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Your search keyword '"He, Jiahao"' showing total 402 results
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402 results on '"He, Jiahao"'

1. GAMF-Net: A Lightweight Network for Semantic Segmentation of Land Cover Recognition in Open-Pit Coal Mining Areas

Author

Li, Jiaqi, Wang, Jiawei, He, Jiahao, Ma, Ming, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hadfi, Rafik, editor, Anthony, Patricia, editor, Sharma, Alok, editor, Ito, Takayuki, editor, and Bai, Quan, editor

Published
2025
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4. Salient Object Detection in RGB-D Videos

Author

Mou, Ao, Lu, Yukang, He, Jiahao, Min, Dingyao, Fu, Keren, and Zhao, Qijun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Given the widespread adoption of depth-sensing acquisition devices, RGB-D videos and related data/media have gained considerable traction in various aspects of daily life. Consequently, conducting salient object detection (SOD) in RGB-D videos presents a highly promising and evolving avenue. Despite the potential of this area, SOD in RGB-D videos remains somewhat under-explored, with RGB-D SOD and video SOD (VSOD) traditionally studied in isolation. To explore this emerging field, this paper makes two primary contributions: the dataset and the model. On one front, we construct the RDVS dataset, a new RGB-D VSOD dataset with realistic depth and characterized by its diversity of scenes and rigorous frame-by-frame annotations. We validate the dataset through comprehensive attribute and object-oriented analyses, and provide training and testing splits. Moreover, we introduce DCTNet+, a three-stream network tailored for RGB-D VSOD, with an emphasis on RGB modality and treats depth and optical flow as auxiliary modalities. In pursuit of effective feature enhancement, refinement, and fusion for precise final prediction, we propose two modules: the multi-modal attention module (MAM) and the refinement fusion module (RFM). To enhance interaction and fusion within RFM, we design a universal interaction module (UIM) and then integrate holistic multi-modal attentive paths (HMAPs) for refining multi-modal low-level features before reaching RFMs. Comprehensive experiments, conducted on pseudo RGB-D video datasets alongside our RDVS, highlight the superiority of DCTNet+ over 17 VSOD models and 14 RGB-D SOD models. Ablation experiments were performed on both pseudo and realistic RGB-D video datasets to demonstrate the advantages of individual modules as well as the necessity of introducing realistic depth. Our code together with RDVS dataset will be available at https://github.com/kerenfu/RDVS/., Comment: IEEE TIP (under major revision)

Published
2023

5. Dynamics of cutaneous atmospheric oxygen uptake in response to mechanical stretch revealed by optical fiber microsensor

Author

Shan, Shengzhou, He, Jiahao, Sun, Qin, Zhu, Kan, Li, Yuanyuan, Reid, Brian, Li, Qingfeng, and Zhao, Min

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, 1.1 Normal biological development and functioning, Optical Fibers, Skin, Epidermis, Cell Proliferation, Oxygen, Stress, Mechanical, cutaneous oxygen uptake, mechanical stretch, optical fiber sensor, skin expansion, surgery, Dermatology & Venereal Diseases, Clinical sciences
Abstract

Skin expands and regenerates in response to mechanical stretch. This important homeostasis process is critical for skin biology and can be exploited to generate extra skin for reconstructive surgery. Atmospheric oxygen uptake is important in skin homeostasis. However, whether and how cutaneous atmospheric oxygen uptake changes during mechanical stretch remains unclear, and relevant research tools to quantify oxygen flux are limited. Herein, we used the scanning micro-optrode technique (SMOT), a non-invasive self-referencing optical fiber microsensor, to achieve real-time measurement of cutaneous oxygen uptake from the atmosphere. An in vivo mechanical stretch-induced skin expansion model was established, and an in vitro Flexcell Tension system was used to stretch epidermal cells. We found that oxygen influx of skin increased dramatically after stretching for 1 to 3 days and decreased to the non-stretched level after 7 days. The enhanced oxygen influx of stretched skin was associated with increased epidermal basal cell proliferation and impaired epidermal barrier. In conclusion, mechanical stretch increases cutaneous oxygen uptake with spatial-temporal characteristics, correlating with cell proliferation and barrier changes, suggesting a fundamental mechanistic role of oxygen uptake in the skin in response to mechanical stretch. Optical fiber microsensor-based oxygen uptake detection provides a non-invasive approach to understand skin homeostasis.

Published
2023

8. Neural-FEBI: Accurate Function Identification in Ethereum Virtual Machine Bytecode

Author

He, Jiahao, Li, Shuangyin, Wang, Xinming, Cheung, Shing-Chi, Zhao, Gansen, and Yang, Jinji

Subjects
Computer Science - Software Engineering
Abstract

Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3 to 99.7, respectively. Its performance on the function boundary identification task is also increased from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that the identified function information can be used to construct more accurate intra-procedural CFGs and call graphs. The experimental results confirm that the proposed framework significantly outperforms state-of-the-art, often based on handcrafted heuristic rules., Comment: 19 pages, 13 figures

Published
2023
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38. Exploration on the Establishment of Endogenous Development Framework in Rural China

Author

Jin, Weidong, He, Jiahao, Zhang, Rui, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Hussain, Rosila Bee Binti Mohd, editor, Parc, Jimmyn, editor, and Li, Jia, editor

Published
2023
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