Computer Science

Permanent URI for this collectionhttps://hdl.handle.net/10315/38508

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Now showing 1 - 20 of 109

Access status: Open Access ,
Deep Generative Models for Trajectory Prediction and Mobility Network Forecasting
(2025-07-23) Nadiri, Amirhossein; Papagelis, Manos
Predicting human mobility is essential for urban planning, traffic management, and epidemiology. This thesis tackles two intertwined challenges: accurately forecasting individual trajectories and inferring the resulting mobility network. First, we introduce TrajLearn, a Transformer‑based deep generative model that treats trajectories as token sequences and employs spatially constrained beam search to predict each individuals’s next k locations with high precision. Building on these forecasts, we present MobiNetForecast, which constructs and predicts the future topology of the mobility network by detecting when independently predicted trajectories intersect in space and time. Across large, real‑world datasets, our unified framework achieves up to 40% relative gains in trajectory accuracy and up to 100x improvement in contact prediction over state-of-the-art baselines. These results demonstrate that combining advanced sequence modeling with explicit contact inference offers a powerful, scalable solution for dynamic mobility network forecasting.
Access status: Open Access ,
Machine Unlearning for Mobility Data: An Algorithmic Perspective
(2025-07-23) Faraji, Ali; Papagelis, Manos
This work addresses machine unlearning for trajectory data, sequences of spatiotemporal points representing movement. Motivated by growing privacy concerns and regulations like GDPR and CCPA, which grant users the right to request deletion of their personal data from trained models (the right to be forgotten), we propose TraceHiding, an algorithmic framework that removes the influence of specific trajectories without full model retraining. TraceHiding estimates the data point importance and applies gradient updates to reverse it proportionally. The framework includes: (i) Estimating data point importance, (ii) a teacher-student architecture, and (iii) a loss function using Importance Scores to compute reversal gradients. We evaluate TraceHiding on benchmark trajectory classification datasets. Results show it outperforms strong baselines and state-of-the-art unlearning methods (Bad-T, SCRUB, NegGrad, and NegGrad+), effectively removing deleted trajectory influence, preserving retained data performance, and improving efficiency over retraining. To our knowledge, this is the first machine unlearning approach designed specifically for trajectory data.
Access status: Open Access ,
Evolving Software Ecosystems: The Role of Community Dynamics in Shaping Software Extensions
(2025-07-23) Onagh, Elmira; Nayebi, Maleknaz
As software ecosystems (SECOs) grow across domains, understanding how tools evolve and differentiate functionally is critical for innovation. This manuscript-based thesis explores the evolution of the software ecosystem and its influence on developers’ motivations to extend their software products in two ecosystems. In the first part, we focus on the evolution of open-source software by analyzing 6,983 GitHub Actions on GitHub Marketplace, revealing a widespread functional redundancy. A graph-based analysis of version histories and release patterns identifies early contributors and offers strategies to reduce duplication and align tools with emerging trends. In the second part, in collaboration with industry partners, we examined proprietary software products, focusing on functional maturity, in particular AI-related features in 116 patient-centric healthcare applications. We find that 86.21% of apps remain in early AI adoption stages, indicating limited advancement toward AI integration. Together, these studies introduce a generalizable, data-driven framework for analyzing functional evolution across domains.
Access status: Open Access ,
Use of Visual Content for Inference and Response in Q/A Forums
(2025-07-23) Ahmed, Faiz; Nayebi, Maleknaz
In the rapidly evolving landscape of developer communities, Q&A platforms serve as crucial resources for crowdsourcing developers' knowledge. A notable trend is the increasing use of images to convey complex queries more effectively. However, the current state-of-the-art method of duplicate question detection has not kept pace with this shift, which predominantly concentrates on text-based analysis. Inspired by advancements in image processing and numerous studies in software engineering illustrating the promising future of image-based communication on social coding platforms, we delved into image-based techniques for identifying duplicate questions on Stack Overflow. When focusing solely on text analysis of Stack Overflow questions and omitting the use of images, our automated models overlook a significant aspect of the question. Previous research has demonstrated the complementary nature of images to text. To address this, we implemented two methods of image analysis: first, integrating the text from images into the question text, and second, evaluating the images based on their visual content using image captions. After a rigorous evaluation of our model, it became evident that the efficiency improvements achieved were relatively modest, approximately an average of 1%. This marginal enhancement falls short of what could be deemed a substantial impact. As an encouraging aspect, our work lays the foundation for easy replication and hypothesis validation, allowing future research to build upon our approach and explore novel solutions for more effective image-driven duplicate question detection.
