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19th IEEE International Symposium on Intelligent Systems and Informatics, SISY 2021

作者： Rigo, Erno Poser, Valeria Eötvös Loránd Research Network Institute for Computer Science and Control Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (纸本)9781665413800

DNS caching is a function of the global name system that is essential for reducing latency of resolution requests. Feasibility, impact and remediation options of a client hijacking focusing on attack amplification based on DNS caching is evaluated. The theoretical attack scenario is described in detail, including remarks and considerations on related technologies and their possible effects. A test environment and methodology for evaluating a representative sample of DNS resolver services is defined. Test results are evaluated within the scope of the attack scenario, exploring possibilities of effective incident response in the described situation. A short overview of potential attack surface is also presented, taking samples of Hungarian financial services. © 2021 IEEE.

关键词： Web services

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Tobacco Plant Disease dataset 14

Tobacco Plant Disease Dataset

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： computer vision

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TPE for JPEG images with Dynamic M-ary Decomposition and Adaptive Threshold Constraints

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Ma, Yakun Chai, Xiuli Long, Guoqiang Gan, Zhihua Zhang, Yushu Henan University School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of Things Zhengzhou450046 China Henan University School of Software Intelligent Data Processing Engineering Research Center of Henan Province Institute of Intelligent Network System Kaifeng475004 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Traditional JPEG image encryption that prioritizes solely confidentiality fails to account for the pressing usability requirements of cloud-based environments, thus boosting the boom in thumbnail-preserving encryption (TPE) to balance image privacy and usability. However, existing TPE schemes for JPEG images face numerous challenges, such as insufficient security, inability to achieve lossless decryption, and high file extension. To address these challenges, we propose a TPE scheme based on dynamic M-ary decomposition and adaptive threshold constraints (TPE-MDTC). First, the valid ranges of quantized DC coefficients for JPEG images are determined. Then, a sum-preserving encryption method for quantized DC coefficients with compliance threshold constraints is designed using the bit-plane permutation to preserve thumbnails with high accuracy. Next, the introduction of dynamic M-ary decomposition effectively changes bit statistical characteristics preserved by bit-plane permutation, enhancing the ciphertext security. Finally, a quantized AC encryption method with RV (Run/Value) pair global permutation is proposed, effectively modifying the unit block features, thereby significantly improving the security and attack resistance of encrypted images. Experimental results show that the proposed TPE-MDTC scheme can reconstruct the original JPEG images without loss, and the generated ciphertext images exhibit significant advantages over previous schemes regarding file extension and security. © 1991-2012 IEEE.

关键词： Ciphertext

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LSMM: A Life-cycle Security Management Mechanism for Secure Image Sharing 2

LSMM: A Life-cycle Security Management Mechanism for Secure ...

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Zhang, Yaoyao Yao, Zhongyuan Pan, Heng Si, Xueming Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

During the storing, transferring, processing, and exchanging phases of the full life-cycle of secure image sharing, there may exist security issues such as information leakage, infringement and abuse. To address those problems, a life-cycle security management mechanism is proposed. By utilizing the Commutative Encryption and Watermarking (CEW), the mechanism ensures that the ownership and confidentiality of one image can hardly be tampered with and compromised. Besides, with the help of the blockchain, the mechanism is enabled to record any changes during the whole process of the image sharing, which makes copyright traceability possible and thus reduces the occurrence of infringement and abuse. The experiment result shows that, in our mechanism, the quality of plaintext image with watermark obtained by decryption is high, and the invisibility of watermark is preserved. Also, the efficient analysis and experimenting of the mechanism exhibits that it is feasible in practice. © 2022 IEEE.

关键词： Life cycle

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A Fair and Reliable data Trading Scheme based on Blockchain 2

A Fair and Reliable Data Trading Scheme based on Blockchain

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Wu, Zhili Pan, Heng Si, Xueming Qian, Haiyang Cai, Fugang Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

To solve the single point of failure and opaque transaction problems in the current centralized data trading model, we propose a blockchain-based data trading scheme. Firstly, we construct a blockchain-based data transaction model to provide users with peer-to-peer trusted transaction services. Then, we design a two-stage one-to-many data pricing method based on smart contracts and the Rubinstein model, which can guarantee the reasonableness of data transaction pricing and achieve a fair price between a single data demander and multiple data owners. Next, we suggest a forensic dispute arbitration method based on on-chain and off-chain data collaboration and a data integrity verification method, which aims to ensure the reliability of de-centralized data transactions and prevent malicious acts such as data owners providing false data, data requesters refusing to pay, and data stored in the cloud being tampered with. Finally, we perform experiments based on consortium blockchain. Experimental results show that the scheme of this paper has better functionality and performance than other schemes of data transactions. © 2022 IEEE.

关键词： Smart contract

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Incentive Scheme for Shared Parking Space based on NFT 2

Incentive Scheme for Shared Parking Space based on NFT

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2nd International Conference on computer science and Blockchain, CCSB 2022

作者： Cai, Fugang Pan, Heng Si, Xueming Wu, Zhili Qian, Haiyang Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

The lack of centralized management causes problems in the system of distributed parking spaces, such as unsecured revenues, poor service quality and malicious valuation. Therefore, this paper proposes a parking sharing scheme based on non-fungible tokens. Firstly, we propose the calculation method of parking space renter reputation value, parking space provider reputation value and the reputation incentive algorithm of parking space sharing, which can ensure the service quality of distributed parking space share and prevent malicious evaluation and implement bidirectional constraints on parking space provider and renter. Secondly, we propose a parking space sharing model with non-fungible tokens as sharing credentials to map parking space assets to on-chain assets via non-fungible tokens and introduce timers based on hash time locks. We propose an intelligent parking rental contract based on improved hash lock to guarantee the revenue of parking space providers in a distributed environment. Finally, experiments based on Ethereum show that the scheme can realize shared parking space at a lower cost;the proposed incentive algorithm can effectively prevent parking space providers and renters from misbehaving. © 2022 IEEE.

