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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Rao, Xiang Wang, Huaimin Shi, Dianxi Chen, Zhenbang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Computing Platform Alibaba Cloud Computing Corporation Hangzhou China

ISBN: (纸本)9781457703751

Extracting fault features with the error logs of fault injection tests has been widely studied in the area of large scale distributed systems for decades. However, the process of extracting features is severely affected by a large amount of noisy logs. While the existing work tries to solve the problem by compressing logs in temporal and spatial views or removing the semantic redundancy between logs, they fail to consider the co-existence of other noisy faults that generate error logs instead of injected faults, for example, random hardware faults, unexpected bugs of softwares, system configuration faults or the error rank of a log severity. During a fault feature extraction process, those noisy faults generate error logs that are not related to a target fault, and will strongly mislead the resulted fault features. We call an error log that is not related to a target fault a noisy error log. To filter out noisy error logs, we present a similarity-based error log filtering method SBF, which consists of three integrated steps: (1) model error logs into time series and use haar wavelet transform to get the approximate time series;(2) divide the approximate time series into sub time series by valleys;(3) identify noisy error logs by comparing the similarity between the sub time series of target error logs and the template of noisy error logs. We apply our log filtering method in an enterprise cloud system and show its effectiveness. Compared with the existing work, we successfully filter out noisy error logs and increase the precision and the recall rate of fault feature extraction.1 © 2011 IEEE.

关键词： Errors

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Network and service management and diagnostics solution of a remote patient monitoring system

Network and service management and diagnostics solution of a...

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3rd IEEE International Symposium on Logistics and Industrial Informatics, LINDI 2011

作者： Kozlovszky, Miklos Meixner, Zsolt Windisch, Gergely Márton, Judit Ács, Sándor Bogdanov, Pál Boruzs, Anikó Kotcauer, Péter Ferenczi, János Kozlovszky, Viktor MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary Óbuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9781457718410

We have developed a combined network and service management and diagnostics solution for our in-house developed remote patient monitoring system. The developed system has included into the ALPHA eHealth/remote patient monitoring system and was successfully used in our large Living Lab infrastructure operating in three different Hungarian regions with 40 patients. In this paper we will identify the key elements of the combined Network and Service Management solution of the remote patient monitoring system. © 2011 IEEE.

关键词： Telemedicine

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

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

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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

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An interface theory based approach to verification of web services

An interface theory based approach to verification of web se...

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30th Annual International Computer Software and Applications Conference, COMPSAC 2006

作者： Zhenbang, Chen Ji, Wang Wei, Dong Zhichang, Qi Yeung, W.L. National Laboratory for Parallel and Distributed Processing Changsha China Department of Computing and Decision Sciences Lingnan University Hong Kong Hong Kong

ISBN: (纸本)0769526551

The verification of web services becomes a challenge in software verification. This paper presents a framework for verification of web service interfaces at various abstraction levels. Its foundation is the interface theory for web services, in which transaction features are incorporated. Within the framework, one may check non mutual invocation, compatibility and refinement of web services at signature, conversation and protocol levels. At protocol level, we present a model checking approach to verifying the protocol properties in Action Set Computation Tree Logic(ASCTL). The paper also discusses the integration of our framework into the web service development. © 2006 IEEE.

关键词： Web services

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GPGPU-based data parallel region growing algorithm for cell nuclei detection

GPGPU-based data parallel region growing algorithm for cell ...

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12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011

作者： Szénási, Sándor Vámossy, Zoltán Kozlovszky, Miklós Óbuda University John Von Neumann Faculty of Informatics Budapest Hungary MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary

ISBN: (纸本)9781457700453

Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps that can detect cell nuclei in a sample image. The aim of our research is developing a new data parallel algorithm that can be implemented even in a GPGPU environment and that is capable of counting hematoxylin eosin (HE) stained cell nuclei and of identifying their exact locations and sizes (using a variation of the region growing method). Our presentation contains the detailed description of the algorithm, the peculiarity of the CUDA implementation, and the evaluation of the created application (regarding its accuracy and the decrease in the execution time). © 2011 IEEE.

关键词： parallel algorithms

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Short fragment sequence alignment on the HP-SEE infrastructure

Short fragment sequence alignment on the HP-SEE infrastructu...

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35th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2012

作者： Kozlovszky, Miklos Windisch, Gergely Balaskó, Ákos MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary Óbuda University John Von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9789532330724

The recently used deep sequencing techniques represent a new data processing challenge: mapping short fragment reads to open-access eukaryotic genomes at the scale of several hundred thousand. This problem is solvable by BLAST, BWA and similar sequence alignment tools. BLAST is one of the most frequently used tool in bioinformatics and BWA is a relative new fast light-weighted tool that aligns effectively short sequences. Local installations of these algorithms are typically not able to handle large problem size therefore the sequence alignment process runs slowly, while web based implementations cannot accept high number of queries. HP-SEE infrastructure allows accessing massively parallel supercomputing infrastructure. With gUSE/WS-PGRADE we have created successfully an online Bioinformatics eScience Gateway, which is capable to serve the short fragment sequence alignment demand of the regional bioinformatics communities within the SEE region. Using workflows we have ported algorithms (BLAST and BWA) to the massively parallel HP-SEE infrastructure. In this paper we describe the created Bioinformatics eScience Gateway, and show as case study how we have implemented the ported BLAST workflow using parameter study. With our online service, researchers can do high throughput sequence alignments against the eukaryotic genomes to search for regulatory mechanisms controlled by short fragments on HP-SEE's supercomputing infrastructure. © 2012 MIPRO.

关键词： Data handling

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Unified Contextualized Knowledge Embedding Method for Static and Temporal Knowledge Graph

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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

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Untrusted Hardware Causes Double-Fetch Problems in the I/O Memory

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Journal of Computer Science & Technology 2018年第3期33卷 587-602页

作者： Kai Lu Peng-Fei Wang Gen Li Xu Zhou College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High-Performance Computing National University of Defense Technology Changsha 410073 China

The double fetch problem occurs when the data is maliciously changed between two kernel reads of the supposedly same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices （aka. the hardware double fetch）. Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O （input and output） memory. Owing to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We propose a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results are categorized and each category is analyzed using case studies to discuss the possibility of causing bugs. We also find four previously unknown double-fetch vulnerabilities, which have been confirmed and fixed after reporting them to the maintainers.

关键词： hardware double fetch double-fetch bug I/O memory peripheral device double-fetch vulnerability

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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

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Frontiers of Computer Science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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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...

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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

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