With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the...With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the existing data sharing schemes have been difficult to meet the growing demand for data sharing,this paper aims at exploring a secure,efficient and privacy-protecting data sharing scheme under web3.0 applications.Specifically,this paper adopts interplanetary file system(IPFS)technology to realize the storage of large data files to solve the problem of blockchain storage capacity limitation,and utilizes ciphertext policy attribute-based encryption(CP-ABE)and proxy re-encryption(PRE)technology to realize secure multi-party sharing and finegrained access control of data.This paper provides the detailed algorithm design and implementation of data sharing phases and processes,and analyzes the algorithms from the perspectives of security,privacy protection,and performance.展开更多
Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These...Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.展开更多
Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing huma...Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated bots.Text-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this verification.However,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency.In our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this purpose.Our approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA images.For the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition accuracy.Our rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our method.The results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.展开更多
Optimizing root system architecture(RSA)is essential for plants because of its critical role in acquiring water and nutrients from the soil.However,the subterranean nature of roots complicates the measurement of RSA t...Optimizing root system architecture(RSA)is essential for plants because of its critical role in acquiring water and nutrients from the soil.However,the subterranean nature of roots complicates the measurement of RSA traits.Recently developed rhizobox methods allow for the rapid acquisition of root images.Nevertheless,effective and precise approaches for extracting RSA features from these images remain underdeveloped.Deep learning(DL)technology can enhance image segmentation and facilitate RSA trait extraction.However,comprehensive pipelines that integrate DL technologies into image-based root phenotyping techniques are still scarce,hampering their implementation.To address this challenge,we present a reproducible pipeline(faCRSA)for automated RSA traits analysis,consisting of three modules:(1)the RSA traits extraction module functions to segment soil-root images and calculate RSA traits.A lightweight convolutional neural network(CNN)named RootSeg was proposed for efficient and accurate segmentation;(2)the data storage module,which stores image and text data from other modules;and(3)the web application module,which allows researchers to analyze data online in a user-friendly manner.The correlation coefficients(R^(2))of total root length,root surface area,and root volume calculated from faCRSA and manually measured results were 0.96**,0.97**,and 0.93**,respectively,with root mean square errors(RMSE)of 8.13 cm,1.68 cm^(2),and 0.05 cm^(3),processed at a rate of 9.74 s per image,indicating satisfying accuracy.faCRSA has also demonstrated satisfactory performance in dynamically monitoring root system changes under various stress conditions,such as drought or waterlogging.The detailed code and deployable package of faCRSA are provided for researchers with the potential to replace manual and semi-automated methods.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U24B20146)the National Key Research and Development Plan in China(Grant No.2020YFB1005500)Beijing Natural Science Foundation Project(No.M21034).
文摘With the rapid development of web3.0 applications,the volume of data sharing is increasing,the inefficiency of big data file sharing and the problem of data privacy leakage are becoming more and more prominent,and the existing data sharing schemes have been difficult to meet the growing demand for data sharing,this paper aims at exploring a secure,efficient and privacy-protecting data sharing scheme under web3.0 applications.Specifically,this paper adopts interplanetary file system(IPFS)technology to realize the storage of large data files to solve the problem of blockchain storage capacity limitation,and utilizes ciphertext policy attribute-based encryption(CP-ABE)and proxy re-encryption(PRE)technology to realize secure multi-party sharing and finegrained access control of data.This paper provides the detailed algorithm design and implementation of data sharing phases and processes,and analyzes the algorithms from the perspectives of security,privacy protection,and performance.
文摘Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.
基金supported by the National Natural Science Foundation of China(Nos.U22A2034,62177047)High Caliber Foreign Experts Introduction Plan funded by MOST,and Central South University Research Programme of Advanced Interdisciplinary Studies(No.2023QYJC020).
文摘Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated bots.Text-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this verification.However,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising efficiency.In our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this purpose.Our approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA images.For the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition accuracy.Our rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our method.The results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.
基金supported by the projects of the National Key Research and Development Program of China(2024YFD2301305)Jiangsu Innovation Support Program for International Science and Technology Cooperation Project(BZ2023049)+2 种基金the projects of the National Natural Science Foundation of China(32272213)the China Agriculture Research System(CARS-03)Jiangsu Collaborative Innovation Center for Modern Crop Production(JCIC-MCP).
文摘Optimizing root system architecture(RSA)is essential for plants because of its critical role in acquiring water and nutrients from the soil.However,the subterranean nature of roots complicates the measurement of RSA traits.Recently developed rhizobox methods allow for the rapid acquisition of root images.Nevertheless,effective and precise approaches for extracting RSA features from these images remain underdeveloped.Deep learning(DL)technology can enhance image segmentation and facilitate RSA trait extraction.However,comprehensive pipelines that integrate DL technologies into image-based root phenotyping techniques are still scarce,hampering their implementation.To address this challenge,we present a reproducible pipeline(faCRSA)for automated RSA traits analysis,consisting of three modules:(1)the RSA traits extraction module functions to segment soil-root images and calculate RSA traits.A lightweight convolutional neural network(CNN)named RootSeg was proposed for efficient and accurate segmentation;(2)the data storage module,which stores image and text data from other modules;and(3)the web application module,which allows researchers to analyze data online in a user-friendly manner.The correlation coefficients(R^(2))of total root length,root surface area,and root volume calculated from faCRSA and manually measured results were 0.96**,0.97**,and 0.93**,respectively,with root mean square errors(RMSE)of 8.13 cm,1.68 cm^(2),and 0.05 cm^(3),processed at a rate of 9.74 s per image,indicating satisfying accuracy.faCRSA has also demonstrated satisfactory performance in dynamically monitoring root system changes under various stress conditions,such as drought or waterlogging.The detailed code and deployable package of faCRSA are provided for researchers with the potential to replace manual and semi-automated methods.
