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Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

Intelligent Automation & Soft Computing 2023年第8期37卷 1637-1654页

作者： Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2R3Canada CCCC Highway Consultants Co. Ltd.Beijing100101China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm *** paper proposes a method for thunderstorm point charge path *** on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization ***,point charge movement paths are obtained after fitting time series localization ***,AEF data losses make it difficult to fit and visualize ***,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost *** paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered ***,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are *** recovered AEF is then input into the fixed SAE-XGBoost *** paths need to be fitted is determined by the interval to which the output PV *** results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关键词： Thunderstorm point charge atmospheric electric field(AEF) recovery

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Research and Application of Speech Transmission Index in Acoustic Leakage Detection

Research and Application of Speech Transmission Index in Aco...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Qiuyun Hao FuQiang Wang Peng Zhang XiaoFeng Ma Jianqiang Zhang Wei Zhao 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Acoustic information in confidential premise can be leaked through channels such as walls, pipes, doors and windows. Acoustic leakage level needs to be measured through specific indicators. Acoustic leakage evaluation indicator includes subjective evaluation indicator and objective evaluation indicator. The subjective evaluation indicator is a qualitative evaluation of acoustic leakage through subjective auditory perception of human ear, which can directly reflect the degree of acoustic leakage in confidential premise. At present, the objective evaluation indicator is mainly sound transmission loss (STL) to reflect objective degree of acoustic leakage in confidential premise. Therefore, it is relatively singular. Through study of Speech Transmission Index (STI), speech transmission index for public address systems (STIPA), which is a simplified form of FULL STI, is applied to acoustic leakage detection in this paper. STI has been proposed as an objective evaluation indicator and applied to acoustic leakage detection and acoustic information security. In order to confirm the effectiveness of objective evaluation indicator, a large number of related experimental studies have been conducted in the paper. Through experiments, it has been proven that STI can objectively evaluate level of acoustic leakage and level of acoustic information security in confidential premise. STI can better predict level of Chinese clarity under various complex transmission conditions. It provides a reliable basis for speech privacy of confidential premise and evaluation of architectural acoustic, sound reinforcement system and voice communication system.

关键词： Privacy Communication systems information security Ear Signal processing Propagation losses Acoustics Indexes Reliability Informatics

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CNN-Enhanced Deep Sparse Representation Network for Polarimetric SAR Image Classification

CNN-Enhanced Deep Sparse Representation Network for Polarime...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi Mengmeng Nie Haiyan Jin Junhuai Li Yuanlin Zhang Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep learning networks can automatically acquire high-level semantic features for polarimetric SAR image classification, while it involves a blind learning procedure without explicit guidance. In contrast, sparse representation methods represent effective non-deep models with a robust mathematical mechanism serving as guidance. However, they can’t capture complex image features and semantic information. To address these issues, we propose a novel approach known as the CNN-enhanced Deep Sparse Representation Network (CE-DSRNet) for PolSAR image classification, which a Sparse Representation (SR) guided deep learning model. Initially, a sparse representation model is constructed for PolSAR images to capture essential features. Subsequently, to solve the sparse model, a Deep Sparse Representation Network (DSRNet) is devised by transforming the Soft Threshold Iterative (ISTA) optimization procedure into a network, enabling automatic learning of sparse coefficients as features. Finally, a CNN-enhanced DSRNet is introduced, integrating DSRNet with CNN to effectively extract deep semantic features and enhance classification accuracy. Experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.

关键词： Deep learning Sparse approximation Semantics Feature extraction Mathematical models Radar polarimetry Iterative algorithms

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E-commerce Scheme Based on Proxy t-out-of-n Oblivious Signature

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International Journal of Network security 2024年第5期26卷 851-860页

作者： Chen, Jingyu Gong, Linming Ma, Xiangxiang Wang, Daoshun The Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi’an Polytechinic University Xi’an710048 China State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University Shaanxi Xi’an710048 China Department of Computer Science and Technology Tsinghua University Beijing100084 China No. 58 Shan-gu Road Lintong Xi’an710600 China

With the development of the internet and the demand for online shopping, intelligent online shopping systems have received widespread attention in recent years. However, existing online shopping systems’ security and privacy issues have not been well addressed. This article proposes a shopping scheme based on proxy t-out-of-n oblivious signatures, which uses t-out-of-n oblivious signature technology to ensure that users select multiple messages from a predetermined set of messages, ensuring the legality and privacy of the selection while using proxy signature technology to provide the attribute of signature unforgeability. We have analyzed the correctness of the proposed scheme and demonstrated its security in terms of integrity, unforgeability, confidentiality of selected messages, selection restriction, non-repetitive, verifiability, distinguishability, non-repudiation, and prevention of abuse. Our scheme has lower computational and communication costs than other oblivious signature schemes. In addition, the privacy-preserving multi-choice shopping scheme we propose enables users to shop more securely and conveniently. © (2024), (International Journal of Network security). All rights reserved.