Access status: Open Access ,
VADViT:Vision Transformer-Driven Memory Forensics for Malicious Process Detection and Explainable Threat Attribution
(2025-07-23) Dehfouli, Yasin; Habibi Lashkari, Arash
Modern malware's increasing complexity limits traditional signature and heuristic-based detection, necessitating advanced memory forensic techniques. Machine learning offers potential but struggles with outdated feature sets, large memory data handling, and forensic explainability. To address these challenges, we propose VADViT, a vision-based transformer model that detects malicious processes by analyzing Virtual Address Descriptor (VAD) memory regions. VADViT converts these structures into Markov, entropy, and intensity-based images, classifying them using a Vision Transformer (ViT) with self-attention to enhance detection accuracy. We also introduce BCCC-MalMem-SnapLog-2025, a dataset logging process identifier (PID) for precise VAD extraction without dynamic analysis. Experimental results show 99% accuracy in binary classification and a 93% macro-average F1 score in multi-class detection. Additionally, attention-based sorting improves forensic analysis by ranking the most relevant malicious VAD regions, narrowing down the search space for forensic investigators.
Access status: Open Access ,
Application and Optimization of Prompt Engineering Techniques for Code Generation in Large Language Models
(2025-07-23) Wang, Chung-Yu; Pham, Hung Viet
Large Language Models have demonstrated remarkable capabilities across various domains, particularly in code generation and task-oriented reasoning. However, their accuracy and reliability in generating correct solutions remain a challenge due to the lack of task-specific prior knowledge and the limitations of existing prompt engineering techniques. Current state-of-the-art approaches, such as PAL, rely on manually crafted prompts and examples but often produce suboptimal results. Additionally, while numerous prompt engineering techniques have been developed to improve performance, selecting the most effective technique for a given task remains difficult since different queries exhibit varying levels of complexity. This work presents an integrated approach to enhance the application and optimization of prompt engineering for code generation. First, it introduces TITAN, a novel framework that refines language model reasoning and task execution through step-back and chain of thought prompting. TITAN eliminates the need for extensive manual task-specific instructions by leveraging analytical and code-generation capabilities, achieving state-of-the-art zero-shot performance in multiple tasks. Second, it proposes PET-Select, a prompt engineering agnostic model that classifies queries based on code complexity and dynamically selects the most suitable prompt engineering technique using contrastive learning. This approach enables Pet-Select to optimize prompt selection, leading to improved accuracy and significant reductions in token usage. Comprehensive evaluations across diverse benchmarks, including HumanEval, MBPP, and APPS, demonstrate the effectiveness of TITAN and Pet-Select. TITAN achieves up to 7.6 percent improvement over existing zero-shot methods, while Pet-Select enhances pass@1 accuracy by up to 1.9 percent and reduces token consumption by 49.9 percent. This work represents a significant advancement in optimizing prompt engineering for code generation in large language models, offering a robust and automated solution for improving performance in complex and diverse programming tasks.
Access status: Open Access ,
Explainability is a Game for Probabilistic Bisimilarity Distances
(2025-07-23) Nanah Ji, Anto; van Breugel, Franck
Software bugs cost trillions annually, requiring better bug detection tools. Testing is widely used but has limitations, especially in non-deterministic software, where code produces different outputs even with fixed inputs due to randomness and concurrency. Labelled Markov chains model randomness but suffer from state space explosion problem, where the number of states grows exponentially with system complexity. One solution is to identify behaviorally equivalent states using probabilistic bisimilarity. However, this method is not robust, small changes in probabilities can affect equivalences. To address this, probabilistic bisimilarity distances were introduced, a quantitative generalization of probabilistic bisimilarity. These distances have game-theoretic characterizations. This thesis illustrates how optimal policies, known as player's strategies, can explain distances. We formulate 1-maximal and 0-minimal policies, argue that they lead to better explanations. We present algorithms for these policies, prove an exponential lower bound for the 1-maximal algorithm, and show that symmetries simplify policies and, hence, explanations.
