We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulati...We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulation characteristics of CB-RLM PAs,a comprehensive objective function is proposed that combines multi-state impedance trajectory constraints with in-band performance deviations.For the saturation and 6 dB power back-off(PBO)states,approximately optimal impedance regions on the Smith chart are derived using impedance constraint circles based on load-pull simulations.These regions are used together with in-band performance deviations(e.g.,saturated efficiency,6 dB PBO efficiency,and saturated output power)for matching network optimization and design.Second,a multi-objective evolutionary algorithm based on decomposition with adaptive weights,neighborhood,and global replacement is integrated with harmonic balance simulations to optimize design parameters and evaluate performance.Finally,to validate the proposed method,a broadband CB-RLM PA operating from 0.6 to 1.8 GHz is designed and fabricated.Measurement results show that the efficiencies at saturation,6 dB PBO,and 8 dB PBO all exceed 43.6%,with saturated output power being maintained at 40.9–41.5 dBm,which confirms the feasibility and effectiveness of the proposed broadband high-efficiency CB-RLM PA optimization and design approach.展开更多
The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The tradi...The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The traditional communication architecture of IoV will easily cause significant delay and low Packet Delivery Ratio(PDR) for disseminating critical security beacons under the condition of high-speed movement, distance-varying communication, and mixed traffic. This paper proposes a novel bandwidth-link resources cooperative allocation strategy to achieve better communication performance under the road conditions of intelligent transportation systems(ITS). Firstly, in traffic scenarios, based on the characteristic to predict the relative position of the mobile transceivers, a strategy is developed to cooperate on the mobile cellular network and the Dedicated Short-Range Communications(DSRC). Secondly, by adopting the general network simulator NS3, the dedicated mobile channel models that are suitable for the data interaction of ITS, is applied to confirm the feasibility and reliability of the strategy. Finally, by the simulation, comparison, and analysis of some critical performance parame-ters, we conclude that the novel strategy does not only reduce the system delay but also improve the other communication performance indicators, such as the PDR and communication capacity.展开更多
Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general public.However,most e-auction schemes involve a trusted auctioneer,which is not always cre...Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general public.However,most e-auction schemes involve a trusted auctioneer,which is not always credible in practice.Some studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of collusion.In this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct.In addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute resolution.Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.展开更多
Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdo...Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater morbidity and mortality. Most patients often have asymptomatic abdominal conditions and symptoms, which are often recognized late;hence the abdomen has been the third most common cause of damage to the human body. That notwithstanding,there may be improved outcomes where the condition of an abdominal organ is detected earlier. Over the years, supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods perform well when the used training data represents the target data, but the methods require large manually annotated data and have adaptation problems. The semi-supervised methods are fast but record poor performance than the supervised if assumptions about the data fail to hold. Current state-of-the-art methods of supervised segmentation are largely based on deep learning techniques due to their good accuracy and success in real world applications. Though it requires a large amount of training data for automatic feature extraction, deep learning can hardly be used. As regards the semi-supervised methods of segmentation, self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrive on their convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches, connection and gaps are identified, and prospective future research opportunities are enumerated.展开更多
The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff systems.One of the most effective and critical steps in the fight agai...The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff systems.One of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging.In this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of COVID-19.Two benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.展开更多
The ensemble is a technique that strategically combines basic models to achieve better accuracy rates.Diversity,combination methods,and selection topology are the main factors determining ensemble performance.Conseque...The ensemble is a technique that strategically combines basic models to achieve better accuracy rates.Diversity,combination methods,and selection topology are the main factors determining ensemble performance.Consequently,it is a challenging task to design an efficient ensemble scheme.Even though numerous paradigms have been proposed to classify ensemble schemes,there is still much room for improvement.This paper proposes a general framework for creating ensembles in the context of classification.Specifically,the ensemble framework consists of four stages:objectives,data preparing,model training,and model testing.It is comprehensive to design diverse ensembles.The proposed ensemble approach can be used for a wide variety of machine learning tasks.We validate our approach on real-world datasets.The experimental results show the efficiency of the proposed approach.展开更多
Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps t...Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor(NN)features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.展开更多
Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid devel...Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid development of information technology and software industry, most of the software has not been rigorously tested before being put in use, so that the hidden vulnerabilities in software will be exploited by the attackers. Therefore, it is of great significance for us to actively de- tect the software vulnerabilities in the security maintenance of information systems. In this paper, we firstly studied some of the common- ly used vulnerability detection methods and detection tools, and analyzed the advantages and disadvantages of each method in different scenarios. Secondly, we designed a set of eval- uation criteria for different mining methods in the loopholes evaluation. Thirdly, we also proposed and designed an integration testing framework, on which we can test the typical static analysis methods and dynamic mining methods as well as make the comparison, so that we can obtain an intuitive comparative analysis for the experimental results. Final- ly, we reported the experimental analysis to verify the feasibility and effectiveness of the proposed evaluation method and the testingframework, with the results showing that the final test results will serve as a form of guid- ance to aid the selection of the most appropri- ate and effective method or tools in vulnera- bility detection activity.展开更多
Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learnin...Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.展开更多
Traffic encryption techniques facilitate cyberattackers to hide their presence and activities.Traffic classification is an important method to prevent network threats.However,due to the tremendous traffic volume and l...Traffic encryption techniques facilitate cyberattackers to hide their presence and activities.Traffic classification is an important method to prevent network threats.However,due to the tremendous traffic volume and limitations of computing,most existing traffic classification techniques are inapplicable to the high-speed network environment.In this paper,we propose a High-speed Encrypted Traffic Classification(HETC)method containing two stages.First,to efficiently detect whether traffic is encrypted,HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows.Second,HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model.The experimental results show that HETC can achieve a 94%F-measure in detecting encrypted flows and a 85%–93%F-measure in classifying fine-grained flows for a 1-KB flow-length dataset,outperforming the state-of-the-art comparison methods.Meanwhile,HETC does not need to wait for the end of the flow and can extract mass computing features.The average time for HETC to process each flow is only 2 or 16 ms,which is lower than the flow duration in most cases,making it a good candidate for high-speed traffic classification.展开更多
With the increasing constraints of hardware devices,there is a growing demand for compact models to be deployed on device endpoints.Knowledge distillation,a widely used technique for model compression and knowledge tr...With the increasing constraints of hardware devices,there is a growing demand for compact models to be deployed on device endpoints.Knowledge distillation,a widely used technique for model compression and knowledge transfer,has gained significant attention in recent years.However,traditional distillation approaches compare the knowledge of individual samples indirectly through class prototypes overlooking the structural relationships between samples.Although recent distillation methods based on contrastive learning can capture relational knowledge,their relational constraints often distort the positional information of the samples leading to compromised performance in the distilled model.To address these challenges and further enhance the performance of compact models,we propose a novel approach,termed contrastive learning-based multi-level knowledge distillation(CLMKD).The CLMKD framework introduces three key modules:class-guided contrastive distillation,gradient relation contrastive distillation,and semantic similarity distillation.These modules are effectively integrated into a unified framework to extract feature knowledge from multiple levels,capturing not only the representational consistency of individual samples but also their higher-order structure and semantic similarity.We evaluate the proposed CLMKD method on multiple image classification datasets and the results demonstrate its superior performance compared to state-of-the-art knowledge distillation methods.展开更多
Steel surface defect detection is a key part of the production process in the steel industry.The traditional manual inspection methods are inefficient and costly.With the rapid development of deep learning technology,...Steel surface defect detection is a key part of the production process in the steel industry.The traditional manual inspection methods are inefficient and costly.With the rapid development of deep learning technology,automatic detection and segmentation of steel surface defects based on deep neural networks has received widespread attention and demonstrated good performance in several real-world scenarios.However,challenges remain due to the obvious inter-class similarity and intra-class variation problems in steel defect images,impacting accuracy and robustness.This paper systematically summarizes the representative researches on solving the inter-class similarity and intra-class variation problems in recent years,and focuses on analyzing the innovations of different methods in network structure design.In addition,this paper also discusses the shortcomings of the current research,and proposes a new idea of fusion of mainstream modeling method.By employing a subcomparison module to enhance feature similarity within classes and differentiate across classes,followed by pyramid feature fusion to optimize computational efficiency,this study aims to advance high-precision intelligent recognition of steel surface defects.This study reveals that the approach not only addresses existing challenges but also provides a foundation for future advancements in steel defect detection technologies.展开更多
