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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Ul Haq, Ikram Khan, Salabat Khan, Mansoor Tamsal, Saher School of Computer Science and Technology Southwest University of Science and Technology Mianyang China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331528324

In today's world, connectivity and performance-driven technology are rapidly advancing each year, and new platforms and mechanisms are being utilized to enhance the security and performance of databases and cloud computing, which are trending in the global market. This examination focused on customer information security in cloud computing, i.e., information stockpiling security issues and how to control unapproved access to data by proposing access control;valuable measures were characterized subsequently, and an entrance control show was prescribed. Cloud computing can be described as an innovation that depends on the web and the sharing of resources. It has an adaptable foundation that customers can use with much of a stretch. With its wide event relationship, there are numerous obstacles to continuing with cloud computing because of security issues, mostly considering customer data security. The proposed study helped hesitant clients sensibly continue forward in the cloud while understanding the dangers of cloud computing. The research objective is to examine the security techniques employed in cloud technology to improve its performance by utilizing data mining within a unified cache system. We will apply the Apriori algorithm as a proposed solution to be used in a single cache while considering the security measures in the cloud. The study expects to apply this method to all cloud providers and vendors by entering data into a single cache mechanism that can be extended into different components and implemented using SPSS modelers to improve the security of the database. © 2024 IEEE.

关键词： Cloud security

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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Salinity sensing by the radio-frequency power based on the Michelson interferometer

Salinity sensing by the radio-frequency power based on the M...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Gui, Lin Chen, Xuhui Mao, Chengxin Li, Qiankun Peng, Yu Wu, Mengmeng School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai China School of Resources and Environmental Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350379266

The salinity sensing technology based on the microwave photonic method is proposed and experimentally verified. The microwave photonic filter is formed by the microwave photonic interferometry of the Michelson interferometer. Contrast to the traditional method based on the optical spectrum measurement, the salinities information can be measured by extracting the minimum values of radio frequency(RF) response curves of microwave photonic filter(MPF). Due to amount of noise and the nonlinearity in the MPF, the minimum values of RF response of MPF vary with time, so the averaged values of several measurements are utilized in data analyzing process. Experimental results show that the salinity sensitivity of 0.284dB/% is achieved with the brine concentration in the range of 31%-40% and the value of R-square can reach 0.88 by linearity fitting for salinity sensing, and the value can be improved to 0.979 by Cubic fitting. © 2024 IEEE.

关键词： Michelson interferometers

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FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Privacy-Preserving Incentive Mechanism for Crowdsensing 5

FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Pri...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Cheng, Jiameng Zhang, Hongwei Tianjin University of Technology School of Computer Science and Engineering Tianjin China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350325294

Privacy-preserving incentive mechanism in crowd-sensing systems is an ongoing research challenge, partly due to the untrusted centralized servers and sensitive data leakage. In this paper, we focus on a secure incentive way with blockchain. First, we analyze the limitations in traditional crowdsensing systems with a trusted third party (TTP) and identify three ways privacy in the incentive mechanism (IM) of crowdsensing: identity, data, and revenues. Then, we propose a novel decentralized system for IM based on a full anonymous blockchain called FAB-D2PIM. In the proposed system, we use a one-way aggregate signature scheme (Boneh et al. EUROCRYPT 2003) for identity privacy and homomorphic Public Key Encryption (Boneh et al. TCC 2005) for sensing data and reward privacy. The proposed scheme not only protects the privacy of the workers but also decreases the operation rate of the task publishing center. © 2023 IEEE.

关键词： Blockchain

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Enhancing deformation characteristics prediction of coarse-grained soils with time-series generative adversarial network-based data augmentation and pre-training

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Frontiers of Structural and Civil engineering 2025年第3期19卷 396-410页

作者： Ying ZHANG Meng JIA Xuedong ZHANG Liping CAO Ziying AN Hongchao WANG Jinyu WANG School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China The State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China

Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.