关键词： Smart contract

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A Novel Hybrid Optimizer Based on Coati Optimization Algorithm and Differential Evolution for Global Optimization and Constrained Engineering Problems

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-70页

作者： Biswas, Saptadeep Maiti, Binanda Singh, Gyan Ezugwu, Absalom E. Saleem, Kashif Abualigah, Laith Smerat, Aseel Bera, Uttam Kumar Department of Mathematics National Institute of Technology Agartala Tripura India Unit for Data Science and Computing North-West University Potchefstroom South Africa Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Computer Science Department Al al-Bayt University Mafraq Jordan Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq

This paper presents a novel hybrid metaheuristic, the Hybrid Coati Optimization Algorithm with Differential Evolution (HCOADE), developed to address complex global optimization tasks and constrained engineering design problems. HCOADE integrates the exploration-driven behaviour of the Coati Optimization Algorithm (COA)-inspired by the social foraging and predation strategies of coatis-with the powerful mutation and crossover mechanisms of Differential Evolution (DE), thereby achieving a balanced and adaptive search process. The hybridization enhances global exploration and local exploitation, enabling the algorithm to efficiently navigate diverse and challenging optimization landscapes. To rigorously evaluate its performance, HCOADE is tested on benchmark suites from CEC 2014, 2017, 2020, and 2022, which encompass unimodal, multimodal, hybrid, and composition functions. It is also applied to real-world constrained engineering problems, such as pressure vessel design, cantilever beam optimization, and reinforced concrete beam design. Comparative experiments against state-of-the-art algorithms—including COA, DE, RSA, PSO, SSA, BBO, QIO, DMOA, and others—demonstrate that HCOADE consistently delivers superior solution quality, faster convergence, and higher robustness. Quantitative results show that HCOADE achieved the 1st place average rank across all four benchmark suites. It obtained top performance on 80% of the functions in CEC 2014, 66.7% in CEC 2017, 70% in CEC 2020, and 66.7% in CEC 2022. Furthermore, HCOADE outperformed or matched CEC competition-winning algorithms such as LSHADE-cnEpSin, LSHADE-SPACMA, and CMA-ES on numerous challenging functions of CEC 2017. Statistical analyses, including Wilcoxon Rank Sum Tests and ranking evaluations, confirm the significance and reliability of HCOADE’s performance. Furthermore, convergence behaviour, measurement of exploration and exploitation, and sensitivity analysis highlight the algorithm’s adaptability and stability

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An Algorithm for Detecting Surface Defects in Industrial Strip Steel based on Receptive Field and Feature Information Supplementation

An Algorithm for Detecting Surface Defects in Industrial Str...

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International Conference on computer Supported Cooperative Work in Design

作者： Qingyu Xu Jiguo Yu Anming Dong Zihao Shang 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 School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute 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 Shandong Data Intelligence Information Technology Co. Ltd. Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the context of Industry 4.0 and the rise of intelligent manufacturing, the quality of industrial products is becoming more and more important. Strip steel surface defect detection, as a key link in industrial production, is crucial to ensure the quality of industrial products. However, due to the irregularity of the defect scale and the inconspicuous defect features in the surface defect image of strip steel, it is difficult for the existing detection algorithms to realize the effective detection of defects. In order to better extract the features of defects and improve the network’s ability to detect defects, this paper proposes an algorithm for detecting surface defects on industrial strip steel based on receptive field and feature information supplementation. First, we design a receptive field (RF) module to replace the residual structure in the C3 module, which we name C3RF. This module can effectively increase the network’s receptive field, allowing the network to fully capture irregular defect features without increasing the cost. Second, for the characteristics that defective features are not obvious and tend to lose detail information as the network deepens, an extra information supplemental branching feature fusion pyramid (EFPN) is proposed on the basis of the original PAFPN architecture to compensate for the detail information that is lost by the fragile features in deeper layers. Finally, convolutional block attention module (CBAM) is introduced to replace the spatial pooling pyramid (SPPF) in the baseline network, which enhances the contrast between defects and backgrounds, and improves the classification and localization ability of the network. Our network achieves an accuracy of 82.2% on the publicly available strip steel defect detection dataset, which is a 4.0% improvement over the baseline. The results show that the network constructed in this paper can realize effective defect detection.

关键词： Radio frequency Location awareness Strips Production Feature extraction Real-time systems Hardware

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Disentangling degree and tie strength heterogeneity in egocentric social networks

arXiv

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arXiv 2024年

作者： Heydari, Sara Iñiguez, Gerardo Kertész, János Saramäki, Jari Department of Computer Science Aalto University School of Science Aalto00076 Finland Faculty of Information Technology and Communication Sciences Tampere University Tampere33720 Finland Department of Network and Data Science Central European University Vienna1100 Austria Centro de Ciencias de la Complejidad Universidad Nacional Autonóma de México Ciudad de México04510 Mexico Complexity Science Hub Vienna1080 Austria

The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear-one possibility is that people's traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual's personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages). Copyright © 2024, The Authors. All rights reserved.

关键词： Communication channels (information theory)

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Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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