Access status: Open Access ,
Guiding Expert Database Tuning with Explainable AI
(2025-07-23) Chai, Andrew Brian Frederick; Szlichta, Jarek
Modern database systems, such as IBM Db2, rely on cost-based optimizers to improve workload performance. However, their decision-making processes are difficult to interpret. Tuning them for specific workloads remains challenging due to their complexity, numerous configuration options, and interaction with unique workload characteristics. Additionally, database systems increasingly rely on black-box machine learning models within the optimizer and automatic tuning tools. These black-box models lack interpretability, hindering expert trust and debugging. We propose GEX, a system that provides interpretable insights into database optimizer behavior using explainable AI techniques. We adapt XAI techniques for generating perturbation-based saliency maps from surrogate models to the domain of SQL queries. With GEX we propose a framework for how saliency scores can be used to guide experts in system tuning tasks such as statistical view creation, configuration parameter adjustment, and query rewrite. We demonstrate the ability of GEX to capture and communicate optimizer behaviour through experimental evaluation in these tasks using the TPC-DS benchmark and IBM Db2.
Access status: Open Access ,
Tuning Big Data Systems Via Deep Learning
(2025-07-23) Bianchi, Alexander Robert; Szlichta, Jarek
Modern database systems, including IBM Db2 have numerous parameters, “knobs,” that require precise configuration to achieve optimal workload performance. Even for experts, manually “tuning” these knobs is a challenging process. We present Db2une, an automatic query-aware tuning system that leverages deep learning to maximize performance while minimizing resource usage. Db2une uses a specialized transformer-based query-embedding pipeline and graph neural networks to feed as input to a stability-oriented deep reinforcement learning model. In Db2une, we introduce a multi-phased, database meta-data driven training approach—which incorporates cost estimates, interpolation of these costs, and database statistics—to efficiently discover optimal tuning configurations without the need to execute queries. Thus, our model scales to large workloads where executing queries repeatedly would be prohibitively expensive. Through experimental evaluation, we demonstrate Db2une’s efficiency and effectiveness over a variety of workloads. We show that Db2une provides recommendations surpassing those of other state-of-the-art systems and IBM experts.
Access status: Open Access ,
Visual Element Property Graphs for Bridging the Symbol Description-Recognition Gap
(2025-07-23) Dehnen, Nicholas Alexander; An, Aijun
This thesis addresses the semantic gap between visual perception and functional significance of symbols used in road vehicles. It presents a novel approach that enables users to identify and understand automotive symbols by describing what they visually perceive, translating visual descriptions into practical implications. A system combining a property graph representation of visual components and semantic relationships with a language model-powered natural language interface is developed. This method explicitly models relationships between visual elements and interpretations, differing from end-to-end vision-language models. Evaluations, using automated metrics and human assessment, demonstrate performance exceeding baseline large language models, with a BERTscore F1 of 0.765, compared to the best baseline's 0.597. Analysis of visual symbol queries reveals human description tendencies, favoring intuitive analogies and basic shapes. Contributions include a symbol decomposition methodology, an advanced property graph schema, natural language query processing, and evidence supporting structured knowledge representation for symbol description-recognition, applicable beyond automotive interfaces.
Access status: Open Access ,
Refining the sample complexity of comparative learning
(2025-07-23) Rahmanian Ashkezari, Sajad; Urner, Ruth
The PAC (Probably Approximately Correct) framework is a well-established theoretical framework for analyzing the statistical (and sometimes computational) complexity of machine learning tasks. Comparative learning is a recently introduced variation of the PAC framework that interpolates between the two standard extreme settings of realizable and agnostic PAC learning. In comparative learning the labeling is assumed to be from one hypothesis class (the source) while the learner's performance is to be measured against another hypothesis class (the benchmark). This setup allows for incorporating more specific prior knowledge into PAC-type learning bounds, which are known to be otherwise overly pessimistic. In this work we study the sample complexity of a variation of this setting we call proper comparative learning where we require the learning algorithm to output a hypothesis from the benchmark class. This setting represents model distillation tasks, where a predictor with specific requirements (e.g., interpretability) is trained on the labels from another model.
