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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhuo, Shaoxuan Li, Guang Wu, Weigang Bian, Jing Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing China Sun Yat-Sen University Guangdong Province Key Laboratory of Computational Science Guangzhou China

ISBN: (纸本)9798350330717

Cryptocurrency phishing scams is a significant treat to Ethereum, one of the most popular blockchain platforms. Most of existing Ethereum phishing detection methods are based on traditional machine learning or graph representation learning, which mostly rely on only statistical and structural features in local scope. In this paper, we propose Multi-transaction-view Graph Attention Network (MTvGAT), a novel phishing scam detection model that can make use of transaction patterns of different scopes. To obtain global-view information, we apply graph clustering and construct the global-view graph with multiple clusters, including all the nodes of the original transaction network. To obtain local view information, we apply neighborhood sampling, and construct local-view graphs with target nodes and their neighborhood nodes. Then, node features, edge features, and attention coefficients are aggregated to merge multi-view information into representation of nodes. We further combine global-view and local-view representations to finally identify phishing addresses from target nodes. Extensive experiments demonstrate that the proposed method can outperform existing ones with significant improvement. © 2023 IEEE.

关键词： Ethereum Graph neural network Phishing scams

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M-DFCPP: A runtime library for multi-machine dataflow computing

M-DFCPP: A runtime library for multi-machine dataflow comput...

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作者： Luo, Qiuming Liu, Senhong Huang, Jinke Li, Jinrong College of Computer Science and Software Engineering Shenzhen University Shenzhen China SKT Group Guangdong Province Key Laboratory of Popular High-Performance Computers Shenzhen China SKT Group Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

This article designs and implements a runtime library for general dataflow programming, DFCPP (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), and builds upon it to design and implement a multi-machine C++ dataflow library, M-DFCPP. In comparison to existing dataflow programming environments, DFCPP features a user-friendly interface and richer expressive capabilities (Luo Q, Huang J, Li J, Du Z. Proceedings of the 52nd International Conference on Parallel Processing Workshops. ACM;2023:145-152.), enabling the representation of various types of dataflow actor tasks (static, dynamic and conditional task). Besides that, DFCPP addresses the memory management and task scheduling for non-uniform memory access architectures, while other dataflow libraries lack attention to these issues. M-DFCPP extends the capability of current dataflow runtime libraries (DFCPP, taskflow, openstream, etc.) and capable of multi-machine computing, while maintains the API compatible with DFCPP. M-DFCPP adopts the concepts of master and follower (Dean J, Ghemawat S. Commun ACM. 2008;51(1):107-113;Ghemawat S, Gobioff H, Leung ST. ACM SIGOPS Operating Systems Review. ACM;2003:29-43.), which form a worksharing framework as many multi-machine system. To shift to the M-DFCPP framework, a filtering layer is inserted to the original DFCPP, transforming it into followers that can cooperate with each other. The master is made of modules for scheduling, data processing, graph partition, state management and so forth. In benchmark tests with workload with directed acyclic graph topology of binary trees and random graphs, DFCPP demonstrated performance improvements of 20% and 8%, respectively, compared to the second fastest library. M-DFCPP consistently exhibits outstanding performance across varying levels of concurrency and task workloads, achieving a maximum speedup of more than 20 over DFCPP, when the task parallelism e

关键词： Libraries

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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Composition of interval-valued fuzzy relations with Bandler-Kohout subproduct in marine pollution assessment

Composition of interval-valued fuzzy relations with Bandler-...

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2022 International Conference on Statistics, data Science, and Computational Intelligence, CSDSCI 2022

作者： Deng, Jianglin School of Information and Engineering Zhejiang Ocean University Zhoushan316000 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510661325

With the increasing exploitation of marine resources, there is worldwide concern about the marine environment. It is a hot topic how to assert the marine pollution. Concentrating on dissolved oxygen and suspended solids in offshore water of yellow river mouth, this paper establishes a model of marine pollution assessment by a compositional interval-valued fuzzy relation with Bandler-Kohout subproduct. And a real-life example is shown for the sake of illustrating the efficiency of our proposed model. © 2023 SPIE.

关键词： Marine pollution

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Making Passive Daytime Radiative Cooling Metafabrics on a Large Scale

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Advanced Fiber Materials 2022年第1期4卷 3-4页

作者： Shaowu Pan Huisheng Peng State Key Laboratory for Modifcation of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua University2999 Renmin RoadShanghai 201620China State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Scienceand Laboratory of Advanced MaterialsFudan UniversityShanghai 200438China

Cooling is pervasive in modern society and contributes signifcantly to global energy use.A hierarchical-morphology metafabric has been recently reported to show efcient passive daytime radiative cooling ability and may be also easily scaled up by industrial textile manufacturing *** above study represents an important advance in personal thermal management through the use of intelligent garments.

关键词： technology. hierarchical intelligent

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Review and prospect of remote sensing monitoring methods for inland black and malodorous water bodies 24

Review and prospect of remote sensing monitoring methods for...