With deep mining of coal mines, prospecting multilayer water-filled goaf has become a new content that results from geophysical exploration in coalfields. The central loop transient electromagnetic （TEM） method is f...With deep mining of coal mines, prospecting multilayer water-filled goaf has become a new content that results from geophysical exploration in coalfields. The central loop transient electromagnetic （TEM） method is favorable for prospecting conductive layers because of the coupling relationship between its field structure and formation. However, the shielding effect of conductive overburden would not only require a longer observation time when prospecting the same depth but also weaken the anomalous response of underlying layers. Through direct time domain numerical simulation and horizontal layered earth forward modeling, this paper estimates the length of observation time required to prospect the target, and the distinguishable criterion of multilayer water-filled goal is presented with observation error according to the effect of noise on observation data. The observed emf curves from Dazigou Coal Mine, Shanxi Province can distinguish multilayer water-filled goaf. In quantitative inversion interpretation of observed curves, using electric logging data as initial parameters restrains the equivalence caused by coal formation thin layers. The deduced three-layer and two-layer water-filled goals are confirmed by the drilling hole. The result suggests that when observation time is long enough and with the anomalous situation of underlying layers being greater than the observation error, the use of the central loop TEM method to orosoect a multilaver water-filled goaf is feasible.展开更多
The integrated modular avionics （IMA） architecture is an open standard in avionics industry, in which the number of functionalities implemented by software is greater than ever before. In the IMA architecture, the r...The integrated modular avionics （IMA） architecture is an open standard in avionics industry, in which the number of functionalities implemented by software is greater than ever before. In the IMA architecture, the reliability of the avionics system is highly affected by the software applications. In order to enhance the fault tolerance feature with regard to software application failures, many industrial standards propose a layered health monitoring/fault management （HM/FM） scheme to periodically check the health status of software application processes and recover the malfunctioning software process whenever an error is located. In this paper, we make an analytical study of the HM/FM system for avionics application software. We use the stochastic Petri nets （SPN） to build a formal model of each component and present a method to combine the components together to form a complete system model with respect to three interlayer query strategies. We further investigate the effectiveness of these strategies in an illustrative system.展开更多
With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great signific...With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great significance in reducing system crashes and maintaining system security and integrity. This paper focuses on detecting three common types of vulnerabilities: Unused_Variable, Use_of_Uninitialized_Variable, and Use_After_ Free. We propose a method for software vulnerability detection based on an improved control flow graph(ICFG) and several predicates of vulnerability properties for each type of vulnerability. We also define a set of grammar rules for analyzing and deriving the three mentioned types of vulnerabilities, and design three vulnerability detection algorithms to guide the process of vulnerability detection. In addition, we conduct cases studies of the three mentioned types of vulnerabilities with real vulnerability program segments from Common Weakness Enumeration(CWE). The results of the studies show that the proposed method can detect the vulnerability in the tested program segments. Finally, we conduct manual analysis and experiments on detecting the three types of vulnerability program segments(30 examples for each type) from CWE, to compare the vulnerability detection effectiveness of the proposed method with that of the existing detection tool Cpp Check. The results show that the proposed method performs better. In summary, the method proposed in this paper has certain feasibility and effectiveness in detecting the three mentioned types of vulnerabilities, and it will also have guiding significance for the detection of other common vulnerabilities.展开更多
A feedback control model of lattice hydrodynamic model is proposed by taking the information of the historic optimal velocity into account for the traffic system. The modern control theory is applied for the linear st...A feedback control model of lattice hydrodynamic model is proposed by taking the information of the historic optimal velocity into account for the traffic system. The modern control theory is applied for the linear stability condition with feedback control signal. The result shows that the stability of traffic flow is closely related to the information of the historic optimal velocity. Furthermore, numerical simulations conform that the new feedback control did increase the stability of traffic flow efficiently, which is in accord with theoretical analysis.展开更多
