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
Rydberg-atom-based superheterodyne receivers integrate self-calibration,high sensitivity,a wide operational frequency range,and phase/frequency resolved detection capabilities,demonstrating broad application prospects...Rydberg-atom-based superheterodyne receivers integrate self-calibration,high sensitivity,a wide operational frequency range,and phase/frequency resolved detection capabilities,demonstrating broad application prospects as nextgeneration microwave receivers.Linear gain and linear dynamic range(LDR)are critical metrics for assessing receiver sensitivity and demodulation fidelity,respectively.We numerically solve the four-level master equation and then employ particle swarm optimization(PSO)algorithm to co-optimize linear gain and LDR in atomic superheterodyne receivers based on balanced homodyne detection.Further,we systematically account for dominant dephasing mechanisms in the simulation,encompassing spontaneous decay,transit dephasing,collision dephasing,laser linewidth dephasing,and Doppler averaging.Homodyne readout utilizes both the real and imaginary parts of polarizability for sensing.In the case of the photon shot noise limit,its signal-to-noise ratio(SNR)expression resembles that of direct optical-intensity readout.However,the inherent coherent subtraction operation in homodyne detection significantly suppresses common-mode noise,while appropriately increasing the reference beam power enhances the gain in practical experiments.Indeed,this co-optimization problem,characterized by a high-dimensional variable space,two objectives,and non-convexity,is well-suited for solution by PSO.In addition,probe and coupling detuning contribute equivalently to polarizability and compensate for each other owing to Doppler averaging,thereby reducing the optimization variable space by one.By adopting a product form of linear gain and LDR as the fitness function,the PSO achieves rapid convergence.Here,the effectiveness of the PSO results is verified via the total harmonic distortion(THD).The relative error-based LDR calculation method we proposed efficiently measures receiver response linearity with consuming fewer computational resources.This research is expected to offer valuable insights into enhancing the performance of Rydberg-atom-based superheterodyne receivers.展开更多
The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is develop...The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is developed.In this representation,an expansion center is adopted within the neighborhood of the sources.Then a high-frequency electromagnetic scattering evaluation algorithm is formulated,combining the refined physical optics(PO)and equivalent edge current(EEC)algorithm.The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field(NF)scattering estimation.Afterwards,two basic concepts of the angular glint are briefly introduced and formulated.The proposed procedure makes preparation for the computation of NF linear deviation.Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm.The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.展开更多
The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing r...The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the ...With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the academic community.Despite notable advances,existing approaches still face critical challenges in both information modeling and modality fusion.On one hand,many current methods rely heavily on encoders to extract global features from each modality,which limits their ability to capture latent fine-grained emotional cues within modalities.On the other hand,prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to adaptively regulate modality interactions.To address these limitations,we propose a novel framework for MSA,termed Multi-Granularity Guided Fusion(MGGF).The proposed framework consists of three core components:(i)Multi-Granularity Feature Extraction Module,which simultaneously captures both global and local emotional features within each modality,and integrates them to construct richer intra-modal representations;(ii)Cross-ModalGuidance Learning Module(CMGL),which introduces a cross-modal scoring mechanism to quantify the divergence and complementarity betweenmodalities.These scores are then used as guiding signals to enable the fusion strategy to adaptively respond to scenarios of modality agreement or conflict;(iii)Cross-Modal Fusion Module(CMF),which learns the semantic dependencies among modalities and facilitates deep-level emotional feature interaction,thereby enhancing sentiment prediction with complementary information.We evaluate MGGF on two benchmark datasets:MVSA-Single and MVSA-Multiple.Experimental results demonstrate that MGGF outperforms the current state-of-the-art model CLMLF on MVSA-Single by achieving a 2.32% improvement in F1 score.On MVSA-Multiple,it surpasses MGNNS with a 0.26% increase in accuracy.These results substantiate the effectiveness ofMGGFin addressing two major limitations of existing methods—insufficient intra-modal fine-grained sentiment modeling and inadequate cross-modal semantic fusion.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
With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raisin...With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.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
