China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various rel...China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.展开更多
Handover authentication in high mobility scenarios is characterized by frequent and shortterm parallel execution.Moreover,the penetration loss and Doppler frequency shift caused by high speed also lead to the deterior...Handover authentication in high mobility scenarios is characterized by frequent and shortterm parallel execution.Moreover,the penetration loss and Doppler frequency shift caused by high speed also lead to the deterioration of network link quality.Therefore,high mobility scenarios require handover schemes with less handover overhead.However,some existing schemes that meet this requirement cannot provide strong security guarantees,while some schemes that can provide strong security guarantees have large handover overheads.To solve this dilemma,we propose a privacy-preserving handover authentication scheme that can provide strong security guarantees with less computational cost.Based on Orthogonal Time Frequency Space(OTFS)link and Key Encapsulation Mechanism(KEM),we establish the shared key between protocol entities in the initial authentication phase,thereby reducing the overhead in the handover phase.Our proposed scheme can achieve mutual authentication and key agreement among the user equipment,relay node,and authentication server.We demonstrate that our proposed scheme can achieve user anonymity,unlinkability,perfect forward secrecy,and resistance to various attacks through security analysis including the Tamarin.The performance evaluation results show that our scheme has a small computational cost compared with other schemes and can also provide a strong guarantee of security properties.展开更多
Quantum key distribution(QKD),rooted in quantum mechanics,offers information-theoretic security.However,practi-cal systems open security threats due to imperfections,notably bright-light blinding attacks targeting sin...Quantum key distribution(QKD),rooted in quantum mechanics,offers information-theoretic security.However,practi-cal systems open security threats due to imperfections,notably bright-light blinding attacks targeting single-photon detectors.Here,we propose a concise,robust defense strategy for protecting single-photon detectors in QKD systems against blinding attacks.Our strategy uses a dual approach:detecting the bias current of the avalanche photodiode(APD)to defend against con-tinuous-wave blinding attacks,and monitoring the avalanche amplitude to protect against pulsed blinding attacks.By integrat-ing these two branches,the proposed solution effectively identifies and mitigates a wide range of bright light injection attempts,significantly enhancing the resilience of QKD systems against various bright-light blinding attacks.This method forti-fies the safeguards of quantum communications and offers a crucial contribution to the field of quantum information security.展开更多
With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to d...With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.展开更多
Multimodal named entity recognition(MNER)and relation extraction(MRE)are key in social media analysis but face challenges like inefficient visual processing and non-optimal modality interaction.(1)Heavy visual embeddi...Multimodal named entity recognition(MNER)and relation extraction(MRE)are key in social media analysis but face challenges like inefficient visual processing and non-optimal modality interaction.(1)Heavy visual embedding:the process of visual embedding is both time and computationally expensive due to the prerequisite extraction of explicit visual cues from the original image before input into the multimodal model.Consequently,these approaches cannot achieve efficient online reasoning;(2)suboptimal interaction handling:the prevalent method of managing interaction between different modalities typically relies on the alternation of self-attention and cross-attention mechanisms or excessive dependence on the gating mechanism.This explicit modeling method may fail to capture some nuanced relations between image and text,ultimately undermining the model’s capability to extract optimal information.To address these challenges,we introduce Implicit Modality Mining(IMM),a novel end-to-end framework for fine-grained image-text correlation without heavy visual embedders.IMM uses an Implicit Semantic Alignment module with a Transformer for cross-modal clues and an Insert-Activation module to effectively utilize these clues.Our approach achieves state-of-the-art performance on three datasets.展开更多
This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit(LEO) satellites. As the high signal-to-noise ratio(SNR) reception cannot always be guarante...This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit(LEO) satellites. As the high signal-to-noise ratio(SNR) reception cannot always be guaranteed at LEO satellites, the recently developed direct position determination(DPD)is adopted. For LEO satellite-based passive localization systems, an efficient DPD is challenging due to the excessive exhaustive search range leading from broad satellite coverage. In order to reduce the computational complexity, we propose a time difference of arrival-assisted DPD(TA-DPD) which minimizes the searching area by the time difference of arrival measurements and their variances. In this way, the size of the searching area is determined by both geometrical constraints and qualities of received signals, and signals with higher SNRs can be positioned more efficiently as their searching areas are generally smaller.Both two-dimensional and three-dimensional passive localization simulations using the proposed TA-DPD are provided to demonstrate its efficiency and validity. The superior accuracy performance of the proposed method, especially at low SNRs conditions, is also verified through the comparison to conventional two-step methods. Providing a larger margin in link budget for satellite-based vessel location acquisition,the TA-DPD can be a competitive candidate for trusted marine location service.展开更多