关键词： coarse-grained soils deformation characteristics TimeGAN data augmentation pre-training

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A General Method for Power Equipment State Classification Using Time Series Data Based on Attention Mechanism

A General Method for Power Equipment State Classification Us...

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2023 IEEE International Conference on Power Science and Technology, ICPST 2023

作者： Zhao, Wenqing Wang, Jifa Chen, Rui Liu, Liang Xu, Lijiao North China Electric Power University School of Control and Computer Engineering Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding China North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350311358

With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is difficult to realize general classification due to the limitation of data types and model characteristics when using deep learning. In the light of this, based on the time series data of various power equipment, an equipment state classification method is proposed by combining Recurrent Neural Network (RNN), Long short-term memory (LSTM) and attention mechanism. Firstly, RNN is used as the bottom frame network, and the LSTM core network integrating attention mechanism is added to construct the complex Feature Extraction Module (FEM). Secondly, main feature extraction is implemented in the LSTM network integrated with attention mechanism. Then, the extracted features are input into the complex FEM model and trained to match the evaluation state and obtain the evaluation results. In this paper, confusion matrix, F1micro and accuracy are used as evaluation indexes. Three cases were selected to verify the method and five groups of comparison models were set for each case. Results show that the proposed method has good accuracy in power equipment state classification and also has strong generalization ability in other data classification. © 2023 IEEE.

关键词： Long short-term memory

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ViT lightweight training at IoT edge based on transfer learning

ViT lightweight training at IoT edge based on transfer learn...

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2024 International Conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Li, Zhixin Long, Yang Miao, Jiahao School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun130012 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132022 China

ISBN: (纸本)9781510685437

Recently, the vision transformer (ViT) model of deep learning has achieved surprising performance in the field of computer vision and has been widely used in IoT edge devices. However, the training of ViT models requires a large amount of data and computing resources, which is a challenge for resource-constrained edge IoT devices. To solve the above problems, this paper proposed a lightweight ViT method based on transfer learning. The primary concept of this method is to train large-scale ViT models in the cloud (CloudViT) and deploying small-scale ViT models at the edge (EdgeViT). Firstly, through the method of transfer learning, some underlying parameters of CloudViT were utilized to construct EdgeViT. The purpose is to enable EdgeViT to learn from CloudViT, acquiring knowledge and improving its performance. Secondly, adding a randomly initialized LayerNorm layer before the MLPHead during the training process of EdgeViT, it can improve further model performance. Finally, Experiment results demonstrated that EdgeViT could achieve 91.3% of CloudViT’s performance with only half the parameters and floating-point operations (FLOPs). Moreover, finetuning EdgeViT with a 60% reduction in training time still allows it to achieve 81.3% of CloudViT’s performance. Relevant conclusions can provide technical support for the proposed method. © 2024 SPIE.

关键词： Data modeling Clouds Internet of things Performance modeling Instrument modeling Neural networks Head Visual process modeling Image classification

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PIM-ST: a New Paraphrase Identification Model Incorporating Sequence and Topic Information 4

PIM-ST: a New Paraphrase Identification Model Incorporating ...

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4th International Symposium on computer Technology and Information Science, ISCTIS 2024

作者： Mao, Kaili Zhao, Qingjuan Beijing Institute of Control Engineering Beijing China University Beijing Computer School Beijing Information Science and Technology Beijing China

ISBN: (纸本)9798350354560

Paraphrase identification, with the objective to determine whether two sentences are paraphrases of each other, has fostered many applications including natural language inference and document retrieval. Traditional machine learning models for paraphrase identification are generally based on hand-crafted features. Recent deep neural network models generally take semantic vectors as inputs and generate classification results directly. However, existing solutions still have much room for improvement. First, existing models do not fully exploit sequence information (i.e., the order of words) and topic information, which are important for text under-standing. Second, although deep models achieve promising results, they require a large amount of labelled data and have low interpretability. To cope with these problems, we propose a new model called PIM-ST (Paraphrase Identification Model Incorporating Sequence and Topic Information). Specifically, besides semantic information, PIM-ST extracts sequence information and topic information, and adopts a simple machine learning model LightGBM as the classifier. We conduct comparative experiments and ablation studies on two public datasets Microsoft Research Paraphrase Corpus (MSRP) and Quora Question Pairs. Experimental results show that PIM-ST achieves better results than existing state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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Improved SqueezeNet-Based Method for Automotive Paint Film Defect Detection

Improved SqueezeNet-Based Method for Automotive Paint Film D...

