arXiv subjects

Weihao Qu

Publications and source records attributed to Weihao Qu.

11 recordsLinked to original sources

arXiv2026

Multimodal Injury Risk Prediction in Tennis

Machine learning has had a significant positive impact on the prediction of athlete performance and injury risk. Most works in this field rely on subjective observations and expert assessments, which restrict their effectiveness. In sports like soccer, basketball, and wrestling, some studies attempt to address this challenge by integrating data from alternative sources, such as readings from wearable devices, alongside traditional subjective observations and expert assessments to enhance accuracy. However, similar research in tennis remains largely unexplored. In this paper, we propose a multimodal Predictive Athlete Readiness framework for Tennis (PART) to assess both performance and injury risk in tennis players. By leveraging machine learning and deep learning techniques, PART processes multiple sources of data collected from nine collegiate tennis players, including physiological metrics, training and match data, sleep data from wearable devices, self-reported information via daily questionnaires, jump assessments, and motion analysis from match play videos. PART captures four characteristics of tennis players: overall wellness, injury risk, physical capability, and playing style. By integrating these four characteristics by supervised learning, it is capable of providing a holistic assessment of the tennis athlete's condition, along with advanced forecasts of specific body areas at risk such as the upper body (e.g., elbows) or lower body (e.g., knees). Our evaluation, conducted with data from nine collegiate tennis players, shows that PART achieves strong performance in predicting both overall wellness and injury risk. Additionally, our framework also shows promise for recreational tennis players, who often suffer from injuries due to incorrect playing techniques.

cs.LG↗

arXiv2026

Security Education in Higher Education through AI-Powered Gamification

Cybersecurity education is facing more challenges as AI-driven attacks are becoming increasingly realistic and difficult to detect. Traditional video-based cybersecurity training in higher education often suffers from both low engagement and limited effectiveness. This dilemma motivates educators to explore innovative approaches, such as AI-powered gamification, which can deliver engaging, meaningful, and personalized learning experiences. By presenting content in a more interactive and user-friendly way, these methods have the potential to significantly improve both learner engagement and educational outcomes. This paper explores AI-powered gamification in cybersecurity education through the development of several short, mobile-friendly games. These games cover a range of topics from password security to text and phone scam recognition, incorporate multiple gamification strategies, including quiz-based, narrative-based, and simulation-based designs, as well as interactive formats such as TikTok Mini-Games. We conducted a two-tiered evaluation with 59 college students (comprising 9 technical experts and 50 general users), and the results indicate the potential of AI-powered gamification to improve engagement and increase attention to cybersecurity topics in higher education.

cs.CY↗

arXiv2026

Multimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning

Machine learning has had a positive impact on the sports industry, with one of its most promising applications being the prediction of athlete performance and injury risk. Recent advances have employed state-of-the-art models to improve prediction accuracy, yet progress remains limited by data availability and the reliance on subjective observations or expert assessments. To address these limitations, researchers in sports such as soccer, basketball, and wrestling have begun integrating heterogeneous data sources, such as wearable device readings, with traditional subjective assessments. However, similar multimodal approaches remain underexplored in tennis. In this work, we propose a multimodal weighted ensemble learning framework, Predictive Athlete Readiness for Tennis (PART), to monitor athlete wellness and estimate near-term injury risk in tennis players. PART processes a wide range of inputs, including physiological metrics, training and match data, sleep information from wearable devices, self-reported questionnaires, vertical jump assessments, and motion analysis from match-play videos. From these modalities, specialized machine learning and deep learning models independently extract four athlete-specific characteristics: overall wellness, injury risk, physical capability, and playing style. To overcome the complexity of combining these diverse modalities, PART employs a supervised weighted ensemble integration strategy, assigning adaptive weights to each predictive model based on its reliability. Evaluation of multimodal data collected from nine collegiate tennis players demonstrates that PART achieves strong performance in monitoring athlete wellness and estimating near-term injury susceptibility. Beyond collegiate athletes, the framework also shows promise for recreational tennis players, offering personalized insights to mitigate injury risk and optimize performance.

