arXiv subjects

Bishenghui Tao

Publications and source records attributed to Bishenghui Tao.

7 recordsLinked to original sources

arXiv2026

Sky-Ear: An Unmanned Aerial Vehicle-Enabled Victim Sound Detection and Localization System

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in search-and-rescue (SAR) missions, yet continuous and reliable victim detection and localization remain challenging due to on-board hardware constraints. This paper designs an UAV-Enabled Victim Sound Detection and Localization System (called ``Sky-Ear'' for brevity) to achieve energy-efficient acoustic sensing and sound detection for SAR. Sky-Ear enables the ``ear'' of the UAV with a circular-shaped microphone array, and the array conducts continuous audio recordings during the UAV's flight. In Sky-Ear, a two-stage (Sentinel and Responder) audio processing method is developed for energy-consuming and highly reliable sound detection. In the Sentinel stage, a Masking autoencoder (MAE)-based sound detection mechanism is designed to analyze frequency-time acoustic features. For improved precision, a continuous localization method is designed by optimizing detected directions from multiple observations. Extensive simulation experiments are conducted to validate the system's performance in terms of victim detection accuracy and localization error.

eess.AS↗

arXiv2025

A Confidence-Constrained Cloud-Edge Collaborative Framework for Autism Spectrum Disorder Diagnosis

Autism Spectrum Disorder (ASD) diagnosis systems in school environments increasingly relies on IoT-enabled cameras, yet pure cloud processing raises privacy and latency concerns while pure edge inference suffers from limited accuracy. We propose Confidence-Constrained Cloud-Edge Knowledge Distillation (C3EKD), a hierarchical framework that performs most inference at the edge and selectively uploads only low-confidence samples to the cloud. The cloud produces temperature-scaled soft labels and distils them back to edge models via a global loss aggregated across participating schools, improving generalization without centralizing raw data. On two public ASD facial-image datasets, the proposed framework achieves a superior accuracy of 87.4\%, demonstrating its potential for scalable deployment in real-world applications.

cs.NI↗

arXiv2025

Cloud-Fog-Edge Collaborative Computing for Sequential MIoT Workflow: A Two-Tier DDPG-Based Scheduling Framework

The Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare workflows deployed over heterogeneous cloud-fog-edge infrastructures. Scheduling these sequential workflows to minimize makespan is an NP-hard problem. To tackle this challenge, we propose a Two-tier DDPG-based scheduling framework that decomposes the scheduling decision into a hierarchical process: a global controller performs layer selection (edge, fog, or cloud), while specialized local controllers handle node assignment within the chosen layer. The primary optimization objective is the minimization of the workflow makespan. Experiments results validate our approach, demonstrating increasingly superior performance over baselines as workflow complexity rises. This trend highlights the frameworks ability to learn effective long-term strategies, which is critical for complex, large-scale MIoT scheduling scenarios.

cs.LG↗

arXiv2025

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models

Automated Program Repair (APR) is essential for ensuring software reliability and quality while enhancing efficiency and reducing developers' workload. Although rule-based and learning-based APR methods have demonstrated their effectiveness, their performance was constrained by the defect type of repair, the quality of training data, and the size of model parameters. Recently, Large Language Models (LLMs) combined with Retrieval-Augmented-Generation (RAG) have been increasingly adopted in APR tasks. However, current code LLMs and RAG designs neither fully address code repair tasks nor consider code-specific features. To overcome these limitations, we propose SelRepair, a novel APR approach with integration of a fine-tuned LLM with a newly-designed dual RAG module. This approach uses a bug-fix pair dataset for fine-tuning and incorporates semantic and syntactic/structural similarity information through an RAG selection gate. This design ensures relevant information is retrieved efficiently, thereby reducing token length and inference time. Evaluations on Java datasets show SelRepair outperforms other APR methods, achieving 26.29% and 17.64% in terms of exact match (EM) on different datasets while reducing inference time by at least 6.42% with controlled input lengths.

cs.SE↗

arXiv2025

Hybrid Satellite-Ground Deployments for Web3 DID: System Design and Performance Analysis

The emerging Web3 has great potential to provide worldwide decentralized services powered by global-range data-driven networks in the future. To ensure the security of Web3 services among diverse user entities, a decentralized identity (DID) system is essential. Especially, a user's access request to Web3 services can be treated as a DID transaction within the blockchain, executed through a consensus mechanism. However, a critical implementation issue arises in the current Web3, i.e., how to deploy network nodes to serve users on a global scale. To address this issue, emerging Low Earth Orbit (LEO) satellite communication systems, such as Starlink, offer a promising solution. With their global coverage and high reliability, these communication satellites can complement terrestrial networks as Web3 deployment infrastructures. In this case, this paper develops three hybrid satellite-ground modes to deploy the blockchain-enabled DID system for Web3 users. Three modes integrate ground nodes and satellites to provide flexible and continuous DID services for worldwide users. Meanwhile, to evaluate the effectiveness of the present hybrid deployment modes, we analyze the complete DID consensus performance of blockchain on three hybrid satellite-ground modes. Moreover, we conduct numerical and simulation experiments to verify the effectiveness of three hybrid satellite-ground modes. The impacts of various system parameters are thoroughly analyzed, providing valuable insights for implementing the worldwide Web3 DID system in real-world network environments.

eess.SY↗

arXiv2022

Is Blockchain for Internet of Medical Things a Panacea for COVID-19 Pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.

cs.DC↗

arXiv2022

Complex Network Analysis of the Bitcoin Transaction Network

In this brief, we conduct a complex-network analysis of the Bitcoin transaction network. In particular, we design a new sampling method, namely random walk with flying-back (RWFB), to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest-path length, connected component, centrality, assortativity, and the rich-club coefficient. We obtain several important observations including the small-world phenomenon, multi-center status, preferential attachment, and non-rich-club effect of the current network. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.

cs.SI↗