Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famo...Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famous for his astute logical reasoning, his ability to adopt almost any disguise, and his use of forensic science skills to solve difficult cases. The paper tries to analyze the characteristics of the Holmes and how did Holmes observe evidences and analyze clues.展开更多
Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story&#...Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story's characteristics as a kind of intellectual game and reader's psychology during reading.展开更多
This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe...This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe's The Murder in the Rue Morgue,and the metaphysical detective story,Paul Auster'City of Glass and Umberto Eco's The Name of the Rose.These two stories investigate the perspectives;the story of crime and the story of investigation.展开更多
In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he d...In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he depicts the return of the colonials from British colonies,mostly India,with physically deformed or ravaged body and traumatic past that haunt and trouble his characters’present life.Doyle allegorically uses returned colonials or poor whites who turn into figures of retributive ghosts that function as pathetic memories and inner fears from British colonies.The seeing of ghostly figures and haunting past events delineated in these stories cause characters’sense of uncanny horror and remind them of their past trauma.These monstrous returned colonials or poor whites often create a fear and a social menace that must be appropriately dealt with when the master sleuth is commissioned to pin down the truth of client’s cases.Why are these bodies of ghostly figures so“irregular”and ravaged?What do these deformities signify?How can returned colonial’s or poor white’s traumatic past be related to retributive ghost?This paper attempts to probe into these issues in order to find out possible answers.展开更多
Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that a...Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that accelerate mobility power in late-Victorian and Edwardian society. In some of these mystery or detective stories especially featuring the well-known sleuth Sherlock Holmes, Doyle tended to integrate an early subject＇s experience of shrunken space and reduced time into an unknown fear by delineating his characters who perceive horror and nervousness while facing or riding on a railway transportation, including mainly the steam railway in mysterious tales like ＂The Lost Special＂ and ＂The Man with the Watches＂ as well as in detective stories like ＂The Adventure of the Engineer＇s Thumb＂, ＂The Adventure of Bruce-Partington Plan＂, ＂Valley of Fear＂ and several others. How can this spatiotemporal mobility be connected to mysterious affairs which lead Doyle＇s quasi-detective characters and police power to spring into investigative action？ Railway, mobility, and horror are woven together into a driving force that facilitates our geographical and forensic exploration of Doyle＇s stories.展开更多
Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, lock...Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, locked-room murder and the arm-chair detective, have become the classical conventions of detective story. The eccentric but brilliant protagonist, Auguste Dupin inhis story, has become a model of the later detectives. Poe has also contributed to define the detective story as some kind of intellec-tual game, the plot of which concentrates on the process of investigation.展开更多
Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,which demonstrates that detective novel is popular in Taiwan,but there are seldom local detective novels to be published.Through the t...Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,which demonstrates that detective novel is popular in Taiwan,but there are seldom local detective novels to be published.Through the theory of field of cultural production by Pierre Bourdieu,the paper analyzed how the creators and cultural intermediaries’form of capitals and aesthetics construct the mechanism of the publishing industry,and how the market of detective novels in Taiwan are dominated by foreign products.The study adopted second documentary analysis and in-depth interview.The former is to calculate the published detective novels from 2001 to September 2015 sold in the dominant on-line bookstore,Books.com.tw,in Taiwan,while the latter is to interview 15 related agencies included writers,editors,translators,and a manager of bookstore.The results contain three following issues.Firstly,local production has re-started since 1980’s after a long-time decline.Considering the large cost to cultivate local writers,Taiwan Residents publishers prefer to produce well-known foreign works.Secondly,literary awards are the vital way in the production of local works.The writers receive symbolic capital through awards,and even obtain more opportunities to publish their works or cooperate with other related organization,which means the acquirement of social capital.Finally,the market of local detective novels is forced to be the field of restricted production as a result of supplanted by translated novels.As a consequence,the production of local detective novels becomes popular literature of niche market.展开更多
Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detecti...Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detective image in the history of the western literature vividly.Based on the stories in which Dupin appeared,concerns on the creation of Dupin,the analysis of his features and the function of the setting fellows,like friend and police,summarizing the traditional image pattern of detective stories created by Poe,revealing the great influence Poe had on the development of detective literature,even on the literature of the whole world.展开更多
