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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Yang, Zhe Xu, Liangkui Bu, Ziyu Chen, Yipeng Information Processing Technology Provincial Key Laboratory for Computer Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9781665407458

Compared with the graph structure, the hyper-graph structure can convey more complex and multivariate relationships. In addition, hypergraphs are more capable of portraying and mining higher-order nonlinear correlations among objects. Therefore, it has been widely used in feature selection, clustering classification, and addressing other issues, and hypergraph learning has also become an important branch in the field of machine learning. Hypergraph learning, however, is a relatively high time consuming learning algorithm, because in its iterative solving process, there are lots of high-dimension matrix computations with large-scale parameters. This paper proposes an optimization of the weights, which is constructed by Gaussian kernel, in hyper-graph transductive learning model the weight matrix is replaced by fixed parameters when constructing the hypergraph laplacian matrix, which reduces the time consuming of weights computation in iterative process. Extensive experimental with semi-supervised node classification experiment results on the UCI and Kaggle datasets show that the optimized model reduces the time consuming of the hypergraph model, while ensuring classification accuracy. After model optimization, the four accuracy indicators fluctuate on average 3.03%, the time consuming is reduced by 28.99% on average, and the maximum saving is 42.84%. © 2022 IEEE.

关键词： Learning algorithms

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

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computers, Materials & Continua 2023年第9期76卷 3623-3651页

作者： Yang Yang Yuhan Long Yijing Lin Zhipeng Gao Lanlan Rui Peng Yu State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Engineering Nanyang Technological University639798Singapore Science and Technology on Communication Networks Laboratory The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050051China

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant *** existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and *** paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge ***,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss *** utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high ***,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side *** propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro *** comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault *** addition,experiments show that our approach has a smooth training process and good balance.

关键词： Fault diagnosis knowledge distillation edge-side lightweight model high similarity

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FusionNet: An Effective Network Phishing Website Detection Framework Based on Multi-Modal Fusion 25

FusionNet: An Effective Network Phishing Website Detection F...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Sun, Yu Liu, Guangqi Han, Xiaohui Zuo, Wenbo Liu, Weihua Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (纸本)9798350330014

Recently, the rapid advancement of communication technology has undoubtedly improved people's lives. Unfortunately, this progress has also given rise to a significant issue: the proliferation of phishing websites. Regrettably, phishing web sites have now emerged as one of the primary threats to cybersecurity. While there have been extensive efforts to develop anti-phishing techniques, many of these approaches focus solely on a single modality of information, such as the website's URL, HTML source code, or visual features. Given the growing sophistication of phishing attacks, relying on just one modality of information is no longer sufficient to accurately identify phishing websites. To tackle this challenge, our research aims to leverage the complementarity of various modalities to enhance phishing website detection. In this paper, we introduce a multi-modal framework called FusionNet to identify phishing web pages. This framework utilizes three distinct information modalities: URLs, HTML source codes, and visual features. Specifically, our approach first designs specific representation learning architectures tailored to each modality's unique properties. Subsequently, we incorpo-rate an attention mechanism to merge these representations, thereby exploiting the synergies between the multi-modal data. The learned feature representations are more discriminative, contributing to significantly improved phishing website detection accuracy. To evaluate the performance of FusionNet, we collect a dataset comprising phishing webpages, each sample contains all three modalities-URLs, HTML source codes, and visual features. Our extensive experiments conducted on this dataset demonstrate that FusionNet outperforms state-of-the-art methods in terms of both robustness and accuracy. © 2023 IEEE.

关键词： Websites

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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GraphMal: A Network Malicious Traffic Identification Method Based on Graph Neural Network

GraphMal: A Network Malicious Traffic Identification Method ...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Lei Shi, Huiling Zhang, Kuichao Sun, Hongyang Zhang, Wei Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Ji'nan Shandong Ji'nan China

ISBN: (纸本)9798350327380

Detecting malicious attacks in normal network traffic is critical to network security. Traditional traffic identification methods often struggle to capture the complex communication patterns in network traffic, hindering their effectiveness. We introduce GraphMal, a framework utilizing Graph Neural networks (GNNs) for the identification of malicious traffic. This approach effectively exploits network topology and traffic characteristics to improve performance. We first apply Pearson's correlation coefficient and random forest approaches to effectively reduce the dimensionality of the dataset. Then, the E-GraphSAGE algorithm is used to extract salient features from the traffic topology graph and construct a robust graph classifier. Additionally, the focal loss function is used to solve the data imbalance problem. Experiments conducted on the UNSW-NB15 dataset demonstrate GraphMal's remarkable performance in both binary and multi-class classification tasks, while improving the learning efficiency of GNNs. © 2023 IEEE.

