In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,...In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,such as more chance of network congestion and serious load imbalance.Due to the dynamically changing traffic patterns,the state-of the-art approaches that do this all require forklift changes to data center networking gear.The root of problem is lack of distinct strategies for elephant and mice flows.Under this condition,it is essential to enforce accurate elephant flow detection and come up with a novel load balancing solution to alleviate the network congestion and achieve high bandwidth utilization.This paper proposed an OpenFlow-based load balancing strategy for datacenter networks that accurately detect elephant flows and enforce distinct routing schemes with different flow types so as to achieve high usage of network capacity.The prototype implemented in Mininet testbed with POX controller and verify the feasibility of our load-balancing strategy when dealing with flow confliction and network degradation.The results show the proposed strategy can adequately generate flow rules and significantly enhance the performance of the bandwidth usage compared against other solutions from the literature in terms of load balancing.展开更多
Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu...Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.展开更多
Live streaming is a booming industry in China,involving an increasing number of Internet users.Previous studies show that trust is a cornerstone to develop ecommerce.Trust in the streaming industry is different from t...Live streaming is a booming industry in China,involving an increasing number of Internet users.Previous studies show that trust is a cornerstone to develop ecommerce.Trust in the streaming industry is different from that of other e-commerce areas.There are two major dimensions of trust in the live streaming context:platform trust and cewebrity trust,which are both important for customers to adopt and reuse a specific live streaming service.We collected questionnaire data from 520 participates who have used live streaming services in China.We model the collected data and identified factors that can influence users’propensity by an extended technology acceptance model(TAM)method.According to our analysis,both cewebrity trust and platform trust will greatly influence users’intention to reuse a certain platform.Moreover,results also indicate that cewebrity trust is far more important than platform trust.These findings can lead to several management strategies to improve the adherence of users to streaming platforms.展开更多
By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the li...By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.展开更多
By efficiently and accurately predicting the adoptability of pets,shelters and rescuers can be positively guided on improving attraction of pet profiles,reducing animal suffering and euthanization.Previous prediction ...By efficiently and accurately predicting the adoptability of pets,shelters and rescuers can be positively guided on improving attraction of pet profiles,reducing animal suffering and euthanization.Previous prediction methods usually only used a single type of content for training.However,many pets contain not only textual content,but also images.To make full use of textual and visual information,this paper proposed a novel method to process pets that contain multimodal information.We employed several CNN(Convolutional Neural Network)based models and other methods to extract features from images and texts to obtain the initial multimodal representation,then reduce the dimensions and fuse them.Finally,we trained the fused features with two GBDT(Gradient Boosting Decision Tree)based models and a Neural Network(NN)and compare the performance of them and their ensemble.The evaluation result demonstrates that the proposed ensemble learning can improve the accuracy of prediction.展开更多
Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production rules.In order to solve the problem of poor self-learnin...Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production rules.In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems,a new Petri net modeling method is proposed by introducing BP(Error Back Propagation)algorithm in neural networks.By judging whether the transition is ignited by continuous function,the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training,which makes Petri network have stronger generalization ability and adaptive function,and the reasoning result is more accurate and credible,which is useful for information services.Finally,a typical example is given to verify the effectiveness and superiority of the parameter optimization method.展开更多
Calculating the semantic similarity of two sentences is an extremely challenging problem.We propose a solution based on convolutional neural networks(CNN)using semantic and syntactic features of sentences.The similari...Calculating the semantic similarity of two sentences is an extremely challenging problem.We propose a solution based on convolutional neural networks(CNN)using semantic and syntactic features of sentences.The similarity score between two sentences is computed as follows.First,given a sentence,two matrices are constructed accordingly,which are called the syntax model input matrix and the semantic model input matrix;one records some syntax features,and the other records some semantic features.By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices,we adopt the most effective way of constructing the matrices.Second,these two matrices are given to two neural networks,which are called the sentence model and the semantic model,respectively.The convolution process of the neural networks of the two models is carried out in multiple perspectives.The outputs of the two models are combined as a vector,which is the representation of the sentence.Third,given the representation vectors of two sentences,the similarity score of these representations is computed by a layer in the CNN.Experiment results show that our algorithm(SSCNN)surpasses the performance MPCPP,which noticeably the best recent work of using CNN for sentence similarity computation.Comparing with MPCNN,the convolution computation in SSCNN is considerably simpler.Based on the results of this work,we suggest that by further utilization of semantic and syntactic features,the performance of sentence similarity measurements has considerable potentials to be improved in the future.展开更多
Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we ...Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.展开更多