Quantum network coding is used to solve the congestion problem in quantum communication,which will promote the transmission efficiency of quantum information and the total throughput of quantum network.We propose a no...Quantum network coding is used to solve the congestion problem in quantum communication,which will promote the transmission efficiency of quantum information and the total throughput of quantum network.We propose a novel controlled quantum network coding without information loss.The effective transmission of quantum states on the butterfly network requires the consent form a third-party controller Charlie.Firstly,two pairs of threeparticle non-maximum entangled states are pre-shared between senders and controller.By adding auxiliary particles and local operations,the senders can predict whether a certain quantum state can be successfully transmitted within the butterfly network based on the Z-{10>,|1>}basis.Secondly,when trans-mission fails upon prediction,the quantum state will not be lost,and it will sill be held by the sender.Subsequently,the controller Charlie re-prepares another three-particle non-maximum entangled state to start a new round.When the predicted transmission is successful,the quantum state can be transmitted successfully within the butterfly network.If the receiver wants to receive the effective quantum state,the quantum measurements from Charlie are needed.Thirdly,when the transmission fails,Charlie does not need to integrate the X-{1+>,1->}basis to measure its own particles,by which quantum resources are saved.Charlie not only controls the effective transmission of quantum states,but also the usage of classical and quantum channels.Finally,the implementation of the quantum circuits,as well as a flow chart and safety analysis of our scheme,is proposed.展开更多
Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices,which expose serious threat to edge computing ...Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices,which expose serious threat to edge computing based distributed systems.Graphbased Sybil detection approaches extract social structures from target distributed systems,refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure.Graph preprocessing is a critical component in such Sybil detection methods,and intuitively,the processing methods will affect the detection performance.Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches.In this paper,we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments.The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.展开更多
Recent years have witnessed the phenomenal growth of wireless technologies and applications on a massively large scale since the fifth generation (5G) wireless technologies were proposed as a key propellent to meet th...Recent years have witnessed the phenomenal growth of wireless technologies and applications on a massively large scale since the fifth generation (5G) wireless technologies were proposed as a key propellent to meet the increasing demands of future networks.Going further,the sixth generation (6G) wireless technologies have already been under preparation.However,wireless communication technologies are faced with new opportunities as well as challenges.展开更多
Unmanned aerial vehicles(UAVs)have attracted growing research interests in recent years,which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points(APs).Thus,it is c...Unmanned aerial vehicles(UAVs)have attracted growing research interests in recent years,which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points(APs).Thus,it is crucial to investigate robust airto-ground(A2G)wireless links for high-speed UAVs.However,the A2G wireless link is unstable as it suffers from large path-loss and severe Doppler effect due to the high mobility of UAVs.In order to meet these challenges,we propose an orthogonal time frequency space(OTFS)-based UAV communication system to relief the Doppler effect.Besides,considering that the energy of UAV is limited,we optimize the trajectory planning of UAV to minimize the energy consumption under the constraints of bit error rate(BER)and transmission rate,where the Doppler compensation is taken into account.Simulation results show that the performance of OTFS-based UAV system is superior to orthogonal frequency division multiplexing(OFDM)-based UAV systems,which can accomplish transmission tasks over shorter distances with lower energy consumption.展开更多
Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time ...Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.展开更多
With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecas...With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.展开更多
In silico prediction of self-interacting proteins(SIPs)has become an important part of proteomics.There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost ...In silico prediction of self-interacting proteins(SIPs)has become an important part of proteomics.There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab experiments.The goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the future.In this review,we first describe the data required for the task of DTIs prediction.Then,some interesting feature extraction methods and computational models are presented on this topic in a timely manner.Afterwards,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding schemes.Overall,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.展开更多
In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion attacks.There has been re...In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion attacks.There has been realistic demand to develop an efficient intrusion detection algorithm for connected devices.Most of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack types.In this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack types.Besides,the blockchain technique is introduced in the federated learning process for the consensus of the entire framework.Experimental results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.展开更多
In this paper, we classify the m-ovoids of finite classical polar spaces that admit a transitive automorphism group acting irreducibly on the ambient vector space. In particular, we obtain several new infinite familie...In this paper, we classify the m-ovoids of finite classical polar spaces that admit a transitive automorphism group acting irreducibly on the ambient vector space. In particular, we obtain several new infinite families of transitive m-ovoids.展开更多
