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作者： Chen, Jing Ma, Kun Ji, Ke Chen, Zhenxiang College of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

In the era of the explosion of digital content of large-scale self-media, user-friendly social platforms such as Twitter and Facebook, provide opportunities for people to express their ideas and opinions freely. Due to lack of restrictions, hateful speech and its exposure can have profound psychological impacts on society. Current social networking platform is over-reliant on the manual check, and it is labor-intensive and time-consuming. Although there are many machines learning methods for the detection of hate speech, short text with character limit on social platforms is more challenging for the detection of hate speech and offensive language. To address the problem of data sparsity, we have proposed a topic memory model for hate speech and offensive language detection (abbreviated as TM-HOL). Potential topics are generated with our encoder and decoder to enrich short text features. Two memory matrices correspond to the topic words and the text, and the hate feature matrix is used to learn the syntactic features. It is demonstrated that our proposed method is effective on three datasets, performing better weighted-F1. © 2021 John Wiley & Sons, Ltd.

关键词： Classification (of information)

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Lithological Facies Classification Using Attention-based Gated Recurrent Unit

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Tsinghua Science and Technology 2024年第4期29卷 1206-1218页

作者： Yuwen Liu Yulan Zhang Xingyuan Mao Xucheng Zhou Jingwen Chang Wenwei Wang Pan Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210023China

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

关键词： attention mechanism facies classification GRU MLP(multilayer perceptron)

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Feature Selection for Video Traffic Identification with Competitive Swarm Optimizer 3

Feature Selection for Video Traffic Identification with Comp...

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3rd IEEE International Conference on Social Sciences and Intelligence Management, SSIM 2023

作者： Deng, Hanbo Deng, Feihu Peng, Lizhi Kang, Shunguang Guilin Tourism University Guangxi Guilin541006 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Shandong Jinan250022 China

ISBN: (纸本)9798350327786

To identify video types in the network traffic, we identified the game video from a large number of videos as an imbalanced problem. An imbalanced data gravitation-based classifier (IDGC) was used to solve the classification of the video type in this study. We collected different types of videos and extracted the original feature dataset of two types of video traffic. We extracted 256 features and then used a competitive swarm optimizer (CSO) to select the effective feature from the origin feature data set. We selected the features by their weights. We experimented collected dataset. The developed feature selection method in this study successfully classified video traffic. © 2023 IEEE.

关键词： Feature Selection

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SIGAN: Self-inhibited Graph Attention network for Text Classification 1

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23rd International Conference on intelligent Systems Design and Applications, ISDA 2023

作者： Fang, Jiaqi Ma, Kun School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Quancheng Laboratory Shandong Jinan250103 China

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

Text classification is a pivotal task within the field of natural language processing (NLP) that has many practical applications. However, there are several challenges of existing methods of Graph Convolutional networks, such as difficulties in capturing long-range dependencies and overfitting issues. To address these challenges, we have proposed a novel model called Self-Inhibited Graph Attention network (abbreviated as SIGAN), which builds upon the foundation of TextGCN but introduces two key contributions: Graph Attention networks (GATs) and Self-Inhibition Structure (SIS). GATs enable adaptive weight allocation to nodes, facilitating a better understanding of complex relationships within graph data, including long-distance dependencies;SIS replaces the Dropout operation used in TextGCN and preserves the overfitting prevention properties of Dropout while avoiding the loss of crucial features by considering the importance levels of individual neurons. Our model is evaluate on four large-scale datasets (R52, MR, R8, Ohsumed), and we compare our methods with several baselines such as TextGCN and TWPGCN. Our results show that SIGAN achieves better improvements in accuracy, with gains of 0.93%, 1.27%, 0.88%, and 0.39%, respectively. Experimental results show the effectiveness of the model enhancements and the structure of self-inhibition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Large datasets

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A Survey of SDN Data Plane Attacks and Defense Strategies 23

A Survey of SDN Data Plane Attacks and Defense Strategies

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2nd International Conference on networks, Communications and Information Technology, CNCIT 2023

作者： Zhang, Wenxiu Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798400700620

