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Semi-supervised Graph Convolutional Neural Network Based Classification for Auto Parts Inventory Management

Semi-supervised Graph Convolutional Neural Network Based Cla...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Fangpeng Weng Bing Guo Xinhua Suo Xin Wu Yuchuan Hu Qin Zheng Computer Science Department Sichuan University Chengdu China

Auto parts inventory management is an important link of the automobiles multi-value chain, which has a certain impact on the upstream procurement, downstream production and other links. However, there are many kinds of auto parts, which will lead to improper allocation of funds and uneven allocation of resource when managed according to a unified standard. therefore, they need to be managed by categories. At present, the industry mainly adopts the ABC methods of stores control for classification. But this method has some shortcomings, such as single classification standard and no consideration of the correlation between materials, etc. In order to solve these problems, we introduce a semi-supervised graph convolutional neural network based classification method, which models the graph of automobile multi-value chain data according to the strong correlation between auto parts, and reclassifies them. through the experiment in Geely's multi-value chain data set, we achieve an 91.7% accuracy rate and obtain a better classification results, which provided new ideas for inventory management classification.

关键词： Procurement Industries Correlation Production Inventory management Data models Resource management

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation

Matching Using Sufficient Dimension Reduction for Heterogene...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Haoran Zhao Yinghao Zhang Debo Cheng Chen Li Zaiwen Feng College of Science Huazhong Agricultural University Wuhan China College of Computer Science and Engineering Guangxi Normal University Guilin China STEM University of South Australia Australia Department of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan College of Informatics Huazhong Agricultural University Wuhan China Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Wuhan China State Key Laboratory of Hybrid Rice Wuhan University Wuhan China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan China

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. the existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. the experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals.

关键词： Dimensionality reduction Smart cities Estimation

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Less is More: Mitigating Tor Traffic Correlation with Distance-Aware Path Selection

Less is More: Mitigating Tor Traffic Correlation with Distan...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Guoqiang Zhang Jiahao Cao Mingwei Xu Qi Li Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China

Traffic correlation attacks can deanonymize Tor users by capturing and analyzing traffic patterns on two end paths, i.e., the path from a client to the anonymity network and the path from the anonymity network to the destination server. Prior studies propose to obtain the two corresponding routes to ensure that no autonomous system (AS) can simultaneously appear on both end paths. However, acquiring accurate AS-level Tor paths in time is challenging due to the inaccuracy of route inference techniques, a shortage of traceroute vantage points, and the inherent dynamics of Internet routing. Moreover, it will fail to infer correct AS-level routes due to the widely deployed content delivery networks (CDNs). In this paper, we present a distance-aware Tor path selection algorithm that mitigates traffic correlation attacks merely by constraining the distances between Tor path nodes. the algorithm aims to lower the probability of traffic correlation attacks while not requiring any prior knowledge of AS-level route paths. the simulation results demonstrate that the distance-aware algorithm reduces the risk of Tor traffic correlation attacks by up to 27% compared to the existing AS-aware algorithm and outperforms it in about 88% of cases.

关键词： Correlation Content distribution networks Autonomous systems Heuristic algorithms Simulation Traffic control Routing

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Optimized Computation for Determinant of Multivariate Polynomial Matrices on GPGPU

Optimized Computation for Determinant of Multivariate Polyno...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Liangyu Chen Jianjun Wei Zhenbing Zeng Min Zhang Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Department of Computer Science and Engineering Washington University in St. Louis St Louis USA Department of Mathematics Shanghai University Shanghai China

the determinant of the matrix is a fundamental concept in linear algebra and has various applications in research and engineering. Compared to the easy-calculating numeric matrix, a symbolic matrix with multivariate polynomial entries is hard to calculate because of immediate expression expansion, which often leads to drastic exhaustion of the physical memory. In this paper, we present a novel four-stage-approach to calculate the accurate determinant of multivariate polynomial matrices on GPGPU, through using the modular method, the Stockham Fast Fourier Transformation(FFT), the inverse FFT, and the Chinese Remainder theorem. In our approach, the computation tasks in each stage are deployed on GPGPU so that the calculation could be recovered without loss at any point in case of inevitable interruptions happened during the parallel processing. In addition, to control the parallel computation time, we propose a time prediction model for the computation of polynomial determinant according to the basic matrix attributes. the experiment results show that our approach owns substantial speedups compared to Maple, allowing memory and time usage in steady increments.

