arXiv subjects

Daqing Hou

Publications and source records attributed to Daqing Hou.

17 recordsLinked to original sources

arXiv2026

Early Adoption of Agentic Coding Tools by GitHub Projects

Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level. In this paper, we analyze 25,264 agentic PRs from 2,361 popular GitHub repositories to investigate (1) the adoption of agentic coding tools, (2) project-level agentic PR productivity, and (3) human-agent collaboration patterns. Our results show that the median repository generates only one to two agentic PRs during a three-month period, indicating that intensive adoption remains concentrated in a small subset of projects. At the same time, small projects (1-5 contributors) exhibit higher participation ratios and average levels of agentic PR activity than medium-sized and large projects. We also observe substantial variation in project-level agentic PR productivity. While a small number of projects exceed an industry-reported estimate of 36 PRs per participant during the three-month observation period, most projects remain below this threshold. Finally, human-agent collaboration is dominated by a single-human oversight model, in which one developer reviews and/or modifies the agent's contributions, while multi-human collaboration patterns remain uncommon. These findings provide early empirical evidence on how open-source projects organize human oversight around agentic coding tools and suggest that successful integration of agent-generated contributions depends not only on advances in agent capabilities but also on the human and organizational processes that govern their use. Because this study captures an early snapshot of agent adoption, future work should continue to track how adoption patterns evolve over time.

cs.SE↗

arXiv2026

Adoption-Ready Project-Based Learning for Computing Education: The FORAP Framework and a Multi-Scale Project Portfolio

This innovative practice full paper presents FORAP (Framework for Organizing Reusable and Adaptable PjBL Projects) and a portfolio of 14 adoption-ready project-based learning (PjBL) project packages built with the framework. PjBL in computing education offers strong educational benefits, yet its adoption remains limited by high instructor workload and recurring student technical challenges. FORAP addresses these barriers by organizing each package around a project designed with aligned learning objectives and described through project attributes, along with coordinated instructor, student, and assessment materials that support adoption and adaptation across diverse computing courses. We report on four years of deployment across 44 classroom trials at seven universities, drawing on feedback from students, instructors, and advisory board members. Results suggest that structured project packaging supports feasible adoption with limited modification effort and that targeted support materials help reduce the technical barriers that commonly hinder student engagement. The contributions of this work include FORAP and a multi-scale portfolio that demonstrates its use across diverse computing domains and project scopes, offering practical guidance for instructors who wish to design, adopt, or adapt reusable PjBL projects in computing education.

cs.CY↗

arXiv2026

LLM-Generated Design Problems for Assessing Higher-Order Thinking in Project-Based Learning

Project-based learning (PjBL) is common in computing education, but traditional assessments of PjBL often fail to capture higher-order thinking (HOT), especially in transfer contexts. This study introduces "design problems" (DPs): concise, scenario-based prompts that require applying project concepts in new situations, to address this gap. We examined instructor perceptions, the ability of large language models (LLMs) to generate DPs, and student experiences. Surveys of 31 instructors, evaluation of 80 LLM-generated DPs, and student performance data showed that while instructors value DPs, creation effort is a barrier. LLMs helped by producing high-quality prompts with strong expert agreement. Students rated DPs from different LLMs similarly, and their performance on DP tasks showed negligible correlation with traditional project grades, suggesting DPs may capture distinct aspects of HOT. Keystroke data also suggested deeper cognitive engagement of students through planning and revision behaviors. Overall, DPs appear to be a useful complement to traditional assessments, especially in situations where AI use or collaboration may undermine individual learning.

cs.CY↗

arXiv2026

Do Privacy Policies Match with the Logs? An Empirical Study of Privacy Disclosure in Android Application Logs

Privacy policies are intended to inform users about how software systems collect and handle data, yet they often remain vague or incomplete. This paper presents an empirical study of patterns in log-related statements within privacy policies and their alignment with privacy disclosures observed in Android application logs. We analyzed 1,000 Android apps across multiple categories, generating 86,836,964 log entries. Our findings reveal that while most applications (88.0%) provide privacy policies, only 28.5% explicitly mention logging practices. Among those that reference logging, most clearly describe what information is logged; however, 27.7% of log-related statements remain overly simplistic or vague, offering limited insight into actual data collection. We further observed widespread privacy leakages in application logs, with 67.6% of apps leaking sensitive information not mentioned in their policies. Alarmingly, only 0.4% of applications demonstrated consistent alignment between declared policy contents and actual logged data. These findings highlight that current privacy policies provide incomplete or ambiguous descriptions of logging practices, which frequently do not align with actual logging behaviors.

cs.CR↗

arXiv2025

Factors Impacting Faculty Adoption of Project-Based Learning in Computing Education: a Survey