关键词： Network security

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Image Inpainting Detection Based on High-Pass Filter Attention Network

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computer Systems science & Engineering 2022年第12期43卷 1145-1154页

作者： Can Xiao Feng Li Dengyong Zhang Pu Huang Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409USA

Image inpainting based on deep learning has been greatly *** original purpose of image inpainting was to repair some broken photos, suchas inpainting artifacts. However, it may also be used for malicious operations,such as destroying evidence. Therefore, detection and localization of imageinpainting operations are essential. Recent research shows that high-pass filteringfull convolutional network (HPFCN) is applied to image inpainting detection andachieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, weintroduce the squeezed excitation blocks (SE) and propose a high-pass filter attention full convolutional network (HPACN). In feature extraction, we apply concurrent spatial and channel attention (scSE) to enhance feature extraction and obtainmore information. Channel attention (cSE) is introduced in upsampling toenhance detection and localization. The experimental results show that the proposed method can achieve improvement on ImageNet.

关键词： Image inpainting detection spatial attention channel attention full convolutional network high-pass filter

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CNN-Based Broad Learning for Cross-Domain Emotion Classification

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Tsinghua science and technology 2023年第2期28卷 360-369页

作者： Rong Zeng Hongzhan Liu Sancheng Peng Lihong Cao Aimin Yang Chengqing Zong Guodong Zhou Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 511400China Laboratory of Language Engineering and Computing Guangdong University of Foreign StudiesGuangzhou 510006China School of Computer Science and Intelligence Education Lingnan Normal UniversityGuangzhou 510006China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Soochow UniversitySuzhou 215031China

Cross-domain emotion classification aims to leverage useful information in a source domain to help predict emotion polarity in a target domain in a unsupervised or semi-supervised *** to the domain discrepancy,an emotion classifier trained on source domain may not work well on target *** researchers have focused on traditional cross-domain sentiment classification,which is coarse-grained emotion ***,the problem of emotion classification for cross-domain is rarely *** this paper,we propose a method,called convolutional neural network(CNN)based broad learning,for cross-domain emotion classification by combining the strength of CNN and broad *** first utilized CNN to extract domain-invariant and domain-specific features simultaneously,so as to train two more efficient classifiers by employing broad ***,to take advantage of these two classifiers,we designed a co-training model to boost together for ***,we conducted comparative experiments on four datasets for verifying the effectiveness of our proposed *** experimental results show that the proposed method can improve the performance of emotion classification more effectively than those baseline methods.

关键词： cross-domain emotion classification CNN broad learning classifier co-training

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PRNU Extraction from Stabilized Video:A Patch Maybe Better than a Bunch

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computer Systems science & Engineering 2021年第1期36卷 189-200页

作者： Bin Ma Yuanyuan Hu Jian Li Chunpeng Wang Meihong Yang Yang Zheng Qilu University of Technology(Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer NetworksJinan250353China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer NetworksJinan250014China University of Connecticut MansfieldCT06269USA

This paper presents an algorithm to solve the problem of Photo-Response Non-Uniformity(PRNU)noise facing stabilized *** stabilized video undergoes in-camera processing like rolling shutter ***,misalignment exists between the PRNU noises in the adjacent frames owing to the global and local frame registration performed by the in-camera *** misalignment makes the reference PRNU noise and the test PRNU noise unable to extract and match *** design a computing method of maximum likelihood estimation algorithm for extracting the PRNU noise from stabilized video ***,unlike most prior arts tending to match the PRNU noise in whole frame,we propose a new patch-based matching strategy,aiming at reducing the influence from misalignment of frame the PRNU *** extracting the reference PRNU noise and the test PRNU noise,this paper adopts the reference and the test PRNU overlapping patch-based *** is different from the traditional matching *** paper conducts different experiments on 224 stabilized videos taken by 13 smartphones in the VISION *** area under curve of the algorithm proposed in this paper is 0.841,which is significantly higher than 0.805 of the whole frame matching in the traditional *** results show good performance and effectiveness the proposed strategy by comparing with the prior arts.

关键词： Photo-response non-uniformity(PRNU) stabilized video frames maximum likelihood estimation patch

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Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

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Intelligent Automation & Soft Computing 2023年第11期38卷 185-202页

作者： Hongshang Xu Bei Dong Xiaochang Liu Xiaojun Wu School of Computer Science Shaanxi Normal UniversityXi’an710119China Key Laboratory of Modern Teaching Technology Ministry of EducationXi’an710062China

Deep neural networks often outperform classical machine learning algorithms in solving real-world ***,designing better networks usually requires domain expertise and consumes significant time and com-puting ***,when the task changes,the original network architecture becomes outdated and requires ***,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network *** NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively ***,complex network structures will bring enormous computing *** cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters *** then a decomposition-based multi-objective stochastic fractal search method is proposed to solve *** view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more ***,two distinct update methods are employed in step size update stage to enhance the global and local search abilities ***,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the *** studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

关键词： Deep neural network neural architecture search multi-objective optimization stochastic fractal search decomposition

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