Objective To analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease(KBD)in China,and provide the basis for formulating prevention and control measures.Methods Fixed-poi...Objective To analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease(KBD)in China,and provide the basis for formulating prevention and control measures.Methods Fixed-point monitoring,moving-point monitoring,and full coverage of monitoring were promoted successively from 1990 to 2023.Some children(7-12 years old)underwent clinical and right-hand X-ray examinations every year.According to the KBD diagnosis criteria,clinical and X-ray assessments were used to confirm the diagnosis.Results In 1990,the national KBD detectable rate was 21.01%.X-ray detection decreased to below 10%in 2003 and below 5%in 2007.Between 2010 and 2018,the prevalence of KBD in children was less than 0.4%,which fluctuated at a low level,and has decreased to 0%since 2019.Spatial epidemiological analysis indicated a spatial clustering of adult patients prevalence rate in the KBD areas.Conclusion The evaluation results of the elimination of KBD in China over the last 5 years showed that all villages in the monitored areas have reached the elimination standard.While the adult KBD patients still need for policy consideration and care.展开更多
A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a...A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.展开更多
Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately ...Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.展开更多
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection...To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.展开更多
Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cam...Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.展开更多
Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibilit...Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.展开更多
The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.De...The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.展开更多
The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during the...The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.展开更多
文摘Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famous for his astute logical reasoning, his ability to adopt almost any disguise, and his use of forensic science skills to solve difficult cases. The paper tries to analyze the characteristics of the Holmes and how did Holmes observe evidences and analyze clues.
文摘Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story's characteristics as a kind of intellectual game and reader's psychology during reading.
文摘This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe's The Murder in the Rue Morgue,and the metaphysical detective story,Paul Auster'City of Glass and Umberto Eco's The Name of the Rose.These two stories investigate the perspectives;the story of crime and the story of investigation.
文摘In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he depicts the return of the colonials from British colonies,mostly India,with physically deformed or ravaged body and traumatic past that haunt and trouble his characters’present life.Doyle allegorically uses returned colonials or poor whites who turn into figures of retributive ghosts that function as pathetic memories and inner fears from British colonies.The seeing of ghostly figures and haunting past events delineated in these stories cause characters’sense of uncanny horror and remind them of their past trauma.These monstrous returned colonials or poor whites often create a fear and a social menace that must be appropriately dealt with when the master sleuth is commissioned to pin down the truth of client’s cases.Why are these bodies of ghostly figures so“irregular”and ravaged?What do these deformities signify?How can returned colonial’s or poor white’s traumatic past be related to retributive ghost?This paper attempts to probe into these issues in order to find out possible answers.
文摘Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that accelerate mobility power in late-Victorian and Edwardian society. In some of these mystery or detective stories especially featuring the well-known sleuth Sherlock Holmes, Doyle tended to integrate an early subject＇s experience of shrunken space and reduced time into an unknown fear by delineating his characters who perceive horror and nervousness while facing or riding on a railway transportation, including mainly the steam railway in mysterious tales like ＂The Lost Special＂ and ＂The Man with the Watches＂ as well as in detective stories like ＂The Adventure of the Engineer＇s Thumb＂, ＂The Adventure of Bruce-Partington Plan＂, ＂Valley of Fear＂ and several others. How can this spatiotemporal mobility be connected to mysterious affairs which lead Doyle＇s quasi-detective characters and police power to spring into investigative action？ Railway, mobility, and horror are woven together into a driving force that facilitates our geographical and forensic exploration of Doyle＇s stories.
文摘Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, locked-room murder and the arm-chair detective, have become the classical conventions of detective story. The eccentric but brilliant protagonist, Auguste Dupin inhis story, has become a model of the later detectives. Poe has also contributed to define the detective story as some kind of intellec-tual game, the plot of which concentrates on the process of investigation.