关键词： Computational modeling Memory management Linear algebra Predictive models Parallel processing Task analysis

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Research on Elastic Extension of Multi Type Resources for OpenMP Program

Research on Elastic Extension of Multi Type Resources for Op...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Junfeng Zhao Xuesong Gao Yuancong Li College of Computer Science Inner Mongolia University Hohhot China

OpenMP is one of the most popular programming models in the field of parallel computing, which is widely used in multi-threaded programming with shared memory. But with the continuous change of program computing scale, the problem of resource supply has become increasingly prominent. Making use of the cloud computing to dynamically provide resources for OpenMP program is a meaningful work. Based on the internal structure characteristics of OpenMP program, a flexible elastic resource allocation strategy is designed, and a load prediction method combining active and reactive prediction based on machine learning is proposed to realize the load prediction of OpenMP program. then, the fuzzy controller is used to coordinate the resources and infer the CPU and memory resources needed by the program in the next cycle. On this basis, the middleware OPEC is designed and implemented to realize the dynamic allocation of cloud resources during the operation of OpenMP program. Parsec, a benchmark test suite based on shared memory, is selected for experimental verification. the experimental results show that the solution proposed in this paper can dynamically provide a variety of resources for OpenMP program to meet program running requirements, effectively improve resource utilization efficiency and reduce program running costs.

关键词： Fuzzy logic Cloud computing Fuzzy control Machine learning Prediction methods Programming Parallel processing

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24th international Symposium on Formal Methods, FM 2021

24th International Symposium on Formal Methods, FM 2021

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24th international Symposium on Formal Methods, FM 2021

ISBN: (纸本)9783030908690

the proceedings contain 46 papers. the special focus in this conference is on Formal Methods. the topics include: Verified Quadratic Virtual Substitution for Real Arithmetic;Business Processes Meet Spatial Concerns: the sBPMN Verification Framework;owicki-Gries Reasoning for C11 Programs with Relaxed Dependencies;Integrating ADTs in KeY and their Application to History-Based Reasoning;Identifying Overly Restrictive Matching Patterns in SMT-Based Program Verifiers;rely/Guarantee Reasoning for Multicopy Atomic Weak Memory Models;hybrid System Falsification for Multiple-Constraint Parameter Synthesis: A Gas Turbine Case Study;gaussian Process-Based Confidence Estimation for Hybrid System Falsification;formal Verification of Intelligent Hybrid systems that are Modeled with Simulink and the Reinforcement Learning Toolbox;model Checking for Verification of Quantum Circuits;Hybrid systems Verification with Isabelle/HOL: Simpler Syntax, Better Models, Faster Proofs;z3str4: A Multi-armed String Solver;Concise Outlines for a Complex Logic: A Proof Outline Checker for TaDA;formal Verification of a JavaCard Virtual Machine with Frama-C;verification of the Incremental Merkle Tree Algorithm with Dafny;congruence Relations for Büchi Automata;featured Team Automata;From Partial to Global Assume-Guarantee Contracts: Compositional Realizability Analysis in FRET;fingerprinting Bluetooth Low Energy Devices via Active Automata Learning;trace Abstraction-Based Verification for Uninterpreted Programs;Verifying Secure Speculation in Isabelle/HOL;HStriver: A Very Functional Extensible Tool for the Runtime Verification of Real-Time Event Streams;cabean 2.0: Efficient and Efficacious control of Asynchronous Boolean Networks;dynamic Reconfiguration via Typed Modalities;on Lexicographic Proof Rules for Probabilistic Termination;Fuel in Markov Decision Processes (FiMDP): A Practical Approach to Consumption;hyperProb: A Model Checker for Probabilistic Hyperproperties.