This research full paper investigates the factors influencing computing educators' adoption of project-based learning (PjBL) in software engineering and computing curricula. Recognized as a student-centered pedagogical approach, PjBL has the potential to enhance student motivation, engagement, critical thinking, collaboration, and problem-solving skills. Despite these benefits, faculty adoption remains inconsistent due to challenges such as insufficient institutional support, time constraints, limited training opportunities, designing or sourcing projects, and aligning them with course objectives. This research explores these barriers and investigates the strategies and resources that facilitate a successful adoption. Using a mixed-methods approach, data from 80 computing faculty were collected through an online survey comprising closed-ended questions to quantify barriers, enablers, and resource needs, along with an open-ended question to gather qualitative insights. Quantitative data were analyzed using statistical methods, while qualitative responses underwent thematic analysis. Results reveal that while PjBL is widely valued, its adoption is often selective and impacted by challenges in planning and managing the learning process, designing suitable projects, and a lack of institutional support, such as time, funding, and teaching assistants. Faculty are more likely to adopt or sustain PjBL when they have access to peer collaboration, professional development, and institutional incentives. In addition, sourcing projects from research, industry partnerships, and borrowing from peers emerged as key facilitators for new projects. These findings underscore the need for systemic support structures to empower faculty to experiment with and scale PjBL practices.

cs.SE↗

arXiv2025

Explainable Face Recognition via Improved Localization

Biometric authentication has become one of the most widely used tools in the current technological era to authenticate users and to distinguish between genuine users and imposters. Face is the most common form of biometric modality that has proven effective. Deep learning-based face recognition systems are now commonly used across different domains. However, these systems usually operate like black-box models that do not provide necessary explanations or justifications for their decisions. This is a major disadvantage because users cannot trust such artificial intelligence-based biometric systems and may not feel comfortable using them when clear explanations or justifications are not provided. This paper addresses this problem by applying an efficient method for explainable face recognition systems. We use a Class Activation Mapping (CAM)-based discriminative localization (very narrow/specific localization) technique called Scaled Directed Divergence (SDD) to visually explain the results of deep learning-based face recognition systems. We perform fine localization of the face features relevant to the deep learning model for its prediction/decision. Our experiments show that the SDD Class Activation Map (CAM) highlights the relevant face features very specifically compared to the traditional CAM and very accurately. The provided visual explanations with narrow localization of relevant features can ensure much-needed transparency and trust for deep learning-based face recognition systems.

cs.CV↗

arXiv2025

An Empirical Study on the Capability of LLMs in Decomposing Bug Reports

Background: Bug reports are essential to the software development life cycle. They help developers track and resolve issues, but are often difficult to process due to their complexity, which can delay resolution and affect software quality. Aims: This study investigates whether large language models (LLMs) can assist developers in automatically decomposing complex bug reports into smaller, self-contained units, making them easier to understand and address. Method: We conducted an empirical study on 127 resolved privacy-related bug reports collected from Apache Jira. We evaluated ChatGPT and DeepSeek using different prompting strategies. We first tested both LLMs with zero-shot prompts, then applied improved prompts with demonstrations (using few-shot prompting) to measure their abilities in bug decomposition. Results: Our findings show that LLMs are capable of decomposing bug reports, but their overall performance still requires further improvement and strongly depends on the quality of the prompts. With zero-shot prompts, both studied LLMs (ChatGPT and DeepSeek) performed poorly. After prompt tuning, ChatGPT's true decomposition rate increased by 140\% and DeepSeek's by 163.64\%. Conclusions: LLMs show potential in helping developers analyze and decompose complex bug reports, but they still need improvement in terms of accuracy and bug understanding.

cs.SE↗

arXiv2025

Physics-Aware POD-Based Learning for Ab initio QEM-Galerkin Simulations of Periodic Nanostructures

Quantum nanostructures offer crucial applications in electronics, photonics, materials, drugs, etc. For accurate design and analysis of nanostructures and materials, simulations of the Schrodinger or Schrodinger-like equation are always needed. For large nanostructures, these eigenvalue problems can be computationally intensive. One effective solution is a learning method via Proper Orthogonal Decomposition (POD), together with ab initio Galerkin projection of the Schrodinger equation. POD-Galerkin projects the problem onto a reduced-order space with the POD basis representing electron wave functions (WFs) guided by the first principles in simulations. To minimize training effort and enhance robustness of POD-Galerkin in larger structures, the quantum element method (QEM) was proposed previously, which partitions nanostructures into generic quantum elements. Larger nanostructures can then be constructed by the trained generic quantum elements, each of which is represented by its POD-Galerkin model. This work investigates QEM-Galerkin thoroughly in multi-element quantum-dot (QD) structures on approaches to further improve training effectiveness and simulation accuracy and efficiency for QEM-Galerkin. To further improve computing speed, POD and Fourier bases for periodic potentials are also examined in QEM-Galerkin simulations. Results indicate that, considering efficiency and accuracy, the POD potential basis is superior to the Fourier potential basis even for periodic potentials. Overall, QEM-Galerkin offers more than a 2-order speedup in computation over direct numerical simulation for multi-element QD structures, and more improvement is observed in a structure comprising more elements.

physics.comp-ph↗

arXiv2025

Impact of Data Breadth and Depth on Performance of Siamese Neural Network Model: Experiments with Three Keystroke Dynamic Datasets

