The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic.However,there is a lack of effective theory to mathematically dem...The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic.However,there is a lack of effective theory to mathematically demonstrate the security of information transmission and processing under nonrandom noise(or vulnerability backdoor attack)conditions in cyberspace.This paper first proposes a security model for cyberspace information transmission and processing channels based on error correction coding theory.First,we analyze the fault tolerance and non-randomness problem of Dynamic Heterogeneous Redundancy(DHR)structured information transmission and processing channel under the condition of non-random noise or attacks.Secondly,we use a mathematical statistical method to demonstrate that for non-random noise(or attacks)on discrete memory channels,there exists a DHR-structured channel and coding scheme that enables the average system error probability to be arbitrarily small.Finally,to construct suitable coding and heterogeneous channels,we take Turbo code as an example and simulate the effects of different heterogeneity,redundancy,output vector length,verdict algorithm and dynamism on the system,which is an important guidance for theory and engineering practice.展开更多
On April 28,2023,the Map Museum of Zhengzhou University was officially opened in the historical Central Plains of China.The museum was founded by Mr.GAO Jun,Distinguished Academician of the Chinese Academy of Sciences...On April 28,2023,the Map Museum of Zhengzhou University was officially opened in the historical Central Plains of China.The museum was founded by Mr.GAO Jun,Distinguished Academician of the Chinese Academy of Sciences and Dean of the School of Geo-Science and Technology at Zhengzhou University.The establishment of the Map Museum reflects the vigorous development of Chinese cartography and its advancement toward world-class level.Additionally,it marks a significant milestone in promoting Chinese map culture.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.How...Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.However,upgrading current IP network to a full SR one can be costly and difficult.Hybrid IP/SR network will last for some time.Aiming at the low flexibility problem of static TE policies in the current SR networks,this paper proposes a Deep Reinforcement Learning(DRL)based TE scheme.The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution.As a result,the network congestion can be mitigated and the performance of the network is improved.Simulation results show that our proposed scheme can improve the throughput of the network by up to 9%than existing schemes.展开更多
Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-...Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.展开更多
To solve the problem of chaining distributed geographic information Web services （GI Web services）, this paper provides an ontology-based method. With this method, semantic service description can be achieved by sem...To solve the problem of chaining distributed geographic information Web services （GI Web services）, this paper provides an ontology-based method. With this method, semantic service description can be achieved by semantic annotation of the elements in a Web service description language（WSDL） document with concepts of geographic ontology, and then a common under-standing about service semantics between customers and providers of Web services is built. Based on the decomposition and formalization of customer requirements, the discovery, composition and execution of GI Web services are explained in detail, and then a chaining of GI Web services is built and used to achieve the customer＇s requirement. Finally, an example based on Web ontology language for service （OWL-S） is provided for testing the feasibility of this method.展开更多
In order to improve the efficiency and success rate of the side channel attack,the utility of side channel information of the attack object must be analyzed and evaluated before the attack implementation.Based on the ...In order to improve the efficiency and success rate of the side channel attack,the utility of side channel information of the attack object must be analyzed and evaluated before the attack implementation.Based on the study of side-channel attack techniques,a method is proposed in this paper to analyze and evaluate the utility of side channel information and the evaluation indexes of comentropy,Signal-to-Noise Ratio(SNR)are introduced.On this basis,the side channel information(power and electromagnetic)of a side channel attack experiment board is analyzed and evaluated,and the Data Encryption Standard(DES)cipher algorithm is attacked with the differential power attack method and differential electromagnetic attack method.The attack results show the effectiveness of the analysis and evaluation method proposed in this paper.展开更多
In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists...In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.展开更多
The study of induced polarization （IP） information extraction from magnetotelluric （MT） sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear i...The study of induced polarization （IP） information extraction from magnetotelluric （MT） sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows： 1） dependency on the initial model, 2） easily falling into the local minimum, and 3） serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture （CUDA） is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization （CO-PSO） algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.展开更多
Aiming at the problem that virtual machine information cannot be extracted incompletely, we extend the typical information extraction model of virtual machine and propose a perception mechanism in virtualization syste...Aiming at the problem that virtual machine information cannot be extracted incompletely, we extend the typical information extraction model of virtual machine and propose a perception mechanism in virtualization system based on storage covert channel to overcome the affection of the semantic gap. Taking advantage of undetectability of the covert channel, a secure channel is established between vip and virtual machine monitor to pass data directly. The vip machine can pass the control information of malicious process to virtual machine monitor by using the VMCALL instruction and shared memory. By parsing critical information in process control structure, virtual machine monitor can terminate the malicious processes. The test results show that the proposed mechanism can clear the user-level malicious programs in the virtual machine effectively and covertly. Meanwhile, its performance overhead is about the same as that of other mainstream monitoring mode.展开更多
