IEEE INFOCOM 2021 - IEEE Conference on Computer Communications最新文献_第6页

PROCESS: Privacy-Preserving On-Chain Certificate Status Service 进程:保护隐私的链上证书状态服务

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488858

Menglin Jia, Kun He, Jing Chen, Ruiying Du, Weihang Chen, Zhihong Tian, S. Ji

{"title":"PROCESS: Privacy-Preserving On-Chain Certificate Status Service","authors":"Menglin Jia, Kun He, Jing Chen, Ruiying Du, Weihang Chen, Zhihong Tian, S. Ji","doi":"10.1109/INFOCOM42981.2021.9488858","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488858","url":null,"abstract":"Clients (e.g., browsers) and servers require public key certificates to establish secure connections. When a client accesses a server, it needs to check the signature, expiration time, and revocation status of the certificate to determine whether the server is reliable. The existing solutions for checking certificate status either have a long update cycle (e.g., CRL, CRLite) or violate clients’ privacy (e.g., OCSP, CCSP), and these solutions also have the problem of trust concentration. In this paper, we present PROCESS, an online privacy-preserving on-chain certificate status service based on the blockchain architecture, which can ensure decentralized trust and provide privacy protection for clients. Specifically, we design Counting Garbled Bloom Filter (CGBF) that supports efficient queries and BlockOriented Revocation List (BORL) to update CGBF timely in the blockchain. With CGBF, we design a privacy-preserving protocol to protect clients’ privacy when they check the certificate statuses from the blockchain nodes. Finally, we conduct experiments and compare PROCESS with another blockchain-based solution to demonstrate that PROCESS is suitable in practice.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

CanalScan: Tongue-Jaw Movement Recognition via Ear Canal Deformation Sensing CanalScan:通过耳道变形感知识别舌颌运动

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488852

Yetong Cao, Huijie Chen, Fan Li, Yu Wang

{"title":"CanalScan: Tongue-Jaw Movement Recognition via Ear Canal Deformation Sensing","authors":"Yetong Cao, Huijie Chen, Fan Li, Yu Wang","doi":"10.1109/INFOCOM42981.2021.9488852","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488852","url":null,"abstract":"Human-machine interface based on tongue-jaw movements has recently become one of the major technological trends. However, existing schemes have several limitations, such as requiring dedicated hardware and are usually uncomfortable to wear. This paper presents CanalScan, a nonintrusive system for tongue-jaw movement recognition using only commodity speaker and microphone mounted on ubiquitous off-the-shelf devices (e.g., smartphones). The basic idea is to send an acoustic signal, then captures its reflections and derive unique patterns of ear canal deformation caused by tongue-jaw movements. A dynamic segmentation method with Support Vector Domain Description is used to segment tongue-jaw movements. To combat sensor position-sensitive deficiency and ear-canal-shape-sensitive deficiency in multi-path reflections, we first design algorithms to assist users in adjusting the acoustic sensors to the same valid zone. Then we propose a data transformation mechanism to reduce the impacts of diversities in ear canal shapes and relative positions between sensors and the ear canal. CanalScan explores twelve unique and consistent features and applies a Random Forest classifier to distinguish tongue-jaw movements. Extensive experiments with twenty participants demonstrate that CanalScan achieves promising recognition for six tongue-jaw movements, is robust against various usage scenarios, and can be generalized to new users without retraining and adaptation.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121265257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Combining Regularization with Look-Ahead for Competitive Online Convex Optimization 正则化与前瞻性相结合的竞争在线凸优化

