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Multi-head Hashing with Orthogonal Decomposition for Cross-modal Retrieval 1

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Liu, Wei Li, Jun Wu, Zhijian Xu, Jianhua Yang, Bo School of Computer and Electronic Information Nanjing Normal University Nanjing210023 China School of Data Science and Engineering East China Normal University Shanghai200062 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Recently, cross-modal hashing has become a promising line of research in cross-modal retrieval. It not only takes advantage of complementary multiple heterogeneous data modalities for improved retrieval accuracy, but also enjoys reduced memory footprint and fast query speed due to efficient binary feature embedding. With the boom of deep learning, convolutional neural network (CNN) has become the de facto method for advanced cross-model hashing algorithm. Recent research demonstrates that dominant role of CNN is challenged by increasingly effective Transformer architectures due to their advantages of long-range modeling by relaxing local inductive bias. However, the absence of inductive bias shatters the inherent geometric structure, which inevitably leads to compromised neighborhood correlation. To alleviate this problem, in this paper, we propose a novel cross-modal hashing method termed Multi-head Hashing with Orthogonal Decomposition (MHOD) for cross-modal retrieval. More specifically, with the multi-modal Transformers used as the backbones, MHOD leverages orthogonal decomposition for decoupling local cues and global features, and further captures their intrinsic correlations through our designed multi-head hash layer. In this way, the global and local representations are simultaneously embedded into the resulting binary code, leading to a comprehensive and robust representation. Extensive experiments on popular cross-modal retrieval benchmarking datasets demonstrate the proposed MHOD method achieves advantageous performance against the other state-of-the-art cross-modal hashing approaches. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Research on air quality prediction based on correlation analysis and XGBoost

Research on air quality prediction based on correlation anal...

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2023 International Conference on Electronics and Devices, Computational science, ICEDCS 2023

作者： Liu, Zijie Chen, Kaijie Ning, Ziyu Wang, Lele Zheng, Zhaoting Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Wuhan University of Science and Technology College of Mechanical and Automation Wuhan China Guangzhou City University of Technology School of Computer Engineering School of Data Science Guangzhou China

ISBN: (纸本)9798350343038

As one of the primary concerns of people, air quality, its pollution and management is an important topic. Air pollution can cause harm to human health, ecological environment, and social economy, and its pollution level is affected by many factors. Exploring the factors of air pollutant concentration and predicting PM2.5 concentration and AQI index more accurately is a common concern for the scientific community and policymakers. In order to make a more accurate prediction of air quality, this paper addresses the problem of air quality prediction. The quantitative features are extracted based on the Pearson correlation model and stepwise regression model, and then the qualitative features are extracted based on the chi-square test. After the feature extraction, an XGBoost model is constructed and the extracted features are used as input to predict the future air quality. The obtained prediction accuracy is much higher than that of the support vector machine and BP neural network. The predicted R2 is 0.905. © 2023 IEEE.

关键词： Air quality

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Proximal Policy Optimization-Based Intrusion Detection Model for Satellite Networks

Proximal Policy Optimization-Based Intrusion Detection Model...

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Shuaishuai Liang Huibin Feng Jiawei Wu Canfei He College of Computer and Data Science Fuzhou University School of Computer and Big Data Minjiang University Fuzhou China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

As satellite network communication systems become an increasingly pivotal role in modern life, The routine maintenance of satellite networks is challenging due to limited resources and their susceptibility to interference. Satellite networks are more reliant on interruption detection systems than terrestrial networks. The importance of interruption detection systems in satellite networks is becoming increasingly evident. In this paper, we propose a novel intrusion detection model based on Proximal Policy Optimization (PPO). This method interacts with the environment to learn and optimize detection strategies, allowing for dynamic adaptation to environmental changes and attack patterns. We evaluated our model on benchmark datasets and compared its performance metrics against existing models. The results demonstrate that our proposed model significantly enhances overall accuracy and precision, and outperforms baseline models in terms of training convergence speed.

关键词： Training Adaptation models Privacy Satellites Accuracy Computational modeling Intrusion detection Stability analysis Robustness Protection

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Collaborative Filtering of Malicious Information from the MULTimedia data Using s 1

Collaborative Filtering of Malicious Information from the MU...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Parasuraman, Manikandan Abirami, G. Arockia Raj, A. Krishnan, Batri Munnangi, Ashok Kumar Kumaresan, M. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Sathyabama Institute of Science and Technology Department of Computer Science Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350341737

Due to high proliferation of unsolicited information in the text, image and video files, the social media analytics engine suffers from losing its user, user privacy and others. The unsolicited information is been a huge threat to the service provides as it affects the reputations of the services provided and hence it is necessary to develop an intelligent model. Modern machine learning techniques are applied to identify and filter unsolicited information. However, some of the conventional methods are designed in an insecure way while processing all the text file, image and video files. © 2023 IEEE.

