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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data Science and Information technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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A Multi-Exposure Generation and Fusion Method for Low-Light Image Enhancement

A Multi-Exposure Generation and Fusion Method for Low-Light ...

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International Joint Conference on Neural networks (IJCNN)

作者： Haiyan Jin Long Li Haonan Su YuanLin Zhang ZhaoLin Xiao Bin Wang Faculty of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the low light image enhancement, single exposure images contains a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi exposure generation and fusion method (MEGF) which simulates multi exposure images and perform feature fusion and enhancement on these images. First, we propose a Multi-Exposure Generation (MEG) method, which constructs the Gaussian Distribution for each exposure level based on multi exposure datasets. MEG can generate images with different exposure levels based on the constructed distribution. Then, the Perceptual Importance based Multi-Exposure Feature Enhancement (PIMEFE) block is developed to fuse the feature of generated multi exposure images using VGG-16 network. Before fusion, the generated images are input to Multi Scale Recursive Feature Enhancement (MSRFE) blocks and obtain the denoised and enhanced features. Finally, the fused feature are input to Curve Adjustment (CA) block for fine tuning and provide the color enhancement on fusion features. We propose the Multiple Exposure Recursive Fusion (MERF) block which estimates the adjusting factors for CA block. Experimental results demonstrate that our method outperforms other techniques in both subjective and objective evaluations on real and synthetic datasets.

关键词： Image color analysis Fuses Neural networks Gaussian distribution Dynamic range Image restoration Image enhancement

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Block-Streaming Service Loading Optimization of Android Dalvik Executable File for Cloud-End Collaboration

Block-Streaming Service Loading Optimization of Android Dalv...

引用

2022 International Conference on networking and network Applications, NaNA 2022

作者： Wang, Yichuan Feng, Yanhua Du, Yanning Hei, Xinhong Tian, Yuying Cui, Xiangjie School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

In recent years, dynamic loading technologies of Android applications have emerged in an endless stream. At present, the existing Android application loading method is to load the entire Android Dalvik Executable (DEX) file for cloud-end collaboration, and there is a problem of finer-grained loading optimization in the loading process. Based on the analysis of Android DEX file in this paper, a formal model of each class is established by using the analysis result of this file, analyzes the user's behavior, and tries to ensure that the class to be used in high probability is loaded into memory first, and a block-streaming service loading optimization model of Android DEX file is put forward. The validity of the model is verified by quantitative analysis of Android DEX file. Trying to load the whole Dalvik Executable file and the classes needed by the application separately, recording and comparing the storage space and loading time overhead. In order to avoid the uncertain factors of the network environment, the above files have been tested and recorded several times. The result shows that the time and storage space for loading the whole DEX file are far greater than the sum of the classes required by the application. With this model, the fine-grained formal analysis of Android DEX file can be performed, which is of great significance for exploring the loading method of application block flow service. © 2022 IEEE.

关键词： Android (operating system)

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Multimodal Multi-objective Particle Swarm Optimization Algorithm Based on Multi-directional Guidance 4

Multimodal Multi-objective Particle Swarm Optimization Algor...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Li, Wei Gao, Yetong Wang, Lei School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

In multimodal multi-objective optimization, the key issue is to find as many Pareto optimal solutions as possible and select promising solutions in the environmental selection. This paper proposes a multimodal multi-objective particle swarm optimization algorithm based on multi-directional guidance (MM-PSO-MG) to solve these problems. In the proposed algorithm, multi-directional guidance strategy is introduced to avoid premature convergence and find more Pareto optimal solutions. Moreover, the rank-based special crowding distance strategy is used to select promising solutions. 11 multimodal multi-objective test problems are used to verify the performance of the proposed algorithm. The results show that the proposed algorithm is competitive. © 2022 IEEE.

关键词： Multiobjective optimization

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Precise and Efficient:A Hybrid Analysis-Based Stack Overflow Detection Model under MIPS 9

Precise and Efficient:A Hybrid Analysis-Based Stack Overflow...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Wang, Yichuan Huang, Chenhao Zhang, Tong Zhao, Li Gu, Zhendong Li, Gangyang Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology China Hanjiang.to.Weihan River Valley Water Diversion Project Construction.co.ltd Xi'an China

ISBN: (纸本)9798350319804

Although the vulnerability detection techniques under the MIPS platform can detect stack overflow vulnerabilities, the stack overflow vulnerability of the stack frame changes is complex and hard to recover. The input generated by fuzzy testing techniques is inefficient and difficult to recover the site after generating crashes. It is a challenge to preserve the context of runtime stack and establish effective constraints under hybrid analysis techniques, which usually makes the solution path more run inefficient and affects the analysis results in accuracy. This paper proposes a method that combines hybrid analysis including taint analysis to detect the vulnerabilities of stack overflow. The method ensures accuracy while reducing the number of execution paths. The three vulnerabilities of stack overflow in the routed firmware selected for testing are successfully detected and the number of detected paths is controlled to about 10. In addition, the method is designed to decouple the vulnerability location and detection modules, providing a basis for subsequent research on a common vulnerability detection platform. © 2023 IEEE.

关键词： binary analysis binary security embedded security MIPS vulnerability discovery

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Robust Voice Activity Detection using Locality-Sensitive Hashing and Residual Frequency-Temporal Attention 25

Robust Voice Activity Detection using Locality-Sensitive Has...

引用

25th Interspeech Conferece 2024

作者： Li, Shu Zhang, Peng Li, Ye Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

For voice activity detection (VAD), recent works focus on learning the attention distribution on contextual information of speech to reduce the impact of irrelevant noise. However, contextual frames selected with specific steps may not be relevant, and these attention mechanisms can not fully discover the structure and characteristics of speech. In this paper, we explore a self-attention-inspired locality-sensitive hashing algorithm (SALSH) for dynamic and efficient contextual frame selection to enrich the frame-level features into a 2D partial spectrogram. Then, we propose a residual frequency-temporal attention model (FTAM) for VAD, consisting of an interval branch, an analogous hourglass structure with channel attention, and an attention learning mechanism for speech based on frequency-temporal attention. On the LibriSpeech and TIMIT datasets, the proposed method outperforms the others in terms of area under the curve (AUC), even under extremely low signal-to-noise ratio of -15dB. © 2024 International Speech Communication Association. All rights reserved.

关键词： Speech recognition

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CurMIM: Curriculum Masked Image Modeling

CurMIM: Curriculum Masked Image Modeling

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked Image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked Image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 IEEE.

关键词： Curriculum Learning Image Representation Masked Image Modeling Visual Pre-training

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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