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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Cheng School of Computer Science and Technology Engineering Research Center of Mine Digitalization China University of Mining and Technology Ministry of Education Jiangsu Xuzhou China

ISBN: (纸本)9798400706868

Quantization index modulation (QIM) based VoIP steganography can conceal secret information in VoIP streams. Malicious users could use this technology to conduct illegal activities, threatening network and public security. Hence, practical steganalysis models that could detect QIM-based VoIP steganography are urged to be developed. In recent years, deep learning (DL) models have been investigated for this task, and exciting outcomes have been achieved. However, existing models are far from practical. Two major challenges are required to be addressed. First, there is still significant room for improvement in detection accuracy. Second, studies that balance the detection accuracy and response time are still insufficient. In this context, our main research topic fits in the QIM-based VoIP steganalysis theme, which aims to detect QIM-based steganography in VoIP streams in a fast and accurate manner. © 2024 ACM.

关键词： covert channel

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Control Flow Divergence Optimization by Exploiting Tensor Cores 24

Control Flow Divergence Optimization by Exploiting Tensor Co...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Pang, Weiguang Jiang, Xu Liu, Songran Qiao, Lei Fu, Kexue Gao, Longxiang Yi, Wang University of Electronic Science and Technology of China China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering Northeastern University China Beijing Institute of Control Engineering Beijing China Uppsala University Sweden

ISBN: (纸本)9798400706011

Kernels are scheduled on Graphics Processing Units (GPUs) in the granularity of GPU warp, which is a bunch of threads that must be scheduled together. When executing kernels with conditional branches, the threads within a warp may execute different branches sequentially, resulting in a considerable utilization loss and unpredictable execution time. This problem is known as the control flow divergence. In this work, we propose a novel method to predict threads' execution path before the launch of the kernel by deploying a branch prediction network on the GPU's tensor cores, which can efficiently parallel run with the kernels on CUDA cores, so that the divergence problem can be eased in a large extent with the lowest overhead. Combined with a well-designed thread data reorganization algorithm, this solution can better mitigate GPUs' control flow divergence problem. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Tensors

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Distributed Secure Storage Scheme Based on Sharding Blockchain

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computers, Materials & Continua 2022年第3期70卷 4485-4502页

作者： Jin Wang Chenchen Han Xiaofeng Yu Yongjun Ren R.Simon Sherratt School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Business Nanjing UniversityNanjing210093China School of Computer School of SoftwareSchool of Cyberspace SecurityEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Department of Biomedical Engineering University of ReadingRG66AYUK

Distributed storage can store data in multiple devices or servers to improve data ***,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challenges such as insufficient performance,data tampering,and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed *** this scheme,the following improvements have been made in this *** paper first analyzes the problems faced by distributed *** proposed to build a new distributed storage blockchain scheme with sharding *** proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology,which can improve the efficiency of data verification between *** addition,this paper uses polynomial commitment to construct a new verifiable secret share scheme called *** new scheme is one of the foundations for building our improved distributed storage blockchain *** with the previous scheme,our new scheme does not require a trusted third party and has some new features such as homomorphic and batch *** security of VSS can be further *** comparisons show that the proposed scheme significantly reduces storage and communication costs.

关键词： Blockchain distributed storage verifiable secret share polynomial commitment

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An image inpainting method based on the high-efficiency transformer

An image inpainting method based on the high-efficiency tran...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Yang, Jiaqi Su, Xuesong School of Computer Science and Engineering Tianjin University of Technology Tianjin China Technology Inspection Center of Shengli Oilfield SINOPEC Shandong Dongying China

ISBN: (纸本)9781510681514

In this paper, we introduce an image restoration network that is based on the High-Efficiency Transformer (HET). The model utilizes fast Fourier convolution for image enhancement, incorporates self-attention mechanisms for capturing contextual information, and employs convolution for feature extraction and classification. Additionally, we improve the network's performance and convergence speed by implementing batch normalization and residual concatenation. Experimental results show substantial enhancements in image restoration achieved through our deep learning-based approach. The reconstructed images demonstrate improved clarity, enhanced details, and reduced dependence on manual interventions. © 2024 SPIE.

