implications for humanity has spurred scholars across disciplines to action, and scholars in the field of Human Resource Management (HR) have certainly joined the fray. As scholars with a variety of experience and scholarship across disciplines, we believe that the burgeoning conversation in the HR literature would significantly benefit from a greater presence of interdisciplinary knowledge.

Detecting Polar Ring Galaxies Via Deep Learning, Fawad Kirmani, Anathavishnu S. Unnii, Varsha P. Kulkarni, Kyle Lackey, John R. Rose

Faculty Publications

Polar ring galaxies (PRGs) are peculiar galaxies that show a ring of stars, gas, and dust oriented roughly over the poles of the central ‘host’ galaxy (i.e. roughly orthogonal to the disc of the host galaxy). The formation models for these rings involve mergers or tidal interactions of the host galaxy with another galaxy. Although the identified PRGs look different from each other, they all have a ring that is not in the same plane as the disc of the host galaxy. Unlike in galaxies such as our Milky Way, where stars form in spiral arms, the rings exemplify an …

Digging Deeper With Deep Ram Networks, Andrew J. Wagner

Dissertations and Theses

While Deep Neural Networks (DNNs) have driven major breakthroughs in artificial intelligence, their internal complexity often makes their behavior hard to explain, resulting in the well-known “black box” dilemma. This thesis addresses the challenge of interpretability in DNNs and deep reinforcement learning (DRL) through two main contributions.

In Part I, we revisit and extend the use of Deep RAM Networks (DRNs) within the Arcade Learning Environment (ALE), showing that, with modern architectures and careful hyperparameter tuning, RAM-based agents can achieve performance competitive with established pixel-based baselines on Atari 2600 games, while offering additional advantages for research and analysis. We also …

Dcrda: Deadline-Constrained Function Scheduling In Serverless-Cloud Platform, Priyanka Ashok Birajdar, Divya Meena, Anurag Satpathy, Sourav Kanti Addya

Computer Science Faculty Research & Creative Works

The serverless computing model frees developers from operational and management tasks, allowing them to focus solely on business logic. This paper addresses the computationally challenging function-container-virtual machine (VM) scheduling problem, especially under stringent deadline constraints. We propose a two-stage holistic scheduling framework called DCRDA targeting deadline-constrained function scheduling. In the first stage, the function-to-container scheduling is modeled as a one-to-one matching game and solved using the classical Deferred Acceptance Algorithm (DAA). The second stage addresses the container-to-VM assignment, modeled as a many-to-one matching problem, and solved using a variant of the DAA, the Revised-Deferred Acceptance Algorithm (RDA), to account for …

Insect-Foundation: A Foundation Model And Large Multimodal Dataset For Vision-Language Insect Understanding, Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation …

Explainabledetector: Exploring Transformer-Based Language Modeling Approach For Sms Spam Detection With Explainability Analysis, Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today's modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult …

Genai Literacy Framework For Library Instruction, Adwoa Boateng, Jennifer Freer, Greyson Pasiak, Erich Short, Ryan Tolnay, Rit Libraries

Presentations and other scholarship

A generative artificial intelligence (genai) framework for library instruction. This short framework is designed to incorporate into existing library instruction across many subject areas. The basic elements of the framework are: know & understand genai, use & evaluate genai, and library research & discovery with genai.

Studying Topic Evolution Based On Bertopic Model And Semantic Function, Jiabin Qu, Mengyang Wang

Journal of Scientific Information Research

[Purpose/significance] Topic evolution analysis can help researchers quickly grasp the research hotspots and development trends of a discipline. However, existing topic models often overlook the semantic functions and structures of texts during topic extraction, making it difficult to reveal the deeper patterns of disciplinary development. This paper proposes an integrated framework for topic evolution analysis that combines the BERTopic model with semantic functions, aiming to enrich and improve the methodological system of topic evolution research.

[Method/process] Firstly, the BERTopic model is used to extract topics, obtaining the“Topic-Word”distribution. Next, a discourse parsing tool analyzes abstracts into five semantic function segments, resulting …

Research On Identification And Evaluation Method Of Medical Experts' Expertise Domains In Online Health Community Based On Knowledge Graph, Yunjiang Xi, Qian Zhang, Man Li, Juan Yu

Journal of Scientific Information Research

[Purpose/significance] This study aims to identify the expertise domains of medical experts, and evaluates their domain levels to provide a basis for community expert recommendation.

