Digital Commons Network^™

Real-Time Testbed For Studying Cyberattacks And Defense In Der-Integrated Smart Inverter Systems, M. Maliha, A. Oluyomi, M. Booge, S. Bhattacharjee, N. Braasch, P. Gomez, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a grid-tied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man-in-the-middle (MITM) attacker. The MITM attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from …

Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but its deployment on resource-constrained devices is hindered by high communication overhead, inefficient energy usage, and poor convergence under non-IID data distributions. To address these challenges, we propose GRACE-FL: a Green Resource-Aware Communication-Efficient Federated Learning framework that explicitly incorporates device energy capacity into training. Each client adapts its learning rate, number of local epochs, and gradient quantization bit-width based on its available energy, allowing high-capacity devices to sustain more intensive training while low-capacity devices operate with lighter configurations. A novel energy-weighted aggregation strategy ensures that clients …

Last Digit Tendency: Lucky Number And Psychological Rounding In Mobile Transactions, Hai Wang, Tian Lu, Yingjie Zhang, Yue Wu, Yiheng Sun, Jingran Dong, Wen Huang

Research Collection School Of Computing and Information Systems

The distribution of digits in numbers obtained from different sources reveals interesting patterns. The well-known Benford’s law states that the first digits in many real-life numerical data sets have an asymmetric, logarithmic distribution in which small digits are more common; this asymmetry diminishes for subsequent digits, and the last digit tends to be uniformly distributed. In this paper, we investigate the digit distribution of numbers in a large mobile transaction data set with 835 million mobile transactions and payments made by approximately 460,000 users in more than 300 cities. Although the first digits of the numbers in these mobile transactions …

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

Automated Program Refinement: Guide And Verify Code Large Language Model With Refinement Calculus, Yufan Cai, Zhe Hou, David Sanan, Xiaokun Luan, Yun Lin, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Recently, the rise of code-centric large language models (LLMs) appears to have reshaped the software engineering world with low-barrier tools like Copilot that can generate code easily. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult for users to understand and trust the generated code. Addressing these challenges is both necessary and critical. In contrast, program refinement transforms high-level specification statements into …

Demo2test: Transfer Testing Of Agent In Competitive Environment With Failure Demonstrations, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Dandan Wang, Qing Wang, Fanjiang Xu

Research Collection School Of Computing and Information Systems

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of …

Learning To Rank Aspects And Opinions For Comparative Explanations, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informative comparisons. To enhance the quality of both the personalized recommendation and the explanation, we incorporate optimization objectives that preserve relative rankings of aspects and opinions, in addition to the classical rankings of overall preferences for items. We integrate the multiple ranking objectives and multi-tensor factorization together. Experiments on datasets of different domains …

Weakly-Supervised Semantic Segmentation With Image-Level Labels: From Traditional Models To Foundation Models, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

The rapid development of deep learning has driven significant progress in image semantic segmentation—a fundamental task in computer vision. Semantic segmentation algorithms often depend on the availability of pixel-level labels (i.e., masks of objects), which are expensive, time consuming, and labor intensive. Weakly supervised semantic segmentation (WSSS) is an effective solution to avoid such labeling. It utilizes only partial or incomplete annotations and provides a cost-effective alternative to fully supervised semantic segmentation. In this article, our focus is on the WSSS with image-level labels, which is the most challenging form of WSSS. Our work has two parts. First, we conduct …

Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun

Research Collection School Of Computing and Information Systems

Human pose estimation (HPE) models underperform in recognizing rare poses because they suffer from data imbalance problems (i.e., there are few image samples for rare poses) in their training datasets. From a data perspective, the most intuitive solution is to synthesize data for rare poses. Specifically, the rule-based methods apply manual manipulations (such as Cutout and GridMask) to the existing data, so the limited diversity of the data constrains the model. An alternative method is to learn the underlying data distribution via deep generative models (such as ControlNet and HumanSD) and then sample “new data” from the distribution. This works …

Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin

Research Collection School Of Computing and Information Systems

The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that clients submit their queries in plaintext. Secondly, these models are susceptible to being impacted by malicious client behavior, for example, enabling clients to potentially engage in arbitrage …