Most recently, due to the demand of immersive communication, region-of-interest-based(ROI) high efficiency video coding(HEVC) approaches in conferencing scenarios have become increasingly important. However, there exi...Most recently, due to the demand of immersive communication, region-of-interest-based(ROI) high efficiency video coding(HEVC) approaches in conferencing scenarios have become increasingly important. However, there exists no objective metric, specially developed for efficiently evaluating the perceived visual quality of video conferencing coding. Therefore, this paper proposes a novel objective quality assessment method, namely Gaussian mixture model based peak signal-tonoise ratio(GMM-PSNR), for the perceptual video conferencing coding. First, eye tracking experiments, together with a real-time technique of face and facial feature extraction, are introduced. In the experiments, importance of background, face, and facial feature regions is identified, and it is then quantified based on eye fixation points over test videos. Next, assuming that the distribution of the eye fixation points obeys Gaussian mixture model, we utilize expectation-maximization(EM) algorithm to generate an importance weight map for each frame of video conferencing coding, in light of a new term eye fixation points/pixel(efp/p). According to the generated weight map, GMM-PSNR is developed for quality assessment by assigning different weights to the distortion of each pixel in the video frame. Finally, we utilize some experiments to investigate the correlation of the proposed GMM-PSNR and other conventional objective metrics with subjective quality metrics. The experimental results show the effectiveness of GMM-PSNR.展开更多
By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the ...By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.展开更多
The cloud computing technology has emerged,developed,and matured in recent years,consequently commercializing remote outsourcing storage services.An increasing number of companies and individuals have chosen the cloud...The cloud computing technology has emerged,developed,and matured in recent years,consequently commercializing remote outsourcing storage services.An increasing number of companies and individuals have chosen the cloud to store their data.However,accidents,such as cloud server downtime,cloud data loss,and accidental deletion,are serious issues for some applications that need to run around the clock.For some mission and business-critical applications,the continuous availability of outsourcing storage services is also necessary to protect users'outsourced data during downtime.Nevertheless,ensuring the continuous availability of data in public cloud data integrity auditing protocols leads to data privacy issues because auditors can obtain the data content of users by a sufficient number of storage proofs.Therefore,protecting data privacy is a burning issue.In addition,existing data integrity auditing schemes that rely on semi-trusted third-party auditors have several security problems,including single points of failure and performance bottlenecks.To deal with these issues,we propose herein a blockchain-based continuous data integrity checking protocol with zero-knowledge privacy protection.We realize a concrete construction by using a verifiable delay function with high efficiency and proof of retrievability,and prove the security of the proposal in a random oracle model.The proposed construction supports dynamic updates for the outsourced data.We also design smart contracts to ensure fairness among the parties involved.Finally,we implement the protocols,and the experimental results demonstrate the efficiency of the proposed protocol.展开更多
Software systems are present all around us and playing their vital roles in our daily life.The correct functioning of these systems is of prime concern.In addition to classical testing techniques,formal techniques lik...Software systems are present all around us and playing their vital roles in our daily life.The correct functioning of these systems is of prime concern.In addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software systems.However,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging task.To mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be applied.It complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully automatically.This paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and verification.First,we review the background and foundations of model learning,which form the basis of subsequent sections.Second,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison table.Third,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.展开更多
Traditional public key infrastructure(PKI)only provides authentication for network communication,and the standard X.509 certificate used in this architecture reveals the user’s identity.This lack of privacy protectio...Traditional public key infrastructure(PKI)only provides authentication for network communication,and the standard X.509 certificate used in this architecture reveals the user’s identity.This lack of privacy protection no longer satisfies the increasing demands for personal privacy.Though an optimized anonymous PKI certificate realizes anonymity,it has the potential to be abused due to the lack of identity tracking.Therefore,maintaining a balance between user anonymity and traceability has become an increasing requirement for current PKI.This paper introduces a novel traceable self-randomization certificate authentication scheme based on PKI architecture that achieves both anonymity and traceability.We propose a traceable self-randomization certificate authentication scheme based on the short randomizable signature.Specifically,certificate users can randomize the initial certificate and public key into multiple anonymous certificates and public keys by themselves under the premise of traceability,which possesses lower computational complexity and fewer interactive operations.Users can exhibit different attributes of themselves in different scenarios,randomizing the attributes that do not necessarily need to be displayed.Through security and performance analysis,we demonstrate the suitability of the improved PKI architecture for practical applications.Additionally,we provide an application of the proposed scheme to the permissioned blockchain for supervision.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2019YFF0301400)the National Natural Science Foundation of China(Grant Nos.61671031,61722102,41722103,and 61961146005)。
文摘China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.
