Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data,this paper takes news text as the research object and proposes LDA text topic clustering algorithm...Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data,this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform.Since the TF-IDF(term frequency-inverse document frequency)algorithm under Spark is irreversible to word mapping,the mapped words indexes cannot be traced back to the original words.In this paper,an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored.Firstly,the text feature is extracted by the TF-IDF algorithm combined CountVectorizer proposed in this paper,and then the features are inputted to the LDA(Latent Dirichlet Allocation)topic model for training.Finally,the text topic clustering is obtained.Experimental results show that for large data samples,the processing speed of LDA topic model clustering has been improved based Spark.At the same time,compared with the LDA topic model based on word frequency input,the model proposed in this paper has a reduction of perplexity.展开更多
The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small ob...The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small objects and objects with obstructions.Therefore,we propose a helmet detection algorithm based on the attention mechanism(AT-YOLO).First of all,a channel attention module is added to the YOLOv3 backbone network,which can adaptively calibrate the channel features of the direction to improve the feature utilization,and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network.Secondly,we use DIoU(Distance Intersection over Union)bounding box regression loss function,it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes,which makes the network more accurate in detecting small objects and faster in convergence.Finally,we explore the training strategy of the network model,which improves network performance without increasing the inference cost.Experiments show that the mAP of the proposed method reaches 96.5%,and the detection speed can reach 27 fps.Compared with other existing methods,it has better performance in detection accuracy and speed.展开更多
Cotton plants are recalcitrant with regards to transformation and induced regeneration.In the present study,5-enolpyruvylshikimate-3-phosphate（EPSPS）,a glyphosate resistant gene from the bacterium Agrobacterium sp.s...Cotton plants are recalcitrant with regards to transformation and induced regeneration.In the present study,5-enolpyruvylshikimate-3-phosphate（EPSPS）,a glyphosate resistant gene from the bacterium Agrobacterium sp.strain CP4,was introduced into an elite Bt transgenic cotton cultivar with a modified technique involving in planta Agrobacteriummediated transformation of shoot apex.Primary transformants were initially screened using a 0.26%glyphosate spray and subsequently by PCR analysis.Five out of 4 000 transformants from T_1 seeds were obtained resulting in an in planta transformation rate of 0.125%.Four homozygous lines were produced by continuous self-fertilization and both PCR-based selection and glyphosate resistance.Transgene insertion was analyzed by Southern blot analysis.Gene transcription and protein expression levels in the transgenic cotton lines were further investigated by RT-PCR,Western blot,and ELISA methods.Transgenic T_3 plants were resistant to as much as 0.4% of glyphosate treatments in field trials.Our results indicate that the cotton shoot apex transformation technique which is both tissue-culture and genotype-independent would enable the exploitation of transgene technology in different cotton cultivars.Since this method does not require sterile conditions,the use of specialized growth media or the application of plant hormones,it can be conducted under the greenhouse condition.展开更多
The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret ...The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret message should not be damaged on the process of the cover media.In order to ensure the invisibility of secret message,complex texture objects should be chosen for embedding information.In this paper,an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message.Firstly,complex texture regions are selected based on a kind of objects detection algorithm.Secondly,three different steganographic methods were used to hide secret message into the selected block region.Experimental results show that the approach enhances the security and robustness.展开更多
Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation ...Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods.展开更多
With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system...With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.展开更多
To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image segmentation.Most existing coverless steganography methods are based...To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image segmentation.Most existing coverless steganography methods are based on whole feature mapping,which has poor robustness when facing geometric attacks,because the contents in the image are easy to lost.To solve this problem,we use ResNet to extract semantic features,and segment the object areas from the image through Mask RCNN for information hiding.These selected object areas have ethical structural integrity and are not located in the visual center of the image,reducing the information loss of malicious attacks.Then,these object areas will be binarized to generate hash sequences for information mapping.In transmission,only a set of stego-images unrelated to the secret information are transmitted,so it can fundamentally resist steganalysis.At the same time,since both Mask RCNN and ResNet have excellent robustness,pre-training the model through supervised learning can achieve good performance.The robust hash algorithm can also resist attacks during transmission.Although image segmentation will reduce the capacity,multiple object areas can be extracted from an image to ensure the capacity to a certain extent.Experimental results show that compared with other coverless image steganography methods,our method is more robust when facing geometric attacks.展开更多