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2024 International Conference on Image Processing, Intelligent Control and Computer engineering, IPICE 2024

作者： Liao, Yansong Huang, Fenghua The Academy of Digital China Fuzhou University China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University China Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing Yango University China College of Artificial Intelligence Yango University China

ISBN: (纸本)9798400710285

Black and malodorous water bodies represent a severe water pollution phenomenon that urgently needs addressing due to the discomfort and health risks they pose. Since the release of the"Action Plan for Prevention and Control of Water Pollution" in 2015 and the subsequent"Action Plan for the Treatment of Black and Malodorous Water Bodies in Urban Areas during the 14th Five-Year Plan Period" in 2022 by the Chinese government, monitoring efforts have become increasingly crucial. Remote sensing technology offers advantages such as high dynamics, low cost, and a macroscopic perspective in ecological environment monitoring, making it advantageous over traditional physical and chemical methods in monitoring black and malodorous water bodies. This paper summarizes remote sensing methods for identifying black and malodorous water bodies, including extraction of information through spectral, spatial, index, and temporal features, and analyzes the monitoring feasibility of these methods while pointing out potential issues. Finally, future research directions for identifying black and malodorous water bodies are discussed. © 2024 Copyright held by the owner/author(s).

关键词： Environmental monitoring

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2D-3D Feature Co-Embedding Network with Sparse Annotation for 3D Medical Image Segmentation

2D-3D Feature Co-Embedding Network with Sparse Annotation fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Mingzhu Guo, Shuyu Wang, Ziwei Jiao, Jianhang Bai, Tian Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China The Second Norman Bethune Hospital Jilin University Department of Orthopedics Changchun China

ISBN: (纸本)9798350386226

Supervised methods on 3D medical image segmentation need large amounts of annotated data, but annotating is time-consuming. Also, existing 3D segmentation methods capture more global structural information but overlook local detailed features, which negatively impacts the segmentation of small tissues. In this paper, we propose a novel weakly-supervised 2D-3D Feature Co-Embedding Network (2D-3D CoENet) that includes 2D and 3D encoding layers, simultaneously extracting 2D local detailed and 3D global structural features. To reduce annotation costs, we use fewer labeled slices as ground truth and pseudo-labels are generated by 2D-3D CoENet for other slices. Additionally, multi-view learning is introduced to capture more 2D local detailed information, and a Multi-view Semantic Consistency loss (MSC loss) is proposed to constrain features from multiple perspectives. To further enhance the local detailed texture features, we propose an Edge Enhancement Module (EEM) in the 3D segmentation network to enhance the edge detail features. Our experimental results on the SKI10 dataset and OAI ZIB dataset demonstrate that our method outperforms the SOTA weakly-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound results. © 2024 IEEE.

关键词： Medical imaging

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CARDnet: A denoiser based on contrast-aware and residual-dense block 19th

CARDnet: A denoiser based on contrast-aware and residual-den...

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Cai, Qiang Cao, Ying Wang, Chen Li, Haisheng Ma, Mengxu School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-Product Quality Traceability Beijing100048 China

ISBN: (纸本)9789819968817

In recent years, deep convolutional neural networks have shown good performance on images with spatially invariant noise, but their performance is limited on real-world noisy images. In order to improve the practicality of denoising algorithms, this paper proposes a simple one-stage blind real image denoising network with a modular structure, which combines the local modeling capability of residual dense convolutional layers with the global modeling capability of spatial and channel attention blocks and inserts them as the main building blocks into widely used encoder–decoder architectures to achieve end-to-end denoising. To preserve image structure information as much as possible during the denoising process, this paper uses different lengths of residuals between different modules to mitigate the flow of low-frequency information, and applies Contrast-Aware channel attention to enhance the dependency relationship of channel activation. Furthermore, this paper evaluates state-of-the-art algorithms on different noise datasets using quantitative metrics and visual quality, experimental results demonstrate the superiority of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Convolution

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A DL Intrusion Detection Method for Low-Rate Denial of Service Attacks

A DL Intrusion Detection Method for Low-Rate Denial of Servi...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Wu, Yuhong Lv, Yunshan Hu, Xiangdong School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Public Big Data Security Technology Chongqing Chongqing China School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665457033

Denial of service (DoS) attacks is among the most harmful network threats. Low-rate DoS (LDoS) attacks feature good concealment and are likely to evade detection by conventional signal analysis-based methods. A LDoS attack detection method that combines the convolutional neural network and the gated recurrent unit is proposed in the present work. To evaluate the proposed method, we collect real user traffic from a university's website, label the traffic with LDoS attacks as normal traffic. In order to get the real attack traffic, this paper has collected some new LDoS attack programs, such as slowloris, slowhttptest, pwnlloris, torhammer and httplog, which can be obtained from public channels, and constructed a LAN environment that conformed to the working scenario of the program. By running the attack program, the attack traffic could be obtained. In a large sample scenario, the average detection rate of our proposed method can reach 98.68%. This article focuses on the research: the average detection rate of the detection method proposed in this article in the case that the security agency can only intercept a small number of attack samples. That is, in the small sample scenario, the detection of the collected traffic shows that the average detection rate of the proposed detection method can reach 89.54% in a small sample scenario. Its effective detection rate is obviously superior to the existing similar detection methods. © 2023 IEEE.

关键词： Denial-of-service attack

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