关键词： Convolution

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End-to-end reference image-based coloring

End-to-end reference image-based coloring

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Jia, Huiying Su, Xuesong School of Computer Science and Engineering Tianjin University of Technology Tianjin China Technology Inspection Center of Shengli Oilfield SINOPEC Shandong Dongying China

ISBN: (纸本)9781510681514

This study proposes an end-to-end method aimed at achieving intelligent colorization of images. This approach enables automatic colorization of grayscale images by taking in a grayscale image and a colored reference image as input. We designed a deep neural network model to learn the mapping relationship between grayscale images and colored reference images in an end-to-end manner. During training, we utilized a large amount of image data with real color labels and optimized the model using generative adversarial networks. Experimental results demonstrate the superiority of our method in color restoration and detail preservation across different types of images. Compared to traditional methods, the end-to-end approach offers higher levels of automation and faster colorization speed, opening up new possibilities in the field of image colorization. © 2024 SPIE.

关键词： Generative adversarial networks

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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A Novel Gas Classification Method Based on The Improved GasNet 43

A Novel Gas Classification Method Based on The Improved GasN...

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43rd Chinese Control Conference, CCC 2024

作者： Qin, Xiaolin Cheng, Lei Tao, Xiang Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan430081 China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

ISBN: (纸本)9789887581581

Accurately and promptly identifying harmful gases in the environment can effectively prevent many accidents. Most existing pattern recognition methods are focused on addressing gas classification problems in static scenarios, but the real world is a dynamic and changing environment. Therefore, researching gas classification in continuous learning scenarios will better meet the needs of suddenly emerging gas classification tasks in real-world environments. To address this issue, an incremental learning model was introduced into gas classification, and an improved deep convolutional network was proposed for gas image feature extraction. The proposed model and other baseline methods experimented on an open-source gas dataset. The proposed model reduces knowledge loss due to catastrophic forgetting and effectively classifies the 10 gas samples in the dataset in continuous learning scenarios compared to non-incremental methods. It outperforms other baseline incremental methods by optimizing the network's shortcut connections, making the feature extraction network easier to train and improving gas classification performance. In dynamic learning scenarios, the proposed model achieved a classification accuracy of 86.30% for 10 gas samples. The experimental results demonstrate that the proposed method effectively mitigates catastrophic forgetting interference and completes gas classification tasks in continuous learning scenarios. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Contrastive Learning

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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

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computers, Materials & Continua 2025年第2期82卷 2637-2664页

作者： Reshma Siyal Muhammad Asim Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug School of Computer Science and Engineering Central South UniversityChangsha410083China EIAS LAB College of Computer and Information Sciencesand Center of Excellence in Quantum and Intelligent ComputingPrince Sultan UniversityRiyadh11586Saudi Arabia Gianforte School of Computing Montana State UniversityBozemanMT 59717USA Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

关键词： Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption

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Centromere Landscapes Resolved from Hundreds of Human Genomes

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Genomics, Proteomics & Bioinformatics 2024年第5期22卷 61-71页

作者： Shenghan Gao Yimeng Zhang Stephen J. Bush Bo Wang Xiaofei Yang Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China Center for Mathematical Medical The First Affiliated HospitalXi'an Jiaotong UniversityXi'an 710061China School of Life Science and Technology Xi'an Jiaotong UniversityXi'an 710049China Faculty of Science Leiden UniversityLeiden 2311 EZThe Netherlands

High-fidelity(HiFi)sequencing has facilitated the assembly and analysis of the most repetitive region of the genome,the ***,our current understanding of human centromeres is based on a relatively small number of telomere-to-telomere assemblies,which have not yet captured its full *** this study,we investigated the genomic diversity of human centromere higher order repeats(HORs)via both HiFi reads and haplotype-resolved assemblies from hundreds of samples drawn from ongoing pangenome-sequencing projects and reprocessed them via a novel HOR annotation pipeline,*** used this wealth of data to provide a global survey of the centromeric HOR landscape;in particular,we found that 23 HORs presented significant copy number variability between *** detected three centromere genotypes with unbalanced population frequencies on chromosomes 5,8,and *** inter-assembly comparison of HOR loci further revealed that while HOR array structures are diverse,they nevertheless tend to form a number of specific landscapes,each exhibiting different levels of HOR subunit expansion and possibly reflecting a cyclical evolutionary transition from homogeneous to nested structures and back.

关键词： Centromere sequence Genome evolution High-fidelity sequencing technology Higher order repeat annotation Human population

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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