A5039928918- Privacy, Security, and Data Protection
- Topic Modeling
- Parallel Computing and Optimization Techniques
- Natural Language Processing Techniques
- Privacy-Preserving Technologies in Data
- Advanced Malware Detection Techniques
- Speech and dialogue systems
- Advanced Data Storage Technologies
- Extracellular vesicles in disease
- Corporate Finance and Governance
- Ethics and Social Impacts of AI
- User Authentication and Security Systems
- Banking stability, regulation, efficiency
- Cardiac Fibrosis and Remodeling
- Digital and Cyber Forensics
- Advanced Memory and Neural Computing
- Peptidase Inhibition and Analysis
- Sexuality, Behavior, and Technology
- Chinese history and philosophy
- Cloud Computing and Resource Management
- COVID-19 Digital Contact Tracing
- Solar-Powered Water Purification Methods
- RNA Interference and Gene Delivery
- Hate Speech and Cyberbullying Detection
- Artificial Intelligence in Healthcare and Education
Beijing Tian Tan Hospital
2025
Capital Medical University
2025
Northeastern University
2024-2025
Nanjing Medical University
2025
Peking University Shenzhen Hospital
2010-2024
Jilin Agricultural University
2024
Universidad del Noreste
2024
Yunnan University
2021-2023
Capital University of Economics and Business
2018-2023
Carnegie Mellon University
2016-2023
The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user requires an in-depth understanding the risks concern users most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures real-world ChatGPT conversations and conducted semi-structured interviews...
Long DRAM latency is a critical performance bottleneck in current systems. access defined by three fundamental operations that take place within the cell array: (i) activation of memory row, which opens row to perform accesses; (ii) precharge, prepares array for next access; and (iii) restoration restores values cells were destroyed due activation. There significant variation each these across single chip irregularity manufacturing process. As result, some are inherently faster access, while...
Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional process. However, low adoption rates have become major issue that prevents these from achieving their full potential. In this paper, we present national-scale survey experiment (
It has become increasingly difficult to understand the complex interaction between modern applications and main memory, composed of Dynamic Random Access Memory (DRAM) chips. Manufacturers researchers are developing many different types DRAM, with each DRAM type catering needs (e.g., high throughput, low power, memory density). At same time, access patterns prevalent emerging rapidly diverging, as these manipulate larger data sets in very ways. As a result, combined DRAM-workload behavior is...
While online developer forums are major resources of knowledge for application developers, their roles in promoting better privacy practices remain underexplored. In this paper, we conducted a qualitative analysis sample 207 threads (4772 unique posts) mentioning different forms personal data from the /r/androiddev forum on Reddit. We started with bottom-up open coding sampled posts to develop typology discussions about use and follow-up analyses understand what types elicited in-depth...
Apple announced the introduction of app privacy details to their App Store in December 2020, marking first ever real-world, large-scale deployment nutrition label concept, which had been introduced by researchers over a decade earlier. The labels are created developers, who self-report app's data practices. In this paper, we present study examining usability and understandability Apple's creation process from developer's perspective. By observing interviewing 12 iOS developers about how they...
The emergence of large language models (LLMs), and their increased use in user-facing systems, has led to substantial privacy concerns. To date, research on these concerns been model-centered: exploring how LLMs lead risks like memorization, or can be used infer personal characteristics about people from content. We argue that there is a need for more focusing the human aspect issues: e.g., design paradigms affect users' disclosure behaviors, mental preferences controls, tools, artifacts...
Abstract Although increases in cardiovascular load (pressure overload) are known to elicit ventricular remodeling including cardiomyocyte hypertrophy and interstitial fibrosis, the molecular mechanisms of pressure overload or AngII -induced cardiac fibrosis remain elusive. In this study, serpinE2/protease nexin-1 was over-expressed a model induced by pressure-overloaded via transverse aortic constriction (TAC) mouse. Knockdown serpinE2 attenuates mouse TAC. At meantime, results showed that...
Abstract Privacy tasks can be challenging for developers, resulting in privacy frameworks and guidelines from the research community which are designed to assist developers considering features applying enhancing technologies early stages of software development. However, how engage with design strategies is not yet well understood. In this work, we look at types privacy-related advice give each other that maps Hoepman’s strategies. We qualitatively analyzed 119 accepted answers on Stack...
Notifications are an indispensable feature of mobile devices, but their delivery can interrupt and distract users. Prior work has examined interventions, such as deferring notification to opportune moments, not systematically studied how users might prefer intelligent system manage notifications. Hence, we directly probed Android smartphone users’ preferences via a one-week experience-sampling study ( N = 35). We found that mitigating undesired interruptions by suppressing alerts over them...
The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts textual data generating human-like responses, enabling researchers delve into complex phenomena. other are emerging regarding validity, privacy, ethics research when involved. This SIG aims at offering an open space for who interested understanding impacts discuss their current practices,...
Apple and Google introduced their versions of privacy nutrition labels to the mobile app stores better inform users apps' data practices. However, these are self-reported by developers have been found contain many inaccuracies due misunderstandings label taxonomy. In this work, we present Matcha, an IDE plugin that uses automated code analysis help create accurate Play safety labels. Developers can benefit from Matcha's ability detect user accesses transmissions while staying in control...
Intracranial hemorrhage (ICH), a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO), is often related to poor outcomes. This study aimed establish predictive model for ICH in ECMO treatment. Adults who received between January 2017 and June 2022 were the subjects of single-center retrospective study. Patients under age 18 years old, with acute before ECMO, less than 24 h support, incomplete data excluded. was diagnosed by head computed tomography scan. The...
This study aims to predict IDH wt with TERTp-mut gliomas using multiparametric MRI sequences through a novel fusion model, while matching model classification metrics patient risk stratification aids in crafting personalized diagnostic and prognosis evaluations.Preoperative T1CE T2FLAIR from 1185 glioma patients were analyzed. A MultiChannel_2.5D_DL 2D DL both based on the cross-scale attention vision transformer (CrossFormer) neural network, along Radiomics developed. These integrated via...
The advancements of Large Language Models (LLMs) have decentralized the responsibility for transparency AI usage. Specifically, LLM users are now encouraged or required to disclose use LLM-generated content varied types real-world tasks. However, an emerging phenomenon, users' secret , raises challenges in ensuring end adhere requirement. Our study used mixed-methods with exploratory survey (125 cases reported) and a controlled experiment among 300 investigate contexts causes behind LLMs. We...
Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest unexpected ways human-computer interaction due to the extensive engagement human subjects. This paper reports on practices related LLM use, drawing 16 semi-structured interviews a survey 50 HCI researchers. We discuss LLMs already being utilized throughout entire pipeline, from ideation system development...
Although app developers are responsible for protecting users' privacy, this task can be very challenging. In paper, we present Coconut, an Android Studio plugin that helps handle privacy requirements by engaging to think about during the development process and providing real-time feedback on potential issues. We start presenting new findings based a series of semi-structured interviews with developers, probing into difficulties face when building apps. Based these findings, implemented...
Mobile apps enable ad networks to collect and track users. App developers are given “configurations” on these platforms limit data collection adhere privacy regulations; however, the prevalence of that violate regulations because third parties, including networks, begs question how work through configurations easy they utilize. We study regulations-related interfaces three widely used using two empirical studies, a systematic review think-aloud sessions with eleven developers, shed light...