The Gradient Puppeteer: Adversarial Domination In Gradient Leakage Attacks Through Model Poisoning, Kunlan Xiang, Haomiao Yang, Meng Hao, Shaofeng Li, Haoxin Wang, Zikang Ding, Wenbo Jiang, Tianwei Zhang

Research Collection School Of Computing and Information Systems

In Federated Learning (FL), clients share gradients with a central server while keeping their data local. However, malicious servers could deliberately manipulate the models to reconstruct clients' data from shared gradients, posing significant privacy risks. Although such Active Gradient Leakage Attacks (AGLAs) have been widely studied, they suffer from two severe limitations: 1) coverage: no existing AGLAs can reconstruct all samples in a batch from the shared gradients; 2) stealthiness: no existing AGLAs can evade principled checks of clients. In this paper, we address these limitations with two core contributions. First, we introduce a new theoretical analysis approach, which uniformly …

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …

Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch

Data Science Faculty Publications

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Llm-Driven Apt Detection For 6g Wireless Networks: A Systematic Review And Taxonomy, Muhammed Golec, Yaser Khamayseh, Suhib Bani Melhem, Abdulmalik Alwarafy

All Works

Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. This paper presents the first systematic review and taxonomy for LLM-assisted APT detection in 6G networks. It …

Streamlining Digital Elevation Model Construction From Historical Aerial Photographs: The Impact Of Reference Elevation Data On Spatial Accuracy, Xin Hong, Christopher H. Roosevelt

All Works

This study proposes a streamlined workflow for producing historical digital elevation models (hDEMs) from scanned 1950s aerial photographs using structure-from-motion and multi-view-stereo (SfM-MVS) techniques along with co-registration methods. We also conducted a sensitivity analysis to assess the impact of DEM references with varying spatial resolutions on the SfM-MVS process and co-registration accuracy. The DEM references included a 30 m SRTM DEM (low resolution), a 12 m TanDEM-X DEM (medium resolution), and a 5 m DEM provided by the General Directorate of Mapping of the Ministry of National Defense, Republic of T & uuml;rkiye (high resolution). Results indicate that higher resolution …

Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar

All Works

Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive …

Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali

All Works

This study examines the impact of two key AI modalities–freedom of choice (FoC) and social proof (SP)–on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in …

Certain Knowledge Of Administrative Decisions Issued By Artificial Intelligence Systems In The Public Sector: A Comparative Analysis, Nayel Musa Alomran, Odai Mohammad Alheilat

All Works

This paper aims to elucidate the matter of certain knowledge pertaining to administrative decisions, those issued by artificial intelligence. It also clarifies the position of the administrative judiciary with regard to the adoption of this presumption and through a comparative analysis of administrative judicial applications in Jor-dan, Egypt, and Morocco. The text addresses the most significant evidence for achieving certain knowledge of an administrative decision – and thereby initiating the appeal period against the appellant. The question at hand is whether the administrative judiciary applies the traditional theory of certain knowledge to its counterpart issued by artificial intelligence systems, especially …

Performance Based Scheduling In Distributed Mixed Criticality Systems, Amjad Ali, Saud Wasly, Asad Masood Khattak, Ihsan Ali, Shahid Iqbal, Bashir Hayat

All Works

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores …

Predicting Early Hospital Readmissions For Diabetic Patients Using Machine Learning, Amanda Ferraz, Leonardo Oliveira

ICT

This project applies machine learning to predict whether diabetic patients will be readmitted to a hospital within 30 days of discharge. Early readmissions are a costly and critical issue in healthcare, often signalling gaps in post-discharge care and risk management. Diabetic patients face unfair high readmission rates compared to the general population. According to the CDC Diabetes Report Card 37.3 million people in the U.S. or 11.3% of the population had diabetes as of 2019 (CDC, 2021). Our goal here is to develop a binary classification model capable of flagging high risk patient (< 30-day readmission) based on their clinical, demographic, and administrative data. This lets healthcare institutions to take measures,

Dogs Emotion System, Muhammad Anas Baig

ICT

For our capstone project, we built a machine learning model that can look at pictures of dogs and figure out how they’re feeling, like if they’re happy, sad, or just chill. The idea came from how important pets are in people’s lives these days and how cool it would be to actually understand their emotions better using tech. This system will allow users to upload images of dogs, which are then analysed by a trained model to classify the dog's emotional states such as happy, sad, or neutral. We followed the CRISP-DM process to build it, which basically means we …

