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User-level failure detection and auto-recovery of parallel programs in HPC systems

Frontiers of computer science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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Adaptive watermark generation mechanism based on time series prediction for stream processing

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Frontiers of computer science 2021年第6期15卷 59-73页

作者： Yang SONG Yunchun LI Hailong YANG Jun XU Zerong LUAN Wei LI Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China College of Life Sciences and Bioengineering Beijing University of TechnologyBeijing 100083China

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in *** reality,streaming data usually arrives out-of-order due to factors such as network *** data stream processing framework commonly adopts the watermark mechanism to address the data *** is a special kind of data inserted into the data stream with a timestamp,which helps the framework to decide whether the data received is late and thus be *** watermark generation strategies are periodic;they cannot dynamically adjust the watermark distribution to balance the responsiveness and *** paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above *** mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result *** implement the proposed mechanism on top of Flink and evaluate it with realworld *** experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

关键词： data stream processing watermark time series based prediction dynamic adjustment

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Disentangling Heterogeneous Knowledge Concept Embedding for Cognitive Diagnosis on Untested Knowledge

arXiv

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arXiv 2024年

作者： Zhang, Miao Wang, Ziming Xing, Runtian Xiao, Kui Li, Zhifei Zhang, Yan Tang, Chang School of Computer Science and Information Engineering Hubei University China Key Laboratory of Intelligent Sensing System and Security China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students’ proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it’s hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel framework for Cognitive Diagnosis called Disentangling Heterogeneous Knowledge Cognitive Diagnosis(DisKCD) on untested knowledge. Specifically, we leverage course grades, exercise questions, and learning resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students’ proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students’ proficiency on UKCs. Our code is available at https://***/Hubuers/DisKCD. © 2024, CC BY.

关键词： Students

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A Composite Decomposition Method for Large-Scale Global Optimization

arXiv

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arXiv 2024年

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China The Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Engineering Westlake University Hangzhou310030 China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74048 United States

Cooperative co-evolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of non-additively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups non-separable variables by recursively considering the interactions between each non-separable variable and the formed non-separable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group non-separable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG series designs. © 2024, CC BY.

关键词： Efficiency

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Ecosystem of Technologies for Smart Agriculture to Improve the Efficiency and Profitability of Indian Farmers

Ecosystem of Technologies for Smart Agriculture to Improve t...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Urvashi Sugandh Swati Nigam Manju Khari Department of Computer Science Faculty of Mathematics and Computing Banasthali Vidyapith Banasthali India School of computing Science Engineering Galgotias University Greater Noida Uttar Pradesh India School of computer and System Sciences Jawaharlal Nehru University New Delhi India

An increasing population demands more food, but the available resources are shrinking. The agriculture sector in India is facing several challenges such as climate change, water scarcity, soil degradation, and declining farm profitability. To address these challenges, there is a growing need for the adoption of smart agriculture practices that leverage technology. This paper presents an overview of the ecosystem of technologies for smart agriculture and their potential to improve the efficiency and profitability of Indian farmers. The paper explores the role of various technologies such as precision agriculture, IoT, artificial intelligence, drones, and robotics in improving crop yields, reducing input costs, and enhancing overall farm management. Additionally, the paper discusses the challenges that need to be overcome to ensure the successful adoption of smart agriculture technologies in India. These include the lack of awareness and access to technology, inadequate infrastructure, and limited availability of financing. The paper concludes that the ecosystem of technologies for smart agriculture holds significant promise for Indian farmers to overcome the challenges and improve the efficiency and profitability of their farms.

关键词： Smart agriculture Farming Blockchains Climate change Ecosystems Energy efficiency Soil measurements Drones Statistics Water resources Profitability Food products

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Towards Ordinal Data science

arXiv

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arXiv 2023年

作者： Stumme, Gerd Dürrschnabel, Dominik Hanika, Tom Knowledge & Data Engineering Group Research Center for Information System Design Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Order is one of the main instruments to measure data. In this paper, we will therefore discuss difthe relationship between objects in (empirical) data. ferent means for measuring and ‘calculating’ with However, compared to methods that use numerical ordinal structures — a specific class of directed properties of objects, the amount of ordinal meth- graphs — and show how to infer knowledge from ods developed is rather small. One reason for this is them. Our aim is to establish Ordinal Data science the limited availability of computational resources as a fundamentally new research agenda. Besides in the last century that would have been required cross-fertilization with other cornerstone machine for ordinal computations. Another reason — partic- learning and knowledge representation methods, a ularly important for this line of research — is that broad range of disciplines will benefit from this en-order-based methods are often seen as too mathe- deavor, including, psychology, sociology, economics, matically rigorous for applying them to real-world web science, knowledge engineering, *** Codes 03G10, 06A15, 68T27, 68T30 Copyright © 2023, The Authors. All rights reserved.

