Yixue Zhao

Meghna Roy Chowdhury, Wei Xuan, Shreyas Sen et al.

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding. To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model that connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. We evaluate I-HOPE on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, I-HOPE distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support. The code is available at https://github.com/roycmeghna/I-HOPE.

Wei Xuan, Meghna Roy Chowdhury, Yi Ding et al.

The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among young adults, while offering numerous benefits, has also been linked to negative outcomes such as addiction and regret, significantly impacting well-being. Leveraging the longest longitudinal dataset collected over four college years through passive mobile sensing, this study is the first to examine the relationship between students' smartphone unlocking behaviors and their mental health at scale in real-world settings. We provide the first evidence demonstrating the predictability of phone unlocking behaviors for mental health outcomes based on a large dataset, highlighting the potential of these novel features for future predictive models. Our findings reveal important variations in smartphone usage across genders and locations, offering a deeper understanding of the interplay between digital behaviors and mental health. We highlight future research directions aimed at mitigating adverse effects and promoting digital well-being in this population.

Emily Zhou, Khushboo Khatri, Yixue Zhao et al.

The field of affective computing focuses on recognizing, interpreting, and responding to human emotions, and has broad applications across education, child development, and human health and wellness. However, developing affective computing pipelines remains labor-intensive due to the lack of software frameworks that support multimodal, multi-domain emotion recognition applications. This often results in redundant effort when building pipelines for different applications. While recent frameworks attempt to address these challenges, they remain limited in reducing manual effort and ensuring cross-domain generalizability. We introduce AffectEval, a modular and customizable framework to facilitate the development of affective computing pipelines while reducing the manual effort and duplicate work involved in developing such pipelines. We validate AffectEval by replicating prior affective computing experiments, and we demonstrate that our framework reduces programming effort by up to 90%, as measured by the reduction in raw lines of code.

Sheng Yu, Narjes Nourzad, Randye J. Semple et al.

The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand beyond traditional clinical settings to support daily life. However, the effectiveness of current mHealth solutions is impeded by the lack of context-awareness, personalization, and modularity to foster their reusability. This paper introduces CAREForMe, a contextual multi-armed bandit (CMAB) recommendation framework for mental health. Designed with context-awareness, personalization, and modularity at its core, CAREForMe harnesses mobile sensing and integrates online learning algorithms with user clustering capability to deliver timely, personalized recommendations. With its modular design, CAREForMe serves as both a customizable recommendation framework to guide future research, and a collaborative platform to facilitate interdisciplinary contributions in mHealth research. We showcase CAREForMe's versatility through its implementation across various platforms (e.g., Discord, Telegram) and its customization to diverse recommendation features.

Yixue Zhao, Siwei Yin, Adriana Sejfia et al.

Prefetching web pages is a well-studied solution to reduce network latency by predicting users' future actions based on their past behaviors. However, such techniques are largely unexplored on mobile platforms. Today's privacy regulations make it infeasible to explore prefetching with the usual strategy of amassing large amounts of data over long periods and constructing conventional, "large" prediction models. Our work is based on the observation that this may not be necessary: Given previously reported mobile-device usage trends (e.g., repetitive behaviors in brief bursts), we hypothesized that prefetching should work effectively with "small" models trained on mobile-user requests collected during much shorter time periods. To test this hypothesis, we constructed a framework for automatically assessing prediction models, and used it to conduct an extensive empirical study based on over 15 million HTTP requests collected from nearly 11,500 mobile users during a 24-hour period, resulting in over 7 million models. Our results demonstrate the feasibility of prefetching with small models on mobile platforms, directly motivating future work in this area. We further introduce several strategies for improving prediction models while reducing the model size. Finally, our framework provides the foundation for future explorations of effective prediction models across a range of usage scenarios.

Yixue Zhao, Justin Chen, Adriana Sejfia et al.

UI testing is tedious and time-consuming due to the manual effort required. Recent research has explored opportunities for reusing existing UI tests from an app to automatically generate new tests for other apps. However, the evaluation of such techniques currently remains manual, unscalable, and unreproducible, which can waste effort and impede progress in this emerging area. We introduce FrUITeR, a framework that automatically evaluates UI test reuse in a reproducible way. We apply FrUITeR to existing test-reuse techniques on a uniform benchmark we established, resulting in 11,917 test reuse cases from 20 apps. We report several key findings aimed at improving UI test reuse that are missed by existing work.

Yixue Zhao

A large number of mobile-app analysis and instrumentation techniques have emerged in the past decade. However, those techniques' components are difficult to extract and reuse outside their original tools, their evaluation results are hard to reproduce, and the tools themselves are hard to compare. This paper introduces DECREE, an infrastructure intended to guide such techniques to be reproducible, practical, reusable, and easy to adopt in practice. DECREE allows researchers and developers to easily discover existing solutions to their needs, enables unbiased and reproducible evaluation, and supports easy construction and execution of replication studies. The paper describes DECREE's three modules and its potential to fundamentally alter how research is conducted in this area.

Yixue Zhao, Nenad Medvidovic

A large number of techniques for analyzing and optimizing mobile apps have emerged in the past decade. However, those techniques' components are notoriously difficult to extract and reuse outside their original tools. This paper introduces MAOMAO, a microservice-based reference architecture for reusing and integrating such components. MAOMAO's twin goals are (1) adoption of available app optimization techniques in practice and (2) improved construction and evaluation of new techniques. The paper uses several existing app optimization techniques to illustrate both the motivation behind MAOMAO and its potential to fundamentally alter the landscape in this area.

Yixue Zhao, Marcelo Schmitt Laser, Yingjun Lyu et al.

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applications

Yixue Zhao, Paul Wat, Marcelo Schmitt Laser et al.

Network latency in mobile software has a large impact on user experience, with potentially severe economic consequences. Prefetching and caching have been shown effective in reducing the latencies in browser-based systems. However, those techniques cannot be directly applied to the emerging domain of mobile apps because of the differences in network interactions. Moreover, there is a lack of research on prefetching and caching techniques that may be suitable for the mobile app domain, and it is not clear whether such techniques can be effective or whether they are even feasible. This paper takes the first step toward answering these questions by conducting a comprehensive study to understand the characteristics of HTTP requests in over 1000 popular Android apps. Our work focuses on the prefetchability of requests using static program analysis techniques and cacheability of resulting responses. We find that there is a substantial opportunity to leverage prefetching and caching in mobile apps, but that suitable techniques must take into account the nature of apps' network interactions and idiosyncrasies such as untrustworthy HTTP header information. Our observations provide guidelines for developers to utilize prefetching and caching schemes in app development, and motivate future research in this area.