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488766

Ming Shi, Xiaojun Lin, Lei Jiao

{"title":"Combining Regularization with Look-Ahead for Competitive Online Convex Optimization","authors":"Ming Shi, Xiaojun Lin, Lei Jiao","doi":"10.1109/INFOCOM42981.2021.9488766","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488766","url":null,"abstract":"There has been significant interest in leveraging limited look-ahead to achieve low competitive ratios for online convex optimization (OCO). However, existing online algorithms (such as Averaging Fixed Horizon Control (AFHC)) that can leverage look-ahead to reduce the competitive ratios still produce competitive ratios that grow unbounded as the coefficient ratio (i.e., the maximum ratio of the switching-cost coefficient and the service-cost coefficient) increases. On the other hand, the regularization method can attain a competitive ratio that remains bounded when the coefficient ratio is large, but it does not benefit from look-ahead. In this paper, we propose a new algorithm, called Regularization with Look-Ahead (RLA), that can get the best of both AFHC and the regularization method, i.e., its competitive ratio decreases with the look-ahead window size when the coefficient ratio is small, and remains bounded when the coefficient ratio is large. We also provide a matching lower bound for the competitive ratios of all online algorithms with look-ahead, which differs from the achievable competitive ratio of RLA by a factor that only depends on the problem size. The competitive analysis of RLA involves a non-trivial generalization of online primal-dual analysis to the case with look-ahead.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127926070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Towards Fine-Grained Spatio-Temporal Coverage for Vehicular Urban Sensing Systems 面向车辆城市传感系统的细粒度时空覆盖

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488787

Guiyun Fan, Yiran Zhao, Zilang Guo, Haiming Jin, Xiaoying Gan, Xinbing Wang

{"title":"Towards Fine-Grained Spatio-Temporal Coverage for Vehicular Urban Sensing Systems","authors":"Guiyun Fan, Yiran Zhao, Zilang Guo, Haiming Jin, Xiaoying Gan, Xinbing Wang","doi":"10.1109/INFOCOM42981.2021.9488787","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488787","url":null,"abstract":"Vehicular urban sensing (VUS), which uses sensors mounted on crowdsourced vehicles or on-board drivers’ smartphones, has become a promising paradigm for monitoring critical urban metrics. Due to various hardware and software constraints difficult for private vehicles to satisfy, for-hire vehicles (FHVs) are usually the major forces for VUS systems. However, FHVs alone are far from enough for fine-grained spatio-temporal sensing coverage, because of their severe distribution biases. To address this issue, we propose to use a hybrid approach, where a centralized platform not only leverages FHVs to conduct sensing tasks during their daily movements of serving passenger orders, but also controls multiple dedicated sensing vehicles (DSVs) to bridge FHVs’ coverage gaps. Specifically, we aim to achieve fine-grained spatio-temporal sensing coverage at the minimum long-term operational cost by systematically optimizing the repositioning policy for DSVs. Technically, we formulate the problem as a stochastic dynamic program, and solve various challenges, including long-term cost minimization, stochastic demand with partial statistical knowledge, and computational intractability, by integrating distributionally robust optimization, primal-dual transformation, and second order conic programming methods. We validate the effectiveness of our methods using a real-world dataset from Shenzhen, China, containing 726,000 trajectories of 3848 taxis spanning overall 1 month in 2017.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129035232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

HAVS: Hardware-accelerated Shared-memory-based VPP Network Stack HAVS:硬件加速的基于共享内存的VPP网络堆栈

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488808

Shujun Zhuang, Jian Zhao, Jian Li, Ping Yu, Yuwei Zhang, Haibing Guan

{"title":"HAVS: Hardware-accelerated Shared-memory-based VPP Network Stack","authors":"Shujun Zhuang, Jian Zhao, Jian Li, Ping Yu, Yuwei Zhang, Haibing Guan","doi":"10.1109/INFOCOM42981.2021.9488808","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488808","url":null,"abstract":"The number of requests to transfer large files is increasing rapidly in web server and remote-storage scenarios, and this increase requires a higher processing capacity from the network stack. However, to fully decouple from applications, many latest userspace network stacks, such as VPP (vector packet processing) and snap, adopt a shared-memory-based solution to communicate with upper applications. During this communication, the application or network stack needs to copy data to or from shared memory queues. In our verification experiment, these multiple copy operations incur more than 50% CPU consumption and severe performance degradation when the transferred file is larger than 32 KB. This paper adopts a hardware-accelerated solution and proposes HAVS which integrates Intel I/O Acceleration Technology into the VPP network stack to achieve high-performance memory copy offloading. An asynchronous copy architecture is introduced in HAVS to free up CPU resources. Moreover, an abstract memcpy accelerator layer is constructed in HAVS to ease the use of different types of hardware accelerators and sustain high availability with a fault-tolerance mechanism. The comprehensive evaluation shows that HAVS can provide an average 50%-60% throughput improvement over the original VPP stack when accelerating the nginx and SPDK iSCSI target application.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114937545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Individual Load Forecasting for Multi-Customers with Distribution-aware Temporal Pooling 基于分布感知时间池的多用户个体负荷预测