AIM To investigate gender-specific liver estrogen receptor(ER)expression in normal subjects and patients with hepatitis C virus(HCV)-related cirrhosis and hepatocellular carcinoma(HCC).METHODS Liver tissues from norma...AIM To investigate gender-specific liver estrogen receptor(ER)expression in normal subjects and patients with hepatitis C virus(HCV)-related cirrhosis and hepatocellular carcinoma(HCC).METHODS Liver tissues from normal donors and patients diagnosed with HCV-related cirrhosis and HCV-related HCC were obtained from the NIH Liver Tissue and Cell Distribution System.The expression of ER subtypes,ERαand ERβ,were evaluated by Western blotting and real-time RT-PCR.The subcellular distribution of ERαand ERβwas further determined in nuclear and cytoplasmic tissue lysates along with the expression ofinflammatory[activated NF-κB and IκB-kinase(IKK)]and oncogenic(cyclin D1)markers by Western blotting and immunohistochemistry.The expression of ERαand ERβwas correlated with the expression of activated NF-κB,activated IKK and cyclin D1 by Spearman's correlation.RESULTS Both ER subtypes were expressed in normal livers but male livers showed significantly higher expression of ERαthan females(P<0.05).We observed significantly higher m RNA expression of ERαin HCV-related HCC liver tissues as compared to normals(P<0.05)and ERβin livers of HCV-related cirrhosis and HCV-related HCC subjects(P<0.05).At the protein level,there was a significantly higher expression of nuclear ERαin livers of HCV-related HCC patients and nuclear ERβin HCV-related cirrhosis patients as compared to normals(P<0.05).Furthermore,we observed a significantly higher expression of phosphorylated NF-κB and cyclin D1 in diseased livers(P<0.05).There was a positive correlation between the expression of nuclear ER subtypes and nuclear cyclin D1 and a negative correlation between cytoplasmic ER subtypes and cytoplasmic phosphorylated IKK in HCV-related HCC livers.These findings suggest that dysregulated expression of ER subtypes following chronic HCVinfection may contribute to the progression of HCVrelated cirrhosis to HCV-related HCC.CONCLUSION Gender differences were observed in ERαexpression in normal livers.Alterations in ER subtype expression observed in diseased livers may influence genderrelated disparity in HCV-related pathogenesis.展开更多
In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image rec...In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.展开更多
New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government ca...New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.展开更多
As soil heavy metal pollution is increasing year by year,the risk assess-ment of soil heavy metal pollution is gradually gaining attention.Soil heavy metal datasets are usually imbalanced datasets in which most of the...As soil heavy metal pollution is increasing year by year,the risk assess-ment of soil heavy metal pollution is gradually gaining attention.Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals.Random Forest(RF)has strong generalization ability and is not easy to overfit.In this paper,we improve the Bagging algorithm and simple voting method of RF.AW-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classifica-tion performance of RF on imbalanced datasets.Adaptive Bagging enables trees in RF to learn information from the positive samples,and weighted voting method enables trees with superior performance to have higher voting weights.Experi-ments were conducted using G-mean,recall and F1-score to set weights,and the results obtained were better than RF.Risk assessment experiments were conducted using W-RF on the heavy metal dataset from agricultural fields around Wuhan.The experimental results show that the RW-RF algorithm,which use recall to calculate the classifier weights,has the best classification performance.At the end of this paper,we optimized the hyperparameters of the RW-RF algorithm by a Bayesian optimization algorithm.We use G-mean as the objective function to obtain the opti-mal hyperparameter combination within the number of iterations.展开更多
As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and ...As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.展开更多
Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health.However,big data faces many ongoing serious challenges in the process of collection,storage,and ...Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health.However,big data faces many ongoing serious challenges in the process of collection,storage,and use.Among them,data security and privacy problems have attracted extensive interest.In an effort to overcome this challenge,this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions(SGX).First of all,we define SGX as a trusted edge computing node,design data access module,data protection module,and data integrity check module,to achieve hardware-enhanced data privacy protection.Then,we design a smart contract framework to realize distributed data access control management in a big data environment.The crucial role of the smart contract was revealed by designing multiple access control contracts,register contracts,and history contracts.Access control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history behavior.Register contract contains user property information,edge computing node information,the access control and history smart contract information,and provides functions such as registration,update,and deletion.History contract records the historical behavior information of malicious users,receives the report information of malicious requestors from the access control contract,implements a misbehavior check method to determines whether the requestor has misbehavior,and returns the corresponding result.Finally,we design decentralized system architecture,prove the security properties,and analysis to verify the feasibility of the system.Results demonstrate that our method can effectively improve the timeliness of data,reduce network latency,and ensure the security,reliability,and traceability of data.展开更多
Today,resource depletion threatens a number of resource-based cities in China.The ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of...Today,resource depletion threatens a number of resource-based cities in China.The ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted cities.Using 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to 2017.The results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe level.However,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety level.The top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest coverage.These 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.展开更多