With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged...With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.展开更多
Quantum light sources are the core resources for photonics-based quantum information processing.We investigate the spectral engineering of photon triplets generated by third-order spontaneous parametric down-conversio...Quantum light sources are the core resources for photonics-based quantum information processing.We investigate the spectral engineering of photon triplets generated by third-order spontaneous parametric down-conversion in micro/nanofiber.The phase mismatching at one-third pump frequency gives rise to non-degenerate photon triplets,the joint spectral intensity of which has an elliptical locus with a fixed eccentricity of√6/3.Therefore,we propose a frequency-division scheme to separate non-degenerate photon triplets into three channels with high heralding efficiency for the first time.Choosing an appropriate pump wavelength can compensate for the fabrication errors of micro/nanofiber and also generate narrowband,non-degenerate photon triplet sources with a high signal-to-noise ratio.Furthermore,the long-period micro/nanofiber grating introduces a new controllable degree of freedom to tailor phase matching,resulting from the periodic oscillation of dispersion.In this scheme,the wavelength of photon triplets can be flexibly tuned using quasi-phase matching.We study the generation of photon triplets from this novel perspective of spectrum engineering,and we believe that this work will accelerate the practical implementation of photon triplets in quantum information processing.展开更多
Teachers are key participants in universities,and the performance appraisal of teacher is an important part of college work.By analyzing the data of behavior generated by different departments in university,analytic h...Teachers are key participants in universities,and the performance appraisal of teacher is an important part of college work.By analyzing the data of behavior generated by different departments in university,analytic hierarchy process(AHP) is used to establish the preliminary library of performance indicators for teachers,and the correlation among all the performance indicators is inspected by using data mining method at this time.On this basis,a more objective,comprehensive and scientific performance appraisal system is constructed through principal components analysis(PCA),which is more suitable for university itself.Finally,in order to solve the problems existed in current performance appraisal system,a dynamic evaluation model is put forward by regulating the weight of indicator according to the historical data,highlighting the continuity of the system.展开更多
In a recent paper, Sacchi （Phys. Rev. Lett. 96 （2006） 220502） studied the information-disturbance tradeoff in estimating an unknown two-qubit maximally entangled state. In this study, we explore the tradeoff in es...In a recent paper, Sacchi （Phys. Rev. Lett. 96 （2006） 220502） studied the information-disturbance tradeoff in estimating an unknown two-qubit maximally entangled state. In this study, we explore the tradeoff in estimating 13 an unknown three-qubit GHZ state. The optimal estimation process supplies a fidelity of 13/54 and the tradeoff interpolates smoothly between non-informative measurement and optimal estimation process.展开更多
With the expanding enrollments in higher education,the quality of col-lege education and the learning gains of students have attracted much attention.It is important to study the influencing factors and mechanisms of ...With the expanding enrollments in higher education,the quality of col-lege education and the learning gains of students have attracted much attention.It is important to study the influencing factors and mechanisms of individual stu-dents’acquisition of learning gains to improve the quality of talent cultivation in colleges.However,in the context of information security,the original data of learning situation surveys in various universities involve the security of educa-tional evaluation data and daily privacy of teachers and students.To protect the original data,data feature mining and correlation analyses were performed at the model level.This study selected 12,181 pieces of data from X University,which participated in the Chinese College Student Survey(CCSS)from 2018 to 2021.A confirmatory factor analysis was conducted and a structural equation modeling was conducted using AMOS 24.0.Through hypothesis testing,this study explored the mechanisms that influence learning gains from the per-spectives of student involvement,teacher involvement,and school support.The results indicated that the quality of student involvement has an important mediat-ing effect on learning gains and that a supportive campus environment has the greatest influence on learning gains.Establishing positive emotional communica-tions between teachers and students is a more direct and effective method than improving the teaching level to improve the quality of student involvement.This study discusses the implications of these results on the research and practice of connotative development in higher education.展开更多