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Parallel-Friendly and Work-Efficient Single Source Shortest Path Algorithm on Single-Node System

Parallel-Friendly and Work-Efficient Single Source Shortest ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Jie Zhang Yuan Zhang Yiming Sun Huawei Cao Xuejun An Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of Information Superbahn CAS Nanjing China

the Single-Source Shortest Path (SSSP) algorithm is one of the most important kernels used by a variety of high-level graph processing applications. Although having been extensively studied for single-node scenarios, SSSP algorithm still faces difficulties when being deployed on scale-free graph, which brings about redundant edge traversals, expensive atomic operations and random memory access. In this paper, three parallel-friendly and work-efficient opti-mizations are introduced to improve the performance of parallel SSSP algorithm with shared memory on the single-node system. First, a label-aware pruning technique is proposed to reduce the redundant edge traversals. then, atomic operations caused by race conditions are eliminated by using destination-driven sub graph partitioning. Finally, we propose a double-ordered bucket strategy to further improve the efficiency of SSSP. the experiment demonstrates that the average speedup reaches to 27.58x compared with original implement for R-MAT graphs. Meanwhile, our method achieves up to 2.12x for R-MAT graphs and 4.45 x for real-world graphs on average compared to previous state-of-the-art parallel implementation.

关键词： Partitioning algorithms Kernel Optimization Faces

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Two Types of Single-Server Queueing systems with threshold-Based Renovation Mechanism 24th

Two Types of Single-Server Queueing Systems with Threshold...

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24th international conference on Distributed and computer and Communication Networks, DCCN 2021

作者： Hilquias, Viana C. C. Zaryadov, I.S. Milovanova, T.A. Miklukho-Maklaya Street 6 Moscow117198 Russia Institute of Informatics Problems FRC CSC RAS IPI FRC CSC RAS 44-2 Vavilova Street Moscow119333 Russia

ISBN: (纸本)9783030925062

this work is devoted to the analysis of two GI/M/1 infinite capacity queuing systems with threshold control of the renovation mechanism, which is responsible for the probabilistic dropping of claims entered into the system upon service completions. In the system of the first type, the threshold value is the value of the queue length, upon passing which the renovation mechanism is activated. For a system of the second type, the threshold value not only activates the renovation, but also specifies the area in the queue where from the customers cannot be dropped. For both systems, the main stationary time-probabilistic characteristics are derived and also the results of simulation, which illustrate the performance of the queues, are presented. © 2021, Springer Nature Switzerland AG.

关键词： GPSS Queue management Renovation mechanism threshold Time-probabilistic characteristic

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Toward the characterization of complex microvessel networks via in-line flow rate sensing 24

Toward the characterization of complex microvessel networks ...

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24th international conference on Miniaturized systems for Chemistry and Life sciences, MicroTAS 2020

作者： Daniele, Michael A. Pozdin, Vladimir A. Erb, Patrick Downey, McKenna Joint Department of Biomedical Engineering NC State University UNC-Chapel Hill United States Department of Electrical and Computer Engineering NC State University United States Department of Electrical and Computer Engineering Florida International University United States

ISBN: (纸本)9781733419017

A limiting factor of a system relying on spontaneous microvessel formation is the inability to precisely quantify the development of the microvessel network and control fluid moving through formed vessels. Herein, we report a strategy to monitor the dynamic formation of microscale fluid networks. the non-invasive, non-destructive monitoring and characterization of the fluid network is achieved via in-line sensing of fluid flow rate and correlating this measurement to the hydrodynamic resistance of the fluid network. © 2020 CBMS-0001

关键词： Flow rate

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Weak Network Oriented Mobile Distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Xinlei Wei Yujuan Tan Duo Liu Daitao Wu Yu Wu Xianzhang Chen Jian Li College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

Node failure is one of the most typical issues in distributed storage systems. the classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. the traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%.

关键词： Data centers Fault tolerant systems Redundancy Distributed databases Memory Bandwidth Maintenance engineering

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