Vehicle–bicycle conflict incurs a higher risk of traffic accidents,particularly as it frequently takes place at intersections.Mastering the traffic characteristics of vehicle–bicycle conflict and optimizing the desi...Vehicle–bicycle conflict incurs a higher risk of traffic accidents,particularly as it frequently takes place at intersections.Mastering the traffic characteristics of vehicle–bicycle conflict and optimizing the design of intersections can effectively reduce such conflict.In this paper,the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object,and T-Analyst video recognition technology was used to obtain data on riding(driving)behavior and vehicle–bicycle conflict at seven intersections in Changsha,China.Herein,eight typical traffic characteristics of vehicle–bicycle conflict are summarized,the causes of vehicle–bicycle conflict are analyzed using 18 factors in three dimensions,the internal relationship between intersection design factors and traffic conflicts is explored,and the guiding of design optimization based on the width of bicycle lanes and the soft separation between vehicles and bicycles is discussed.The results showed that colored paved bicycle lanes were better,performing better at a width of 2.5 m compared to 1.5 m.However,the colored pavement was not suitable for the entire road and had to be set at the position,at which the trajectories of a bicycle and motor vehicle overlapped.Thus,a 2.5-m-wide bicycle lane provides good safety.However,there are still defects in the existing safety indicators,so it is necessary to develop new indicators to reflect real vehicle–bicycle conflict situations more comprehensively.展开更多
With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet a...With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography.For a given group of medical images of one patient,DenseNet is used to regroup the images based on feature similarity comparison.Then the mapping indexes can be constructed based on LBP feature and hash generation.After mapping the privacy information with the hash sequences,the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user.The user can extract the privacy information successfully with a similar method of feature analysis and index construction.The simulation results show good performance of robustness.And the hiding success rate also shows good feasibility and practicability for application.Since the medical images are kept original without embedding and modification,the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.展开更多
Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things ap...Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things applications.However,in all existing single chaining watermark schemes,how to ensure the synchronization between the data sender and the receiver is still an unsolved problem.Once the synchronization points are attacked by the adversary,existing data integrity authentication schemes are difficult to work properly,and the false negative rate might be up to 50 percent.And the additional fixed group delimiters not only increase the data size,but are also easily detected by adversaries.In this paper,we propose an effective dual-chaining watermark scheme,called DCW,for data integrity protection in smart campus IoT applications.The proposed DCW scheme has the following three characteristics:(1)In order to authenticate the integrity of the data,fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping;(2)Instead of additional fixed group delimiters,chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered;(3)To achieve lossless integrity authentication,a reversible watermarking technique is applied.The experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.展开更多
Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image an...Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness.We propose a coverless video steganography method based on frame sequence perceptual distance mapping.In this method,we introduce Learned Perceptual Image Patch Similarity(LPIPS)to quantify the similarity between consecutive video frames to obtain the sequential features of the video.Then we establish the relationship map between features and the hash sequence for information hiding.In addition,the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process.Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks.Compared with the existing methods,the robustness to Gaussian noise and speckle noise is improved by more than 40%,and the algorithm has better practicability and feasibility.展开更多
Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network ...Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network methods can only distinguish foreground from the non-semantic background.The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and shadows.In this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments.Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second.In terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.展开更多
Nowadays,as lightweight mobile clients become more powerful and widely used,more and more information is stored on lightweight mobile clients,user sensitive data privacy protection has become an urgent concern and pro...Nowadays,as lightweight mobile clients become more powerful and widely used,more and more information is stored on lightweight mobile clients,user sensitive data privacy protection has become an urgent concern and problem to be solved.There has been a corresponding rise of security solutions proposed by researchers,however,the current security mechanisms on lightweight mobile clients are proven to be fragile.Due to the fact that this research field is immature and still unexplored in-depth,with this paper,we aim to provide a structured and comprehensive study on privacy protection using trusted execution environment(TEE)for lightweight mobile clients.This paper presents a highly effective and secure lightweight mobile client privacy protection system that utilizes TEE to provide a new method for privacy protection.In particular,the prototype of Lightweight Mobile Clients Privacy Protection Using Trusted Execution Environments(LMCPTEE)is built using Intel software guard extensions(SGX)because SGX can guarantee the integrity,confidentiality,and authenticity of private data.By putting lightweight mobile client critical data on SGX,the security and privacy of client data can be greatly improved.We design the authentication mechanism and privacy protection strategy based on SGX to achieve hardware-enhanced data protection and make a trusted connection with the lightweight mobile clients,thus build the distributed trusted system architecture.The experiment demonstrates that without relying on the performance of the blockchain,the LMCPTEE is practical,feasible,low-performance overhead.It can guarantee the privacy and security of lightweight mobile client private data.展开更多
基金This work is supported by the Science Research Projects of Hunan Provincial Education Department(Nos.18A174,18C0262)the National Natural Science Foundation of China(No.61772561)+2 种基金the Key Research&Development Plan of Hunan Province(Nos.2018NK2012,2019SK2022)the Degree&Postgraduate Education Reform Project of Hunan Province(No.209)the Postgraduate Education and Teaching Reform Project of Central South Forestry University(No.2019JG013).