[Method/process] This study utilized the improved OneRel model to structure community historical Q&A into entity relation triples. Then used the knowledge graph triples to test the consistency between the medical knowledge in the community Q&A and the domain knowledge, and finally obtained the doctor's domain levels by aggregating in each expertise domain.

[Result/conclusion] Using the data example from xywy.com website, 214 doctors in the community were ranked in terms of their average level of expertise …

Adaptive Security Metric For Optimizing Post-Quantum Cryptography In Constrained Iot Devices, Aisha Nasser Ahmed

Theses

Quantum Computing poses real threat to Classical Public-Key Cryptography requiring the use of Post-Quantum Cryptography for all Internet of Things Devices. However, there are greater computational, memory and communication overheads in PQC algorithms that create additional burdens on resource constrained IoT devices. At this time, there are no standard measures for systems developers to determine optimal PQC settings for the various IoT Device Classes. This Thesis develops a new framework of metrics for determining the most suitable PQC settings based on Security Strength, Performance Indicators (Latency, Memory, Energy), Communication Overhead and Reliability for each IoT device class. The Research introduces …

Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …

Stroke2sketch: Harnessing Stroke Attributes For Training-Free Sketch Generation, Rui Yang, Huining Li, Yiyi Long, Xiaojun Wu, Shengfeng He

Research Collection School Of Computing and Information Systems

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semanticfocused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely …

Mitigating Cross-Modal Representation Bias For Multicultural Image-To-Recipe Retrieval, Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food image only reflects the visual outcome of a cooked dish and not the underlying cooking process. Consequently, learning cross-modal representations to bridge the modality gap between images and recipes tends to ignore subtle, recipe-specific details that are not visually apparent but are crucial for recipe retrieval. Specifically, the representations are biased to capture the dominant visual elements, resulting in difficulty in ranking similar recipes with subtle differences in use of ingredients and cooking methods. …

Diffusionmat: Alpha Matting As Deterministic Sequential Refinement Learning, Yangyang Xu, Shengfeng He, Wenqi Shao, Yong Du, Kwan-Yee K. Wong, Yu Qiao, Jun Yu, Ping Luo

Research Collection School Of Computing and Information Systems

In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a deterministic sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring …

Advances In Iot, Ai, And Sensor‑Based Technologies For Disease Treatment, Health Promotion, Successful Ageing, And Ageing Well, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

Recent advancements in the Internet of Things (IoT) and artificial intelligence (AI) are unlocking transformative opportunities across society. One of the most critical challenges addressed by these technologies is the ageing population, which presents mounting concerns for healthcare systems and quality of life worldwide. By supporting continuous monitoring, personal care, and data-driven decision-making, IoT and AI are shifting healthcare delivery from a reactive approach to a proactive one. This paper presents a comprehensive overview of IoT-based systems with a particular focus on the Internet of Healthcare Things (IoHT) and their integration with AI, referred to as the Artificial Intelligence of …

Polyqent: A Polynomial Quantified Entailment Solver, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Milad Saadat, Maximilian Seeliger, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Polynomial quantified entailments with existentially and universally quantified variables arise in many problems of verification and program analysis. We present PolyQEnt which is a tool for solving polynomial quantified entailments in which variables on both sides of the implication are real valued or unbounded integers. Our tool provides a unified framework for polynomial quantified entailment problems that arise in several papers in the literature. Our experimental evaluation over a wide range of benchmarks shows the applicability of the tool as well as its benefits as opposed to simply using existing SMT solvers to solve such constraints.

Ai-Enabled Multi-Layer Security Operations: A Combined Siem, Ids, And Threat Intelligence Model For Adaptive Cyber Defense, Mohamad Khayat

Dissertations

This dissertation presents a comprehensive framework for the evolution of Security Operation Centers (SOCs) through the integration of advanced artificial intelligence (AI), blockchain, and optimization techniques. Motivated by the increasing complexity of cyber threats and the limitations of traditional reactive SOC strategies, this work begins with a systematic literature review that identifies critical gaps in current SOC operations. Based on these insights, a reference architecture is proposed to guide the integration of intelligent components into SOC environments. To address the challenge of secure and trustworthy information sharing, a blockchain-based threat intelligence platform is developed, leveraging Byzantine Fault Tolerance and Zero-Knowledge …

Topoimages: Incorporating Local Topology Encoding Into Deep Learning Models For Medical Image Classification, Pengfei Gu, Hongxiao Wang, Yejia Zhang, Huimin Li, Chaoli Wang, Danny Z. Chen