基金supported by Natural Science Foundation of China(No.62002006,U2241213,U21B2021,62172025,61932011,61932014,61972018,61972019,61772538,32071775,91646203)Defense Industrial Technology Development Program(No.JCKY2021211B017)。
文摘Handover authentication in high mobility scenarios is characterized by frequent and shortterm parallel execution.Moreover,the penetration loss and Doppler frequency shift caused by high speed also lead to the deterioration of network link quality.Therefore,high mobility scenarios require handover schemes with less handover overhead.However,some existing schemes that meet this requirement cannot provide strong security guarantees,while some schemes that can provide strong security guarantees have large handover overheads.To solve this dilemma,we propose a privacy-preserving handover authentication scheme that can provide strong security guarantees with less computational cost.Based on Orthogonal Time Frequency Space(OTFS)link and Key Encapsulation Mechanism(KEM),we establish the shared key between protocol entities in the initial authentication phase,thereby reducing the overhead in the handover phase.Our proposed scheme can achieve mutual authentication and key agreement among the user equipment,relay node,and authentication server.We demonstrate that our proposed scheme can achieve user anonymity,unlinkability,perfect forward secrecy,and resistance to various attacks through security analysis including the Tamarin.The performance evaluation results show that our scheme has a small computational cost compared with other schemes and can also provide a strong guarantee of security properties.
基金This work was supported by the Major Scientific and Technological Special Project of Anhui Province(202103a13010004)the Major Scientific and Technological Special Project of Hefei City(2021DX007)+1 种基金the Key R&D Plan of Shandong Province(2020CXGC010105)the China Postdoctoral Science Foundation(2021M700315).
文摘Quantum key distribution(QKD),rooted in quantum mechanics,offers information-theoretic security.However,practi-cal systems open security threats due to imperfections,notably bright-light blinding attacks targeting single-photon detectors.Here,we propose a concise,robust defense strategy for protecting single-photon detectors in QKD systems against blinding attacks.Our strategy uses a dual approach:detecting the bias current of the avalanche photodiode(APD)to defend against con-tinuous-wave blinding attacks,and monitoring the avalanche amplitude to protect against pulsed blinding attacks.By integrat-ing these two branches,the proposed solution effectively identifies and mitigates a wide range of bright light injection attempts,significantly enhancing the resilience of QKD systems against various bright-light blinding attacks.This method forti-fies the safeguards of quantum communications and offers a crucial contribution to the field of quantum information security.
基金Project supported by the National Natural Science Foundation of China(Grant No.T2293771)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.
文摘Multimodal named entity recognition(MNER)and relation extraction(MRE)are key in social media analysis but face challenges like inefficient visual processing and non-optimal modality interaction.(1)Heavy visual embedding:the process of visual embedding is both time and computationally expensive due to the prerequisite extraction of explicit visual cues from the original image before input into the multimodal model.Consequently,these approaches cannot achieve efficient online reasoning;(2)suboptimal interaction handling:the prevalent method of managing interaction between different modalities typically relies on the alternation of self-attention and cross-attention mechanisms or excessive dependence on the gating mechanism.This explicit modeling method may fail to capture some nuanced relations between image and text,ultimately undermining the model’s capability to extract optimal information.To address these challenges,we introduce Implicit Modality Mining(IMM),a novel end-to-end framework for fine-grained image-text correlation without heavy visual embedders.IMM uses an Implicit Semantic Alignment module with a Transformer for cross-modal clues and an Insert-Activation module to effectively utilize these clues.Our approach achieves state-of-the-art performance on three datasets.