文摘Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data,this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform.Since the TF-IDF(term frequency-inverse document frequency)algorithm under Spark is irreversible to word mapping,the mapped words indexes cannot be traced back to the original words.In this paper,an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored.Firstly,the text feature is extracted by the TF-IDF algorithm combined CountVectorizer proposed in this paper,and then the features are inputted to the LDA(Latent Dirichlet Allocation)topic model for training.Finally,the text topic clustering is obtained.Experimental results show that for large data samples,the processing speed of LDA topic model clustering has been improved based Spark.At the same time,compared with the LDA topic model based on word frequency input,the model proposed in this paper has a reduction of perplexity.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/+6 种基金in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022,author Y.T,http://kjt.hunan.gov.cn/in part by the Graduate Science and Technology Innovation Fund Project of Central South University of Forestry and Technology under Grant CX2020107,author Q.Z,https://jwc.csuft.edu.cn/。
文摘The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small objects and objects with obstructions.Therefore,we propose a helmet detection algorithm based on the attention mechanism(AT-YOLO).First of all,a channel attention module is added to the YOLOv3 backbone network,which can adaptively calibrate the channel features of the direction to improve the feature utilization,and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network.Secondly,we use DIoU(Distance Intersection over Union)bounding box regression loss function,it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes,which makes the network more accurate in detecting small objects and faster in convergence.Finally,we explore the training strategy of the network model,which improves network performance without increasing the inference cost.Experiments show that the mAP of the proposed method reaches 96.5%,and the detection speed can reach 27 fps.Compared with other existing methods,it has better performance in detection accuracy and speed.
基金supported by the National Biotechnology Development Plan, China (2016ZX08005-004)
文摘Cotton plants are recalcitrant with regards to transformation and induced regeneration.In the present study,5-enolpyruvylshikimate-3-phosphate（EPSPS）,a glyphosate resistant gene from the bacterium Agrobacterium sp.strain CP4,was introduced into an elite Bt transgenic cotton cultivar with a modified technique involving in planta Agrobacteriummediated transformation of shoot apex.Primary transformants were initially screened using a 0.26%glyphosate spray and subsequently by PCR analysis.Five out of 4 000 transformants from T_1 seeds were obtained resulting in an in planta transformation rate of 0.125%.Four homozygous lines were produced by continuous self-fertilization and both PCR-based selection and glyphosate resistance.Transgene insertion was analyzed by Southern blot analysis.Gene transcription and protein expression levels in the transgenic cotton lines were further investigated by RT-PCR,Western blot,and ELISA methods.Transgenic T_3 plants were resistant to as much as 0.4% of glyphosate treatments in field trials.Our results indicate that the cotton shoot apex transformation technique which is both tissue-culture and genotype-independent would enable the exploitation of transgene technology in different cotton cultivars.Since this method does not require sterile conditions,the use of specialized growth media or the application of plant hormones,it can be conducted under the greenhouse condition.
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242in part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret message should not be damaged on the process of the cover media.In order to ensure the invisibility of secret message,complex texture objects should be chosen for embedding information.In this paper,an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message.Firstly,complex texture regions are selected based on a kind of objects detection algorithm.Secondly,three different steganographic methods were used to hide secret message into the selected block region.Experimental results show that the approach enhances the security and robustness.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/+5 种基金in part by the Science Research Projects of Hunan Provincial Education Department under Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022,author Y.T,http://kjt.hunan.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant CX20200730,author G.H,http://kjt.hunan.gov.cn/in part by the Graduate Science and Technology Innovation Fund Project of Central South University of Forestry and Technology under Grant CX20202038,author G.H,http://jwc.csuft.edu.cn/.