文摘Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,which demonstrates that detective novel is popular in Taiwan,but there are seldom local detective novels to be published.Through the theory of field of cultural production by Pierre Bourdieu,the paper analyzed how the creators and cultural intermediaries’form of capitals and aesthetics construct the mechanism of the publishing industry,and how the market of detective novels in Taiwan are dominated by foreign products.The study adopted second documentary analysis and in-depth interview.The former is to calculate the published detective novels from 2001 to September 2015 sold in the dominant on-line bookstore,Books.com.tw,in Taiwan,while the latter is to interview 15 related agencies included writers,editors,translators,and a manager of bookstore.The results contain three following issues.Firstly,local production has re-started since 1980’s after a long-time decline.Considering the large cost to cultivate local writers,Taiwan Residents publishers prefer to produce well-known foreign works.Secondly,literary awards are the vital way in the production of local works.The writers receive symbolic capital through awards,and even obtain more opportunities to publish their works or cooperate with other related organization,which means the acquirement of social capital.Finally,the market of local detective novels is forced to be the field of restricted production as a result of supplanted by translated novels.As a consequence,the production of local detective novels becomes popular literature of niche market.
文摘Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detective image in the history of the western literature vividly.Based on the stories in which Dupin appeared,concerns on the creation of Dupin,the analysis of his features and the function of the setting fellows,like friend and police,summarizing the traditional image pattern of detective stories created by Poe,revealing the great influence Poe had on the development of detective literature,even on the literature of the whole world.
基金supported by the Central government subsidies to local public health special funds,National Key Research and Development Program of China[2022YFC2503101]Basic Research and Development Funds for Heilongjiang Province-affiliated Universities[2023-KYYWF-0272].
文摘Objective To analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease(KBD)in China,and provide the basis for formulating prevention and control measures.Methods Fixed-point monitoring,moving-point monitoring,and full coverage of monitoring were promoted successively from 1990 to 2023.Some children(7-12 years old)underwent clinical and right-hand X-ray examinations every year.According to the KBD diagnosis criteria,clinical and X-ray assessments were used to confirm the diagnosis.Results In 1990,the national KBD detectable rate was 21.01%.X-ray detection decreased to below 10%in 2003 and below 5%in 2007.Between 2010 and 2018,the prevalence of KBD in children was less than 0.4%,which fluctuated at a low level,and has decreased to 0%since 2019.Spatial epidemiological analysis indicated a spatial clustering of adult patients prevalence rate in the KBD areas.Conclusion The evaluation results of the elimination of KBD in China over the last 5 years showed that all villages in the monitored areas have reached the elimination standard.While the adult KBD patients still need for policy consideration and care.
文摘A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.
基金Supported by Shandong Province Medical and Health Science and Technology Development Plan Project,No.202203030713Clinical Research Funding of Shandong Medical Association-Qilu Specialization,No.YXH2022ZX02031Science and Technology Program of Yantai Affiliated Hospital of Binzhou Medical University,No.YTFY2022KYQD06.
文摘Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金supported in part by the National Key R&D Program of China(No.2022YFB3904503)National Natural Science Foundation of China(No.62172418)。
文摘To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.
基金supported by the Ningxia Key Research and Development Program(Talent Introduction Special Project)Project(2022YCZX0013)North Minzu University 2022 School-Level Scientific Research Platform“Digital Agriculture Enabling Ningxia Rural Revitalization Innovation Team”(2022PT_S10)+1 种基金Yinchuan City University-Enterprise Joint Innovation Project(2022XQZD009)Ningxia Key Research and Development Program(Key Project)Project(2023BDE02001).
文摘Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.
基金supported by a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT),Republic of KoreaThe authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding Program Grant Code(NU/RG/SERC/13/40)+2 种基金Also,the authors are thankful to Prince Satam bin Abdulaziz University for supporting this study via funding from Prince Satam bin Abdulaziz University project number(PSAU/2024/R/1445)This work was also supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R54)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.
基金supported by the National Key Research and Development Plan in China(Grant No.2020YFB1005500)。
文摘The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.
文摘The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.