Driven by the continuous penetration of high data rate services and applications,a large amount of unregulated visible light spectrum is used for communication to fully meet the needs of 6th generation(6G)mobile techn...Driven by the continuous penetration of high data rate services and applications,a large amount of unregulated visible light spectrum is used for communication to fully meet the needs of 6th generation(6G)mobile technologies.Visible light communication(VLC)faces many challenges as a solution that complements existing radio frequency(RF)networks.This paper studies the optimal configuration of LEDs in indoor environments under the constraints of illumination and quality of experience(QoE).Based on the Voronoi tessellation(VT)and centroidal Voronoi tessellation(CVT)theory,combined with the Lloyd’s algorithm,we propose two approaches for optimizing LED deployments to meet the illumination and QoE requirements of all users.Focusing on(i)the minimization of the number of LEDs to be installed in order to meet illumination and average QoE constraints,and(ii)the maximization of the average QoE of users to be served with a fixed number of LEDs.Monte Carlo simulations are carried out for different user distribution compared with hexagonal,square and VT deployment.The simulation results illustrate that under the same conditions,the proposed deployment approach can provide less LEDs and achieve better QoE performance.展开更多
It is well known that today's inter-domain routing protocol, Border Gateway Protocol (BGP), converges slowly during network failures. Due to the distribution nature of Internet routing decisions and the rate-limit...It is well known that today's inter-domain routing protocol, Border Gateway Protocol (BGP), converges slowly during network failures. Due to the distribution nature of Internet routing decisions and the rate-limiting timer Minimum Route Advertisement Interval (MRAI) of BGP, unavoidable convergence latency is introduced in reaction to network changes. During the period of convergence temporarily routing table inconsistencies cause short-term routing blackholes and loops which result in widespread temporary burst packet loss. In this paper, we present ROute-Free Forwarding (ROFF) - a novel technique for packet delivering continuously during periods of convergence. With slightly modifications on IP packet header and BGP, route loops and blackholes can be avoided. Our preliminary evaluation demonstrates that ROFF succeeds in reducing the number of Autonomous Systems (ASes) which experience burst packet loss and the duration of packet loss.展开更多
Non-rigid shape deformation without tearing or stretching is called isometry. There are many difficulties to research non-rigid shape in Euclidean space. Therefore, non-rigid shapes are firstly embedded into a none-Eu...Non-rigid shape deformation without tearing or stretching is called isometry. There are many difficulties to research non-rigid shape in Euclidean space. Therefore, non-rigid shapes are firstly embedded into a none-Euclidean space. Spectral space is chosen in this paper. Then three descriptors are proposed based on three spectral distances. The existence of zero-eigenvalue has negative effects on computation of spectral distance, Therefore the spectral distance should be computed from the first non-zcro-eigenvalue. Experiments show that spectral distance distributions are very effective to describe the non-rigid shapes.展开更多
Smart environment is being used in many areas to deliver more services to individuals in a physical space, such as a hospital. In the UK, the National Health Service(NHS) provides free and high quality healthcare serv...Smart environment is being used in many areas to deliver more services to individuals in a physical space, such as a hospital. In the UK, the National Health Service(NHS) provides free and high quality healthcare service for all residents. Smart hospital environment is able to support NHS and provide more convenience. Patient flow scheduling is a crucial section in a smart hospital environment. Smart hospital environment aims to provide a smart environment in the hospital to facilitate individual experience and improve the quality of healthcare service.First of all, this paper investigates a real world patient flow scenario of a hospital in the UK and models a general scheduling scheme based on the scenario using a compositional formal approach, i.e. performance evaluation process algebra(PEPA). This scheduling scheme uses an easy-implemented solution(the grouping scheme) to reduce the waiting queue in the hospital. Secondly, fluid flow analysis is used for the performance analysis by generating a set of ordinary differential equations(ODEs) in terms of the PEPA model.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62171204,62171129,62001192).
文摘We propose an optimization method based on evolutionary computation for the design of broadband high-efficiency current-biased reverse load-modulation power amplifiers(CB-RLM PAs).First,given the reverse load-modulation characteristics of CB-RLM PAs,a comprehensive objective function is proposed that combines multi-state impedance trajectory constraints with in-band performance deviations.For the saturation and 6 dB power back-off(PBO)states,approximately optimal impedance regions on the Smith chart are derived using impedance constraint circles based on load-pull simulations.These regions are used together with in-band performance deviations(e.g.,saturated efficiency,6 dB PBO efficiency,and saturated output power)for matching network optimization and design.Second,a multi-objective evolutionary algorithm based on decomposition with adaptive weights,neighborhood,and global replacement is integrated with harmonic balance simulations to optimize design parameters and evaluate performance.Finally,to validate the proposed method,a broadband CB-RLM PA operating from 0.6 to 1.8 GHz is designed and fabricated.Measurement results show that the efficiencies at saturation,6 dB PBO,and 8 dB PBO all exceed 43.6%,with saturated output power being maintained at 40.9–41.5 dBm,which confirms the feasibility and effectiveness of the proposed broadband high-efficiency CB-RLM PA optimization and design approach.