文摘Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods.
基金This work was supported by the Hainan Provincial Natural Science Foundation of China[2019RC041,2019RC098]National Natural Science Foundation of China[61762033]+3 种基金Hainan University Doctor Start Fund Project[kyqd1328]Hainan University Youth Fund Project[qnjj1444]Ministry of education humanities and social sciences research program fund project(19YJA710010)The Opening Project of Shanghai Trusted Industrial Control Platform(Grant No.TICPSH202003005-ZC).
文摘With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,http://kjt.hunan.gov.cn/+3 种基金in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/and in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/.
文摘To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image segmentation.Most existing coverless steganography methods are based on whole feature mapping,which has poor robustness when facing geometric attacks,because the contents in the image are easy to lost.To solve this problem,we use ResNet to extract semantic features,and segment the object areas from the image through Mask RCNN for information hiding.These selected object areas have ethical structural integrity and are not located in the visual center of the image,reducing the information loss of malicious attacks.Then,these object areas will be binarized to generate hash sequences for information mapping.In transmission,only a set of stego-images unrelated to the secret information are transmitted,so it can fundamentally resist steganalysis.At the same time,since both Mask RCNN and ResNet have excellent robustness,pre-training the model through supervised learning can achieve good performance.The robust hash algorithm can also resist attacks during transmission.Although image segmentation will reduce the capacity,multiple object areas can be extracted from an image to ensure the capacity to a certain extent.Experimental results show that compared with other coverless image steganography methods,our method is more robust when facing geometric attacks.
基金Supported by Cancer Sucks,Bixby,Oklahoma Research grantto Kaul R
文摘AIM To investigate gender-specific liver estrogen receptor(ER)expression in normal subjects and patients with hepatitis C virus(HCV)-related cirrhosis and hepatocellular carcinoma(HCC).METHODS Liver tissues from normal donors and patients diagnosed with HCV-related cirrhosis and HCV-related HCC were obtained from the NIH Liver Tissue and Cell Distribution System.The expression of ER subtypes,ERαand ERβ,were evaluated by Western blotting and real-time RT-PCR.The subcellular distribution of ERαand ERβwas further determined in nuclear and cytoplasmic tissue lysates along with the expression ofinflammatory[activated NF-κB and IκB-kinase(IKK)]and oncogenic(cyclin D1)markers by Western blotting and immunohistochemistry.The expression of ERαand ERβwas correlated with the expression of activated NF-κB,activated IKK and cyclin D1 by Spearman's correlation.RESULTS Both ER subtypes were expressed in normal livers but male livers showed significantly higher expression of ERαthan females(P<0.05).We observed significantly higher m RNA expression of ERαin HCV-related HCC liver tissues as compared to normals(P<0.05)and ERβin livers of HCV-related cirrhosis and HCV-related HCC subjects(P<0.05).At the protein level,there was a significantly higher expression of nuclear ERαin livers of HCV-related HCC patients and nuclear ERβin HCV-related cirrhosis patients as compared to normals(P<0.05).Furthermore,we observed a significantly higher expression of phosphorylated NF-κB and cyclin D1 in diseased livers(P<0.05).There was a positive correlation between the expression of nuclear ER subtypes and nuclear cyclin D1 and a negative correlation between cytoplasmic ER subtypes and cytoplasmic phosphorylated IKK in HCV-related HCC livers.These findings suggest that dysregulated expression of ER subtypes following chronic HCVinfection may contribute to the progression of HCVrelated cirrhosis to HCV-related HCC.CONCLUSION Gender differences were observed in ERαexpression in normal livers.Alterations in ER subtype expression observed in diseased livers may influence genderrelated disparity in HCV-related pathogenesis.
基金the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,http://kjt.hunan.gov.cn/+5 种基金in part by the Key Research and Development Plan of Hunan Province under Grant 2019SK2022,author Y.T,http://kjt.hunan.gov.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,http://kxjsc.gov.hnedu.cn/in part by the Science Research Projects of Hunan Provincial Education Department under Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/,in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/.Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.
