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arXiv 2024年

作者： Lin, Yuji Lyu, Xianqiang Hou, Junhui Zhao, Qian Meng, Deyu The Department of Computer Science City University of Hong Kong Hong Kong The School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'An710049 China The School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi 710049 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China Guangdong Guangzhou510555 China

In this paper, we delve into the realm of 4-D light fields (LFs) to enhance underwater imaging plagued by light absorption, scattering, and other challenges. Contrasting with conventional 2-D RGB imaging, 4-D LF imaging excels in capturing scenes from multiple perspectives, thereby indirectly embedding geometric information. This intrinsic property is anticipated to effectively address the challenges associated with underwater imaging. By leveraging both explicit and implicit depth cues present in 4-D LF images, we propose a progressive, mutually reinforcing framework for underwater 4-D LF image enhancement and depth estimation. Specifically, our framework explicitly utilizes estimated depth information alongside implicit depth-related dynamic convolutional kernels to modulate output features. The entire framework decomposes this complex task, iteratively optimizing the enhanced image and depth information to progressively achieve optimal enhancement results. More importantly, we construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods, comprising 75 underwater scenes and 3675 high-resolution 2K pairs. To craft vibrant and varied underwater scenes, we build underwater environments with various objects and adopt several types of degradation. Through extensive experimentation, we showcase the potential and superiority of 4-D LF-based underwater imaging vis-a-vis traditional 2-D RGB-based approaches. Moreover, our method effectively corrects color bias and achieves state-of-the-art performance. The dataset and code will be publicly available at https://***/linlos1234/LFUIE. Copyright © 2024, The Authors. All rights reserved.

关键词： Underwater imaging

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Reversible Data Hiding Simultaneously Using Substitution of MSB and Compression of LSB of Encrypted Image 14

Reversible Data Hiding Simultaneously Using Substitution of ...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Sui, Liansheng Liu, Jie Cheng, Ying Xiao, Zhaolin Tian, Ailing School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Student’s Affairs Division Xi’an University of Technology Xi’an710048 China Shaanxi Province Key Lab of Thin Film Technology and Optical Test Xi’an Technological University Xi’an710048 China

ISBN: (纸本)9781510657564

With the development of privacy protection, reversible data hiding methods in encrypted image have drawn extensive research interest. Among them, a new method is proposed based on embedding prediction errors, i.e., EPE-based method, where secret information is embedded in the encrypted most significant bit plane. Not only the original image can be recovered with high quality but also the payload can reach close to 1 bit per pixel (bpp). However, there are potential errors in the process of extracting secret data, because most significant bits of a part of pixels are used as flags to mark prediction error location. In this paper, a reversible data hiding method in encrypted image with high capacity is proposed by combining most significant bit prediction with least significant bit compression. At first, most significant bit of each pixel is predicted and a location map of prediction errors in the original image is generated. In the same time, the original image is encrypted using a stream cipher method. Then, the location map is embedded into the vacated space generated with compressing least significant bits and the secret data is embedded into most significant bits of a part of pixels without prediction errors. In this way, the marked encrypted image is obtained. Finally, the original image can be recovered without any error and the secret information can be extracted correctly from the marked encrypted image. Experimental results show that the proposed method has better performance than EPE-based and other methods. © 2022 SPIE.

关键词： Pixels

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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing information retrieval Data mining Task analysis

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Enabling Efficient and Secure Health Data Sharing for Healthcare Iot Systems

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SSRN 2023年

作者： Zhu, Liehuang Xie, Yumeng Zhou, Yuao Fan, Qing Zhang, Chuan Liu, Ximeng School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou350108 China

The Healthcare Internet of Things (HIoT) system enables remote monitor patient health state and feedback on medical treatments timely. The success of HIoT relies on how to share health data securely and efficiently. Existing solutions either fail to ensure the security or incur significant overheads. In this paper, we put forward an efficient and secure health data sharing scheme for HIoT systems, named ESDS, to solve this dilemma. In ESDS, we first signcrypt the health data collected by IoT devices to protect patient privacy and ensure data authenticity. Next, we aggregate N signatures for N pieces of different health data into a short signature, which significantly reduces storage space and transmission size, i.e., from O(N) to O(1). Then, we apply local verifiability to verify one piece of health data under the aggregated signature. With this property, a doctor only needs one auxiliary message generated by the patient to check the target message without obtaining whole health data, which improves system verification efficiency. Extensive security analysis and performance evaluation show that ESDS is secure and efficient. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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MOAFL: Potential Seed Selection with Multi-Objective Particle Swarm Optimization 21

MOAFL: Potential Seed Selection with Multi-Objective Particl...

