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Static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Human Resource Application Development using Person-Job Matching for Placement of Prospective Employees 5

Human Resource Application Development using Person-Job Matc...

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5th International Conference on Advancement in Data Science, E-Learning and Information systems, ICADEIS 2023

作者： Putri, Alifiah Suakanto, Sinung Siswanto, Joko Arifin, Farah Fitriavida Darmawan, Irfan Angela, Dina Telkom University Faculty of Software Engineering Department of Information System Bandung Indonesia Bandung of Institute Technology Industrial Engineering Research Group Bandung Indonesia Faculty of Industrial Engineering Institut Teknologi Harapan Bangsa Information Techology Research Group Bandung Indonesia

ISBN: (纸本)9798350303414

The recruitment process presents a significant challenge for Human Resource (HR) staff, particularly in their ability to objectively assess candidate competencies and align them with job requirements. While qualitative comparisons between resumes and position requirements may yield candidates who appear suitable, this approach proves ineffective in terms of time and cost. Furthermore, many recruiters lack sufficient knowledge of the positions they are hiring for, making it difficult to justify their selection decisions. This research aims to address this challenge by developing an HR person-job matching application for the recruitment of prospective employees. The study focuses on analyzing and designing applications that can quantitatively measure the competencies of candidates and compare them with job requirements using a person-job matching method. The effectiveness of this approach was evaluated by testing it on a sample of employees from small and medium-sized enterprises (SMEs). The study found that out of the 16 employees across three job positions, the person-job matching application achieved an accuracy rate of 75%. This accuracy percentage was determined by comparing the fit value results with the actual conditions of the employees in their respective workplaces. The findings highlight the potential of the HR person-job matching application to enhance the efficiency and effectiveness of recruitment efforts. © 2023 IEEE.

关键词： Job satisfaction

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OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs 29

OptiTwin: Data-Driven Machining Process Optimization Platfor...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Abadía, José Joaquín Peralta Barrenechea, Felix Larrinaga Zabaljauregui, Mikel Cuesta Aiestaran, Xabier Badiola Zubiaurre, Aitor Duo Mendia, Gorka Olalde Lázare, Txema Pérez Mondragon Goi Eskola Politeknikoa Software and System Engineering Research Group Arrasate Spain Mondragon Goi Eskola Politeknikoa High Performance Machining Research Group Arrasate Spain Mondragon Goi Eskola Politeknikoa Electronic Power Systems Applied to the Control Electric Energy Research Group Arrasate Spain Mondragon Goi Eskola Politeknikoa Data Analysis and Cybersecurity Research Group Arrasate Spain

ISBN: (纸本)9798350361230

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance. © 2024 IEEE.

关键词： Machining centers

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research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Automatic Chest Radiology Report Generation Using Reinforcement Learning 7th

Automatic Chest Radiology Report Generation Using Reinforcem...

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7th International Conference on Intelligent Technologies, ICIT 2022

作者： Kourav, Mohit Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Rodriguez, Ciro Multimedia and Information Security Research Group ABV-Indian Institute of Information Technology & Management Gwalior India Faculty of System Engineering Universidad Nacional Federico Villarreal Lima Peru Faculty of Software Engineering Universidad Nacional Mayor de San Marcos Lima Peru

ISBN: (纸本)9789819919116

Radiology report generation is the manual task assigned to radiologists which analyze the given chest image (x-ray) of patient and generates the report based on that analysis. Many of the research is undergoing in this field to make this process automatic. However, there are a large number of difficulties which have been faced by the researchers while developing the efficient automatic report generation model. The main difficulties include the generation of clinically inaccurate reports when analyzed with the reports of the expert radiologist. The introduction of deep learning based approaches achieved tremendous advancement in these areas and can produce chest X-ray reports in a more accurate manner. BY further optimizing the deep learning based approaches, we proposed a reinforcement learning-based model that generates automated X-ray reports based on input chest X-ray images. We also developed a lookahead inference mechanism to overcome the limitation of beam search, which selects words based on policy and value networks at each particular step of time. The proposed model achieved 0.454, 0.546, 0.466, 0.410, and 0.424 for the Bleu-1, Bleu-2, Bleu-3, Bleu-4, and Rouge evaluation metrics, respectively, surpassing all available state-of-the-art algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Design and analysis of GPS satellite-based augmentation system based on low earth orbit constellations 3

Design and analysis of GPS satellite-based augmentation syst...