基金supported in part by the National Key Research and Development Program of China under Grant No. 2019YFB1803200the National Natural Science Foundation of China (NSFC) under Grant No. 61901020the Civil Aviation Administration of China。
文摘This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit(LEO) satellites. As the high signal-to-noise ratio(SNR) reception cannot always be guaranteed at LEO satellites, the recently developed direct position determination(DPD)is adopted. For LEO satellite-based passive localization systems, an efficient DPD is challenging due to the excessive exhaustive search range leading from broad satellite coverage. In order to reduce the computational complexity, we propose a time difference of arrival-assisted DPD(TA-DPD) which minimizes the searching area by the time difference of arrival measurements and their variances. In this way, the size of the searching area is determined by both geometrical constraints and qualities of received signals, and signals with higher SNRs can be positioned more efficiently as their searching areas are generally smaller.Both two-dimensional and three-dimensional passive localization simulations using the proposed TA-DPD are provided to demonstrate its efficiency and validity. The superior accuracy performance of the proposed method, especially at low SNRs conditions, is also verified through the comparison to conventional two-step methods. Providing a larger margin in link budget for satellite-based vessel location acquisition,the TA-DPD can be a competitive candidate for trusted marine location service.
基金This work is supported by NSFC(Grant Nos.92046001,61571024,61671087,61962009,61971021)the Aeronautical Science Foundation of China(2018ZC51016)+4 种基金the Fundamental Research Funds for the Central Universities(Grant No.2019XD-A02)the Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data(Grant Nos.2018BDKFJJ018,2019BDKFJJ010,2019BDKFJJ014)the Open Research Project of the State Key Laboratory of Media Convergence and Communication,Communication University of China,China(Grant No.SKLMCC2020KF006)Huawei Technologies Co.Ltd(Grant No.YBN2020085019)the Scientific Research Foundation of North China University of Technology.
文摘Quantum network coding is used to solve the congestion problem in quantum communication,which will promote the transmission efficiency of quantum information and the total throughput of quantum network.We propose a novel controlled quantum network coding without information loss.The effective transmission of quantum states on the butterfly network requires the consent form a third-party controller Charlie.Firstly,two pairs of threeparticle non-maximum entangled states are pre-shared between senders and controller.By adding auxiliary particles and local operations,the senders can predict whether a certain quantum state can be successfully transmitted within the butterfly network based on the Z-{10>,|1>}basis.Secondly,when trans-mission fails upon prediction,the quantum state will not be lost,and it will sill be held by the sender.Subsequently,the controller Charlie re-prepares another three-particle non-maximum entangled state to start a new round.When the predicted transmission is successful,the quantum state can be transmitted successfully within the butterfly network.If the receiver wants to receive the effective quantum state,the quantum measurements from Charlie are needed.Thirdly,when the transmission fails,Charlie does not need to integrate the X-{1+>,1->}basis to measure its own particles,by which quantum resources are saved.Charlie not only controls the effective transmission of quantum states,but also the usage of classical and quantum channels.Finally,the implementation of the quantum circuits,as well as a flow chart and safety analysis of our scheme,is proposed.
基金the National Key R&D Program of China(No.2017YFB0802403)the Beijing Natural Science Foundation(No.4202036)+1 种基金the National Natural Science Foundation of China(No.U1733115,No.61871023)the Opening Project of Shanghai Key Laboratory of Inte grated Administration Technologies for Information Security(No.AGK2019001).
文摘Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices,which expose serious threat to edge computing based distributed systems.Graphbased Sybil detection approaches extract social structures from target distributed systems,refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure.Graph preprocessing is a critical component in such Sybil detection methods,and intuitively,the processing methods will affect the detection performance.Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches.In this paper,we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments.The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.
