查看更多>>摘要：Probe machine(PM)is a recently repor-ted mathematic model with massive parallelism.Herein,we presented searching the maximum clique of an undir-ected graph with six vertices.We constructed data lib-rary containing n sublibraries,each sublibrary correspon-ded to a vertex in the given graph.Then,probe library according to the induced subgraph was designed in order to search and generate all maximal cliques.Subsequently,we performed probe operation,and all maximal cliques were generated in parallel.The advantages of the pro-posed model lie in two aspects.On one hand,solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution space.On the other hand,the proposed model is highly parallel.The work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.
查看更多>>摘要：The function of the Internet proxy is to check and convert the data exchanged between client and server.In fact,the two-party secure communication pro-tocol with good security is turned into an unsafe multi-party protocol.At present,there are relatively few proxy protocols that can be applied in practice.This paper ana-lyzes the classic agent protocol mcTLS and pointed out the security issues.We focus on the security of TLS 1.3 and proposed a lattice-based multi-party proxy protocol:LaTLS.LaTLS can be proved secure in the eCK model,it can resist key-sharing attacks,counterfeiting attacks,re-play attacks,and achieve forward security.Compared with traditional DH and ECDH schemes,LaTLS is more effcient.Its security is based on the shortest vector prob-lem,therefor it has anti-quantum attack properties.
查看更多>>摘要：The devices in the Internet of things(IoT)gain capability of sustainable operation when they harvest energy from ambient sources.Fluctuation in the harvested energy may cause the energy-harvesting IoT devices to suffer from frequent energy shortage,which may bring in intolerable packet delay or packet discard-ing.It is important to design a low-delay packet delivery scheme that adapts to variation in the harvested energy.In this paper,we present the timely data delivery(TDD)scheme for the IoT devices.Using Markov chain,we de-velop a probability model for the TDD scheme,which leads to the expected number of packets delivered in an operation cycle,the expected numbers of packets waiting in the data buffer in an operation cycle and an energy-harvesting cycle,and the expected packet delay.Addi-tionally,we formulate the optimization problem that min-imizes the packet delay in the TDD scheme,and the solu-tion to the optimization problem yields the optimal para-meters for the IoT devices to determine when to harvest energy and when to deliver data under the TDD scheme.The simulation results show that the proposed TDD scheme outperforms the existing schemes in terms of packet delay.
查看更多>>摘要：Chinese part-of-speech(POS)tagging is an essential task for Chinese downstream natural lan-guage processing tasks.The accuracy of the Chinese POS task will drop dramatically by word-based methods be-cause of the segmentation errors and the word sparsity.Also,there are several Chinese POS tagging sets with dif-ferent criteria.Some of them only have a small-scale an-notated corpus and are hard to train.To this end,we propose a modified word-based transformer neural net-work architecture.Meanwhile,we utilize an adversarial transfer learning method that splits the architecture into shared and private parts.This work directly improves the ability of the word-based model,instead of adopting a joint character-based method.Extensive experiments show that our method achieves state-of-the-art perform-ance on all datasets,and more importantly,our method improves performance effectively for the word-based Chinese sequence labeling task.
查看更多>>摘要：Numerous microbes inhabit human body,making a vast difference in human health.Hence,discov-ering associations between microbes and diseases is bene-ficial to disease prevention and treatment.In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly,a heterogeneous network is integrated by known microbe-disease associations and multiple similarities.Based on microbe Gaussian interaction profile(GIP)ker-nel similarity,we consider different effects of these mi-crobes on organs in the human body to further improve microbe similarity.For disease similarity network,we combine GIP kernel similarity,disease semantic similar-ity and disease-symptom similarity.And then,an embed-ding algorithm called GraRep is used to learn global structural information for this network.According to vec-tor feature of every node,we utilize support vector ma-chine classifier to calculate the score for each microbe-dis-ease pair.HNGFL achieves a reliable performance in cross validation,outperforming the compared methods.In addi-tion,we carry out case studies of three diseases.Results show that HNGFL can be considered as a reliable meth-od for microbe-disease association prediction.
查看更多>>摘要：Cross-project defect prediction is a hot topic in the field of defect prediction.How to reduce the difference between projects and make the model have bet-ter accuracy is the core problem.This paper starts from two perspectives:feature selection and distance-weight in-stance transfer.We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA.We have tested on AEEEM and ReLink datasets,and the results show that our method has an average improvement of 23％compared with TCA+algorithm on AEEEM datasets,and an average improvement of 5％on ReLink datasets.
查看更多>>摘要：Considering the issues of premature con-vergence and low solution accuracy in solving high-dimen-sional problems with the basic chicken swarm optimiza-tion algorithm,an adaptive simplified chicken swarm op-timization algorithm based on inverted S-shaped inertia weight(ASCSO-S)is proposed.Firstly,a simplified chick-en swarm optimization algorithm is presented by remov-ing all the chicks from the chicken swarm.Secondly,an inverted S-shaped inertia weight is designed and intro-duced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm.Thirdly,in order to enhance the explora-tion ability of the algorithm,an adaptive updating strategy is added to the updating process of the hens.Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison al-gorithms in terms of convergence speed,solution accur-acy,and solution stability.In addition,ASCSO-S is ap-plied to the parameter estimation of Richards model,and the test results indicate that ASCSO-S has the best fit-ting results compared with other three algorithms.
查看更多>>摘要：Knowledge graph completion(KGC)can solve the problem of data sparsity in the knowledge graph.A large number of models for the KGC task have been proposed in recent years.However,the underutilisa-tion of the structure information around nodes is one of the main problems of the previous KGC model,which leads to relatively single encoding information.To this end,a new KGC model that encodes and decodes the fea-ture information is proposed.First,we adopt the sub-graph sampling method to extract node structure.Moreover,the graph convolutional network(GCN)intro-duced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information.Eventually,the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scor-ing function.The experimental results show that the model performs well on the datasets used.
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