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2024 International Conference on Artificial Intelligence and Digital Technology, ICAIDT 2024

作者： Yan, Shengnan Shen, Yihui Minjiang University School of Computer and Control Engineering Fujian China Fujian Medical University College of Arts and Sciences Fujian China

ISBN: (纸本)9798350386905

To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovatively suggests a classification method combining Selective Kernel Networks (SKNet) with SqueezeNet model. Initially, the DDIM is used for dataset expansion, followed by an evaluation of the similarity between generated and captured paint film defect images using multiscale structural similarity (MS-SSIM). The training and generation effects of DDIM are then compared with those of DCGAN. In the SqueezeNet model, a Selective Kernel module is added following the fire7 module to enhance the model's attention mechanism. The results show that all types of paint film defect images generated by DDIM have MS-SSIM indices above 0.64, with most exceeding 0.7. The combined approach of Selective Kernel Networks(SKNet) and SqueezeNet outperforms other attention mechanisms, achieving an accuracy above 96.3%. The method demonstrates promising prospects for paint film defect detection, enhancing identification efficiency and accuracy while reducing detection costs, and is applicable to mobile or embedded devices. © 2024 IEEE.

关键词： Classification (of information)

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Changes in physiology, antioxidant system, and gene expression in Microcystis aeruginosa under fenoxaprop-p-ethyl stress

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Environmental Science and Pollution Research 2024年第19期31卷 28754-28763页

作者： Liu, Sijia Ni, Jiawei Guan, Ying Tao, Jianwei Wu, Liang Hou, Meifang Wu, Shichao Xu, Wenwu Zhang, Chu Ye, Jing School of Chemical and Environmental Engineering Shanghai Institute of Technology Shanghai201418 China Los Angeles Regional Water Quality Control Board Los AngelesCA90013 United States School of Ecological Technology and Engineering Shanghai Institute of Technology Shanghai201418 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Fenoxaprop-p-ethyl (FE) is one of the typical aryloxyphenoxypropionate herbicides. FE has been widely applied in agriculture in recent years. Human health and aquatic ecosystems are threatened by the cyanobacteria blooms caused by Microcystis aeruginosa, which is one of the most common cyanobacteria responsible for freshwater blooming. Few studies have been reported on the physiological effects of FE on M. aeruginosa. This study analyzed the growth curves, the contents of chlorophyll a and protein, the oxidative stress, and the microcystin-LR (MC-LR) levels of M. aeruginosa exposed to various FE concentrations (i.e., 0, 0.5, 1, 2, and 5 mg/L). FE was observed to stimulate the cell density, chlorophyll a content, and protein content of M. aeruginosa at 0.5- and 1-mg/L FE concentrations but inhibit them at 2 and 5 mg/L FE concentrations. The superoxide dismutase and catalase activities were enhanced and the malondialdehyde concentration was increased by FE. The intracellular (intra-) and extracellular (extra-) MC-LR contents were also affected by FE. The expression levels of photosynthesis-related genes psbD1, psaB, and rbcL varied in response to FE exposure. Moreover, the expressions of microcystin synthase-related genes mcyA and mcyD and microcystin transportation-related gene mcyH were significantly inhibited by the treatment with 2 and 5 mg/L FE concentrations. These results might be helpful in evaluating the ecotoxicity of FE and guiding the rational application of herbicides in modern agriculture. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Gene expression

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