Rocky K. C. Chang
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View article: Organ-Agents: Virtual Human Physiology Simulator via LLMs
Organ-Agents: Virtual Human Physiology Simulator via LLMs Open
Recent advances in large language models (LLMs) have enabled new possibilities in simulating complex physiological systems. We introduce Organ-Agents, a multi-agent framework that simulates human physiology via LLM-driven agents. Each Simu…
View article: When Program Analysis Meets Bytecode Search: Targeted and Efficient Inter-procedural Analysis of Modern Android Apps in BackDroid
When Program Analysis Meets Bytecode Search: Targeted and Efficient Inter-procedural Analysis of Modern Android Apps in BackDroid Open
Widely-used Android static program analysis tools, e.g., Amandroid and FlowDroid, perform the whole-app inter-procedural analysis that is comprehensive but fundamentally difficult to handle modern (large) apps. The average app size has inc…
View article: Understanding Open Ports in Android Applications: Discovery, Diagnosis, and Security Assessment
Understanding Open Ports in Android Applications: Discovery, Diagnosis, and Security Assessment Open
Open TCP/UDP ports are traditionally used by servers to provide application services, but they are also found in many Android apps. In this paper, we present the first open-port analysis pipeline, covering the discovery, diagnosis, and sec…
View article: Robust Estimation of Value-at-Risk through Distribution-Free and Parametric Approaches Using the Joint Severity and Frequency Model: Applications in Financial, Actuarial, and Natural Calamities Domains
Robust Estimation of Value-at-Risk through Distribution-Free and Parametric Approaches Using the Joint Severity and Frequency Model: Applications in Financial, Actuarial, and Natural Calamities Domains Open
Value-at-Risk (VaR) is a well-accepted risk metric in modern quantitative risk management (QRM). The classical Monte Carlo simulation (MCS) approach, denoted henceforth as the classical approach, assumes the independence of loss severity a…
View article: MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance
MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance Open
Crowdsourcing mobile user's network performance has become an effective way of understanding and improving mobile network performance and user quality-of-experience. However, the current measurement method is still based on the landline me…
View article: MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance
MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance Open
Crowdsourcing mobile user's network performance has become an effective way of understanding and improving mobile network performance and user quality-of-experience. However, the current measurement method is still based on the landline me…
View article: MopEye: Monitoring Per-app Network Performance with Zero Measurement Traffic
MopEye: Monitoring Per-app Network Performance with Zero Measurement Traffic Open
Mobile network performance measurement is important for understanding mobile user experience, problem diagnosis, and service comparison. A number of crowdsourcing measurement apps (e.g., MobiPerf [4, 6] and Netalyzr [5, 7]) have been embar…