<div style="text-align:justify;"> In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” due to th...<div style="text-align:justify;"> In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” due to the extension of its object space and the geographic information it contains, which brings new challenges to map information organization. This paper analyzes the concept and information characteristics of the ubiquitous map. Based on that, it proposes a ubiquitous map information organization model oriented to location-based aggregation. This new model includes three parts as “ubiquitous map instance”, “location-based aggregation mode” and “map scene”. This paper focuses on the “map scene” part which is the core of the model and contains two mutually mapped aspects as “content scene” and “representation scene”. And both aspects are divided into three levels as “features” ←→ “elements” ←→ “scenes” according to ubiquitous map information characteristics and location-based aggregation mode. With cases of map decomposition, the application of the model is explained to illustrate its effectiveness. The model is expected to provide powerful data organization and management capabilities for ubiquitous map production and use. </div>展开更多
This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In t...This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.展开更多
View synthesis is an important building block in three dimension（3D） video processing and communications.Based on one or several views,view synthesis creates other views for the purpose of view prediction（for compr...View synthesis is an important building block in three dimension（3D） video processing and communications.Based on one or several views,view synthesis creates other views for the purpose of view prediction（for compression） or view rendering（for multiview-display）.The quality of view synthesis depends on how one fills the occlusion area as well as how the pixels are created.Consequently,luminance adjustment and hole filling are two key issues in view synthesis.In this paper,two views are used to produce an arbitrary virtual synthesized view.One view is merged into another view using a local luminance adjustment method,based on local neighborhood region for the calculation of adjustment coefficient.Moreover,a maximum neighborhood spreading strength hole filling method is presented to deal with the micro texture structure when the hole is being filled.For each pixel at the hole boundary,its neighborhood pixels with the maximum spreading strength direction are selected as candidates;and among them,the pixel with the maximum spreading strength is used to fill the hole from boundary to center.If there still exist disocclusion pixels after once scan,the filling process is repeated until all hole pixels are filled.Simulation results show that the proposed method is efficient,robust and achieves high performance in subjection and objection.展开更多
基金supported by National Key R&D Program of China for Young Scientists:Cyberspace Endogenous Security Mechanisms and Evaluation Methods(No.2022YFB3102800).
文摘The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic.However,there is a lack of effective theory to mathematically demonstrate the security of information transmission and processing under nonrandom noise(or vulnerability backdoor attack)conditions in cyberspace.This paper first proposes a security model for cyberspace information transmission and processing channels based on error correction coding theory.First,we analyze the fault tolerance and non-randomness problem of Dynamic Heterogeneous Redundancy(DHR)structured information transmission and processing channel under the condition of non-random noise or attacks.Secondly,we use a mathematical statistical method to demonstrate that for non-random noise(or attacks)on discrete memory channels,there exists a DHR-structured channel and coding scheme that enables the average system error probability to be arbitrarily small.Finally,to construct suitable coding and heterogeneous channels,we take Turbo code as an example and simulate the effects of different heterogeneity,redundancy,output vector length,verdict algorithm and dynamism on the system,which is an important guidance for theory and engineering practice.
文摘On April 28,2023,the Map Museum of Zhengzhou University was officially opened in the historical Central Plains of China.The museum was founded by Mr.GAO Jun,Distinguished Academician of the Chinese Academy of Sciences and Dean of the School of Geo-Science and Technology at Zhengzhou University.The establishment of the Map Museum reflects the vigorous development of Chinese cartography and its advancement toward world-class level.Additionally,it marks a significant milestone in promoting Chinese map culture.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金supported by the National Key R&D Project(No.2020YFB1804803)the Research and Development Program in Key Areas of Guangdong Province(No.2018B010113001)。
文摘Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.However,upgrading current IP network to a full SR one can be costly and difficult.Hybrid IP/SR network will last for some time.Aiming at the low flexibility problem of static TE policies in the current SR networks,this paper proposes a Deep Reinforcement Learning(DRL)based TE scheme.The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution.As a result,the network congestion can be mitigated and the performance of the network is improved.Simulation results show that our proposed scheme can improve the throughput of the network by up to 9%than existing schemes.
基金National Key Research and Development Program of China,No.2016YFB0502300。
文摘Coronavirus disease 2019(COVID-19)is continuing to spread globally and still poses a great threat to human health.Since its outbreak,it has had catastrophic effects on human society.A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information.This analysis reveals the spread of the epidemic,from the perspective of spatio-temporal objects,to provide references for related research and the formulation of epidemic prevention and control measures.The case information is abstracted,descripted,represented,and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects,multi-level visual expressions,and spatial correlation analysis.The rationality of the method is verified through visualization scenarios of case information statistics for China,Henan cases,and cases related to Shulan.The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic,the discovery of the transmission law,and the spatial traceability of the cases.It has a good portability and good expansion performance,so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.