Computer Science Faculty Publications

Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). Despite remarkable successes of numerous image processing methods that rely on appearance information, these methods often lack sensitivity to topological structures when used in general deep learning (DL) frameworks. In this paper, we introduce a new general approach, called TopoImages (for Topology Images), which computes a new representation of input images by encoding local topology of patches. In TopoImages, we leverage persistent homology (PH) to encode geometric and topological features inherent in image patches. Our main objective is …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

The Pastor As Romantic Author: Ai, Preaching, And The Unacknowledged Inheritance Of Authenticity, Daniel Plate, James Hutson

Faculty Scholarship

This article interrogates contemporary reactions to sermons produced with generative technologies through a historical–conceptual lens, arguing that widespread judgments of such outputs as “soulless,” “generic,” or lacking a “beating heart” are best explained by an unacknowledged inheritance from nineteenth-century Romantic expressivism. Rather than treating resistance to machine authorship as a theological verdict on computational incapacity, the study reconstructs how Romanticism centered authorship in sincere self-expression and solitary genius, displacing earlier heraldic expectations that prized fidelity to a received message. Methodologically, the analysis combines intellectual history with discourse analysis of global Christian experiments in synthetic composition (2020–2025), denominational guidance, and media …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Building An Inclusive Ai Chatbot For Diverse Student Communities At Cal Poly: Uplift Ai, Gideon Telahun

College of Engineering Summer Undergraduate Research Program

This research project will investigate the ability of advanced Large Language Models (LLMs) to identify and assess misinformation across diverse forms of media, including text, images, and video. In an age where misleading content spreads rapidly across digital platforms, evaluating the reliability and integrity of AI systems tasked with fact-checking is critical. We will develop a comprehensive dataset composed of factual and misleading examples drawn from various well-known and reliable fact-checking organizations. Each item will be independently reviewed and transparently labeled to ensure reproducibility. We will then prompt a curated group of state-of-the-art LLMs—including GPT-4, Claude, Gemini, Perplexity, Grok, and …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Meshless Discrete Velocity Boltzmann Model For Porous Media Flow, Amandine Maidenberg

Doctoral Dissertations and Master's Theses

This dissertation explores the combination of two sophisticated techniques for addressing computational fluid dynamics: the discrete velocity Boltzmann equation (DVBE) and the localized collocation meshless model with upwinding (U-LCMM). The DVBE is a high-level model that describes the foundations of transport phenomena by addressing the microscale motions of particles themselves and the effect of their aggregate behaviors on continuum principles. This equation integrates multiple scales of phenomena; while it can be used for fluid flow at Navier-Stokes scales, it can also resolve fine features that can only be described at the molecular level. This type of model is necessary for …

Unresolved Image Simulation For Space Situational Awareness Applications, Fox Coniglario

Doctoral Dissertations and Master's Theses

The knowledge of what lies in orbit around Earth is at best a guess. Decades of spaceflight, debris buildup, and vehicle collisions have contributed to a large number of objects that are simply not able to be catalogued. Ongoing efforts to catalog debris in orbit have reached limits by conventional measures and as such, research is active in the field of in-orbit space situational awareness. This thesis intends to help fill a hole in the development of such orbital platforms by assisting the development of image processing software pipelines though the simulation of unresolved space imagery. The simulation uses accurate …

The Complexity Of Long-Distance Dependencies And Their Impact On Language Models, Abhijit Shrikant Mahalunkar

Doctoral

Sequential data modeling is an important challenge in various fields and in particular in natural language processing. Building effective sequential models faces a notable challenge in the form of Long-Distance Dependencies (LDDs) within the sequence data. Hence, successfully overcoming this challenge is imperative for developing robust and accurate sequential models across various domains and applications. To tackle this challenge, the first step is to conduct a detailed analysis of the complexity of LDDs observed in various sequence datasets. This thesis offers a thorough exploration and documentation of this analysis. An important finding from this thesis is the consistent patterns of …

Analysis Of The Status And Thematic Trends Of Ai For Science Research Abroad From 2015 To 2024, Fangyuan Wang, Huiting Xu, Jinghua Xue

Journal of Scientific Information Research

[Purpose/significance] This paper analyzes the relevant literature in the field of AI for Science（AI4S）in the WoS core database from 2015 to 2024, and sorts out the research status and development trends in this field, aiming to provide forward-looking insights for the application of AI technology in scientific research.

[Method/process] This paper combines bibliometric analysis with the BERTopic model to analyze the publication trends, publishing countries, core authors, and topic identification and development trends in the field of AI4S.

[Result/conclusion] Through bibliometric analysis, this paper reveals the exponential growth trend of AI4S-related literature, and finds that China ranks first in the …