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2022 International Conference on networking and network Applications, NaNA 2022

作者： Fan, Fan Zhang, Hongbin Zhao, Dongmei Wang, Yanxia Liu, Bin Liu, Jian Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei Geological Workers' University Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Trust plays a vital role in ensuring the security of the Social Internet of Things. Due to the increased mobility of intelligent devices, there are frequent interactions between machines and changes in social relationships. It leads to a possible data sparsity problem in the Social Internet of Things. Therefore, it is presented that the trust evaluation model is based on various social relationships. The model considers the impact of multiple dynamically changing social relationships between nodes on trust. The implicit social relationship is mined based on the stated social relationship and potential attributes connecting nodes. It addresses the scarcity issue and the slow start of the Social Internet of Things. Simulations on the Santander Smart Cities dataset demonstrate that the approach improves the accuracy and convergence of trust evaluation in both sparse and regular networks. © 2022 IEEE.

关键词： Internet of things

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

arXiv

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arXiv 2024年

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defence Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2024, CC BY-NC-ND.

关键词： Fake detection

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Design and Implementation of Security Vulnerability Sharing Platform Based on Web Crawler 11th

Design and Implementation of Security Vulnerability Sharing ...

引用

11th International Conference on Computer Engineering and networks, CENet2021

作者： Wang, Zhiqiang Wang, Ziyi Wang, Zhuoyue Zhang, Zhirui Yang, Tao Beijing Electronic Science and Technology Institute Beijing100070 China State Information Center Beijing100045 China Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (纸本)9789811665530

network attacks affect the security of basic network facilities and critical information systems and cause serious damage to the national security. It is vital and important to establish a platform of software and hardware security vulnerability for collecting, verifying, sharing vulnerabilities. Aiming at the problems of low automation, high labor cost in the current domestic and foreign vulnerability sharing platform, this paper designs and implements a security vulnerability sharing platform based on a web crawler. The crawler technology is used to crawling the vulnerabilities’ data automatically. Moreover, more comprehensive vulnerability information query and sharing are realized, which is convenient for early warning and repair of security vulnerabilities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

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LSketch: A label-Enabled Graph Stream Sketch Toward Time-Sensitive Queries

arXiv

引用

arXiv 2023年

作者： Zeng, Yiling Song, Chunyao Li, Yuhan Ge, Tingjian Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin300350 China Department of Computer Science University of Massachusetts Lowell United States

Graph streams represent data interactions in real applications. The mining of graph streams plays an important role in network security, social network analysis, and traffic control, among others. However, the sheer volume and high dynamics cause great challenges for efficient storage and subsequent query analysis on them. Current studies apply sketches to summarize graph streams. We propose LSketch that works for heterogeneous graph streams, which effectively preserves the label information carried by the streams in real scenes, thereby enriching the expressive ability of sketches. In addition, as graph streams continue to evolve over time, edges too old may lose their practical significance. Therefore, we introduce the sliding window model into LSketch to eliminate the expired edges automatically. LSketch uses sub-linear storage space and can support structure based queries and time-sensitive queries with high accuracy. We perform extensive experiments over four real datasets, demonstrating the superiority of the proposed method over state-of-the-art methods, in aspects of query accuracy and time efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： network security

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End-to-end Deep Video Compression Based on Hierarchical Temporal Context Learning

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IEEE Transactions on Multimedia 2025年

作者： Wu, Kejun Li, Zhenxing Yang, You Liu, Qiong Zhang, Xiao-Ping Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Tsinghua University Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School Shenzhen China

Emerging learning-based video compression suffers from error propagation in long group of pictures (GOP), yielding limited coding performance. To address this problem, a novel end-to-end Deep Video Compression method based on Hierarchical Temporal Context Learning (DVCH) is proposed in this paper. DVCH aims to fully exploit temporal contexts and suppress error propagation for better coding performance. It first divides video frames into several hierarchies with different compression qualities. The frames in lower hierarchies have high compression quality, and serve as reference frames. To mine high-quality reference information, we propose a Hierarchical Temporal Context Learning (HTCL) network as the fundamental module of our DVCH. The informative temporal context features from hierarchical prediction structure can be extracted by the network. Motion vectors (MVs) between the to-be-coded frame and its reference frames are estimated by the MV Learning module and used to align the extracted contexts. The contexts are fed into Context Coding module to generate the prediction of the decoded frame. Moreover, a multi-stage training strategy is developed to solve the imbalanced training challenge. Experimental results demonstrate that the proposed DVCH exceeds x264 and other end-to-end video compression methods, regardless of objective, subjective, error propagation suppression, GOP sizes, and sequence length evaluations. As much as 49.27% bitrate savings and 2.52 dB PSNR gains can be achieved in large GOP. © 1999-2012 IEEE.

关键词： Contrastive Learning

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Improved Far-field Speaker Recognition Method Based Geometry Acoustic Simulation and SpecAugment

Improved Far-field Speaker Recognition Method Based Geometry...

