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Commonsense Knowledge Construction with Concept and Pretrained Model 19th

Commonsense Knowledge Construction with Concept and Pretrain...

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19th International Conference on Web Information systems and Applications, WISA 2022

作者： Cai, Hanjun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Commonsense knowledge (CSK) is the information that people use in daily life but do not often mention. It summarizes the practical knowledge about how the world works. Existing machines have knowledge but lack commonsense because they do not understand and master commonsense knowledge in the same way that humans do. In the latest works, crowdsourcing-based method is costly and has low coverage, knowledge base completion method can highly fit samples, and methods extracted from unstructured data have the defects of low quality. CG &BF is commonsense knowledge construction with a concept-based generator and a BERT-based filter. We utilize semantic search for node matching and entropy encoder for filtering triples with high abstraction. Two algorithms based on concept aggregation and path credibility are proposed to obtain high-quality CSK triples. We subsequently finetuning a BERT to filter incorrect triples. We obtain 500,000 CSK triples based on ConceptNet, which is superior to other construction methods in novelty and quality. In the reading comprehension task, the three-way attention network is selected as the basic model and the knowledge we generate enables the base model to perform better, which proves that the output of CG &BF has higher quality and ease of use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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A multi-dimensional index for privacy-preserving queries in cloud computing

A multi-dimensional index for privacy-preserving queries in ...

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作者： Xu, Hui Ding, Xiaofeng Jin, Hai Yu, Qing National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles. © 2019 John Wiley & Sons, Ltd.

关键词： Sensitive data

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DroidAuditor: A framework for auditing covert communication on Android

DroidAuditor: A framework for auditing covert communication ...

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作者： Qiang, Weizhong Xin, Shifan Jin, Hai Sun, Guozhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China Co. Ltd. Beijing100193 China

Exploitation of covert channels in smartphone operating systems may lead to furtive data transmission between applications with different permissions, which might threaten users' privacy. Restricting the access to shared system resources can effectively prevent the exploitation of known covert channels. However, it inevitably limits the normal usage of those resources. In this paper, we propose a general method that detects covert channel attack at runtime without impacting the accessibility of shared resources in the system. The main idea of the method is to track and audit the use of system resources known as potential covert channel variables and impose interferences on those channels to reduce their capacity once violations are detected. We implement a prototype framework, which is able to audit and interfere covert communication both in the application layer and in the native layer of Android. The experimental results demonstrate that our method can effectively reduce the data rate of user-defined covert channels while the overhead is negligible. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Android (operating system)

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MR-Scope: A real-time tracing tool for MapReduce 10

MR-Scope: A real-time tracing tool for MapReduce

引用

Proceedings of the 19th ACM International Symposium on High Performance Distributed computing

作者： Huang, Dachuan Shi, Xuanhua Ibrahim, Shadi Lu, Lu Liu, Hongzhang Wu, Song Jin, Hai Cluster and Grid Computing Lab. Services Computing Technique and System Lab. Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781605589428

MapReduce programming model is emerging as an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by both academia and enterprise. Recently, lots of efforts have been done on improving the performance of MapReduce system and on analyzing the MapReduce process based on the log files generated during the Hadoop execution. Visualizing log files seems to be a very useful tool to understand the behavior of the Hadoop process. In this paper, we present MRScope, a real-time MapReduce tracing tool. MR-Scope provides a real-time insight of the MapReduce process, including the ongoing progress of every task hosted in Task Tracker. In addition, it displays the health of the Hadoop cluster data nodes, the distribution of the file system's blocks and their replicas and the content of the different block splits of the file system. We implement MR-Scope in native Hadoop 0.1. Experimental results demonstrat that MR-Scope's overhead is less than 4% when running wordcount benchmark. Copyright 2010 ACM.

关键词： MapReduce

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High Performance DDoS Attack Detection system Based on Distribution Statistics 16th

High Performance DDoS Attack Detection System Based on Distr...

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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Xie, Xia Li, Jinpeng Hu, Xiaoyang Jin, Hai Chen, Hanhua Ma, Xiaojing Huang, Hong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030307080

Nowadays, web servers often face the threat of distributed denial of service attacks and their intrusion prevention systems cannot detect those attacks effectively. Many existing intrusion prevention systems detect attacks by the state of per-flow and current processing speed cannot fulfill the requirements of real-time detection due to the high speed traffic. In this paper, we propose a powerful system TreeSketchShield which can improve sketch data structure and detect attacks quickly. First, we discuss a novel structure TreeSketch to obtain statistics of network flow, which utilizes the stepped structure of binary tree to map the distribution and reduces the complexity of the statistic calculation. Second, we present a two-level detection scheme that could make a compromise between the detection speed and detection accuracy. Experimental results show that our method can process more than 100,000 records per second. The false alarm rate can achieve 2% to 25% performance improvement. © 2019, IFIP International Federation for Information Processing.