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Project supported by the National Natural Science Foundation of China(Grant Nos.62331024 and 62571549)the National Key Research and Development Program of China(Grant No.2022YFB2802804)。
文摘Rydberg-atom-based superheterodyne receivers integrate self-calibration,high sensitivity,a wide operational frequency range,and phase/frequency resolved detection capabilities,demonstrating broad application prospects as nextgeneration microwave receivers.Linear gain and linear dynamic range(LDR)are critical metrics for assessing receiver sensitivity and demodulation fidelity,respectively.We numerically solve the four-level master equation and then employ particle swarm optimization(PSO)algorithm to co-optimize linear gain and LDR in atomic superheterodyne receivers based on balanced homodyne detection.Further,we systematically account for dominant dephasing mechanisms in the simulation,encompassing spontaneous decay,transit dephasing,collision dephasing,laser linewidth dephasing,and Doppler averaging.Homodyne readout utilizes both the real and imaginary parts of polarizability for sensing.In the case of the photon shot noise limit,its signal-to-noise ratio(SNR)expression resembles that of direct optical-intensity readout.However,the inherent coherent subtraction operation in homodyne detection significantly suppresses common-mode noise,while appropriately increasing the reference beam power enhances the gain in practical experiments.Indeed,this co-optimization problem,characterized by a high-dimensional variable space,two objectives,and non-convexity,is well-suited for solution by PSO.In addition,probe and coupling detuning contribute equivalently to polarizability and compensate for each other owing to Doppler averaging,thereby reducing the optimization variable space by one.By adopting a product form of linear gain and LDR as the fitness function,the PSO achieves rapid convergence.Here,the effectiveness of the PSO results is verified via the total harmonic distortion(THD).The relative error-based LDR calculation method we proposed efficiently measures receiver response linearity with consuming fewer computational resources.This research is expected to offer valuable insights into enhancing the performance of Rydberg-atom-based superheterodyne receivers.
文摘The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work.First of all,a more refined expression of the Green function is developed.In this representation,an expansion center is adopted within the neighborhood of the sources.Then a high-frequency electromagnetic scattering evaluation algorithm is formulated,combining the refined physical optics(PO)and equivalent edge current(EEC)algorithm.The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field(NF)scattering estimation.Afterwards,two basic concepts of the angular glint are briefly introduced and formulated.The proposed procedure makes preparation for the computation of NF linear deviation.Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm.The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.
文摘The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB3102904in part by the National Natural Science Foundation of China under Grant No.U23A20305 and No.62472440.
文摘With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the academic community.Despite notable advances,existing approaches still face critical challenges in both information modeling and modality fusion.On one hand,many current methods rely heavily on encoders to extract global features from each modality,which limits their ability to capture latent fine-grained emotional cues within modalities.On the other hand,prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to adaptively regulate modality interactions.To address these limitations,we propose a novel framework for MSA,termed Multi-Granularity Guided Fusion(MGGF).The proposed framework consists of three core components:(i)Multi-Granularity Feature Extraction Module,which simultaneously captures both global and local emotional features within each modality,and integrates them to construct richer intra-modal representations;(ii)Cross-ModalGuidance Learning Module(CMGL),which introduces a cross-modal scoring mechanism to quantify the divergence and complementarity betweenmodalities.These scores are then used as guiding signals to enable the fusion strategy to adaptively respond to scenarios of modality agreement or conflict;(iii)Cross-Modal Fusion Module(CMF),which learns the semantic dependencies among modalities and facilitates deep-level emotional feature interaction,thereby enhancing sentiment prediction with complementary information.We evaluate MGGF on two benchmark datasets:MVSA-Single and MVSA-Multiple.Experimental results demonstrate that MGGF outperforms the current state-of-the-art model CLMLF on MVSA-Single by achieving a 2.32% improvement in F1 score.On MVSA-Multiple,it surpasses MGNNS with a 0.26% increase in accuracy.These results substantiate the effectiveness ofMGGFin addressing two major limitations of existing methods—insufficient intra-modal fine-grained sentiment modeling and inadequate cross-modal semantic fusion.
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 Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education of China(KLCS20240211)。
文摘With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.
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.
