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计算机科学技术学报（英文版）
计算机科学技术学报（英文版）

李国杰

双月刊

1000-9000

jcst@ict.ac.cn

010-62610746

100080

北京中关村科学院南路6号 《计算机科学技术学报（英）》编辑部

计算机科学技术学报（英文版）/Journal Journal of Computer Science and TechnologyCSCDCSTPCD北大核心EISCI
查看更多>>Journal of Computer Science and Technology(JCST)是中国计算机科学技术领域国际性学术期刊。 JCST于1986 年创刊, 双月刊, 国内外公开发行, 由Springer Science + Business Media代理国际出版发行。 JCST是中国计算机学会会刊, 由中国科学院计算技术研究所承办。JCST由数十位国际计算机界的著名专家和学者联袂编审，把握世界计算机科学技术最新发展趋势。JCST荟萃了国内外计算机科学技术领域中有指导性和开拓性的学术论著，定期组织热点专辑或专题栏目，部分文章邀请了世界著名计算机科学专家撰写。
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    4D-MAP:Multipath Adaptive Packet Scheduling for Live Streaming over QUIC

    宋丛溪韩彪苏金树
    159-176页
    查看更多>>摘要：In recent years,live streaming has become a popular application,which uses TCP as its primary transport protocol.Quick UDP Internet Connections(QUIC)protocol opens up new opportunities for live streaming.However,how to leverage QUIC to transmit live videos has not been studied yet.This paper first investigates the achievable quality of experience(QoE)of streaming live videos over TCP,QUIC,and their multipath extensions Multipath TCP(MPTCP)and Multipath QUIC(MPQUIC).We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission reliability.However,network fluctuations may cause heterogeneous paths,high path loss,and band-width degradation,resulting in significant QoE deterioration.Motivated by the above observations,we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP,a multipath adaptive packet scheduling scheme over QUIC.Specifically,a linear upper confidence bound(LinUCB)-based online learning algorithm,along with four novel scheduling mechanisms,i.e.,Dispatch,Duplicate,Discard,and Decompensate,is proposed to conquer the above problems.4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission schemes.Experimental results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Identity-Preserving Adversarial Training for Robust Network Embedding

    岑科廷沈华伟曹婍徐冰冰...
    177-191页
    查看更多>>摘要：Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    SMEC:Scene Mining for E-Commerce

    王罡李翔郭子义殷大伟...
    192-210页
    查看更多>>摘要：Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given scene.However,scenes are typically unknown in advance,which necessitates scene discovery for E-commerce.In this article,we study scene discovery for E-commerce systems.We first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,respectively.We then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the HIN.To solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its convergence.Using six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Minimal Context-Switching Data Race Detection with Dataflow Tracking

    郑龙李洋辛杰刘海峰...
    211-226页
    查看更多>>摘要：Data race is one of the most important concurrent anomalies in multi-threaded programs.Emerging con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race detector.However,this constraint-based approach has serious limitations on helping programmers analyze and understand data races.First,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the program.Second,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving process.This ad hoc recommendation imposes too many context switches,which complicates the data race analysis.To address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context switches.Specifically,we reduce the false positives by ana-lyzing and tracking the dataflow in the program.By this means,DFTracker thus reduces the unnecessary analysis of false race schedules.We further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data race.Our experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race detec-tor.Therefore,DFTracker can be used as an effective tool to understand the data race for programmers.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network

    崔诗尧郁博文从鑫柳厅文...
    227-242页
    查看更多>>摘要：Event detection(ED)seeks to recognize event triggers and classify them into the predefined event types.Chinese ED is formulated as a character-level task owing to the uncertain word boundaries.Prior methods try to incorpo-rate word-level information into characters to enhance their semantics.However,they experience two problems.First,they fail to incorporate word-level information into each character the word encompasses,causing the insufficient word-charac-ter interaction problem.Second,they struggle to distinguish events of similar types with limited annotated instances,which is called the event confusing problem.This paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network(L-HGAT)to address these two problems.Specifically,we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions,and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and words.Furthermore,we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each character.Experimental results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.
      原文链接:
    • NETL
    • NSTL
    • 万方数据