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2024 3rd International Conference on Aerospace and Control engineering, ICoACE 2024

作者： Yang, Jianlei Jin, Zhengzhen Zheng, Shuaiyong Huang, Shiyong Ma, Guoyu Ji, Shiran Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang050081 China The 54 Research Institute China Electronics Technology Group Shijiazhuang050081 China School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China College of Software Tianjin Normal University Tianjin300387 China

Addressing the need for improved GPS accuracy, especially for low-dynamic users, this study introduces a GPS augmentation system employing Low Earth Orbit (LEO) satellites. We detail the principles of satellite navigation and augmentation and simulate the GPS-LEO constellation to analyse its performance in terms of satellite visibility and Position Dilution of Precision (PDOP). The results demonstrate substantial augmentation in navigation accuracy with the integrated system, as verified by UAV flight experiments. The integration of LEO satellites is anticipated to advance global navigation capabilities, with implications for intelligent transportation, logistics, and precision agriculture. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Global positioning system

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Human Resource Application Development using Person-Job Matching for Placement of Prospective Employees

Human Resource Application Development using Person-Job Matc...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), International Conference on

作者： Alifiah Putri Sinung Suakanto Joko Siswanto Farah Fitriavida Arifin Irfan Darmawan Dina Angela Department of Information System Faculty of Software Engineering Telkom University Bandung Indonesia Industrial Engineering Research Group Bandung of Institute Technology Bandung Indonesia Information Techology Research Group Faculty of Industrial Engineering Institut Teknologi Harapan Bangsa Bandung Indonesia

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OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs

OptiTwin: Data-Driven Machining Process Optimization Platfor...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： José Joaquín Peralta Abadía Felix Larrinaga Barrenechea Mikel Cuesta Zabaljauregui Xabier Badiola Aiestaran Aitor Duo Zubiaurre Gorka Olalde Mendia Txema Perez Lazare Software and System Engineering Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain High Performance Machining Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Electronic Power Systems Applied to the Control Electric Energy Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Data Analysis and Cybersecurity Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

关键词： Manufacturing industries Productivity Drilling Condition monitoring Quality assurance Data acquisition Real-time systems Data models Optimization Manufacturing automation

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Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis 25

Enhancing Web Service Anomaly Detection via Fine-grained Mul...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xixuan Yang Xin Huang Chiming Duan Tong Jia Shandong Dong Ying Li Gang Huang Peking University Beijing China Nanyang Normal University Nanyang Henan China Peking University Beijing China and National Key Laboratory of Data Space Technology and System Beijing China Alibaba Group Hangzhou Zhejiang China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400713316

Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies through data fusion approaches. They associate logs and metrics using coarse-grained time window alignment and capture the normal patterns of system operation through reconstruction. However, these methods have two issues that limit their performance in anomaly detection. First, due to asynchrony between logs and metrics, coarse-grained time window alignment cannot achieve a precise association between the two modalities. Second, reconstruction-based methods suffer from severe overgeneralization problems, resulting in anomalies being accurately reconstructed. In this paper, we propose a novel anomaly detection method named FFAD to address these two issues. On the one hand, FFAD employs graph-based alignment to mine and extract associations between the modalities from the constructed log-metric relation graph, achieving precise associations between logs and metrics. On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection. We validated the effectiveness of our model on two real-world industrial datasets and one open-source dataset. The results show that our method achieves an average anomaly detection F1-score of 93.6%, representing an 8.8% improvement over previous state-of-the-art methods.

关键词： anomaly detection

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SMOG: Accelerating Subgraph Matching on GPUs

SMOG: Accelerating Subgraph Matching on GPUs

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

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