文摘In recent years,with the development of machine learning and deep learning,it is possible to identify and even control crop diseases by using electronic devices instead of manual observation.In this paper,an image recognition method of citrus diseases based on deep learning is proposed.We built a citrus image dataset including six common citrus diseases.The deep learning network is used to train and learn these images,which can effectively identify and classify crop diseases.In the experiment,we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,model size,accuracy.Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy.Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal,and put forward relevant suggestions.
基金This work was supported by the Hainan Provincial Natural Science Foundation of China[2018CXTD333,617048]National Natural Science Foundation of China[61762033,61702539]+3 种基金Hainan University Doctor Start Fund Project[kyqd1328]Hainan University Youth Fund Project[qnjj1444]Ministry of Education Humanities and Social Sciences Research Program Fund Project[19YJA710010]the Opening Project of Shanghai Trusted Industrial Control Platform.
文摘New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.
基金This work was supported in part by the Major Technical Innovation Projects of Hubei Province under Grant 2018ABA099in part by the National Science Fund for Youth of Hubei Province of China under Grant 2018CFB408+2 种基金in part by the Natural Science Foundation of Hubei Province of China under Grant 2015CFA061in part by the National Nature Science Foundation of China under Grant 61272278in part by Research on Key Technologies of Intelligent Decision-making for Food Big Data under Grant 2018A01038.
文摘As soil heavy metal pollution is increasing year by year,the risk assess-ment of soil heavy metal pollution is gradually gaining attention.Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals.Random Forest(RF)has strong generalization ability and is not easy to overfit.In this paper,we improve the Bagging algorithm and simple voting method of RF.AW-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classifica-tion performance of RF on imbalanced datasets.Adaptive Bagging enables trees in RF to learn information from the positive samples,and weighted voting method enables trees with superior performance to have higher voting weights.Experi-ments were conducted using G-mean,recall and F1-score to set weights,and the results obtained were better than RF.Risk assessment experiments were conducted using W-RF on the heavy metal dataset from agricultural fields around Wuhan.The experimental results show that the RW-RF algorithm,which use recall to calculate the classifier weights,has the best classification performance.At the end of this paper,we optimized the hyperparameters of the RW-RF algorithm by a Bayesian optimization algorithm.We use G-mean as the objective function to obtain the opti-mal hyperparameter combination within the number of iterations.
基金This work is supported by the National Natural Science Foundation of China(No.61772561)the Key Research&Development Plan of Hunan Province(No.2018NK2012)+2 种基金the Postgraduate Research and Innovation Project of Hunan Province(No.CX2018B447)the Postgraduate Science and Technology Innovation Foundation of Cent ral South University of Forestry and Technology(20183027)the Key Laboratory for Dig ital Dongting Lake Basin of Hunan Province.
文摘As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.
基金This work was supported by the National Natural Science Foundation of China(Grant No.61762033)Hainan Provincial Natural Science Foundation of China(Grant Nos.2019RC041 and 2019RC098)+2 种基金Opening Project of Shanghai Trusted Industrial Control Platform(Grant No.TICPSH202003005-ZC)Ministry of Education Humanities and Social Sciences Research Program Fund Project(Grant No.19YJA710010)Zhejiang Public Welfare Technology Research(Grant No.LGF18F020019).
文摘Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health.However,big data faces many ongoing serious challenges in the process of collection,storage,and use.Among them,data security and privacy problems have attracted extensive interest.In an effort to overcome this challenge,this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions(SGX).First of all,we define SGX as a trusted edge computing node,design data access module,data protection module,and data integrity check module,to achieve hardware-enhanced data privacy protection.Then,we design a smart contract framework to realize distributed data access control management in a big data environment.The crucial role of the smart contract was revealed by designing multiple access control contracts,register contracts,and history contracts.Access control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history behavior.Register contract contains user property information,edge computing node information,the access control and history smart contract information,and provides functions such as registration,update,and deletion.History contract records the historical behavior information of malicious users,receives the report information of malicious requestors from the access control contract,implements a misbehavior check method to determines whether the requestor has misbehavior,and returns the corresponding result.Finally,we design decentralized system architecture,prove the security properties,and analysis to verify the feasibility of the system.Results demonstrate that our method can effectively improve the timeliness of data,reduce network latency,and ensure the security,reliability,and traceability of data.
基金This work was supported by the Technology R&D Program of Changsha City(nos.kc1702045 and kq1901145)the Key Technology R&D Program of Hunan Province(nos.2016TP2007,2017TP2006,and 2016TP1014).