Deep learning models, such as the Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impacts of dataset breadth (i.e., the number of subjects) and depth (e.g., the amount of training samples per subject) on the performance of these models is often informally assumed, and remains under-explored. To this end, we have conducted extensive experiments using the concepts of "feature space" and "density" to guide and gain deeper understanding on the impact of dataset breadth and depth on three publicly available keystroke datasets (Aalto, CMU and Clarkson II). Through varying the number of training subjects, number of samples per subject, amount of data in each sample, and number of triplets used in training, we found that when feasible, increasing dataset breadth enables the training of a well-trained model that effectively captures more inter-subject variability. In contrast, we find that the extent of depth's impact from a dataset depends on the nature of the dataset. Free-text datasets are influenced by all three depth-wise factors; inadequate samples per subject, sequence length, training triplets and gallery sample size, which may all lead to an under-trained model. Fixed-text datasets are less affected by these factors, and as such make it easier to create a well-trained model. These findings shed light on the importance of dataset breadth and depth in training deep learning models for behavioral biometrics and provide valuable insights for designing more effective authentication systems.

cs.LG↗

arXiv2024

A large-scale study of performance and equity of commercial remote identity verification technologies across demographics

As more types of transactions move online, there is an increasing need to verify someone's identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver's license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was inequitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.

cs.CV↗

arXiv2023

Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction

Fingerprints are widely recognized as one of the most unique and reliable characteristics of human identity. Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process. Various types of fingerprint sensors, such as optical, capacitive, and ultrasonic sensors, employ distinct techniques to gather and analyze fingerprint data. This dependency on specific hardware or sensors creates a barrier or challenge for the broader adoption of fingerprint based biometric systems. This limitation hinders the widespread adoption of fingerprint authentication in various applications and scenarios. Border control, healthcare systems, educational institutions, financial transactions, and airport security face challenges when fingerprint sensors are not universally available. To mitigate the dependence on additional hardware, the use of contactless fingerprints has emerged as an alternative. Developing precise fingerprint segmentation methods, accurate fingerprint extraction tools, and reliable fingerprint matchers are crucial for the successful implementation of a robust contactless fingerprint authentication system. This paper focuses on the development of a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. Our system leverages deep learning techniques to achieve high segmentation accuracy and reliable extraction of fingerprints from contactless fingerprint images. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%. These results demonstrate the effectiveness of our novel contactless fingerprint segmentation and extraction tools.

cs.CV↗

arXiv2023

A Hybrid Optimization and Deep Learning Algorithm for Cyber-resilient DER Control

With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.

eess.SY↗

arXiv2023

Keystroke Dynamics: Concepts, Techniques, and Applications

Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques, such as biometric authentication systems, have been developed in recent years. This paper provides a detailed review of keystroke-based authentication systems and their applications. Keystroke dynamics is a behavioral biometric that is emerging as an important tool for cybersecurity as it promises to be non-intrusive and cost-effective. In addition, no additional hardware is required, making it convenient to deploy. This survey covers novel keystroke datasets, state-of-the-art keystroke authentication algorithms, keystroke authentication on touch screen and mobile devices, and various prominent applications of such techniques beyond authentication. The paper covers all the significant aspects of keystroke dynamics and can be considered a reference for future researchers in this domain. The paper includes a discussion of the latest keystroke datasets, providing researchers with an up-to-date resource for analysis and experimentation. In addition, this survey covers the state-of-the-art algorithms adopted within this domain, offering insights into the cutting-edge techniques utilized for keystroke analysis. Moreover, this paper explains the diverse applications of keystroke dynamics, particularly focusing on security, verification, and identification uses. Furthermore, this paper presents a summary of future research opportunities, highlighting potential areas for exploration and development within the realm of keystroke dynamics. This forward-looking perspective aims to inspire further inquiry and innovation, guiding the trajectory of future studies in this dynamic field.

cs.CR↗

arXiv2022

Statistical Methods for Assessing Differences in False Non-Match Rates Across Demographic Groups

Biometric recognition is used across a variety of applications from cyber security to border security. Recent research has focused on ensuring biometric performance (false negatives and false positives) is fair across demographic groups. While there has been significant progress on the development of metrics, the evaluation of the performance across groups, and the mitigation of any problems, there has been little work incorporating statistical variation. This is important because differences among groups can be found by chance when no difference is present. In statistics this is called a Type I error. Differences among groups may be due to sampling variation or they may be due to actual difference in system performance. Discriminating between these two sources of error is essential for good decision making about fairness and equity. This paper presents two novel statistical approaches for assessing fairness across demographic groups. The first methodology is a bootstrapped-based hypothesis test, while the second is simpler test methodology focused upon non-statistical audience. For the latter we present the results of a simulation study about the relationship between the margin of error and factors such as number of subjects, number of attempts, correlation between attempts, underlying false non-match rates(FNMR's), and number of groups.

stat.ME↗

arXiv2022

Mouse Dynamics Behavioral Biometrics: A Survey

Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish an individual based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this paper, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. Lastly, we end the paper with presenting challenges and promising research opportunities.

cs.CR↗

arXiv2021

Machine Learning at the Network Edge: A Survey

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

cs.LG↗

arXiv2020

Fast Free-text Authentication via Instance-based Keystroke Dynamics

Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.

cs.CR↗