With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy an...With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy and low latency is critical to face recognition systems.After analyzing the two-tier architecture“client-cloud”face recognition systems,it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded,and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network.This paper proposes a flexible and efficient edge computing accelerated architecture.By offloading part of the computing tasks to the edge server closer to the data source,edge computing resources are used for image preprocessing to reduce the number of images to be transmitted,thus reducing the network transmission overhead.Moreover,the application code does not need to be rewritten and can be easily migrated to the edge server.We evaluate our schemes based on the open source Azure IoT Edge,and the experimental results show that the three-tier architecture“Client-Edge-Cloud”face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.展开更多
Wearable devices are becoming more popular in our daily life.They are usually used to monitor health status,track fitness data,or even do medical tests,etc.Since the wearable devices can obtain a lot of personal data,...Wearable devices are becoming more popular in our daily life.They are usually used to monitor health status,track fitness data,or even do medical tests,etc.Since the wearable devices can obtain a lot of personal data,their security issues are very important.Motivated by the consideration that the current pairing mechanisms of Bluetooth Low Energy(BLE)are commonly impractical or insecure for many BLE based wearable devices nowadays,we design and implement a security framework in order to protect the communication between these devices.The security framework is a supplement to the Bluetooth pairing mechanisms and is compatible with all BLE based wearable devices.The framework is a module between the application layer and the GATT(Generic Attribute Profile)layer in the BLE architecture stack.When the framework starts,a client and a server can automatically and securely establish shared fresh keys following a designed protocol;the services of encrypting and decrypting messages are provided to the applications conveniently by two functions;application data are securely transmitted following another protocol using the generated keys.Prudential principles are followed by the design of the framework for security purposes.It can protect BLE based wearable devices from replay attacks,Man-in-The-Middle attacks,data tampering,and passive eavesdropping.We conduct experiments to show that the framework can be conveniently deployed with practical operational cost of power consumption.The protocols in this framework have been formally verified that the designed security goals are satisfied.展开更多
Bitcoin is known as the first decentralized digital currency around the world.It uses blockchain technology to store transaction data in a distributed public ledger,is a distributed ledger that removes third-party tru...Bitcoin is known as the first decentralized digital currency around the world.It uses blockchain technology to store transaction data in a distributed public ledger,is a distributed ledger that removes third-party trust institutions.Since its invention,bitcoin has achieved great success,has a market value of about$200 billion.However,while bitcoin has brought a wide and far-reaching impact in the financial field,it has also exposed some security problems,such as selfish mining attacks,Sybil attack,eclipse attacks,routing attacks,EREBUS attacks,and so on.This paper gives a comprehensive overview of various attack scenarios that the bitcoin network may be subjected to,and the methods used to implement the attacks,and reviews the solutions and countermeasures proposed against these attacks.Finally,we summarized other security challenges and proposed further optimizations for the security of the bitcoin network.展开更多
基金This work was supported by the CETC Joint Advanced Research Foundation(Grant Nos.6141B08010102,6141B08080101)the National Science and Technology Major Project for IND(investigational new drug)(Project No.2018ZX09201014).
文摘In today’s datacenter network,the quantity growth and complexity increment of traffic is unprecedented,which brings not only the booming of network development,but also the problem of network performance degradation,such as more chance of network congestion and serious load imbalance.Due to the dynamically changing traffic patterns,the state-of the-art approaches that do this all require forklift changes to data center networking gear.The root of problem is lack of distinct strategies for elephant and mice flows.Under this condition,it is essential to enforce accurate elephant flow detection and come up with a novel load balancing solution to alleviate the network congestion and achieve high bandwidth utilization.This paper proposed an OpenFlow-based load balancing strategy for datacenter networks that accurately detect elephant flows and enforce distinct routing schemes with different flow types so as to achieve high usage of network capacity.The prototype implemented in Mininet testbed with POX controller and verify the feasibility of our load-balancing strategy when dealing with flow confliction and network degradation.The results show the proposed strategy can adequately generate flow rules and significantly enhance the performance of the bandwidth usage compared against other solutions from the literature in terms of load balancing.
基金This study was supported by National Key Research and Development Project(Project No.2017YFD0301506)National Social Science Foundation(Project No.71774052)+1 种基金Hunan Education Department Scientific Research Project(Project No.17K04417A092).
文摘Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
基金This study was supported by National Social Science Foundation(Project No:12CGL046).
文摘Live streaming is a booming industry in China,involving an increasing number of Internet users.Previous studies show that trust is a cornerstone to develop ecommerce.Trust in the streaming industry is different from that of other e-commerce areas.There are two major dimensions of trust in the live streaming context:platform trust and cewebrity trust,which are both important for customers to adopt and reuse a specific live streaming service.We collected questionnaire data from 520 participates who have used live streaming services in China.We model the collected data and identified factors that can influence users’propensity by an extended technology acceptance model(TAM)method.According to our analysis,both cewebrity trust and platform trust will greatly influence users’intention to reuse a certain platform.Moreover,results also indicate that cewebrity trust is far more important than platform trust.These findings can lead to several management strategies to improve the adherence of users to streaming platforms.
基金The authors would like to acknowledge the financial support from the National Natural Science Foundation of China(No.61379145)the Joint Funds of CETC(Grant No.20166141B08020101).
文摘By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.
基金This work is supported by The National Key Research and Development Program of China(2018YFB1800202,2016YFB1000302,SQ2019ZD090149,2018YFB0204301).