文摘Today,resource depletion threatens a number of resource-based cities in China.The ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted cities.Using 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to 2017.The results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe level.However,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety level.The top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest coverage.These 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.
基金This work was supported in part by the Ministry of Education of the People’s Republic of China Project of Humanities and Social Sciences under Grant No.19YJCZH208,author X.X,http://www.moe.gov.cn/in part by the Philosophy and Social Science Foundation Project of Hunan Province under Grant No.15YBA406,author X.X,http://www.hnsk.gov.cn/+3 种基金in part by the Social Science Evaluation Committee Project of Hunan Province under Grant No.XSP18YBZ125,author X.X,http://www.hnsk.gov.cn/in part by the Social Sciences Federation Think Tank Project of Hunan Province under Grant No.ZK2019025,author X.X,http://www.hnsk.gov.cn/in part by the Education Bureau Research Foundation Project of Hunan Province under Grant No.20A531,author X.X,http://jyt.hunan.gov.cn/in part by the Science and Technology Project of Changsha City,under Grant No.kq2004092,author X.X,http://kjj.changsha.gov.cn/.
文摘Vehicle–bicycle conflict incurs a higher risk of traffic accidents,particularly as it frequently takes place at intersections.Mastering the traffic characteristics of vehicle–bicycle conflict and optimizing the design of intersections can effectively reduce such conflict.In this paper,the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object,and T-Analyst video recognition technology was used to obtain data on riding(driving)behavior and vehicle–bicycle conflict at seven intersections in Changsha,China.Herein,eight typical traffic characteristics of vehicle–bicycle conflict are summarized,the causes of vehicle–bicycle conflict are analyzed using 18 factors in three dimensions,the internal relationship between intersection design factors and traffic conflicts is explored,and the guiding of design optimization based on the width of bicycle lanes and the soft separation between vehicles and bicycles is discussed.The results showed that colored paved bicycle lanes were better,performing better at a width of 2.5 m compared to 1.5 m.However,the colored pavement was not suitable for the entire road and had to be set at the position,at which the trajectories of a bicycle and motor vehicle overlapped.Thus,a 2.5-m-wide bicycle lane provides good safety.However,there are still defects in the existing safety indicators,so it is necessary to develop new indicators to reflect real vehicle–bicycle conflict situations more comprehensively.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61772561,author J.Q,http://www.nsfc.gov.cn/in part by the Key Research and Development Plan of Hunan Province under Grant 2018NK2012,author J.Q,and 2019SK2022,author H.T,http://kjt.hunan.gov.cn/+4 种基金in part by the Science Research Projects of Hunan Provincial Education Department under Grant 18A174,author X.X,and Grant 19B584,author Y.T,http://kxjsc.gov.hnedu.cn/in part by the Degree&Postgraduate Education Reform Project of Hunan Province under Grant 2019JGYB154,author J.Q,http://xwb.gov.hnedu.cn/in part by the National Natural Science Foundation of Hunan under Grant 2019JJ50866,author L.T,2020JJ4140,author Y.T,and 2020JJ4141,author X.X,http://kjt.hunan.gov.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/and in part by the Postgraduate Education and Teaching Reform Project of Central South University of Forestry&Technology under Grant 2019JG013,author X.X,http://jwc.csuft.edu.cn/.
文摘With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more critical.In this paper,we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography.For a given group of medical images of one patient,DenseNet is used to regroup the images based on feature similarity comparison.Then the mapping indexes can be constructed based on LBP feature and hash generation.After mapping the privacy information with the hash sequences,the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized user.The user can extract the privacy information successfully with a similar method of feature analysis and index construction.The simulation results show good performance of robustness.And the hiding success rate also shows good feasibility and practicability for application.Since the medical images are kept original without embedding and modification,the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.
基金This work is supported by the Major Program of the National Social Science Fund of China under Grant No.17ZDA092by the Electronic Information and Control of Fujian University Engineering Research Center Fund under Grant No.EIC1704+3 种基金by the National Key R&D Program of China under grant 2018YFB1003205by the National Natural Science Foundation of China under grant 61173136,U1836208,U1536206,U1836110,61602253,61672294by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things applications.However,in all existing single chaining watermark schemes,how to ensure the synchronization between the data sender and the receiver is still an unsolved problem.Once the synchronization points are attacked by the adversary,existing data integrity authentication schemes are difficult to work properly,and the false negative rate might be up to 50 percent.And the additional fixed group delimiters not only increase the data size,but are also easily detected by adversaries.In this paper,we propose an effective dual-chaining watermark scheme,called DCW,for data integrity protection in smart campus IoT applications.The proposed DCW scheme has the following three characteristics:(1)In order to authenticate the integrity of the data,fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping;(2)Instead of additional fixed group delimiters,chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered;(3)To achieve lossless integrity authentication,a reversible watermarking technique is applied.The experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.