cs.LG↗

arXiv2026

Time-Aware Tranformer-Based Prediction Model for AECOPD

The rapid symptom change of Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) makes it critical to have time-sensitive prediction models. However, most current machine learning models studying AECOPD use clinical and laboratory data, which will inevitably cause latency. To ensure timely detection of AECOPD and minimize latency, this paper focuses on home monitoring scenarios where only respiratory data from daily-use ventilators is available. We introduce a Time-Aware transformer-based AECOPD prediction model, which generates meaningful patient representations using the Time-Aware transformer to capture the symptoms and their temporal progression in ventilator data. Our experimental results demonstrate that our Time-Aware transformer-based approach outperforms traditional methods in multiple classification tasks, highlighting its potential to enhance AECOPD prediction accuracy.

cs.LG↗

arXiv2026

Enhancing User Resilience Against AI-Augmented Phishing: A Two-Stage Framework for Detection and Personalized Training

The rapid development of artificial intelligence, including agents and deepfake techniques, has accelerated phishing attacks and lowered the threshold for attackers. Modern phishing attacks now blend multiple tactics, including social engineering, URL spoofing, and AI deepfakes enabling adversaries to craft highly convincing messages that exploit human vulnerabilities and bypass traditional detection systems. At the same time, current security awareness education struggles to keep up with the speed, sophistication, and complexity of these evolving threats. To address this challenge, we propose a two-stage anti-phishing framework, CyberGLA, that combines technical defense and user-centered security education. In the Detection stage, we introduce EmailKnight, a spoof detection tool that performs multi-level email analysis. To enhance user awareness, the Training stage incorporates a large language model (LLM)-based security coach that dynamically selects personalized training modules based on the outcomes of the Detection stage. This dual purpose design philosophy enables effective protection against the evolving threats of modern email phishing attacks.

cs.CR↗

arXiv2026

A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) can worsen rapidly, making timely prediction a clinical priority. Most existing machine learning approaches rely on episodically collected clinical variables, introducing delays that limit their practical utility in home monitoring settings. Home ventilators offer a lower-latency alternative, producing a near-continuous record of respiratory status during daily use. However existing ventilator-based approaches either compress the waveform into handcrafted features or focus primarily on binary risk classification, leaving the timing of an impending event unresolved. In this paper, we present a two-stage framework that operates directly on raw pressure and flow waveforms from the most recent seven days of home ventilator use. The first-stage classification model identifies patients at high risk of a severe exacerbation. The second-stage regression model then estimates how many days remain before the event occurs. Our experimental results demonstrate that the two-stage model outperforms traditional baseline models on both risk classification and time-to-event estimation, with our selected Stage 1 classifier achieving F1 = 0.91 and our Stage 2 regression model achieving RMSE = 1.00 days and R^2 = 0.76, giving clinicians both an early warning and actionable lead time before a severe exacerbation occurs.

cs.LG↗

arXiv2026

Program Analysis for Adaptive Data Analysis

Data analyses are usually designed to identify some property of the population from which the data are drawn, generalizing beyond the specific data sample. For this reason, data analyses are often designed to produce a low generalization error, so that the result of an analysis on sample data does not differ too much from the result one would achieve over the entire population. An adaptive data analysis can be seen as a process composed of multiple queries interrogating some data, where the choice of which query to run next may rely on the results of previous queries. The generalization error of each individual query can be controlled using well-established statistical techniques. However, when queries are arbitrarily composed, errors can propagate through the chain of queries and lead to high generalization error. To address this issue, several techniques guarantee bounds not only on single queries but also on composed analyses. The choice of technique often depends on the chain of queries that an adaptive data analysis can generate. In this work, we consider adaptive data analyses implemented as while-like programs and design a program analysis to help identify which technique to use to control their generalization errors. More specifically, we formalize the intuitive notion of adaptivity as a quantitative property of programs. Based on this definition, we design a program analysis for soundly approximating this quantity. The analysis represents the data analysis as a weighted dependency graph, where weights upper-bound how often variables can be reached, and uses a path-search strategy to upper-bound adaptivity. We implement our program analysis and show that it can analyze several concrete data analyses with different adaptivity structures.