基金supported in part by the National Natural Science Foundation of China (No.61573171)the Major Information Projects of State Ministry of Transportation (No.2013-364-836-900)
文摘The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The traditional communication architecture of IoV will easily cause significant delay and low Packet Delivery Ratio(PDR) for disseminating critical security beacons under the condition of high-speed movement, distance-varying communication, and mixed traffic. This paper proposes a novel bandwidth-link resources cooperative allocation strategy to achieve better communication performance under the road conditions of intelligent transportation systems(ITS). Firstly, in traffic scenarios, based on the characteristic to predict the relative position of the mobile transceivers, a strategy is developed to cooperate on the mobile cellular network and the Dedicated Short-Range Communications(DSRC). Secondly, by adopting the general network simulator NS3, the dedicated mobile channel models that are suitable for the data interaction of ITS, is applied to confirm the feasibility and reliability of the strategy. Finally, by the simulation, comparison, and analysis of some critical performance parame-ters, we conclude that the novel strategy does not only reduce the system delay but also improve the other communication performance indicators, such as the PDR and communication capacity.
基金supported by the National Key R&D Program of China (No.2020YFB1005500)the Leading-edge Technology Program of Jiangsu Natural Science Foundation (No.BK20202001)+1 种基金the Fundamental Research Funds for the Central Universities (No.XJSJ23040)the Postdoctoral Science Foundation of Jiangsu Province (No.2021K596C)。
文摘Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general public.However,most e-auction schemes involve a trusted auctioneer,which is not always credible in practice.Some studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of collusion.In this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct.In addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute resolution.Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.
基金supported by National Natural Science Foundation of China(Nos.61772242,61976106 and 61572239)the China Postdoctoral Science Foundation(No.2017M611737)+1 种基金the Six Talent Peaks Project in Jiangsu Province(No.DZXX-122)the Key Special Project of Health and Family Planning Science and Technology in Zhenjiang City(No.SHW2017019)。
文摘Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater morbidity and mortality. Most patients often have asymptomatic abdominal conditions and symptoms, which are often recognized late;hence the abdomen has been the third most common cause of damage to the human body. That notwithstanding,there may be improved outcomes where the condition of an abdominal organ is detected earlier. Over the years, supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods perform well when the used training data represents the target data, but the methods require large manually annotated data and have adaptation problems. The semi-supervised methods are fast but record poor performance than the supervised if assumptions about the data fail to hold. Current state-of-the-art methods of supervised segmentation are largely based on deep learning techniques due to their good accuracy and success in real world applications. Though it requires a large amount of training data for automatic feature extraction, deep learning can hardly be used. As regards the semi-supervised methods of segmentation, self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrive on their convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches, connection and gaps are identified, and prospective future research opportunities are enumerated.
基金This project is supported by National Natural Science Foundation of China(NSFC)(Nos.61902158,61806087)Graduate student innovation program for academic degrees in general university in Jiangsu Province(No.KYZZ16-0337).
文摘The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff systems.One of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging.In this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of COVID-19.Two benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.
文摘The ensemble is a technique that strategically combines basic models to achieve better accuracy rates.Diversity,combination methods,and selection topology are the main factors determining ensemble performance.Consequently,it is a challenging task to design an efficient ensemble scheme.Even though numerous paradigms have been proposed to classify ensemble schemes,there is still much room for improvement.This paper proposes a general framework for creating ensembles in the context of classification.Specifically,the ensemble framework consists of four stages:objectives,data preparing,model training,and model testing.It is comprehensive to design diverse ensembles.The proposed ensemble approach can be used for a wide variety of machine learning tasks.We validate our approach on real-world datasets.The experimental results show the efficiency of the proposed approach.
基金the National Natural Science Foundation of China(No.61502208)the Natural Science Foundation of Jiangsu Province of China(No.BK20150522)+1 种基金the Scientific and Technical Program of City of Huizhou(Nos.2016X0422037 and 2017C0405021)the Natural Science Foundation of Huizhou University(Nos.hzux1201606 and hzu201701)
文摘Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor(NN)features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.