基金This work was supported in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133the Natural Science Foundation of Hunan Province under Grant 2020JJ4141,2020JJ4140the National Natural Science Foundation of China under Grant 62002392.
文摘Most existing coverless video steganography algorithms use a particular video frame for information hiding.These methods do not reflect the unique sequential features of video carriers that are different from image and have poor robustness.We propose a coverless video steganography method based on frame sequence perceptual distance mapping.In this method,we introduce Learned Perceptual Image Patch Similarity(LPIPS)to quantify the similarity between consecutive video frames to obtain the sequential features of the video.Then we establish the relationship map between features and the hash sequence for information hiding.In addition,the MongoDB database is used to store the mapping relationship and speed up the index matching speed in the information hiding process.Experimental results show that the proposed method exhibits outstanding robustness under various noise attacks.Compared with the existing methods,the robustness to Gaussian noise and speckle noise is improved by more than 40%,and the algorithm has better practicability and feasibility.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62002392,author Y.T,http://www.nsfc.gov.cn/in part by the Natural Science Foundation of Hunan Province(No.2020JJ4140),author Y.T,http://kjt.hunan.gov.cn/+3 种基金and in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant[2019]370-133,author J.Q,http://xwb.gov.hnedu.cn/in part by the Postgraduate Scientific Research Innovation Project of Hunan Province under Grant CX20210878,author Z.W,http://jyt.hunan.gov.cn/and in part by Scientific Innovation Fund for Post-graduates of Central South University of Forestry and Technology under Grant CX202102056,author Z.W,https://jwc.csuft.edu.cn/.
文摘Visual object tracking is a hot topic in recent years.In the meanwhile,Siamese networks have attracted extensive attention in this field because of its balanced precision and speed.However,most of the Siamese network methods can only distinguish foreground from the non-semantic background.The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC)can achieve higher precision under interferences,but the tracking accuracy is still not ideal,especially in the environment with more target interferences,dim light,and shadows.In this paper,we propose crisscross attentional Siamese networks for object tracking(SiamCC).To solve the imbalance between foreground and non-semantic background,we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments.Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second.In terms of detection accuracy,the accuracy of shadow sequences is greatly improved,especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC,and the success rate score is improved from 0.07 to 0.55.
基金supported by the National Natural Science Foundation of China(Grant No.61762033)Hainan Provincial Natural Science Foundation of China(Grant Nos.2019RC041 and 2019RC098)+2 种基金Opening Project of Shanghai Trusted Industrial Control Platform(Grant No.TICPSH202003005-ZC)Ministry of Education Humanities and Social Sciences Research Program Fund Project(Grant No.19YJA710010)Zhejiang Public Welfare Technology Research(Grant No.LGF18F020019).
文摘Nowadays,as lightweight mobile clients become more powerful and widely used,more and more information is stored on lightweight mobile clients,user sensitive data privacy protection has become an urgent concern and problem to be solved.There has been a corresponding rise of security solutions proposed by researchers,however,the current security mechanisms on lightweight mobile clients are proven to be fragile.Due to the fact that this research field is immature and still unexplored in-depth,with this paper,we aim to provide a structured and comprehensive study on privacy protection using trusted execution environment(TEE)for lightweight mobile clients.This paper presents a highly effective and secure lightweight mobile client privacy protection system that utilizes TEE to provide a new method for privacy protection.In particular,the prototype of Lightweight Mobile Clients Privacy Protection Using Trusted Execution Environments(LMCPTEE)is built using Intel software guard extensions(SGX)because SGX can guarantee the integrity,confidentiality,and authenticity of private data.By putting lightweight mobile client critical data on SGX,the security and privacy of client data can be greatly improved.We design the authentication mechanism and privacy protection strategy based on SGX to achieve hardware-enhanced data protection and make a trusted connection with the lightweight mobile clients,thus build the distributed trusted system architecture.The experiment demonstrates that without relying on the performance of the blockchain,the LMCPTEE is practical,feasible,low-performance overhead.It can guarantee the privacy and security of lightweight mobile client private data.