Access status: Open Access ,
SWE-Bench+: Enhanced Coding Benchmark for LLMs
(2025-07-23) Aleithan, Reem; Wang, Song
Large Language Models (LLMs) in Software Engineering (SE) can offer valuable assistance for coding tasks. To facilitate a rigorous evaluation of LLMs in practical coding contexts, Carlos et al. introduced the SWE-bench dataset, which comprises 2,294 real-world GitHub issues. Several impressive LLM-based toolkits have recently been developed and evaluated on this dataset. However, a systematic evaluation of the quality of SWE-bench remains missing. In this thesis, we address this gap by presenting an empirical analysis of the SWE-bench dataset. We manually screen instances where SWE-Agent + GPT-4 successfully resolved the issues by comparing model-generated patches with developer-written pull requests. Our analysis reveals two critical issues: (1) 33.47% of patches have solution leakage, where the fix is directly or indirectly revealed in the issue report or comments; and (2) 24.70% of successful patches are suspicious due to weak test cases that fail to detect incorrect, incomplete, or irrelevant fixes. Filtering out these problematic instances drops SWE-Agent + GPT-4’s resolution rate from 12.47% to 4.58%. Motivated by these findings, we propose SWE-Bench+, a refined version of the benchmark using two LLM-based tools: SoluLeakDetector to identify solution-leak issues and TestEnhancer to reduce weak test cases. SWE-Bench+ identifies solution-leak issues with 86% accuracy and reduces suspicious patches by 19%. To reduce the risk of potential data leakage, we collect a new set of post-cutoff GitHub issues. We then evaluate models on this dataset, observing a consistent performance drop across all models. This highlights the impact of solution leakage and weak tests in inflating resolution rates in current benchmarks.
Access status: Open Access ,
Analyzing Turning Movement Counts at Intersections through Multi-Camera Ground-Plane Reasoning
(2025-07-23) Pakdamansavoji, Sajjad; Elder, James Harvey
Classifying vehicle trajectories at intersections, known as turning movement counts (TMC), is a critical task for traffic management. Traditional approaches rely on a detect, track, count (DTC) paradigm that employs rule-based methods on image-plane data from a single camera. In this thesis, we propose a novel maximum likelihood approach that operates on the ground plane to perform trajectory classification. Our method demonstrates superior performance compared to image plane techniques and shows promising preliminary results for integrating multi-camera data on ground plane at the counting stage.
Access status: Open Access ,
Assessing and Enhancing the Quality of News Headlines Using Machine Learning
(2025-07-23) Omidvar, Amin; An, Aijun
Headlines play a pivotal role in capturing readers' attention, and their quality is critical for engaging audiences. In this thesis, we propose various solutions to assist news media in crafting high-quality headlines. First, we delve into headline quality assessment, devising four innovative indicators that automatically evaluate headlines' quality. Our proposed model empowers news outlets to automatically determine the quality of published headlines. We evaluate the quality of headlines from The Globe and Mail using these four indicators and provide insightful results. We then use this labeled data to train our novel headline quality prediction model to predict the quality of unpublished headlines, assisting journalists in selecting high-quality headlines for their articles. Furthermore, we facilitate journalists' work by recommending high-quality headlines for their articles. To accomplish this, we propose a headline generative model that learns to generate headlines using Reinforcement Learning (RL). Our model can be optimized not only with respect to a non-differentiable metric but also based on a combination of two different metrics simultaneously. Additionally, we enhance headline generation in terms of both training speed and the quality of the generated headlines by proposing a novel architecture utilizing state-of-the-art transformer models. In our architecture, after generating candidate headlines using state-of-the-art models, we select the most popular headline using our headline popularity prediction model. Moreover, we establish a popularity benchmark for evaluating headline generation models based on their ability to generate popular headlines. Lastly, we forecast changes in how people consume news articles, envisioning a shift towards interacting with agents instead of navigating news portals. To address existing challenges and enable this transition, we introduce Semantic In-Context Learning (S-ICL), an innovative approach enabling Large Language Models (LLMs) to deliver updated news in a conversational format, enhancing user engagement and comprehension for news media.