基金the National Natural Science Fundation ofChina (60774041)
文摘To solve the problem of chaining distributed geographic information Web services （GI Web services）, this paper provides an ontology-based method. With this method, semantic service description can be achieved by semantic annotation of the elements in a Web service description language（WSDL） document with concepts of geographic ontology, and then a common under-standing about service semantics between customers and providers of Web services is built. Based on the decomposition and formalization of customer requirements, the discovery, composition and execution of GI Web services are explained in detail, and then a chaining of GI Web services is built and used to achieve the customer＇s requirement. Finally, an example based on Web ontology language for service （OWL-S） is provided for testing the feasibility of this method.
文摘In order to improve the efficiency and success rate of the side channel attack,the utility of side channel information of the attack object must be analyzed and evaluated before the attack implementation.Based on the study of side-channel attack techniques,a method is proposed in this paper to analyze and evaluate the utility of side channel information and the evaluation indexes of comentropy,Signal-to-Noise Ratio(SNR)are introduced.On this basis,the side channel information(power and electromagnetic)of a side channel attack experiment board is analyzed and evaluated,and the Data Encryption Standard(DES)cipher algorithm is attacked with the differential power attack method and differential electromagnetic attack method.The attack results show the effectiveness of the analysis and evaluation method proposed in this paper.
基金This work was supported by the National Natural Science Foundation of China(grant no.61602515).
文摘In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.
基金Projects(41604117,41204054)supported by the National Natural Science Foundation of ChinaProjects(20110490149,2015M580700)supported by the Research Fund for the Doctoral Program of Higher Education,China+1 种基金Project(2015zzts064)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(16B147)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘The study of induced polarization （IP） information extraction from magnetotelluric （MT） sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows： 1） dependency on the initial model, 2） easily falling into the local minimum, and 3） serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture （CUDA） is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization （CO-PSO） algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (2009AA012200)Henan Province Science and Technology Funding Projects ( SP09JH11158)
文摘Aiming at the problem that virtual machine information cannot be extracted incompletely, we extend the typical information extraction model of virtual machine and propose a perception mechanism in virtualization system based on storage covert channel to overcome the affection of the semantic gap. Taking advantage of undetectability of the covert channel, a secure channel is established between vip and virtual machine monitor to pass data directly. The vip machine can pass the control information of malicious process to virtual machine monitor by using the VMCALL instruction and shared memory. By parsing critical information in process control structure, virtual machine monitor can terminate the malicious processes. The test results show that the proposed mechanism can clear the user-level malicious programs in the virtual machine effectively and covertly. Meanwhile, its performance overhead is about the same as that of other mainstream monitoring mode.
基金the National Natural Science Foundation of China Youth Project(62302520).
文摘With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.
基金Project supported by the National Natural Science Foundation of China(Grant No.61605249)the Science and Technology Key Project of Henan Province of China(Grant Nos.182102210577 and 232102211086).
文摘Quantum light sources are the core resources for photonics-based quantum information processing.We investigate the spectral engineering of photon triplets generated by third-order spontaneous parametric down-conversion in micro/nanofiber.The phase mismatching at one-third pump frequency gives rise to non-degenerate photon triplets,the joint spectral intensity of which has an elliptical locus with a fixed eccentricity of√6/3.Therefore,we propose a frequency-division scheme to separate non-degenerate photon triplets into three channels with high heralding efficiency for the first time.Choosing an appropriate pump wavelength can compensate for the fabrication errors of micro/nanofiber and also generate narrowband,non-degenerate photon triplet sources with a high signal-to-noise ratio.Furthermore,the long-period micro/nanofiber grating introduces a new controllable degree of freedom to tailor phase matching,resulting from the periodic oscillation of dispersion.In this scheme,the wavelength of photon triplets can be flexibly tuned using quasi-phase matching.We study the generation of photon triplets from this novel perspective of spectrum engineering,and we believe that this work will accelerate the practical implementation of photon triplets in quantum information processing.
基金985 Construction Projects of Tongji,China(No.4218142801)
文摘Teachers are key participants in universities,and the performance appraisal of teacher is an important part of college work.By analyzing the data of behavior generated by different departments in university,analytic hierarchy process(AHP) is used to establish the preliminary library of performance indicators for teachers,and the correlation among all the performance indicators is inspected by using data mining method at this time.On this basis,a more objective,comprehensive and scientific performance appraisal system is constructed through principal components analysis(PCA),which is more suitable for university itself.Finally,in order to solve the problems existed in current performance appraisal system,a dynamic evaluation model is put forward by regulating the weight of indicator according to the historical data,highlighting the continuity of the system.