The Software-Defined networking (SDN) paradigm proposes the decoupling of control and data planes and a centralized software-oriented management approach based on a central controller, easing the development of new applications and services, and fundamentally changed the network architecture. This paper introduces the present situation of SDN and its data plane research, but as with traditional networks, there are multiple vulnerabilities in the security aspects of the SDN architecture, which can be used to perform malicious operations with these vulnerabilities to affect the network and its services. In this paper, the SDN data plane security issues are mainly discussed, the attacks on the SDN data plane are summarized, and the existing attack defense policies are summarized and compared. Finally, the research direction of SDN data plane security is proposed and the whole paper is summarized. CCS CONCEPTS •Security and privacy •network security •Security protocols © 2023 ACM.

关键词： Information management

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A Novel Adaptive Distribution Distance-based Feature Selection Method for Video Traffic Identification 9th

A Novel Adaptive Distribution Distance-Based Feature Selecti...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Licheng Liu, Shuaili Yang, Qingsheng Qu, Zhongfeng Peng, Lizhi Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Mathematical Sciences University of Jinan Jinan China Quancheng Laboratory Jinan China

ISBN: (纸本)9783031466731

The rapid proliferation of video applications in recent years has triggered an unprecedented surge in Internet video traffic, which in turn has presented substantial challenges for effective network management. However, existing methods for extracting features from video traffic primarily focus on conventional traffic attributes, resulting in suboptimal identification accuracy. Furthermore, the challenge of handling high-dimensional data is a common hurdle in video traffic identification, necessitating a robust approach to select the most pertinent features crucial for accurate identification. Despite the abundance of studies utilizing feature selection to enhance identification performance, there exists a notable lack of research that addresses the quantification of feature distributions with small or no overlap. This study proposes, firstly, the extraction of features relevant to videos, thereby assembling an expansive feature repertoire. Secondly, in the pursuit of forming an effective subset of features, the current research introduces the adaptive distribution distance-based feature selection (ADDFS) methodology. Using the Wasserstein distance metric to quantify the differences between feature distributions. To gauge the efficacy of this proposal, a dataset comprising video traffic from various platforms within a campus network environment was collected, and a series of experiments were conducted using these datasets. The experimental results indicate that the proposed method can achieve highly accurate identification performance for video traffic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： network management

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Retrievable Image Encryption based on Adaptive Block Compressed Sensing

Retrievable Image Encryption Based on Adaptive Block Compres...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Fang, Jiuchuan Zhao, Hui Zheng, Mingwen Wang, Zijian Niu, Sijie Gao, Xizhan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan250022 China School of Mathematics and Statistics Shandong University of Technology Zibo255000 China

ISBN: (纸本)9789819708543

The increasing popularity of the internet and mobile devices, cloud storage has emerged as a popular storage method. However, because data carries a lot of information, untrustworthy servers can access users’ private information. This paper introduces an approach based on adaptive block compressed sensing and hash retrieval. The objective of this paper is to reduce the storage space required by encryption algorithms while simultaneously improving interaction efficiency. Firstly, compress images using block compressed sensing, and use BCS-SPL algorithm to reconstruct. And curves are fitted based on the gradient features and root mean square values of the blocks. Then, the sub-rates are allocated based on the gradient characteristics of the image using a fitting curve, and then the image is compressed. Next, encrypt the image and save it. The features of the queried image are used for retrieval, the results are decrypted, and then reconstructed. Finally, through a series of security analyses, the method is effective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud storage

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Graph Attention network for Short Text Type News 23

Graph Attention Network for Short Text Type News

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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Sun, Zhijie School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9798400707667

