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arXiv 2023年

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Shen Yuan Honors College Beihang University Beijing100191 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Hebei University of Technology Tianjin300401 China

Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we first propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, enabling nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Ponzi Scheme Detection Based on Control Flow Graph Feature Extraction

Ponzi Scheme Detection Based on Control Flow Graph Feature E...

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IEEE International Conference on Web Services (ICWS)

作者： Shunhui Ji Congxiong Huang Pengcheng Zhang Hai Dong Yan Xiao Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer and Information Hohai University Nanjing China School of Computing Technologies RMIT University Melbourne Australia School of Cyber Science and Technology Sun Yat-sen University Shenzhen China

The blockchain ecosystem is expanding as a result of advancements in blockchain technology and the emergence of BaaS (Blockchain as a Service) platforms. Smart contracts are designed to carry out diverse business operations, but there is a risk of Ponzi schemes being concealed within them. These schemes masquerade as investment agreements and deceive users, resulting in substantial losses for the blockchain community. Detecting Ponzi schemes in smart contracts is crucial. This study introduces a machine learning approach to identify Ponzi schemes by extracting features from smart contracts using the control flow graph. During the construction of the control flow graph for the smart contract’s bytecode, elements unrelated to its functionality are identified and eliminated. We utilize the control flow graph to extract n-gram Term Frequency and n-gram Term Frequency-Inverse Document Frequency features. These features are respectively employed to construct a Random Forest model for Ponzi scheme detection. To address the issue of imbalanced samples, the SVM_SMOTE oversampling algorithm is applied to balance the number of positive and negative samples. The results from experiments conducted on a real-world dataset demonstrate the effectiveness of our approach. The feature extraction method based on the control flow graph outperforms the method based on continuous text. Additionally, the Random Forest model utilizing SVM_SMOTE outperforms four existing models.

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Online Tensor Decomposition for Hyperspectral data Representation

Online Tensor Decomposition for Hyperspectral Data Represent...

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2021 China Automation Congress, CAC 2021

作者： He, Jinrong Shi, Yanxin Xie, Chengwang Bao, Zeyu Bai, Zongwen College of Mathematics and Computer Science Yan'an University Shaanxi China School of Computer and Information Engineering South China Normal University Shanwei China School of the Gifted Young University of Science and Technology of China Hefei China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Shaanxi China

ISBN: (纸本)9781665426473

Tensor is a natural representation of visual data, such as hyperspectral image data. Other than vector representation based feature extraction methods, tensor-based representation methods are more informative. Most existing data representation methods only work on vector spaces which are implemented by stacking the original tensor elements into a long vector. However, the vectorization will inevitably lost the underlying structural information and lead to high computational complexity. In order to reduce the memory requirements, we proposed an online tensor decomposition (OTD) method for hyperspectral image feature extraction. It does not require to load the whole tensor data set into main memory, and with the insertion of new tensor samples, it can update projection matrices incrementally, which is desirable for large data sets. In the experiments, the proposed method was applied into the task of apple hyperspectral data classification. The classification results confirm the effectiveness of online tensor decom-position method. © 2021 IEEE

关键词： Principal component analysis

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Ipmmg: Information Propagation with Multi-Granularity Morphology-Guided for Nuclear Segmentation and Classification

SSRN

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SSRN 2025年

作者： Fan, Dawei Li, Jun Cai, Chengfei Lin, Lihui Chen, Riqing Chen, Yanping Wei, Lifang College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China College of Future Technology Fujian Agriculture and Forestry University Fuzhou350002 China Department of Pathology Clinical Oncology School of Fujian Medical University Fujian Cancer Hospital Fuzhou350014 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan354300 China