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2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Lian, Yingzi Pang, Jing Inner Mongolia University of Technology College of Science Hohhot China Inner Mongolia Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781665437301

Deep learning method has made a great breakthrough in the field of speaker recognition. However, the performance of deep learning method based neural network often degrade in far-field speaker recognition task, with respect to the near-field. One of the indispensable factors is that deep learning performance has a great impact on the quality and diversity of data. Utterance in far-field scenario always mixed with variant reverberation and noise which could interfere the prediction of model. In this paper, we proposed a novel method comprised of geometry acoustic simulation (GAS) and SpecAugment, which makes the performance of deep learning method on far-field speaker recognition task more stable and excellent. Based on near-field speech dataset, by means of physical simulation, far-field speech with different types and characters can be generated to provide as many scenes as possible for speaker recognition. Simultaneously, the SpecAugment method has been applied to enhance the diversity of utterance data on mel-spectrogram. Then, ResNet34 and AM-Softmax Loss were used as the most commonly used methods for feature extraction and model training. Our proposed method has been trained on Voxceleb1 and V oxceleb2 dataset, and test on Vox celeb original test set. Evaluations show that our proposed method has 46% and 40% of EER and minDCF relatively improvement on far-field speaker recognition task. © 2021 IEEE.

关键词： Deep learning

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Recent advances on environmental corrosion behavior and mechanism of high-entropy alloys

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Journal of Materials science & technology 2021年第21期80卷 217-233页

作者： Yu Fu Jun Li Hong Luo Cuiwei Du Xiaogang Li Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijingJ 00083China Key Laboratory for Corrosion and Protection of The Ministry of Education(MOE) Beijing100083China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing100083China National Materials Corrosion and Protection Scientific Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing100083China BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan528000China

In the past decade, the sudden rise of high-entropy alloys(HEAs) has become a research hotspot in the domain of metal materials. HEAs break through the design concept of traditional single-principal element alloys, and the four core effects, especially the high entropy and cocktail effects, make HEAs exhibit much better corrosion resistance than traditional corrosion-resistant metal materials, e.g., stainless steels, copper-nickel alloys, and high-nickel alloys. Currently, the corrosion resistance of HEAs causes great concern in the field of corrosion research. This article reviews the corrosion behavior and mechanism of HEAs in various aqueous solutions, revealing the correlation among the composition, microstructure and corrosion resistance of HEAs, and elaborates the influence of heat treatment, anodizing treatment and preparation methods on the corrosion behavior of HEAs. This knowledge will benefit the on-demand design of corrosion-resistant HEAs, which is an important trend of future development. Finally, perspectives regarding the corrosion research of HEAs are outlined to guide future studies.

关键词： High-entropy alloys Corrosion behavior Corrosion mechanism Passive film Alloy design

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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OAA: An Abstraction for Efficient Accelerator Adaptation in Deep Learning Frameworks

OAA: An Abstraction for Efficient Accelerator Adaptation in ...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Zhengxian Lu Chengkun Du Xueshuo Xie Qiankun Dong Cheng Gong Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Huawei Technologies Zhejiang China Haihe Lab of ITAI Tianjin China College of Software Nankai University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Deep learning frameworks rely on specific runtime and computation libraries to rewrite the backend for the adaptation of specialized accelerators, which is inefficient and hard to guarantee performance. This paper solves the issue by proposing an Operator Adaptation Abstraction (OAA) that lies between the framework and the libraries. In addition, this paper optimizes training performance based on the hardware characteristics of the accelerator. We designed experiments based on Ascend 910 and OneFlow to verify the effectiveness of OAA. Our adaptation includes more than 50 Ascend operators, achieving up to 2.0x throughput compared to the official adaptation version of PyTorch. The experiments validate that the adaptation method proposed in this paper can effectively retain the advantages of both the framework and the accelerator.

关键词： Deep learning Training Runtime Neural networks Throughput Libraries Computational efficiency

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label Distribution Learning with Correlation Information

SSRN

引用

SSRN 2024年

作者： Wu, Yilin Lin, Yaojin Guo, Wenzhong Ding, Weiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou363000 China School of Information Science and Technology Nantong University Nantong226019 China

label distribution learning quantifies the label space for each instance and has widely applicability in different fields. However, most existing works primarily focus on label correlation, but they still have a limitation in capturing instance correlation. Meanwhile, traditional label distribution learning operates on individual labels sequentially, leading to limitation in applying common features. Therefore, in this paper, we propose a novel approach for label distribution learning with correlation information i.e. instance correlation and label correlation. Specifically, the instance correlation and label correlation are identified by an optimization function and Pearson correlation coefficient, respectively. Besides, we conduct $\ell_{2,1}-$norm regularization to exploit common features. Comprehensive experiments manifest the superiority of our proposed approach against other well-established label distribution learning algorithms. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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