To address the current situation where many small enterprises lack efficient management of customer data, this paper proposes a design and implementation plan of a customer relationship management (CRM) system based o...To address the current situation where many small enterprises lack efficient management of customer data, this paper proposes a design and implementation plan of a customer relationship management (CRM) system based on email service. It aims to solve the problems of data dispersion, untimely update and information redundancy in customer management of small and medium-sized enterprises. The system includes four core functional modules: historical email analysis, lead pool management, customer management and email archiving. Through email mining and web crawler technology, the system can extract potential customer information from historical emails and enrich lead data;the lead pool management module supports lead information maintenance, status tracking and conversion of high-value leads;the customer management module realizes the maintenance and dynamic tracking of customer information;the email management module provides the archiving of emails and attachments and the structured storage of basic email information. The system provides automated and intelligent customer information management, improves the work efficiency of sales staff, and provides an efficient customer relationship management solution for enterprises.展开更多
Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled ...Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model’s detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network’s average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.展开更多
Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable se...Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.展开更多
Traditional email systems can only achieve one-way communication,which means only the receiver is allowed to search for emails on the email server.In this paper,we propose a blockchain-based certificateless bidirectio...Traditional email systems can only achieve one-way communication,which means only the receiver is allowed to search for emails on the email server.In this paper,we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption(CL-BSE)by combining the storage function of cloud server with the communication function of email server.In the new model,not only can the data receiver search for the relevant content by generating its own trapdoor,but the data owner also can retrieve the content in the same way.Meanwhile,there are dual authentication functions in our model.First,during encryption,the data owner uses the private key to authenticate their identity,ensuring that only legal owner can generate the keyword ciphertext.Second,the blockchain verifies the data owner’s identity by the received ciphertext,allowing only authorized members to store their data in the server and avoiding unnecessary storage space consumption.We obtain a formal definition of CL-BSE and formulate a specific scheme from the new system model.Then the security of the scheme is analyzed based on the formalized security model.The results demonstrate that the scheme achieves multikeyword ciphertext indistinguishability andmulti-keyword trapdoor privacy against any adversary simultaneously.In addition,performance evaluation shows that the new scheme has higher computational and communication efficiency by comparing it with some existing ones.展开更多
Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Te...Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks.The study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron algorithms.The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching 0.99.This study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.展开更多
Mobile agent currently is a hot spot among research fields of Internet technology. The deployment of mo-bile agents over network usually needs extra infrastructure for agent migration and communication,which adds to t...Mobile agent currently is a hot spot among research fields of Internet technology. The deployment of mo-bile agents over network usually needs extra infrastructure for agent migration and communication,which adds to thedifficulty of popularizing MA systems. We present in this paper an Email-box-based mechanism of agent migrationand communication,which is built on top of the formerly developed MOON-EAMS system. This mechanism,basedon Email formatting skills,utilizes Email for data transfer,and offers a loosely coupled option of agent migration andcommunication,which ,compared to related works ,obtains the advantage of easy implementation,and reduces the riskof network connection failure.展开更多
文摘To address the current situation where many small enterprises lack efficient management of customer data, this paper proposes a design and implementation plan of a customer relationship management (CRM) system based on email service. It aims to solve the problems of data dispersion, untimely update and information redundancy in customer management of small and medium-sized enterprises. The system includes four core functional modules: historical email analysis, lead pool management, customer management and email archiving. Through email mining and web crawler technology, the system can extract potential customer information from historical emails and enrich lead data;the lead pool management module supports lead information maintenance, status tracking and conversion of high-value leads;the customer management module realizes the maintenance and dynamic tracking of customer information;the email management module provides the archiving of emails and attachments and the structured storage of basic email information. The system provides automated and intelligent customer information management, improves the work efficiency of sales staff, and provides an efficient customer relationship management solution for enterprises.
基金Funded by Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,China(No.GZKF-202219)。
文摘Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model’s detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network’s average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:71-829-2024).
文摘Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.
基金supported by the National Natural Science Foundation of China(Nos.62172337,62241207)Key Project of GansuNatural Science Foundation(No.23JRRA685).
文摘Traditional email systems can only achieve one-way communication,which means only the receiver is allowed to search for emails on the email server.In this paper,we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption(CL-BSE)by combining the storage function of cloud server with the communication function of email server.In the new model,not only can the data receiver search for the relevant content by generating its own trapdoor,but the data owner also can retrieve the content in the same way.Meanwhile,there are dual authentication functions in our model.First,during encryption,the data owner uses the private key to authenticate their identity,ensuring that only legal owner can generate the keyword ciphertext.Second,the blockchain verifies the data owner’s identity by the received ciphertext,allowing only authorized members to store their data in the server and avoiding unnecessary storage space consumption.We obtain a formal definition of CL-BSE and formulate a specific scheme from the new system model.Then the security of the scheme is analyzed based on the formalized security model.The results demonstrate that the scheme achieves multikeyword ciphertext indistinguishability andmulti-keyword trapdoor privacy against any adversary simultaneously.In addition,performance evaluation shows that the new scheme has higher computational and communication efficiency by comparing it with some existing ones.
文摘Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks.The study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron algorithms.The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching 0.99.This study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.
文摘Mobile agent currently is a hot spot among research fields of Internet technology. The deployment of mo-bile agents over network usually needs extra infrastructure for agent migration and communication,which adds to thedifficulty of popularizing MA systems. We present in this paper an Email-box-based mechanism of agent migrationand communication,which is built on top of the formerly developed MOON-EAMS system. This mechanism,basedon Email formatting skills,utilizes Email for data transfer,and offers a loosely coupled option of agent migration andcommunication,which ,compared to related works ,obtains the advantage of easy implementation,and reduces the riskof network connection failure.