基金partly supported by National Natural Science Foundation of China (NSFC grant numbers: 61202110 and 61502205)the project of Jiangsu provincial Six Talent Peaks (Grant numbers: XYDXXJS-016)
文摘Software an important way to vulnerability mining is detect whether there are some loopholes existing in the software, and also is an important way to ensure the secu- rity of information systems. With the rapid development of information technology and software industry, most of the software has not been rigorously tested before being put in use, so that the hidden vulnerabilities in software will be exploited by the attackers. Therefore, it is of great significance for us to actively de- tect the software vulnerabilities in the security maintenance of information systems. In this paper, we firstly studied some of the common- ly used vulnerability detection methods and detection tools, and analyzed the advantages and disadvantages of each method in different scenarios. Secondly, we designed a set of eval- uation criteria for different mining methods in the loopholes evaluation. Thirdly, we also proposed and designed an integration testing framework, on which we can test the typical static analysis methods and dynamic mining methods as well as make the comparison, so that we can obtain an intuitive comparative analysis for the experimental results. Final- ly, we reported the experimental analysis to verify the feasibility and effectiveness of the proposed evaluation method and the testingframework, with the results showing that the final test results will serve as a form of guid- ance to aid the selection of the most appropri- ate and effective method or tools in vulnera- bility detection activity.
文摘Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.
基金supported by the National Natural Science Foundation of China under Grant No.U1736216。
文摘Traffic encryption techniques facilitate cyberattackers to hide their presence and activities.Traffic classification is an important method to prevent network threats.However,due to the tremendous traffic volume and limitations of computing,most existing traffic classification techniques are inapplicable to the high-speed network environment.In this paper,we propose a High-speed Encrypted Traffic Classification(HETC)method containing two stages.First,to efficiently detect whether traffic is encrypted,HETC focuses on randomly sampled short flows and extracts aggregation entropies with chi-square test features to measure the different patterns of the byte composition and distribution between encrypted and unencrypted flows.Second,HETC introduces binary features upon the previous features and performs fine-grained traffic classification by combining these payload features with a Random Forest model.The experimental results show that HETC can achieve a 94%F-measure in detecting encrypted flows and a 85%–93%F-measure in classifying fine-grained flows for a 1-KB flow-length dataset,outperforming the state-of-the-art comparison methods.Meanwhile,HETC does not need to wait for the end of the flow and can extract mass computing features.The average time for HETC to process each flow is only 2 or 16 ms,which is lower than the flow duration in most cases,making it a good candidate for high-speed traffic classification.
基金supported in part by National Natural Science Foundation of China(Grants 62262005,61976107,and 61962010)the Research Foundation for Talented Scholars of Southwest University(Grant SWU-KR24002)+3 种基金the High-level Innovative Talents in Guizhou Province(Grant GCC[2023]033)the Natural Science Research Project of Department of Education of Guizhou Province(Grant QJJ[2024]009)the Guizhou Provincial Department of Science and Technology(Grant QKHCG-DXGA[2025]-ZD002)the High Performance Computing(HPC)clusters at Southwest University.
文摘With the increasing constraints of hardware devices,there is a growing demand for compact models to be deployed on device endpoints.Knowledge distillation,a widely used technique for model compression and knowledge transfer,has gained significant attention in recent years.However,traditional distillation approaches compare the knowledge of individual samples indirectly through class prototypes overlooking the structural relationships between samples.Although recent distillation methods based on contrastive learning can capture relational knowledge,their relational constraints often distort the positional information of the samples leading to compromised performance in the distilled model.To address these challenges and further enhance the performance of compact models,we propose a novel approach,termed contrastive learning-based multi-level knowledge distillation(CLMKD).The CLMKD framework introduces three key modules:class-guided contrastive distillation,gradient relation contrastive distillation,and semantic similarity distillation.These modules are effectively integrated into a unified framework to extract feature knowledge from multiple levels,capturing not only the representational consistency of individual samples but also their higher-order structure and semantic similarity.We evaluate the proposed CLMKD method on multiple image classification datasets and the results demonstrate its superior performance compared to state-of-the-art knowledge distillation methods.
