检索结果-内蒙古大学图书馆

Towards estimating expected sizes of probabilistic skylines

Science China(Information Sciences) 2011年第12期54卷 2574-2584页

作者： YANG YongTao & WANG YiJie national key laboratory for parallel and distributed Processing, School of Computer, national University of Defense Technology, Changsha 410073, China 1. National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

We consider the maximal vector problem on uncertain data, which has been recently posed by the study on processing skyline queries over a probabilistic data stream in the database context. Let D n be a set of n points in a d-dimensional space and q (0 < q 1) be a probability threshold; each point in D n has a probability to occur. Our problem is concerned with how to estimate the expected size of the probabilistic skyline, which consists of all the points that are not dominated by any other point in D n with a probability not less than q. We prove that the upper bound of the expected size is O(min{n, (- ln q)(ln n) d-1 }) under the assumptions that the value distribution on each dimension is independent and the values of the points along each dimension are distinct. The main idea of our proof is to find a recurrence about the expected size and solve it. Our results reveal the relationship between the probability threshold q and the expected size of the probabilistic skyline, and show that the upper bound is poly-logarithmic when q is not extremely small.

关键词： probabilistic skyline probabilistic data streams expected size

来源：评论

学校读者我要写书评

暂无评论

AutoMal: Automatic clustering and signature generation for malwares based on the network flow

引用

Security and Communication Networks 2015年第10期8卷 1845-1854页

作者： Hao, Sun Wang, Wen Lu, Huabiao Ren, Peige School of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

The volume of malwares is growing at an exponential speed nowadays. This huge growth makes it extremely hard to analyse malware manually. Most existing signatures extracting methods are based on string signatures, and string matching is not accurate and time consuming. Therefore, this paper presents AutoMal, a system for automatically extracting signatures from large-scale malwares. Firstly, the system proposes to represent the network flows by using feature hashing, which can dramatically reduce the high-dimensional feature spaces that are general in malware analysis. Then, we design a clustering and median filtering method to classify the malware vectors into different types. Finally, it introduces the signature generation algorithm based on Bayesian method. The system can extract both the byte signature and the hash signature of malwares from its network flow with low false positive and zero false negative. Our evaluation shows that AutoMal can generate strongly noise-resisted signatures that exactly depict the characteristics of malware. © 2014 John Wiley & Sons, Ltd.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

引用

12th national CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Unified Contextualized Knowledge Embedding Method for Static and Temporal Knowledge Graph

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 33卷 82-95页

作者： Gao, Yifu Qiao, Linbo Huang, Zhen Kan, Zhigang He, Yongquan Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology Changsha410000 China Intelligent Game and Decision Lab Beijing China Meituan Beijing100102 China

Recent years, there is a growing interest in knowledge graph embedding (KGE), which maps symbolic entities and relations into low-dimensional vector space to effectively represent structured data from the knowledge graph. In addition, the concept of temporal knowledge graph is proposed to document dynamically changing facts in the real world. Existing works attempt to incorporate temporal information into static KGE methods to accomplish temporal knowledge representations. However, existing static or temporal KGE approaches focus on the single query fact and ignore the query-relevant contextual information in the graph structure. This paper moves beyond the traditional way of scoring facts in distinct vector space and proposes a unified framework with pre-trained language models (PLM) to learn dynamic contextualized static/temporal knowledge graph embeddings, called CoS/TKGE. Given the query-specific subgraph, our model transforms it into an input sequence and uses the PLM to obtain the contextualized knowledge representations, which is flexible adaptive to the input graph contexts. We reformulate the link prediction task as a mask prediction problem to fine-tune the pre-trained language model. And the contrastive learning technique is employed to align dynamic contextual embeddings with static global embeddings. Experimental results on three widely used static and temporal KG datasets show the superiority of our model. © 2014 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Study of a code comment decision method based on structural features

Study of a code comment decision method based on structural ...

引用

2019 International Conference on Intelligent computing, Automation and Systems, ICICAS 2019

作者： Wang, Renmin Wang, Tao Wang, Huaimin Key Lab. of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (纸本)9781728161068

Code comment is one of the most effective ways to help programmers to understand the source code. High-quality comment decisions can not only cover the core code snippets in the software system but also avoid generating redundant code comments. However, in actual development, there is no uniform comment specification, and most of the comment decisions depend on personal experience and domain knowledge. This paper has learned a common comment decision specification from a large number of code comment examples to assist programmers in making appropriate comment decisions during code development. This paper proposes a method to extract the code structure from the context code of the current code line, and use the machine learning algorithm to determine the possibility that the current code needs to add comments. The proposed method was evaluated on 8 well-known open-source Python projects in GitHub and the experimental results show the feasibility and effectiveness of the method in some code types. © 2019 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Large-Scale Study of Failures on Petascale Supercomputers

引用

Journal of Computer Science & Technology 2018年第1期33卷 24-41页

作者： Rui-Tao Liu Zuo-Ning Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 213215 China National Research Center of Parallel Computer Engineering and Technology Beijing 100190 China