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Proceedings of the 7th International Conference on Communication and information Processing

作者： Jinman Jiang Rui Ma Xiajing Wang Jinyuan He Donghai Tian Jiating Li Beijing Key Laboratory of Software Security Engineering Technology School of Computer Science&Technology Beijing Institute of Technology China CASIC General Department of Network&Information China and CASIC Network&Information Development Corporation Limited China

ISBN: (纸本)9781450385190

Fuzzing has become one of the most widely used technology for discovering software vulnerabilities thanks to its effectiveness. However, even the state-of-the-art fuzzers are not very efficient at identifying promising seeds. Coverage-guided fuzzers like American Fuzzy Lop (AFL) usually employ single criterion to evaluate the quality of seeds that may pass up potential seeds. To overcome this problem, we design a potential seed selection scheme, called MOAFL. The key idea is to measure seed potential utilizing multiple objectives and prioritize promising seeds that are more likely to generate interesting seeds via mutation. More specifically, MOAFL leverages lightweight swarm intelligence techniques like Multi-Objective Particle Swarm Optimization (MOPSO) to handle multi-criteria seed selection, which allows MOAFL to choose promising seeds effectively. We implement this scheme based on AFL and our evaluations on LAVA-M dataset and 7 popular real-world programs demonstrate that MOAFL significantly increases the code coverage over AFL.

关键词： Multi-Objective Particle Swarm Optimization (MOPSO) AFL Multiple Criteria Seed Selection

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Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Comprehensive Feature Processing Based on Attention Mechanis...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma 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 Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

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Adaptive Interacting Multiple Model for Underwater Maneuvering Target Tracking with One-Step Randomly Delayed Measurements

SSRN

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SSRN 2023年

作者： Li, Xiaohua Lu, Bo Li, Yuxing Lu, Xiaofeng Jin, Haiyan School of Computer Science and Engineering Xi'an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China School of Automation and Information Engineering Xi'an University of Technology Xi’an710048 China

The existing interacting multiple model (IMM) suffers from two problems when tracking an underwater maneuvering target: 1) the conventional nonlinear filters in IMM cannot work on the condition of delayed measurement, 2) the inefficiency of model switching in IMM may degrade the tracking performance. In order to handle the delayed measurement, an augmented state vector including target state and measurement noise is designed. Thus, under two Gaussian assumptions, the estimation of the target state can be divided into two steps: measurement noise filter and target state filter. Then, the new extended Klaman filter (EKF) and unscented Kalman filter (UKF) with delayed measurement are implemented as two variations of the proposed filter. In addition, to solve the problem of low switching efficiency between IMM models, a transfer probability corrected function is designed to adaptively update the transfer probability matrix (TPM) in IMM. The improved TPM can make full use of the current and past model information. The simulation results show that the proposed algorithm outperforms the existing IMM-EKF and IMM-UKF. © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Disentangling Interest and Conformity Representation to Mitigate Popularity Bias for Sequential Recommendation

Disentangling Interest and Conformity Representation to Miti...

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International Joint Conference on Neural networks (IJCNN)

作者： Wenyue Hu Zhenyu Yang Yan Huang Zhibo Zhang Baojie Xu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The objective of sequential recommendation is to predict user preferences for items based on historical interaction sequences. This process often leads to a phenomenon known as popularity bias, where popular items are excessively recommended. Conformity, the tendency of users to follow popular items, is a significant factor contributing to this issue. Previous methods have not adequately disentangled conformity and interest, failing to accurately model users’ true intent. To address this, we propose a novel Disentangled Interest and Conformity Sequential Recommendation method (DICSRec) to mitigate the popularity bias. Specifically, we first design an Intent Encoding Module (IEM), which includes two independent encoders for conformity and interest to model their representations. To better disentangle these two factors, we design a disentangling task with proxy-based self-supervised learning and orthogonal regularization. Furthermore, to provide the Intent Encoding Module with more global information, we design a Global Conformity-aware Module (GCM), which supplies item popularity information and aids in enhancing user conformity representation. Lastly, recognizing the varying significance of user conformity and interest, we propose an adaptive Fusion Prediction Module (FPM) that adaptively aggregates user conformity and interest representations for final prediction. Experiments on four real-world datasets consistently demonstrate the superiority of our method over advanced sequential recommendation models. Code implementation is available at: https://***/lyra0611/DICSRec.

关键词： Adaptation models Codes Fuses Aggregates Neural networks Self-supervised learning Multitasking

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