关键词： Collaborative filtering

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Faster Local Solvers for Graph Diffusion Equations

arXiv

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arXiv 2024年

作者： Bai, Jiahe Zhou, Baojian Yang, Deqing Xiao, Yanghua The School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative methods require accessing the whole graph per iteration, making them time-consuming for large-scale graphs. While existing local solvers approximate diffusion vectors through heuristic local updates, they often operate sequentially and are typically designed for specific diffusion types, limiting their applicability. Given that diffusion vectors are highly localizable, as measured by the participation ratio, this paper introduces a novel framework for approximately solving GDEs using a local diffusion process. This framework reveals the suboptimality of existing local solvers. Furthermore, our approach effectively localizes standard iterative solvers by designing simple and provably sublinear time algorithms. These new local solvers are highly parallelizable, making them well-suited for implementation on GPUs. We demonstrate the effectiveness of our framework in quickly obtaining approximate diffusion vectors, achieving up to a hundred-fold speed improvement, and its applicability to large-scale dynamic graphs. Our framework could also facilitate more efficient local message-passing mechanisms for GNNs. © 2024, CC BY-NC-SA.

关键词： Iterative methods

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mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

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IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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GestureID:Gesture-Based User Authentication on Smart Devices Using Acoustic Sensing

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Sound & Vibration 2024年第1期58卷 151-169页

作者： Jizhao Liu Jiang Hui Zhaofa Wang School of Computer Science Zhongyuan University of TechnologyZhengzhou450007China Henan Development and Innovation Laboratory of Industrial Internet Security Big Data(Zhongyuan University of Technology) Zhengzhou450007China

User authentication on smart devices is crucial to protecting user privacy and device *** to the development of emerging attacks,existing physiological feature-based authentication methods,such as fingerprint,iris,and face recognition are vulnerable to forgery and *** this paper,GestureID,a system that utilizes acoustic sensing technology to distinguish hand features among users,is *** involves using a speaker to send acoustic signals and a microphone to receive the echoes affected by the reflection of the hand movements of the *** ensure system accuracy and effectively distinguish users’gestures,a second-order differential-based phase extraction method is *** method calculates the gradient of received signals to separate the effects of the user’s hand movements on the transmitted signal from the background ***,the secondorder differential phase and phase-dependent acceleration information are used as inputs to a Convolutional Neural Networks-Bidirectional Long Short-Term Memory(CNN-BiLSTM)model to model hand motion *** decrease the time it takes to collect data for new user registration,a transfer learning method is *** involves creating a user authentication model by utilizing a pre-trained gesture recognition *** a result,accurate user authentication can be achieved without requiring extensive amounts of training *** demonstrate that GestureID can achieve 97.8%gesture recognition accuracy and 96.3%user authentication accuracy.

关键词： Acoustic sensing hand gesture user authentication

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Simultaneous Speech Extraction for Multiple Target Speakers Under Meeting Scenarios

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Zeng, Bang Suo, Hongbin Wan, Yulong Li, Ming School of Computer Science Wuhan University Wuhan430027 China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan Jiangsu215316 China Data & amp AI Engineering System OPPO Beijing100125 China

The common target speech separation directly estimates the target source, ignoring the interrelationship between different speakers at each frame. We propose a multiple-target speech separation (MTSS) model to simultaneously extract each speaker’s voice from the mixed speech rather than just optimally estimating the target source. Moreover, we propose a speaker diarization (SD) aware MTSS system (SD-MTSS). By exploiting the target speaker voice activity detection (TSVAD) and the estimated mask, our SD-MTSS model can extract the speech signal of each speaker concurrently in a conversational recording without additional enrollment audio in advance. Experimental results show that our MTSS model achieves improvements of 1.38 dB signal-to-distortion ratio (SDR), 1.34 dB scale-invariant signal-to-distortion ratio (SISDR), and 0.13 perceptual evaluation of speech quality (PESQ) over the baseline on the WSJ0-2mix-extr dataset, separately. The SD-MTSS system makes a 19.2% relative speaker dependent character error rate reduction on the Alimeeting dataset. © Shanghai Jiao Tong University 2024.

关键词： Speech analysis

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Reversible data hiding scheme using prediction neural network and adaptive modulation mapping

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Multimedia Tools and Applications 2025年第9期84卷 6665-6686页

作者： Chen, Si-Sheng Chang, Chin-Chen Horng, Ji-Hwei School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fujian Fuzhou35030 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Electrical Engineering National Quemoy University Kinmen89250 Taiwan

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mapping

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Deep mining algorithm for traffic accident data on highways under severe weather conditions

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第SPEICAL ISSUE期1卷 47-58页

作者： Zhang, J.Y. Cao, Y. Jiangsu Youth Science and Technology Center Nanjing210000 China School of Computer Engineering Jiangsu Second Normal University Nanjing210000 China Jiangsu Province Engineering Research Center of Basic Education Big Data A PPlication Nanjing210013 China

Analyzing traffic accident data is crucial for pinpointing contributing factors’ forecasting accident patterns’ and informing effective safety measures. This insight leads to enhanced road safety’ decreased fatalities’ and better resource allocation in transportation planning. Severe weather conditions’ including rain’ fog’ and sandstorms’ can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly’ collect traffic accident data under adverse weather conditions and complete attribute reduction’ denoising’ and normalization processing. Then’ by improving the decision tree algorithm’ the attribute selection criteria of the decision tree algorithm were enhanced;Finally’ the improved decision tree algorithm is aPPlied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%’ an accuracy rate of over 94.3%’ a recall rate of over 92.7%’ and a stable F1 value of around 0.8’ demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision suPPort for traffic management departments. © 2024, Aracne Editrice. All rights reserved.

关键词： data mining

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