关键词： Differential privacy

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes 4

Seamless Decision-Making in the Big Data Era: A Modular Appr...

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4th International Conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Zemmouri, Mohamed Laalam, Fatma Zohra Kazar, Okba Himeur, Yassine Oulefki, Adel Toumi, Chahrazad Mansoor, Wathiq Attala, Shadi Artificial intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla30000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Mohamed Khider University Department of Computer Science LINFI Laboratory Biskra07000 Algeria United Arab Emirate University Department of Information Systems and Security College of Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369694

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source. © 2023 IEEE.

关键词： Big data

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SUMI-IFL: An information-Theoretic Framework for Image Forgery Localization with Sufficiency and Minimality Constraints 39

SUMI-IFL: An Information-Theoretic Framework for Image Forge...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao Cao, Xiaochun School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University China State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macao School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China

ISBN: (纸本)157735897X

Image forgery localization (IFL) is a crucial technique for preventing tampered image misuse and protecting social safety. However, due to the rapid development of image tampering technologies, extracting more comprehensive and accurate forgery clues remains an urgent challenge. To address these challenges, we introduce a novel information-theoretic IFL framework named SUMI-IFL that imposes sufficiency-view and minimality-view constraints on forgery feature representation. First, grounded in the theoretical analysis of mutual information, the sufficiency-view constraint is enforced on the feature extraction network to ensure that the latent forgery feature contains comprehensive forgery clues. Considering that forgery clues obtained from a single aspect alone may be incomplete, we construct the latent forgery feature by integrating several individual forgery features from multiple perspectives. Second, based on the information bottleneck, the minimality-view constraint is imposed on the feature reasoning network to achieve an accurate and concise forgery feature representation that counters the interference of task-unrelated features. Extensive experiments show the superior performance of SUMI-IFL to existing state-of-the-art methods, not only on in-dataset comparisons but also on cross-dataset comparisons. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Correlation theory

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Infringement Detection and Traceability Scheme Based on Ciphertext Feature Extraction

Infringement Detection and Traceability Scheme Based on Ciph...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Lianhai Wang Xinlei Wang Tianrui Liu Yingxiaochun Wang Qi Li 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller’s address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy.

关键词： Economics Data privacy Technological innovation Data security Smart contracts Cyberspace Medical services Data science Feature extraction Homomorphic encryption

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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection 39

GLCF: A Global-Local Multimodal Coherence Analysis Framework...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou 510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 Macao

ISBN: (纸本)157735897X

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Public risks

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Resampling Factor Estimation via Dual-Stream Convolutional Neural Network

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computers, Materials & Continua 2021年第1期66卷 647-657页

作者： Shangjun Luo Junwei Luo Wei Lu Yanmei Fang Jinhua Zeng Shaopei Shi Yue Zhang School of Data and Computer Science Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhou510006China Academy of Forensic Science Shanghai200063China College of Information Science and Technology Jinan UniversityGuangzhou510632China Department of Computer Science University of Massachusetts LowellLowellMA 01854USA

The estimation of image resampling factors is an important problem in image *** all the resampling factor estimation methods,spectrumbased methods are one of the most widely used methods and have attracted a lot of research ***,because of inherent ambiguity,spectrum-based methods fail to discriminate upscale and downscale operations without any prior *** general,the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled ***,the resampling process will introduce correlations between neighboring *** this case,a set of periodic pixels that are correlated to their neighbors can be found in a resampled ***,the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear ***,in this paper,we propose a dual-stream convolutional neural network for image resampling factors *** of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled *** other is frequency stream that discovers the differences of spectrum between rescaled and original *** features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain,which is later fed into softmax layer for resampling factor *** results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.

关键词： Image forensics image resampling detection parameter estimation convolutional neural network

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