文摘Recent years have witnessed the phenomenal growth of wireless technologies and applications on a massively large scale since the fifth generation (5G) wireless technologies were proposed as a key propellent to meet the increasing demands of future networks.Going further,the sixth generation (6G) wireless technologies have already been under preparation.However,wireless communication technologies are faced with new opportunities as well as challenges.
基金supported by the National Key Research and Development Program of China(Grant 2020YFB1804800)the National Natural Science Foundation of China(Grant U22B2008 and Grant 61922010)the Beijing Natural Science Foundation(Grant JQ20019)。
文摘Unmanned aerial vehicles(UAVs)have attracted growing research interests in recent years,which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points(APs).Thus,it is crucial to investigate robust airto-ground(A2G)wireless links for high-speed UAVs.However,the A2G wireless link is unstable as it suffers from large path-loss and severe Doppler effect due to the high mobility of UAVs.In order to meet these challenges,we propose an orthogonal time frequency space(OTFS)-based UAV communication system to relief the Doppler effect.Besides,considering that the energy of UAV is limited,we optimize the trajectory planning of UAV to minimize the energy consumption under the constraints of bit error rate(BER)and transmission rate,where the Doppler compensation is taken into account.Simulation results show that the performance of OTFS-based UAV system is superior to orthogonal frequency division multiplexing(OFDM)-based UAV systems,which can accomplish transmission tasks over shorter distances with lower energy consumption.
基金supported by the National Natural Science Foundation of China(Grant No.U1636208,No.61862008,No.61902013)the Beihang Youth Top Talent Support Program(Grant No.YWF-21-BJJ-1039)。
文摘Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.
基金Our work is supported by the National Key R&D Program of China(2021YFB2012400).
文摘With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.
基金This work was supported by the National Key R&D Program of China(2020YFA0908700 and 2018AAA0100100)the National Natural Science Foundation of China(Grant Nos.62002297,61902342,U1713212,61836005,and 62073225)+2 种基金the Natural Science Foundation of Guangdong Province-Outstanding Youth Program(2019B151502018)the Technology Research Project of Shenzhen City(JSGG20180507182904693)Public Technology Platform of Shenzhen City(GGFW2018021118145859).
文摘In silico prediction of self-interacting proteins(SIPs)has become an important part of proteomics.There is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab experiments.The goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the future.In this review,we first describe the data required for the task of DTIs prediction.Then,some interesting feature extraction methods and computational models are presented on this topic in a timely manner.Afterwards,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding schemes.Overall,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.
基金This work was supported in part by the National Key R&D Program of China(2018AAA0101100)the National Natural Science Foundation of China(Grant Nos.62022008 and 92067204).
文摘In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion attacks.There has been realistic demand to develop an efficient intrusion detection algorithm for connected devices.Most of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack types.In this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack types.Besides,the blockchain technique is introduced in the federated learning process for the consensus of the entire framework.Experimental results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.
基金supported by National Natural Science Foundation of China (Grant No. 12171428)the Sino-German Mobility Programme M-0157Shandong Provincial Natural Science Foundation (Grant No. ZR2022QA069)。
文摘In this paper, we classify the m-ovoids of finite classical polar spaces that admit a transitive automorphism group acting irreducibly on the ambient vector space. In particular, we obtain several new infinite families of transitive m-ovoids.
文摘Most recently, due to the demand of immersive communication, region-of-interest-based(ROI) high efficiency video coding(HEVC) approaches in conferencing scenarios have become increasingly important. However, there exists no objective metric, specially developed for efficiently evaluating the perceived visual quality of video conferencing coding. Therefore, this paper proposes a novel objective quality assessment method, namely Gaussian mixture model based peak signal-tonoise ratio(GMM-PSNR), for the perceptual video conferencing coding. First, eye tracking experiments, together with a real-time technique of face and facial feature extraction, are introduced. In the experiments, importance of background, face, and facial feature regions is identified, and it is then quantified based on eye fixation points over test videos. Next, assuming that the distribution of the eye fixation points obeys Gaussian mixture model, we utilize expectation-maximization(EM) algorithm to generate an importance weight map for each frame of video conferencing coding, in light of a new term eye fixation points/pixel(efp/p). According to the generated weight map, GMM-PSNR is developed for quality assessment by assigning different weights to the distortion of each pixel in the video frame. Finally, we utilize some experiments to investigate the correlation of the proposed GMM-PSNR and other conventional objective metrics with subjective quality metrics. The experimental results show the effectiveness of GMM-PSNR.