cs.PL↗

arXiv2026

Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-12 and Undergraduates

K-12 students often possess creative engineering ideas but lack technical skills to build them, while undergraduates have coding expertise but few opportunities to lead real-world projects or mentor others. The rapid development of AI-assisted tools offers a potential bridge to connect these groups, yet the structure for effective K-12 and university collaborations remains underexplored. This paper introduces a multi-tiered mentorship framework enabling high school students to engage in authentic engineering through AI-assisted development using large language models and AI agents, while undergraduate mentors provide architectural oversight. We test this framework through LuckyTag, a privacy-preserving NFC-based lost-and-found system. The model positions high schoolers as product leads, undergraduates as technical architects, and faculty as minimal-intervention advisors. A pilot with four high school students, three undergraduates and two faculty yielded survey data showing high perceived barrier removal and gains in system architecture understanding. Thematic analysis reveals that AI amplifies rather than supplants mentoring demands, requiring human oversight for logic and security. These findings suggest a hybrid model for equitable K-12 and university collaboration on computing integration that emphasizes "AI micromanagement" and architectural reasoning over traditional syntax.

cs.CY↗

arXiv2026

LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University

As generative AI reshapes professional and educational practice, institutions face a challenge: how to support diverse learners, from non-coders to advanced students, in building confidence and practice with AI-supported problem solving. Most institutional responses bifurcate into conceptual workshops for general audiences or technical courses for computer science majors, leaving few spaces where mixed-ability learners can engage common AI tasks at levels matched to their prior experience. This experience report presents the LearnAI Framework, a two-layer model for just-in-time AI co-creation piloted at a comprehensive teaching university. The Wide-Exposure Layer embeds short presentations in existing courses to build AI awareness at scale, reaching students and faculty across 18 courses in five disciplines. The Customized Co-Creation Layer provides opt-in, one-on-one sessions where clients work with trained undergraduate tutors through a 5-Stage Pedagogical Script: Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, and Ethical Reflection. Over two semesters, 35 clients co-created 36 portfolio websites and over 20 deployed web applications. Interviews with five clients and two tutors suggest a recurring change in how clients described AI use, shifting from treating AI as a passive answer machine to engaging it as a collaborative tool under human direction. A small paired pre/post AI readiness dataset (N = 7) provides preliminary descriptive context, and tutor accounts document how the pedagogical script was enacted and adapted across client types. We report on boundary cases including clients who felt overwhelmed and respondents who deliberately rejected AI use. This paper contributes a practical, adoptable framework with initial evidence from a single institution.

cs.CY↗

arXiv2019

Relational Cost Analysis for Functional-Imperative Programs

Relational cost analysis aims at formally establishing bounds on the difference in the evaluation costs of two programs. As a particular case, one can also use relational cost analysis to establish bounds on the difference in the evaluation cost of the same program on two different inputs. One way to perform relational cost analysis is to use a relational type-and-effect system that supports reasoning about relations between two executions of two programs. Building on this basic idea, we present a type-and-effect system, called ARel, for reasoning about the relative cost of array-manipulating, higher-order functional-imperative programs. The key ingredient of our approach is a new lightweight type refinement discipline that we use to track relations (differences) between two arrays. This discipline combined with Hoare-style triples built into the types allows us to express and establish precise relative costs of several interesting programs which imperatively update their data.

cs.PL↗

arXiv2018

Bidirectional Type Checking for Relational Properties

Relational type systems have been designed for several applications including information flow, differential privacy, and cost analysis. In order to achieve the best results, these systems often use relational refinements and relational effects to maximally exploit the similarity in the structure of the two programs being compared. Relational type systems are appealing for relational properties because they deliver simpler and more precise verification than what could be derived from typing the two programs separately. However, relational type systems do not yet achieve the practical appeal of their non-relational counterpart, in part because of the lack of a general foundations for implementing them. In this paper, we take a step in this direction by developing bidirectional relational type checking for systems with relational refinements and effects. Our approach achieves the benefits of bidirectional type checking, in a relational setting. In particular, it significantly reduces the need for typing annotations through the combination of type checking and type inference. In order to highlight the foundational nature of our approach, we develop bidirectional versions of several relational type systems which incrementally combine many different components needed for expressive relational analysis.

cs.PL↗