Access status: Open Access ,
Perception of Materials in Virtual Reality Based on Their Audiovisual Properties
(2025-07-23) Koppisetty, Harshitha; Allison, Robert S.
This study examined the effects of cue conflicts between auditory and visual material information in a virtual environment. All combinations of impact sounds and visual textures for four materials were paired, creating sixteen conditions. Participants, wearing a VR headset, viewed the rendered target object and heard the paired sound when it was struck with a virtual metal rod. To study the effect of agency, half the trials involved an agent striking the target (agent-interaction), while in the other half, participants struck it themselves (self-interaction). Once they classified the material of the target object, their responses and response times were recorded. Results show that participants relied largely on auditory properties when classifying materials, no significant difference was found between agent-interaction and self-interaction modes, and in four conditions, potential audiovisual illusions were observed. These findings underscore the importance of high-quality auditory cues in VR, as discordant signals can distort perceived material properties.
Access status: Open Access ,
Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks
(2025-04-10) Gouge, Justin; Wang, Ping
Within the realm of federated learning (FL), adversarial entities can poison models, slowing down or destroying the FL training process. Therefore, attack prevention and mitigation are crucial for FL. Real-world scenarios may necessitate additional separation or abstraction between clients and servers. When considering multi-layer FL systems, which contain edge server layers, the structural differences warrant new strategies to handle adversaries. While existing works primarily address attack prevention and mitigation in conventional two-layer FL systems, research on attack prevention and mitigation in multi-layer federated learning systems remains limited. This thesis aims to address this gap by investigating the defense strategies in a multi-layered FL system. We propose new methods for anomaly detection and removal of attackers/adversarial entities from training in a multi-layer FL system. First, we train a variational autoencoder (VAE) using the model updates collected from the edge servers. This allows the VAE to discern between benign and adversarial model updates. Following that, we deploy the VAE to detect which edge servers at the cohort level contain malicious clients. Subsequently, we devise two malicious client exclusion strategies: the scoring-based method, which applies a score for each client based upon its appearances within cohorts labeled as benign or malicious, and the Bayesian-based method, which uses Bayesian inference to predict if a specific client is malicious based on the statistical performance of the autoencoder. Both approaches are aimed at mitigating potential harm caused by malicious clients during model training. The experimental results demonstrate the superiority of the proposed methods over previous works for traditional FL mitigation under a variety of scenarios.
Access status: Open Access ,
Tor User De-Anonymization: Client-Side Originating Watermark
(2025-04-10) Brown, Daniel; Vlajic, Natalija
Traditional techniques for Tor user de-anonymization through a side-channel by means of traffic-watermarks are generally implemented through utilization/modulation of server-side originating traffic (SSOW). However, the effectiveness of SSOW is often hindered by significant amounts of traffic noise that accumulates along Tor’s communication pathways. In this thesis, we outline the key ideas behind our novel user de-anonymization technique that utilizes client-side originating watermarks (CSOW). We describe some potential ways this scheme could be implemented in practice while not requiring the control of any Tor node or other resource. We also demonstrate significantly superior real-world performance of our CSOW approach vs. those previously discussed in the literature. Finally, we propose the use of Long Short-Term Memory (LSTM) DNN for the purpose of more effective watermark detection. The real-world experimentations demonstrate excellent potential of our proposed LSTM-Based CSOW watermark detection system to accurately de-anonymize Tor users while keeping the number of false positives (e.g., users mistakenly accused of wrongdoing) at an absolute 0.