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488816

Eunju Yang, Chan-Hyun Youn

{"title":"Individual Load Forecasting for Multi-Customers with Distribution-aware Temporal Pooling","authors":"Eunju Yang, Chan-Hyun Youn","doi":"10.1109/INFOCOM42981.2021.9488816","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488816","url":null,"abstract":"For smart grid services, accurate individual load forecasting is an essential element. When training individual forecasting models for multi-customers, discrepancies in data distribution among customers should be considered; there are two simple ways to build the models considering multi-customers: constructing each model independently or training as one model encompassing multi-customers. The independent approach shows higher accuracy than the latter. However, it deploys copious models, causing resource/management inefficiency; the latter is the opposite. A compromise between these two could be clustering-based forecasting. However, the previous studies are limited in applying to individual forecasting in that they focus on aggregated load and do not consider concept drift, which degrades accuracy over time. Therefore, we propose a distribution-aware temporal pooling framework that is enhanced clustering-based forecasting. For the clustering, we propose Variational Recurrent Deep Embedding (VaRDE) working in a distribution-aware manner, so it is suitable to process individual load. It allocates clusters to customers every time, so the clusters, where customers are assigned, are dynamically changed to resolve distribution change. We conducted experiments with real data for evaluation, and the result showed better performance than previous studies, especially with a few models even for unseen data, leading to high scalability.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133602061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Sample-level Data Selection for Federated Learning 联邦学习的样本级数据选择

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488723

Anran Li, Lan Zhang, Juntao Tan, Yaxuan Qin, Junhao Wang, Xiangyang Li

{"title":"Sample-level Data Selection for Federated Learning","authors":"Anran Li, Lan Zhang, Juntao Tan, Yaxuan Qin, Junhao Wang, Xiangyang Li","doi":"10.1109/INFOCOM42981.2021.9488723","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488723","url":null,"abstract":"Federated learning (FL) enables participants to collaboratively construct a global machine learning model without sharing their local training data to the remote server. In FL systems, the selection of training samples has a significant impact on model performances, e.g., selecting participants whose datasets have erroneous samples, skewed categorical distributions, and low content diversity would result in low accuracy and unstable models. In this work, we aim to solve the exigent optimization problem that selects a collection of high-quality training samples for a given FL task under a monetary budget in a privacy-preserving way, which is extremely challenging without visibility to participants’ local data and training process. We provide a systematic analysis of important data related factors affecting the model performance and propose a holistic design to privately and efficiently select high-quality data samples considering all these factors. We verify the merits of our proposed solution with extensive experiments on a real AIoT system with 50 clients, including 20 edge computers, 20 laptops, and 10 desktops. The experimental results validates that our solution achieves accurate and efficient selection of high-quality data samples, and consequently an FL model with a faster convergence speed and higher accuracy than that achieved by existing solutions.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132382124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 55