文摘By efficiently and accurately predicting the adoptability of pets,shelters and rescuers can be positively guided on improving attraction of pet profiles,reducing animal suffering and euthanization.Previous prediction methods usually only used a single type of content for training.However,many pets contain not only textual content,but also images.To make full use of textual and visual information,this paper proposed a novel method to process pets that contain multimodal information.We employed several CNN(Convolutional Neural Network)based models and other methods to extract features from images and texts to obtain the initial multimodal representation,then reduce the dimensions and fuse them.Finally,we trained the fused features with two GBDT(Gradient Boosting Decision Tree)based models and a Neural Network(NN)and compare the performance of them and their ensemble.The evaluation result demonstrates that the proposed ensemble learning can improve the accuracy of prediction.
文摘Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production rules.In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems,a new Petri net modeling method is proposed by introducing BP(Error Back Propagation)algorithm in neural networks.By judging whether the transition is ignited by continuous function,the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training,which makes Petri network have stronger generalization ability and adaptive function,and the reasoning result is more accurate and credible,which is useful for information services.Finally,a typical example is given to verify the effectiveness and superiority of the parameter optimization method.
文摘Calculating the semantic similarity of two sentences is an extremely challenging problem.We propose a solution based on convolutional neural networks(CNN)using semantic and syntactic features of sentences.The similarity score between two sentences is computed as follows.First,given a sentence,two matrices are constructed accordingly,which are called the syntax model input matrix and the semantic model input matrix;one records some syntax features,and the other records some semantic features.By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices,we adopt the most effective way of constructing the matrices.Second,these two matrices are given to two neural networks,which are called the sentence model and the semantic model,respectively.The convolution process of the neural networks of the two models is carried out in multiple perspectives.The outputs of the two models are combined as a vector,which is the representation of the sentence.Third,given the representation vectors of two sentences,the similarity score of these representations is computed by a layer in the CNN.Experiment results show that our algorithm(SSCNN)surpasses the performance MPCPP,which noticeably the best recent work of using CNN for sentence similarity computation.Comparing with MPCNN,the convolution computation in SSCNN is considerably simpler.Based on the results of this work,we suggest that by further utilization of semantic and syntactic features,the performance of sentence similarity measurements has considerable potentials to be improved in the future.
基金Supported by the National Natural Science Foundation of China (61901308)。
文摘Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.
基金This work is supported by the National Key Research and Development Program of China under Grant(No.2016YFB1000302)the National Natural Science Foundation of China under Grant(No.61832020).
文摘With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy and low latency is critical to face recognition systems.After analyzing the two-tier architecture“client-cloud”face recognition systems,it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded,and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network.This paper proposes a flexible and efficient edge computing accelerated architecture.By offloading part of the computing tasks to the edge server closer to the data source,edge computing resources are used for image preprocessing to reduce the number of images to be transmitted,thus reducing the network transmission overhead.Moreover,the application code does not need to be rewritten and can be easily migrated to the edge server.We evaluate our schemes based on the open source Azure IoT Edge,and the experimental results show that the three-tier architecture“Client-Edge-Cloud”face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.
文摘Wearable devices are becoming more popular in our daily life.They are usually used to monitor health status,track fitness data,or even do medical tests,etc.Since the wearable devices can obtain a lot of personal data,their security issues are very important.Motivated by the consideration that the current pairing mechanisms of Bluetooth Low Energy(BLE)are commonly impractical or insecure for many BLE based wearable devices nowadays,we design and implement a security framework in order to protect the communication between these devices.The security framework is a supplement to the Bluetooth pairing mechanisms and is compatible with all BLE based wearable devices.The framework is a module between the application layer and the GATT(Generic Attribute Profile)layer in the BLE architecture stack.When the framework starts,a client and a server can automatically and securely establish shared fresh keys following a designed protocol;the services of encrypting and decrypting messages are provided to the applications conveniently by two functions;application data are securely transmitted following another protocol using the generated keys.Prudential principles are followed by the design of the framework for security purposes.It can protect BLE based wearable devices from replay attacks,Man-in-The-Middle attacks,data tampering,and passive eavesdropping.We conduct experiments to show that the framework can be conveniently deployed with practical operational cost of power consumption.The protocols in this framework have been formally verified that the designed security goals are satisfied.
文摘Bitcoin is known as the first decentralized digital currency around the world.It uses blockchain technology to store transaction data in a distributed public ledger,is a distributed ledger that removes third-party trust institutions.Since its invention,bitcoin has achieved great success,has a market value of about$200 billion.However,while bitcoin has brought a wide and far-reaching impact in the financial field,it has also exposed some security problems,such as selfish mining attacks,Sybil attack,eclipse attacks,routing attacks,EREBUS attacks,and so on.This paper gives a comprehensive overview of various attack scenarios that the bitcoin network may be subjected to,and the methods used to implement the attacks,and reviews the solutions and countermeasures proposed against these attacks.Finally,we summarized other security challenges and proposed further optimizations for the security of the bitcoin network.