基金supported in part by the National Natural Science Foundation of China under Grant No.61972371Youth Innovation Promotion Association of Chinese Academy of Sciences(CAS)under Grant No.Y202093.
文摘By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.
基金This work is supported by the National Natural Science Foundation of China(61872229,U19B2021)the Shaanxi Provincial Science Fund for Distinguished Young Scholars(2022JC-47)+1 种基金the Blockchain Core Technology Strategic Research Program of Ministry of Education of China(2020KJ010301)the Key Research and Development Program of Shaanxi(2021ZDLGY06-04,2020ZDLGY09-06).
文摘The cloud computing technology has emerged,developed,and matured in recent years,consequently commercializing remote outsourcing storage services.An increasing number of companies and individuals have chosen the cloud to store their data.However,accidents,such as cloud server downtime,cloud data loss,and accidental deletion,are serious issues for some applications that need to run around the clock.For some mission and business-critical applications,the continuous availability of outsourcing storage services is also necessary to protect users'outsourced data during downtime.Nevertheless,ensuring the continuous availability of data in public cloud data integrity auditing protocols leads to data privacy issues because auditors can obtain the data content of users by a sufficient number of storage proofs.Therefore,protecting data privacy is a burning issue.In addition,existing data integrity auditing schemes that rely on semi-trusted third-party auditors have several security problems,including single points of failure and performance bottlenecks.To deal with these issues,we propose herein a blockchain-based continuous data integrity checking protocol with zero-knowledge privacy protection.We realize a concrete construction by using a verifiable delay function with high efficiency and proof of retrievability,and prove the security of the proposal in a random oracle model.The proposed construction supports dynamic updates for the outsourced data.We also design smart contracts to ensure fairness among the parties involved.Finally,we implement the protocols,and the experimental results demonstrate the efficiency of the proposed protocol.
基金the National Natural Science Foundation of China(NSFC)(Grant Nos.61872016,61932007 and 61972013).
文摘Software systems are present all around us and playing their vital roles in our daily life.The correct functioning of these systems is of prime concern.In addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software systems.However,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging task.To mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be applied.It complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully automatically.This paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and verification.First,we review the background and foundations of model learning,which form the basis of subsequent sections.Second,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison table.Third,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.
基金This work was supported by the National Key R&D Program of China(No.2020YFB1005600)Beijing Natural Science Foundation(No.M21031)+4 种基金the Natural Science Foundation of China(Nos.U21A20467,61932011,62002011,and 61972019)the Populus Euphratica Foundation(No.CCF-HuaweiBC2021009)the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province(No.ZCL21007)Zhejiang Soft Science Research Program(No.2023C35081)the Youth Top Talent Support Program of Beihang University(No.YWF-22-L-1272).
文摘Traditional public key infrastructure(PKI)only provides authentication for network communication,and the standard X.509 certificate used in this architecture reveals the user’s identity.This lack of privacy protection no longer satisfies the increasing demands for personal privacy.Though an optimized anonymous PKI certificate realizes anonymity,it has the potential to be abused due to the lack of identity tracking.Therefore,maintaining a balance between user anonymity and traceability has become an increasing requirement for current PKI.This paper introduces a novel traceable self-randomization certificate authentication scheme based on PKI architecture that achieves both anonymity and traceability.We propose a traceable self-randomization certificate authentication scheme based on the short randomizable signature.Specifically,certificate users can randomize the initial certificate and public key into multiple anonymous certificates and public keys by themselves under the premise of traceability,which possesses lower computational complexity and fewer interactive operations.Users can exhibit different attributes of themselves in different scenarios,randomizing the attributes that do not necessarily need to be displayed.Through security and performance analysis,we demonstrate the suitability of the improved PKI architecture for practical applications.Additionally,we provide an application of the proposed scheme to the permissioned blockchain for supervision.