Finding Critical Files from a Packet 从数据包中查找关键文件

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488914

Junnyung Hur, Hahoon Jeon, Hyeon Gy Shon, Young Jae Kim, Myungkeun Yoon

{"title":"Finding Critical Files from a Packet","authors":"Junnyung Hur, Hahoon Jeon, Hyeon Gy Shon, Young Jae Kim, Myungkeun Yoon","doi":"10.1109/INFOCOM42981.2021.9488914","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488914","url":null,"abstract":"Network-based intrusion detection and data leakage prevention systems inspect packets to detect if critical files such as malware or confidential documents are transferred. However, this kind of detection requires heavy computing resources in reassembling packets and only well-known protocols can be interpreted. Besides, finding similar files from a storage requires pairwise comparisons. In this paper, we present a new network-based file identification scheme that inspects packets independently without reassembly and finds similar files through inverted indexing instead of pairwise comparison. We use a contents-based chunking algorithm to consistently divide both files and packets into multiple byte sequences, called chunks. If a packet is a part of a file, they would have common chunks. The challenging problem is that packet chunking and inverted-index search should be fast and scalable enough for packet processing. The file identification should be accurate although many chunks are noises. In this paper, we use a small Bloom filter and a delayed query strategy to solve the problems. To the best of our knowledge, this is the first scheme that identifies a specific critical file from a packet over unknown protocols. Experimental results show that the proposed scheme can successfully identify a critical file from a packet.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128120356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Maximizing the Benefit of RDMA at End Hosts 实现终端主机RDMA的最大效益

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488875

Xiaoliang Wang, Hexiang Song, Cam-Tu Nguyen, Dongxu Cheng, Tiancheng Jin

{"title":"Maximizing the Benefit of RDMA at End Hosts","authors":"Xiaoliang Wang, Hexiang Song, Cam-Tu Nguyen, Dongxu Cheng, Tiancheng Jin","doi":"10.1109/INFOCOM42981.2021.9488875","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488875","url":null,"abstract":"RDMA is increasingly deployed in data center to meet the demands of ultra-low latency, high throughput and low CPU overhead. However, it is not easy to migrate existing applications from the TCP/IP stack to the RDMA. The developers usually need to carefully select communication primitives and manually tune the parameters for each single-purpose system. After operating the high-speed RDMA network, we identify multiple hidden costs which may cause degraded and/or unpredictable performance of RDMA-based applications. We demonstrate these hidden costs including the combination of complicated parameter settings, scalability of Reliable Connections, two-sided memory management and page alignment, resource contention among diverse traffics, etc. Furthermore, to address these problems, we introduce Nem, a suite that allows developers to maximize the benefit of RDMA by i) eliminating the resource contention at NIC cache through asynchronous resource sharing; ii) introducing hybrid page management based on messages sizes; iii) isolating flows of different traffic classes based hardware features. We implement the prototype of Nem and verify its effectiveness by rebuilding the RPC message service, which demonstrates the high throughput for large messages, low latency for small messages without compromising the low CPU utilization and good scalability performance for a large number of active connections.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128135284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

MIERank: Co-ranking Individuals and Communities with Multiple Interactions in Evolving Networks MIERank:在不断发展的网络中对具有多重交互作用的个人和社区进行联合排序

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488753

Shan Qu, Luoyi Fu, Xinbing Wang

{"title":"MIERank: Co-ranking Individuals and Communities with Multiple Interactions in Evolving Networks","authors":"Shan Qu, Luoyi Fu, Xinbing Wang","doi":"10.1109/INFOCOM42981.2021.9488753","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488753","url":null,"abstract":"Ranking has significant applications in real life. It aims to evaluate the importance (or popularity) of two categories of objects, i.e., individuals and communities. Numerous efforts have been dedicated to these two types of rankings respectively. Instead, in this paper, we for the first time explore the co-ranking of both individuals and communities. Our insight lies in that co-ranking may enhance the mutual evaluation on both sides. To this end, we first establish an Evolving Coupled Graph that contains a series of smoothly weighted snapshots, each of which characterizes and couples the intricate interactions of both individuals and communities till a certain evolution time into a single graph. Then we propose an algorithm, called MIERank to implement the co-ranking of individuals and communities in the proposed evolving graph. The core idea of MIERank lies in a novel unbiased random walk, which, when sampling the interplay among nodes over different generation times, incorporates the preference knowledge of ranking by utilizing nodes’ future actions. MIERank returns the co-ranking of both individuals and communities by iteratively alternating between their corresponding stationary probabilities of the unbiased random walk in a mutually-reinforcing manner. We prove the efficiency of MIERank in terms of its convergence, optimality and extensiblity. Our experiments on a big scholarly dataset of 606862 papers and 1215 fields further validate the superiority of MIERank with fast convergence and an up to 26% ranking accuracy gain compared with the separate counterparts.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128333963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0