关键词： Binary trees

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An Efficient Data Prefetch Strategy for Deep Learning Based on Non-volatile Memory 15th

An Efficient Data Prefetch Strategy for Deep Learning Based ...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Jiang, Wenbin Liu, Pai Jin, Hai Peng, Jing National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030642426

Deep learning (DL) systems usually utilize asynchronous prefetch to improve data reading performance. However, the efficiency of the data transfer path from hard disk to DRAM is still limited by disk performance. The emerging non-volatile memory (NVRAM) provides a novel solution for this problem, while few existing researches have considered it. We propose a novel efficient data prefetch strategy for DL based on a heterogeneous memory system combining NVRAM with DRAM. Benefitting from the large capacity and fast reading speed of NVRAM, the strategy uses an asynchronous reading method named sliding NVRAM cache (SNC) to improve the performance of the data transfer paths. A sliding window is applied to map the data from disk to NVRAM and continuously update the data, while non-ideal writing performance of NVRAM can be remitted to a large extent in this strategy. Experiments show that SNC can improve the time performance of diverse deep neural networks training by more than 30%. © 2020, Springer Nature Switzerland AG.

关键词： Dynamic random access storage

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Xenrelay: An efficient data transmitting approach for tracing guest domain

Xenrelay: An efficient data transmitting approach for tracin...

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2nd IEEE International Conference on Cloud computing technology and Science, CloudCom 2010

作者： Jin, Hai Cao, Wenzhi Yuan, Pingpeng Xie, Xia Cluster and Grid Computing Lab. Services Computing Technique and System Lab. Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769543024

As the degree of virtualization is growing considerably, improving performance of virtual machine environments motivates deeper investigation of the internal processes and performance implications of virtualization. Several tools are currently available to help analyze performance in virtual machine environments. However, all of these cannot support users to add their own trace information. In this paper, we present Xenrelay, a unified, efficient, and simple mechanism for transferring large amounts of data from guest domain kernel to privileged domain user-space. Xenrelay allows users (who trace subsystems of guest domain to analyze performance) to record and fast relay data between domains. We provide a case of Xenrelay's value in revealing behavior of virtualization. We build a block trace toolkit (using Xenrelay as an engine). This toolkit is used to track the block I/O virtualization procedures and evaluates overheads in the Xen environment. Our trace results identify that the main delay is the time of request transmission and block device operation. Besides, the results reveal impact of the I/O schedules on the disk performance. © 2010 IEEE.

关键词： Virtualization

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Dynamic processor resource configuration in virtualized environments

Dynamic processor resource configuration in virtualized envi...

引用

International Conference on services computing

作者： Jin, Hai Deng, Li Wu, Song Shi, Xuanhua Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769544625

Virtualization can provide significant benefits in data centers, such as dynamic resource configuration, live virtual machine migration. services are deployed in virtual machines (VMs) and resource utilization can be greatly improved. In this paper, we present VScheduler, a system that dynamically adjusts processor resource configuration of virtual machines, including the amount of virtual resource and a new mapping of virtual machines and physical nodes. VScheduler implements a two-level resource configuration scheme - local resource configuration (LRC) for an individual virtual machine and global resource configuration (GRC) for a whole cluster or data center. GRC especially takes variation tendency of workloads into account when remapping virtual machines to physical nodes. We implement our techniques in Xen and conduct a detailed evaluation using RUBiS and dbench. The experimental results show that VScheduler not only satisfies resource demands of services, but also reduces the number of virtual machines migration, which can provide a stable VM distribution on physical nodes in data centers. © 2011 IEEE.

关键词： Virtual machine

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Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain systems 28th

Anole: A Lightweight and Verifiable Learned-Based Index for...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chang, Jian Li, Binhong Xiao, Jiang Lin, Licheng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031306365

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index’s error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 × and reduces the storage overhead by 99.4 % compared with the state-of-the-art vChain+. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Failure order: A missing piece in disk failure processing of data centers 21

Failure order: A missing piece in disk failure processing of...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Yi, Yusheng Xiao, Jiang Wu, Song Li, Huichuwu Jin, Hai National Engineering Research Center for Big Data Technology System Services Computing Technology System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

To avoid data loss, data centers adopt disk failure prediction (DFP) technology to raise warnings ahead of actual disk failures, and process the warnings in the order they are raised, i.e., a first-in-first-out (FIFO) warning order. The FIFO-guided warning order can process warnings timely when disk failures are rare in data centers. With the growing scale of data centers, the increasing number of disk failures leads to a complex situation that multiple warnings are raised simultaneously, where the FIFO-guided warning order neither processes warnings timely, nor manages warnings properly due to lack of the priority of warnings. Thus, a real-time and finer-grained priority guidance for warning order management is an urgent need. To this end, we turn our attention to the failures since each warning corresponds to a fail event. The key insight is that the interdependence of failures, i.e., the order failure occurred, indicates the order of warning processing. With an accurate failure order, data centers can decrease the probability of data loss and the downtime of latency-sensitive applications by processing urgent warnings in advance. In this paper, we predict the failure order with a LambdaMART model, which is a state-of-the-art ranking algorithm in information retrieval. To avoid overly concerning on the correctness of high-rank warnings in information retrieval, we design a symmetric metric to evaluate the prediction evaluation of failure order. Experiment on a public dataset, provided by the Backblaze company, shows that our model outperforms the FIFO order and the order from previous DFP models. © 2019 IEEE.

关键词： Information retrieval

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