In the era of self-media, the spread of fake news is more widespread and rapid because anyone can become an editor of the news. It leads to a large number of short news texts. However, the unchecked dissemination and sharing of news information have led to a continuous emergence of fake news events. It not only misleads readers but also has a detrimental impact on society. Besides, short news texts are semantically sparse and need more contextual solid connections. It is difficult to extract text features and achieve high error detection efficiency. Therefore, this paper has proposed a heterogeneous graph attention network that includes multiple text-related features. The network breaks the traditional way of only connecting text features in graph neural networks, extracts various external knowledge and text feature information from the news to construct the graph network, and establishes connections for different text features to enhance semantic understanding. Additionally, connecting external knowledge bases is helpful in eliminating news entity word ambiguities. Then, the heterogeneous graph is embedded into a dual-attention mechanism at both node and pattern levels, capturing the importance of different adjacent nodes, reducing the weight of noisy nodes, and accurately identifying valid information. Experimental results demonstrate that the proposed method in this paper outperforms better than several baseline models, such as TextGCN in terms of accuracy. © 2023 ACM.

关键词： Graph neural networks

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Loading uniform Ag_(3)PO_(4)nanoparticles on three-dimensional peony-like WO_(3)for good stability and excellent selectivity towards NH_(3)at room temperature

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Chinese Physics B 2023年第8期32卷 251-259页

作者：邵星炎贾福超刘婷婷刘健诚王小梅尹广超吕娜周通 Ramachandran Rajan 刘波 Laboratory of Functional Molecules and Materials School of Physics and Optoelectronic EngineeringShandong University of TechnologyZibo 255000China School of Material Science and Engineering Shandong University of TechnologyZibo 255000China Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Translational Medical Center Zibo Central HospitalZibo 255036China

A heterojunction structure design is a very good method for improving the properties of semiconductors in many research *** method is employed in the present study to promote the gas-sensing performance of Ag_(3)PO_(4)nanocomposites at room temperature(25℃).A nanocomposite of Ag_(3)PO_(4)nanoparticles and three-dimensional peony-like WO_(3)(WO_(3)/Ag_(3)PO_(4))was successfully prepared by the precipitation *** crystalline phases were analyzed by xray diffraction and the microstructure was characterized by scanning electron microscopy and transmission electron *** chemical bonding states were analyzed by x-ray photoelectron *** gas-sensing performance of WO_(3)/Ag_(3)PO_(4)sensors was systematically explored at room *** composite sensors possessed a higher response and lower detection limit(1 ppm)to NH_(3)than those made of a single type of material;this is ascribed to the synergistic effect achieved by the heterojunction *** the different composite sensors tested,gas sensor A5W5(Ag_(3)PO_(4):WO3mass ratio of 5:5)displayed the highest response to NH_(3)at room ***,the A5W5 gas sensor exhibited relatively good stability and excellent selectivity to NH_(3).The A5W5 sensor also displayed a relatively good response under high *** gas-sensing mechanism of the WO_(3)/Ag_(3)PO_(4)sensors is explained in *** together,the as-prepared sensor is highly efficient at detecting NH_(3)and could be suitable for practical *** addition,this study also provides a new method for developing Ag_(3)PO_(4)-based sensors in the gas-sensing field.

关键词： Ag_(3)PO_(4) peony-like WO_(3) NH_(3) room temperature

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Yolo-based Lightweight Object Detection with Structure Simplification and Attention Enhancement 48

Yolo-Based Lightweight Object Detection with Structure Simpl...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sun, Shuqi Yang, Xiaohui Peng, Jingliang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China University of Jinan School of Information Science and Engineering Jinan250022 China

ISBN: (纸本)9781728163277

In this paper we propose a lightweight object detector by optimizing the structure of YOLOv3. The optimization is conducted in two aspects: simplifying the structural components by lightweight substitutes and introducing the attention mechanism to increase the detection accuracy. For the simplification, we remodel the backbone based on MobileNet v2 and replace every 3×3 convolution in the detection neck and head by the fusion of a 3 × 3 depthwise separable convolution and a squeeze and excitation block (DSConv+SE);for the attention enhancement, we introduce the high-frequency wavelets of the original image to the input, a simplified non- local block to the simplified backbone and convolutional block attention modules to the simplified detection neck. In addition, local 3×3 convolution branches are introduced to the simplified backbone for enhanced learning capability. Experiments demonstrate that the proposed detector outperforms each compared state-of-the-art work in one or more aspects. © 2023 IEEE.

关键词： Object detection

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