基金Supported by the National Basic Research Programme of China, the National Natural Science Foundation of China under Grant Nos 10674128 and 60121503, the Knowledge Innovation Project and the Hundreds of Talents Programme of Chinese Academy of Sciences, and the Doctoral Foundation of the Education Ministry of China under Grant No 20060358043.
文摘In a recent paper, Sacchi （Phys. Rev. Lett. 96 （2006） 220502） studied the information-disturbance tradeoff in estimating an unknown two-qubit maximally entangled state. In this study, we explore the tradeoff in estimating 13 an unknown three-qubit GHZ state. The optimal estimation process supplies a fidelity of 13/54 and the tradeoff interpolates smoothly between non-informative measurement and optimal estimation process.
基金This work was supported by the Education Department of Henan,China.The fund was obtained from the general project of the 14th Plan of Education Science of Henan Province in 2021(No.2021YB0037).
文摘With the expanding enrollments in higher education,the quality of col-lege education and the learning gains of students have attracted much attention.It is important to study the influencing factors and mechanisms of individual stu-dents’acquisition of learning gains to improve the quality of talent cultivation in colleges.However,in the context of information security,the original data of learning situation surveys in various universities involve the security of educa-tional evaluation data and daily privacy of teachers and students.To protect the original data,data feature mining and correlation analyses were performed at the model level.This study selected 12,181 pieces of data from X University,which participated in the Chinese College Student Survey(CCSS)from 2018 to 2021.A confirmatory factor analysis was conducted and a structural equation modeling was conducted using AMOS 24.0.Through hypothesis testing,this study explored the mechanisms that influence learning gains from the per-spectives of student involvement,teacher involvement,and school support.The results indicated that the quality of student involvement has an important mediat-ing effect on learning gains and that a supportive campus environment has the greatest influence on learning gains.Establishing positive emotional communica-tions between teachers and students is a more direct and effective method than improving the teaching level to improve the quality of student involvement.This study discusses the implications of these results on the research and practice of connotative development in higher education.
文摘<div style="text-align:justify;"> In the era of information and communication technology (ICT) and big data, the map gradually shows a new qualitative feature of “spatiotemporal ubiquitous” due to the extension of its object space and the geographic information it contains, which brings new challenges to map information organization. This paper analyzes the concept and information characteristics of the ubiquitous map. Based on that, it proposes a ubiquitous map information organization model oriented to location-based aggregation. This new model includes three parts as “ubiquitous map instance”, “location-based aggregation mode” and “map scene”. This paper focuses on the “map scene” part which is the core of the model and contains two mutually mapped aspects as “content scene” and “representation scene”. And both aspects are divided into three levels as “features” ←→ “elements” ←→ “scenes” according to ubiquitous map information characteristics and location-based aggregation mode. With cases of map decomposition, the application of the model is explained to illustrate its effectiveness. The model is expected to provide powerful data organization and management capabilities for ubiquitous map production and use. </div>
基金Supported by the Degree Dissertation of Doctor Natural Science Innovation Foundation of Information Engineering University(2007)
文摘This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.
基金supported by the National Natural Science Foundation of China(61075013)
文摘View synthesis is an important building block in three dimension（3D） video processing and communications.Based on one or several views,view synthesis creates other views for the purpose of view prediction（for compression） or view rendering（for multiview-display）.The quality of view synthesis depends on how one fills the occlusion area as well as how the pixels are created.Consequently,luminance adjustment and hole filling are two key issues in view synthesis.In this paper,two views are used to produce an arbitrary virtual synthesized view.One view is merged into another view using a local luminance adjustment method,based on local neighborhood region for the calculation of adjustment coefficient.Moreover,a maximum neighborhood spreading strength hole filling method is presented to deal with the micro texture structure when the hole is being filled.For each pixel at the hole boundary,its neighborhood pixels with the maximum spreading strength direction are selected as candidates;and among them,the pixel with the maximum spreading strength is used to fill the hole from boundary to center.If there still exist disocclusion pixels after once scan,the filling process is repeated until all hole pixels are filled.Simulation results show that the proposed method is efficient,robust and achieves high performance in subjection and objection.