关键词： Data science

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VARIATIONAL AUTO-ENCODER BASED SOLUTIONS TO INTERACTIVE DYNAMIC INFLUENCE DIAGRAMS

arXiv

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arXiv 2024年

作者： Pan, Yinghui Ma, Biyang Zhang, Hanyi Zeng, Yifeng National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science Minnan Normal University Zhangzhou China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom

Addressing multiagent decision problems in AI, especially those involving collaborative or competitive agents acting concurrently in a partially observable and stochastic environment, remains a formidable challenge. While Interactive Dynamic Influence Diagrams (I-DIDs) have offered a promising decision framework for such problems, they encounter limitations when the subject agent encounters unknown behaviors exhibited by other agents that are not explicitly modeled within the I-DID. This can lead to sub-optimal responses from the subject agent. In this paper, we propose a novel data-driven approach that utilizes an encoder-decoder architecture, particularly a variational autoencoder, to enhance I-DID solutions. By integrating a perplexity-based tree loss function into the optimization algorithm of the variational autoencoder, coupled with the advantages of Zig-Zag One-Hot encoding and decoding, we generate potential behaviors of other agents within the I-DID that are more likely to contain their true behaviors, even from limited interactions. This new approach enables the subject agent to respond more appropriately to unknown behaviors, thus improving its decision quality. We empirically demonstrate the effectiveness of the proposed approach in two well-established problem domains, highlighting its potential for handling multi-agent decision problems with unknown behaviors. This work is the first time of using neural networks based approaches to deal with the I-DID challenge in agent planning and learning problems. © 2024, CC BY.

关键词： Decoding

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Robust Dynamic Broadcasting for Multi-Hop Wireless Networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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Estimating Person Location in Bathroom with Spatial Ultrasound and Variational autoencoder

Estimating Person Location in Bathroom with Spatial Ultrasou...

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IEEE Symposium (IUS) Ultrasonics

作者： Shun Sato Yuto Yasuda Ryotaro Ohara Riku Hamabe Takayuki Genda Shoya Imanaka Shintaro Izumi Hiroshi Kawaguchi Graduate School of System Informatics Kobe University Kobe Japan Department of Computer Science and Systems Engineering Kobe University Kobe Japan Graduate School of Science Technology and Innovation Kobe University Kobe Japan

In bathrooms, a fall can lead to severe injuries or even drowning. While taking preventive measures against such accidents is crucial, it's equally important to swiftly detect and request rescue when they occur. In such scenarios, knowing the exact location of the person in the bathroom becomes critical. However, concerns related to privacy and costs make the use of cameras and lidar impractical. To address these issues, we propose a method for estimating the 3D location of a person within the bathroom using ultrasound correlation values as input for a deep neural network. To validate our approach, we conduct experiments using two sensors in this study. The results are promising, as we achieved a mean square error of just 10.48 cm.

关键词：

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GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network 6

GraphEye: A Novel Solution for Detecting Vulnerable Function...

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Zhou, Li Huang, Minhuan Li, Yujun Nie, Yuanping Li, Jin Liu, Yiwei School Of Information And Communication Engineering University Of Electronic Science And Technology Of China Chengdu China National Key Laboratory Of Science And Technology On Information System Security Beijing China School Of Computer Science And Engineering University Of Electronic Science And Technology Of China Chengdu China

ISBN: (纸本)9781665418157

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatically detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification problem. GraphEye is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention networks, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6%, 95.6%, 96.1%, 92.6%, and 96.1% respectively, which validate the effectiveness of the proposed solution. © 2021 IEEE.

关键词： Semantics

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