文摘Steel surface defect detection is a key part of the production process in the steel industry.The traditional manual inspection methods are inefficient and costly.With the rapid development of deep learning technology,automatic detection and segmentation of steel surface defects based on deep neural networks has received widespread attention and demonstrated good performance in several real-world scenarios.However,challenges remain due to the obvious inter-class similarity and intra-class variation problems in steel defect images,impacting accuracy and robustness.This paper systematically summarizes the representative researches on solving the inter-class similarity and intra-class variation problems in recent years,and focuses on analyzing the innovations of different methods in network structure design.In addition,this paper also discusses the shortcomings of the current research,and proposes a new idea of fusion of mainstream modeling method.By employing a subcomparison module to enhance feature similarity within classes and differentiate across classes,followed by pyramid feature fusion to optimize computational efficiency,this study aims to advance high-precision intelligent recognition of steel surface defects.This study reveals that the approach not only addresses existing challenges but also provides a foundation for future advancements in steel defect detection technologies.
基金supported by the National Science Foundation of China(No.41374129)Science and Technology Project of Shanxi Province(No.20100321066)Research and Development Project of National Major Scientifi c Research Equipment(No.ZDYZ2012-1-05-04)
文摘With deep mining of coal mines, prospecting multilayer water-filled goaf has become a new content that results from geophysical exploration in coalfields. The central loop transient electromagnetic （TEM） method is favorable for prospecting conductive layers because of the coupling relationship between its field structure and formation. However, the shielding effect of conductive overburden would not only require a longer observation time when prospecting the same depth but also weaken the anomalous response of underlying layers. Through direct time domain numerical simulation and horizontal layered earth forward modeling, this paper estimates the length of observation time required to prospect the target, and the distinguishable criterion of multilayer water-filled goal is presented with observation error according to the effect of noise on observation data. The observed emf curves from Dazigou Coal Mine, Shanxi Province can distinguish multilayer water-filled goaf. In quantitative inversion interpretation of observed curves, using electric logging data as initial parameters restrains the equivalence caused by coal formation thin layers. The deduced three-layer and two-layer water-filled goals are confirmed by the drilling hole. The result suggests that when observation time is long enough and with the anomalous situation of underlying layers being greater than the observation error, the use of the central loop TEM method to orosoect a multilaver water-filled goaf is feasible.
基金supported by the National Grand Fundamental Research Program of China (Nos. 2010CB328105, 2009CB320504)the Tsinghua University Initiative Scientific Research Programthe National Natural Science Foundation of China (Nos. 61070182,60973107, 60973144, 61173008, 61070021)
文摘The integrated modular avionics （IMA） architecture is an open standard in avionics industry, in which the number of functionalities implemented by software is greater than ever before. In the IMA architecture, the reliability of the avionics system is highly affected by the software applications. In order to enhance the fault tolerance feature with regard to software application failures, many industrial standards propose a layered health monitoring/fault management （HM/FM） scheme to periodically check the health status of software application processes and recover the malfunctioning software process whenever an error is located. In this paper, we make an analytical study of the HM/FM system for avionics application software. We use the stochastic Petri nets （SPN） to build a formal model of each component and present a method to combine the components together to form a complete system model with respect to three interlayer query strategies. We further investigate the effectiveness of these strategies in an illustrative system.
基金Supported by the National Natural Science Foundation of China(61202110 and 61502205)the Project of Jiangsu Provincial Six Talent Peaks(XYDXXJS-016)
文摘With the rapid development of software technology, software vulnerability has become a major threat to computer security. The timely detection and repair of potential vulnerabilities in software, are of great significance in reducing system crashes and maintaining system security and integrity. This paper focuses on detecting three common types of vulnerabilities: Unused_Variable, Use_of_Uninitialized_Variable, and Use_After_ Free. We propose a method for software vulnerability detection based on an improved control flow graph(ICFG) and several predicates of vulnerability properties for each type of vulnerability. We also define a set of grammar rules for analyzing and deriving the three mentioned types of vulnerabilities, and design three vulnerability detection algorithms to guide the process of vulnerability detection. In addition, we conduct cases studies of the three mentioned types of vulnerabilities with real vulnerability program segments from Common Weakness Enumeration(CWE). The results of the studies show that the proposed method can detect the vulnerability in the tested program segments. Finally, we conduct manual analysis and experiments on detecting the three types of vulnerability program segments(30 examples for each type) from CWE, to compare the vulnerability detection effectiveness of the proposed method with that of the existing detection tool Cpp Check. The results show that the proposed method performs better. In summary, the method proposed in this paper has certain feasibility and effectiveness in detecting the three mentioned types of vulnerabilities, and it will also have guiding significance for the detection of other common vulnerabilities.