Dogs Emotion System- Poster, Muhammad Anas Baig

ICT

This project is all about a deep learning-based “Dog Emotion System” that can figure out how dogs are feeling just by looking at their faces. We used a balanced set of 4,000 dog images with four different emotion categories and followed the CRISP-DM process to build it. The model was trained from scratch using a Convolutional Neural Network (CNN) without any pre-existing models. It is deployed using Steamlit, where people can upload pictures of their dogs and get their emotional state predicted in real time. The goal of this tech is to make it easier for pet owners to understand …

Strategic Analysis Of Employment Permit Statistics And Predictive Analytics For Workforce Planning In Ireland, Amy Souza, Thaynna Vieira

ICT

By analysing historical employment permit data from Enterprise.gov.ie (Enterprise.gov.ie, 2024), this project has the aim to use Data Analytics and Machine Learning to make predictions of employment permits trends across sectors and companies, providing insights to optimize workforce planning for Recruitment Agencies and guide international job seekers requiring work visas. The insights gained are intended to enhance strategic recruitment practices and empower job seekers to make informed career decisions in Ireland’s competitive labour market.

Data-Driven Public Transport Planning For Dublin: A Clustering And Forecasting Approach, Magdalena Burtinik Urueta, Mirae Yu

ICT

Dublin has been experiencing severe traffic congestion due to rapid economic and population growth, with residents losing an average of 158 hours per year in traffic during rush hour (Europe Data, 2025). A 2022 European Commission study found that 76% of Irish people use a car as their primary mode of transport on a typical day—an 8% increase from 2019, compared to the EU average of 47% (MacCarthaigh, 2022).

This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method …

Data Driven Public Transport Planning In Dublin : A Clustering And Forecasting Approach, Magdalena Burtinik Urueta, Mirae Yu

ICT

Dublin faces increasing traffic congestions with over 76% of Irish residents relying on private cars for daily transport, well above the EU average (MacCarthaigh, 2022). This contributes to increased greenhouse gas emissions, challenging Ireland’s goals to reduce emissions by 55% by 2030. This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method was applied to cluster EDs based on similarities in population dynamics. Forecasts were generated in 5-year intervals, revealing key growth corridors across Dublin using a clustered VAR …

Detecting Fake News Using Ai, Gustavo Lambert, Lucas Schultz

ICT

This is a document that presents a strategic analysis of the project “Detecting Fake News using AI”. Developed as part of the BSc (Hons) in Computing in IT at CCT College Dublin. The goal is to analyse the potential advantages, exploiting the viability and the impact of applying Artificial intelligence to check, verify and alert about misinformation found and to answer the question “How can Artificial Intelligence be leveraged to accurately detect and combat fake news while ensuring data privacy and compliance with regulations?” and “To what extent can AI-driven misinformation detection help reduce the spread of fake news on …

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification, Bernardo Gandara, Ignacio Varela

ICT

The advancement of generative AI technologies has made it increasingly difficult to distinguish synthetic images from authentic ones. This capstone project addresses the challenge by developing a binary image classification model using deep learning techniques to differentiate AI-generated images from real photographs. Guided by the CRISP-DM methodology, we employed the DeepGuardDB dataset, consisting of 13,000 balanced image samples, evenly split between real and synthetic sources. We implemented and compared three Convolutional Neural Network (CNN) architectures through transfer learning, standardising input pipelines and integrating custom classification heads. Following a performance evaluation across multiple metrics, the best-performing model was selected for further …

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …

Hunting And Fishing Ceos: Environmental Plunderers Or Saviors?, Thomas Covington, Steve Widler, Keven Yost

Finance Faculty Works

CEOs who participate in hunting and fishing benefit by appreciating natural environments and permanently consuming natural resources. We examine whether CEOs who hunt and fish make different environmental decisions and find that firms led by CEOs who obtain the most hunting and fishing licenses have lower environmental performance as measured by MSCI-KLD. This effect is strongest in the environmental category of climate change but also extends to pollution, waste, and the protection of natural capital. Furthermore, firms led by CEOs with the most hunting and fishing licenses are significantly more likely to pay a regulatory settlement for an environmental regulatory …