With the rapid development of supercomputers, the scale and complexity are ever increasing, and the reliability and resilience are faced with larger challenges. There are many important technologies in fault tolerance, such as proacrive failure avoidance technologies based on fault prediction, reactive fault tolerance based on checkpoint, and scheduling technologies to improve reliability. Both qualitative and quantitative descriptions on characteristics of system faults are very critical for these technologies, This study analyzes the source of failures on two typical petascale supercomputers called Sunway BlueLight （based on multi-core CPUs） and Sunway TaihuLight （based on heterogeneous manycore CPUs）. It uncovers some interesting fault characteristics and finds unknown correlation relationship among main components＇ faults. Finally the paper analyzes the failure time of the two supercomputers in various grains of resource and different time spans, and builds a uniform multi-dimensional failure time model for petascale supereomputers.

关键词： petascale supercomputer fault characteristic correlation relationship multi-dimension failure time model

来源：评论

学校读者我要写书评

暂无评论

A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Speculative symbolic execution

Speculative symbolic execution

引用

2012 IEEE 23rd International Symposium on Software Reliability Engineering, ISSRE 2012

作者： Zhang, Yufeng Chen, Zhenbang Wang, Ji National Laboratory for Parallel and Distributed Processing Department of Computing Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769548883

Symbolic execution is an effective path oriented and constraint based program analysis technique. Recently, there is a significant development in the research and application of symbolic execution. However, symbolic execution still suffers from the scalability problem in practice, especially when applied to large-scale or very complex programs. In this paper, we propose a new fashion of symbolic execution, named Speculative Symbolic Execution (SSE), to speed up symbolic execution by reducing the invocation times of constraint solver. In SSE, when encountering a branch statement, the search procedure may speculatively explore the branch without regard to the feasibility. Constraint solver is invoked only when the speculated branches are accumulated to a specified number. In addition, we present a key optimization technique that enhances SSE greatly. We have implemented SSE and the optimization technique on Symbolic Pathfinder (SPF). Experimental results on six programs show that, our method can reduce the invocation times of constraint solver by 20.7% to 48.7% (with an average of 29.9%), and save the search time from 23.6% to 43.6% (with an average of 30%). © 2012 IEEE.

关键词： Java programming language

来源：评论

学校读者我要写书评

暂无评论

Two improved GPU acceleration strategies for force-directed graph layout

Two improved GPU acceleration strategies for force-directed ...

引用

International Conference on Computer Application and System Modeling

作者： Wang, Yong-Xian Li, Zong-Zhe Yao, Lu Cao, Wei Wang, Zheng-Hua National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781424472369

Force directed approach is one of the most widely used methods in graph drawing research. However, the running time is increased intolerablely along with the enlargement of the graph size, which restricts the algorithm's practicability. By the aid of GPU (graphics processing unit) computing platform, we can speed-up the graph layout with low cost, but the existing GPU implementation mainly employees an "one-by-one" style to update the vertex' coordination per iteration, which has a lower convergent rate than the "batch" style which is instead used commonly in traditional CPU implementation. As a result, the aesthetics of graph layout would be decreased if the total running time is restricted. It is hard to achieve both a high speedup factor of GPU over CPU and a high convergent rate in existing GPU computing implementation. In order to solve this problem partially, this paper presents two new strategies to implement the large-scale graph layout on CPU+GPU heteromerous platform to accelerate the force directed layout for graph drawing problem. The numerical computation results show that our GPU implementation can dramatically improve the performance of force-direct layout and is 20 times on a NVIDIA GeForce 9800 GT GPU at 1.44 GHz faster than the one on single-CPU core of Intel Pentium 4 PC at 3.0 GHz for the graph layout with moderate size (typically 1000 vertices). © 2010 IEEE.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

Reduced complexity tree-structured conditional random field for all-words word sense disambiguation

引用

Journal of Computational Information Systems 2012年第3期8卷 1177-1186页

作者： Zhou, Yun Wang, Ting Wang, Zhiyuan Xie, Songxian Computer School National University of Defense Technology Changsha 410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

In this paper, we apply tree-structured conditional random field (TCRF) to all-words word sense disambiguation (WSD), where the graphical structure of TCRF is the dependency syntax tree produced by Minipar. The extremely large label space and high time complexity of TCRF hinders its application in semantic parsing, especially in all-words WSD, and we focus on reducing the time complexity of TCRF in this paper. The running time of TCRF is reduced significantly by edge feature selection and parallelization in training phase and beam search strategy in decoding phase. We evaluate the TCRF method on the English all-words task of Senseval-3, and the F1 value of TCRF method is 0.668, which outperforms the best result 0.652 in Senseval-3. For convenience of comparison, our training set only includes a part of Semcor, which reserves the space for improvement. Furthermore, the practical problem in the fields of information retrieval and natural language processing, often has a large label space and tree-structured information, which can also be solved by the TCRF method proposed in this paper. 1553-9105/Copyright © 2012 Binary Information Press.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：