Access status: Open Access ,
A Solution for Scale Ambiguity in Generative Novel View Synthesis
(2025-04-10) Forghani, Fereshteh; Brubaker, Marcus
Generative Novel View Synthesis (GNVS) involves generating plausible unseen views of a scene given an initial view and the relative camera motion between the input and target views using generative models. A key limitation of current generative methods lies in their susceptibility to scale ambiguity, an inherent challenge in multi-view datasets caused by the use of monocular techniques to estimate camera positions from uncalibrated video frames. In this work, we present a novel approach to tackle this scale ambiguity in multi-view GNVS by optimizing the scales as parameters in an end-to-end fashion. We also introduce Sample Flow Consistency (SFC), a novel metric designed to assess scale consistency across samples with the same camera motion. Through various experiments, we demonstrate our approach yields improvements in terms of SFC, providing more consistent and reliable novel view synthesis.
Access status: Open Access ,
Dual-Agent Deep Reinforcement Learning Approach to GPU Job Scheduling
(2025-04-10) Shao, Yiming; An, Aijun
Public cloud GPU clusters are increasingly used for distributed deep learning tasks, making the job scheduler critical for minimizing job waiting and completion times. However, scheduling is inherently complex and NP-hard. Current approaches typically address job scheduling and GPU allocation separately, leading to suboptimal performance. DRL-based scheduling methods, while flexible, often overlook two challenges. Firstly, they focus on minimizing the total job completion time and ignore fairness in waiting times. Secondly, distributed training speed is significantly influenced by GPU communication costs, often overlooked. To address this, we introduce AttentiveSched, a DRL-based framework that simultaneously optimizes job selection and GPU assignment. AttentiveSched considers cluster topology for informed scheduling. Its two agents (job and GPU) use attention mechanisms to capture global relationships in the input sequence. By addressing fairness, job completion time, and communication costs in its rewards, AttentiveSched outperforms heuristics-based, meta-heuristics-based, and other DRL-based schedulers on real-world datasets.
Access status: Open Access ,
From Discrete to Continuous: Learning 3D Geometry from Unstructured Points by Random Continuous Space Queries
(2025-04-10) Jia, Meng; Kyan, Matthew J.
In this dissertation, we focus on generalizing recent point convolution methods and building well-behaved point-cloud 3D shape features to achieve more robust, invariant, and versatile implicit neural representations (INR) of 3D shapes. In recent efforts to explore point-cloud based learning methods to improve 3D shape analysis, there has been much attention paid to the use of INR-based frameworks. Existing methods, however, mostly formulate models with an encoder-decoder architecture that incorporates a global shape embedding space, which often fails to model fine-grained local details efficiently, limiting overall generalization performance. To overcome this problem, we propose a convolutional feature space sampling operation (Dual-Feature Sampling or DFS) and develop a novel INR learning framework (Stochastic Continuous Function Learning or SCFL). This framework is first adapted and evaluated for its use in surface reconstruction of generic objects from sparsely sampled point clouds, which is a task that has been extensively used to bench-mark INR 3D shape learning methods. This study demonstrates impressive capabilities of our method, namely: 1) an ability to faithfully recover fine details and uncommon shape characteristics; 2) improved robustness to point-cloud rotation; 3) flexibility to handle different levels of sparsity in the input point clouds; 4) significantly better generalization in the presence of unseen shape categories. In addition, the proposed DFS operator proposed for this framework is well-formulated and general enough that it can be easily made compatible for integration into existing systems designed to address more complex 3D shape tasks. In this work, we harness this powerful ability to represent shape, within a newly proposed SCFL-based occupancy network, applied to shape based processing problems in medical image registration and segmentation. Specifically, our network is adapted and applied to two different, traditionally challenging problems: 1) liver image-to-physical registration; and 2) tumour-bearing whole brain segmentation. In both of these tasks, significant deformation can severely degrade and hinder performance. We illustrate however, that accuracy in both tasks can be considerably improved over baseline methods using our proposed network. Finally, through the course of the investigations conducted, an intensive effort has been made throughout the dissertation to review, analyze and offer speculative insights into the features of these proposed innovations, their role in the configurations presented, as well as possible utility in other scenarios and configurations that may warrant future investigation. It is our hope that the work in this dissertation may help to spark new ideas to advance the state of the art in learning-based representation of 3D shapes and encourage more interest in novel applications of INR to solve real-world problems.

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