查看更多>>摘要：Motif-based graph local clustering(MGLC)is a popular method for graph mining tasks due to its various applications.However,the traditional two-phase approach of precomputing motif weights before performing local clustering loses locality and is impractical for large graphs.While some attempts have been made to address the efficiency bottleneck,there is still no applicable algorithm for large scale graphs with billions of edges.In this paper,we propose a purely local and index-free method called Index-free Triangle-based Graph Local Clustering(TGLC*)to solve the MGLC problem w.r.t.a triangle.TGLC*directly estimates the Personalized PageRank(PPR)vector using random walks with the desired triangle-weighted distribution and proposes the clustering result using a standard sweep procedure.We demonstrate TGLC*'s scalability through theoretical analysis and its practical benefits through a novel visualization layout.TGLC*is the first algorithm to solve the MGLC problem without precomputing the motif weight.Extensive experiments on seven real-world large-scale datasets show that TGLC*is applicable and scalable for large graphs.
查看更多>>摘要：Knowledge tracing aims to track students'knowledge status over time to predict students'future performance accurately.In a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge status.Markov chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over time.However,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over time.In addition,the existing MCKT models only consider the relationship between students'knowledge status and problems when modeling students'responses but ignore the relationship between knowledge concepts in the same problem.To address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students'numerous knowledge concepts mastery probabilities over time.To solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students'future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly.To better model students'exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students'exercise responses by considering the relationship among students'knowledge status,knowledge concept,and problems.We conduct experiments with four real-world datasets in three knowledge-driven tasks.The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students'future performance and can learn the relationship among students,knowledge concepts,and problems from students'exercise sequences.We also conduct several case studies.The case studies show that TRACED exhibits excellent interpretability and thus has the potential for personalized automatic feedback in the real-world educational environment.
查看更多>>摘要：Due to the advantages of high volume of transactions and low resource consumption,Directed Acyclic Graph(DAG)-based Distributed Ledger Technology(DLT)has been considered a possible next-generation alternative to block-chain.However,the security of the DAG-based system has yet to be comprehensively understood.Aiming at verifying and evaluating the security of DAG-based DLT,we develop a Multi-Agent based IOTA Simulation platform called MAIOTASim.In MAIOTASim,we model honest and malicious nodes and simulate the configurable network environment,including network topology and delay.The double-spending attack is a particular security issue related to DLT.We perform the security verification of the consensus algorithms under multiple double-spending attack strategies.Our simulations show that the consensus algorithms can resist the parasite chain attack and partially resist the splitting attack,but they are ineffective under the large weight attack.We take the cumulative weight difference of transactions as the evaluation criterion and analyze the effect of different consensus algorithms with parameters under each attack strategy.Besides,MAIOTASim enables users to perform large-scale simulations with multiple nodes and tens of thousands of transactions more efficiently than state-of-the-art ones.
查看更多>>摘要：With the widespread use of network infrastructures such as 5G and low-power wide-area networks,a large number of the Internet of Things(IoT)device nodes are connected to the network,generating massive amounts of data.Therefore,it is a great challenge to achieve anonymous authentication of IoT nodes and secure data transmission.At present,blockchain technology is widely used in authentication and s data storage due to its decentralization and immutability.Recently,Fan et al.proposed a secure and efficient blockchain-based IoT authentication and data sharing scheme.We studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT nodes.To overcome these security flaws,this paper proposes an enhanced authentication and data transmission scheme,which is verified by formal security proofs and informal security analysis.Furthermore,Scyther is applied to prove the security of the proposed scheme.Moreover,it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.
Antonio SANTOS-OLMOLuis Enrique SáNCHEZDavid G.ROSADOManuel A.SERRANO...
199-216页
查看更多>>摘要：The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets.The availability of these systems is now vital for the protection and evolution of companies.However,several factors have led to an increasing need for more accurate risk analysis approaches.These are:the speed at which technologies evolve,their global impact and the growing requirement for companies to collaborate.Risk analysis processes must consequently adapt to these new circumstances and new technological paradigms.The objective of this paper is,therefore,to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management process.This analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new needs.The paper also presents a summary of MARISMA,the risk analysis and management framework designed by our research group.The basis of our framework is the main existing risk standards and proposals,and it seeks to address the weaknesses found in these proposals.MARISMA is in a process of continuous improvement,as is being applied by customers in several European and American countries.It consists of a risk data management module,a methodology for its systematic application and a tool that automates the process.
查看更多>>摘要：Protein acetylation refers to a process of adding acetyl groups(CH3CO-)to lysine residues on protein chains.As one of the most commonly used protein post-translational modifications,lysine acetylation plays an important role in different organisms.In our study,we developed a human-specific method which uses a cascade classifier of complex-valued polynomial model(CVPM),combined with sequence and structural feature descriptors to solve the problem of imbalance between positive and negative samples.Complex-valued gene expression programming and differential evolution are utilized to search the optimal CVPM model.We also made a systematic and comprehensive analysis of the acetylation data and the prediction results.The performances of our proposed method are 79.15％in Sp,78.17％in Sn,78.66％in ACC 78.76％in Fl,and 0.5733 in MCC,which performs better than other state-of-the-art methods.
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