基金Supported by the National Natural Science Foundation of China under Grant No.61673168
文摘A feedback control model of lattice hydrodynamic model is proposed by taking the information of the historic optimal velocity into account for the traffic system. The modern control theory is applied for the linear stability condition with feedback control signal. The result shows that the stability of traffic flow is closely related to the information of the historic optimal velocity. Furthermore, numerical simulations conform that the new feedback control did increase the stability of traffic flow efficiently, which is in accord with theoretical analysis.
基金This work was supported by National Natural Science Foundation of China(No.61772243)Jiangsu Provincial Key Research and Development Program(BE2018108)Six talent peak high level talent plan projects of Jiangsu Province(XYDXX-115).
文摘Driven by the continuous penetration of high data rate services and applications,a large amount of unregulated visible light spectrum is used for communication to fully meet the needs of 6th generation(6G)mobile technologies.Visible light communication(VLC)faces many challenges as a solution that complements existing radio frequency(RF)networks.This paper studies the optimal configuration of LEDs in indoor environments under the constraints of illumination and quality of experience(QoE).Based on the Voronoi tessellation(VT)and centroidal Voronoi tessellation(CVT)theory,combined with the Lloyd’s algorithm,we propose two approaches for optimizing LED deployments to meet the illumination and QoE requirements of all users.Focusing on(i)the minimization of the number of LEDs to be installed in order to meet illumination and average QoE constraints,and(ii)the maximization of the average QoE of users to be served with a fixed number of LEDs.Monte Carlo simulations are carried out for different user distribution compared with hexagonal,square and VT deployment.The simulation results illustrate that under the same conditions,the proposed deployment approach can provide less LEDs and achieve better QoE performance.
基金Supported by Major State Basic Research Development Program (973 Program) (No. 2007CB307102, No. 2007CB 307100)
文摘It is well known that today's inter-domain routing protocol, Border Gateway Protocol (BGP), converges slowly during network failures. Due to the distribution nature of Internet routing decisions and the rate-limiting timer Minimum Route Advertisement Interval (MRAI) of BGP, unavoidable convergence latency is introduced in reaction to network changes. During the period of convergence temporarily routing table inconsistencies cause short-term routing blackholes and loops which result in widespread temporary burst packet loss. In this paper, we present ROute-Free Forwarding (ROFF) - a novel technique for packet delivering continuously during periods of convergence. With slightly modifications on IP packet header and BGP, route loops and blackholes can be avoided. Our preliminary evaluation demonstrates that ROFF succeeds in reducing the number of Autonomous Systems (ASes) which experience burst packet loss and the duration of packet loss.
基金Partly Supported by NKBRPC(2004CB318006)NNSFC(60873164 and 60533090)
文摘Non-rigid shape deformation without tearing or stretching is called isometry. There are many difficulties to research non-rigid shape in Euclidean space. Therefore, non-rigid shapes are firstly embedded into a none-Euclidean space. Spectral space is chosen in this paper. Then three descriptors are proposed based on three spectral distances. The existence of zero-eigenvalue has negative effects on computation of spectral distance, Therefore the spectral distance should be computed from the first non-zcro-eigenvalue. Experiments show that spectral distance distributions are very effective to describe the non-rigid shapes.
基金the National Natural Science Foundation of China(Nos.61502206 and 61472343)the Natural Science Foundation of Jiangsu Province(Nos.BK20160543 and BK20150523)the Open Project of Key Laboratory of Jiangsu Province(No.BM20082061507)
文摘Smart environment is being used in many areas to deliver more services to individuals in a physical space, such as a hospital. In the UK, the National Health Service(NHS) provides free and high quality healthcare service for all residents. Smart hospital environment is able to support NHS and provide more convenience. Patient flow scheduling is a crucial section in a smart hospital environment. Smart hospital environment aims to provide a smart environment in the hospital to facilitate individual experience and improve the quality of healthcare service.First of all, this paper investigates a real world patient flow scenario of a hospital in the UK and models a general scheduling scheme based on the scenario using a compositional formal approach, i.e. performance evaluation process algebra(PEPA). This scheduling scheme uses an easy-implemented solution(the grouping scheme) to reduce the waiting queue in the hospital. Secondly, fluid flow analysis is used for the performance analysis by generating a set of ordinary differential equations(ODEs) in terms of the PEPA model.