Nuclear segmentation and classification play a pivotal role in pathological image analysis. However, it is often hindered by blurred nuclear boundaries and complex structures in digital pathology slides, caused by factors such as staining techniques and imaging methods, presenting a significant challenge for accurate segmentation and classification. To address this issue, we propose a novel and efficient approach for nuclear identification, termed Information Propagation with Multi-Granularity Morphology-Guided Network (IPMMG). IPMMG progressively captures edge morphology information from different network layers while simultaneously incorporating structural morphology features at multiple granularities. By explicitly propagating features related to both the edge and structure, our approach constrains semantic features to focus on the region of interest's contour, thereby alleviating the challenge of blurred morphology. Experiments on public datasets demonstrate that IPMMG achieves state-of-the-art performance in segmentation, as measured by Dice and IoU scores, while also attaining competitive results in classification with DQ, SQ, and PQ metrics. In particular, it excels in handling nuclei with blurred edges and complex structures. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Pre-classification Supporting Reasoning for Document-level Relation Extraction 21

Pre-classification Supporting Reasoning for Document-level R...

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Proceedings of the 10th International Joint Conference on Knowledge Graphs

作者： Jiehao Zhao Guiduo Duan Tianxi Huang School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China Department of Fundamental Courses Chengdu Textile College China

ISBN: (纸本)9781450395656

The document-level relation extraction task aims to extract relational triples from a document consisting of multiple sentences. Most previous models focus on modeling the dependency between the entities and neglect the reasoning mechanism. Some other models construct paths implicitly between co-sentence entities to find semantic relations. However, they ignore that there are interactions between different triples, especially some triples play an import role in predicting others. In this short research paper, we propose a new two stage framework PCSR(Pre-classification Supporting Reasoning) which captures the interactions between triples and utilizes these information for reasoning. Specifically, we make a pre-classification for each entity pair in the first stage. Then we aggregate the embeddings of predicted triples to enhance entity representation and make a new classification. Since the second classification could find triples missed in the first stage, we take the result as the supplement of the prior one. Experiments on DocRED show that our method achieves an F1 score of 62.11. Compared with the previous state-of-the-art model, our model increase by 0.81 on the test set, which demonstrates the effectiveness of our reasoning mechanism.

关键词： BERT Information Extraction Document-level Relation Extraction Reasoning Mechanism

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_data$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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Bioinformatic analysis of chromatin organization and biased expression of duplicated genes between two poplars with a common whole-genome duplication

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Horticulture Research 2021年第1期8卷 806-817页

作者： Le Zhang Jingtian Zhao Hao Bi Xiangyu Yang Zhiyang Zhang Yutao Su Zhenghao Li Lei Zhang Brian J.Sanderson Jianquan Liu Tao Ma College of Computer Science&Medical Big Data Center of Sichuan University&Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education&College of Life Sciences Sichuan University610065 ChengduChina Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of Sciences310024 HangzhouChina Department of Biology West Virginia UniversityMorgantownWV 26506USA State Key Laboratory of Grassland Agro-Ecosystem Institute of Innovation Ecology&College of Life SciencesLanzhou University730000 LanzhouChina

The nonrandom three-dimensional organization of chromatin plays an important role in the regulation of gene ***,it remains unclear whether this organization is conserved and whether it is involved in regulating gene expression during speciation after whole-genome duplication(WGD)in *** this study,high-resolution interaction maps were generated using high-throughput chromatin conformation capture(Hi-C)techniques for two poplar species,Populus euphratica and Populus alba ***,which diverged~14 Mya after a common *** examined the similarities and differences in the hierarchical chromatin organization between the two species,including A/B compartment regions and topologically associating domains(TADs),as well as in their DNA methylation and gene expression *** found that chromatin status was strongly associated with epigenetic modifications and gene transcriptional activity,yet the conservation of hierarchical chromatin organization across the two species was *** divergence of gene expression between WGD-derived paralogs was associated with the strength of chromatin interactions,and colocalized paralogs exhibited strong similarities in epigenetic modifications and expression ***,the spatial localization of duplicated genes is highly correlated with biased expression during the diploidization *** study provides new insights into the evolution of chromatin organization and transcriptional regulation during the speciation process of poplars after WGD.

关键词： expression chromatin analysis

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