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XTREE: Traversal-Based Partitioning for Extreme-Scale Graph Processing on Supercomputers 38

XTREE: Traversal-Based Partitioning for Extreme-Scale Graph ...

引用

38th IEEE international conference on data engineering (ICDE)

作者： Gan, Xinbiao Zhang, Yiming Zeng, Ruigeng Liu, Jie Wang, Ruibo Li, Tiejun Chen, Li Lu, Kai Natl Univ Def Technol Changsha Peoples R China Xiamen Univ Xiamen Peoples R China Huawei Changsha Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Graph algorithms, such as Breadth First Search (BFS), Single Source Shortest Path (SSSP), PageRank (PR), and Connected Components (CC), are increasingly important in big data processing and analytics. As graph scales (numbers of vertices and edges) have increased from billions to trillions, Supercomputers have huge numbers (up to hundreds of thousands) of computing nodes (CNs) that can provide ultra-high aggregate computing power and memory capacity, thus being particularly suitable for processing extreme-scale graphs with trillions of vertices and edges. However, existing cluster-based graph-parallel systems perform poorly when deployed on supercomputers, since their partitioning methods overlook the hierarchical nature of supercomputer networks and incur prohibitive communication storm. This paper presents XTREE, an efficient traversal-based partitioning method for minimizing communication overhead of graph processing on supercomputers. We observe that supercomputers' huge numbers of CNs are usually organized into hierarchical communication domains, which can be modeled as a domain tree where communication in lower-level domains is significantly faster than that in higher-level ones. Therefore, the key idea of XTREE's partitioning is to exploit hierarchical locality by viewing the graph as a BFS tree and leveraging the topology knowledge to map the graph's BFS tree onto the domain tree, We evaluate the effectiveness of XTREE by running various graph algorithms, on both real-world big graphs and synthetic trillion-scale graphs. XTREE substantially reduces communication overhead and achieves orders of magnitude speedup against the Graph500 reference implementations with the state-of-the-art 2D-decomposition partitioning.

关键词： Social networking (online) Scalability conferences Blogs data engineering Supercomputers Libraries

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Do Process Analysts Care About the Metadata of Event Logs? 44

Do Process Analysts Care About the Metadata of Event Logs?

引用

44th international conference on Information systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Zhou, Hui Zerbato, Francesca Weber, Barbara Indulska, Marta Sadiq, Shazia School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD Australia Institute of Computer Science University of St. Gallen Switzerland St. Gallen Switzerland School of Business The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9781713893622

Traditional approaches to evaluate data quality typically require pre-defined user requirements or data models of the source datasets. However, analysts are often faced with unfamiliar and repurposed data, for which they have little or no prior knowledge. In particular, process analysts rely on event log data but encounter challenges in evaluating the quality of such data. The situation is complicated by the typical inadequacy of metadata, despite various standardisation initiatives. Specifically, process analysts are frequently confronted with event logs from a variety of sources and different domains which often contain inadequate metadata. In this paper, we aim to gain an empirical understanding of the role of metadata in evaluating the quality of repurposed event logs. Through a semi-structured interview with 41 experienced process mining analysts, we extract, identify, and describe challenges, current practices and approaches, and preferences for the use of metadata in the context of event logs. Copyright © 2023 Hui Zhou, Francesca Zerbato, Barbara Weber, Marta Indulska, Shazia Sadiq.

关键词： Metadata

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Cyber Security Integration with Smart New Age Sustainable Startup Business, Risk Management, Automation and Scaling System for Entrepreneurs: An Artificial Intelligence Approach 7

Cyber Security Integration with Smart New Age Sustainable St...

引用

7th international conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Dasawat, Shagun Singh Sharma, Sachin Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350397253

Artificial Intelligence and cybersecurity play a crucial role in the success of a new-age sustainable startup business. In terms of risk management, Artificial Intelligenoe can be used to detect and prevent cyber threats, while also providing valuable insights into potential vulnerabilities. Automation can also improve the efficiency of security processes and help scale the business. However, entrepreneurs must be aware of the potential risks associated with AI and cybersecurity, including the possibility of data breaches and ethical issues. Strong security measures, such encryption and routine software updates, as well as knowledge of the laws and policies governing AI and cybersecurity are necessary to mitigate these dangers. A systematic literature review has been done in this paper. This paper provides an overview of new-age sustainable startup businesses and the importance of emerging technologies in new-age sustainable startup businesses, risk management, automation and scaling of entrepreneurs. Further, this paper tells about the impact of AI and Cyber Security in new age sustainable startup businesses. The advantages and challenges of Artificial Intelligence and Cyber Security in a new-age sustainable startup business are also discussed. © 2023 IEEE.

关键词： Risk management

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Revolutionizing Neurological Diagnosis: Innovations in ErrP for Enhanced Detection

Revolutionizing Neurological Diagnosis: Innovations in ErrP ...

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2024 IEEE international conference on Signal Processing, Informatics, Communication and Energy systems, SPICES 2024

作者： Nair, Praseeda B Godwinraj, D. Mathew, Little Therese Krishna, N Sreedu Varghese, Sheryl Amaljyothi College of Engineering Department of Electronics and Communication Kottayam India Amaljyothi College of Engineering Electronics and Communicztion Kottayam India

ISBN: (纸本)9798350376135

The primary focus of the work is the extraction and visualization of ErrP data, specifically comparing individuals with learning disabilities and hyperactivity to healthy *** advanced methodologies, our goal is to uncover hidden patterns within neural signals, illuminating the intricate features of neurodegenerative diseases. This research aims to investigate real-time neural dynamics in individuals with varied cognitive profiles, such as those with learning disabilities, intellectual disabilities, hyperactivity, and related conditions. Using advanced techniques like Short-Time Fourier Analysis (STFA), the research aims to elucidate error perception and cognitive control mechanisms in diverse populations beyond just those with schizophrenia. Through extensive experimentation involving more than 10 individuals representing a spectrum of cognitive profiles, including learning disabilities, mental retardation, and hyperactivity, garnered valuable insights into real-time neural responses during error monitoring tasks.. This nuanced understanding of real-time neural dynamics not only enhances comprehension of error processing mechanisms but also paves the way for tailored interventions and treatments for cognitive dysfunction in diverse populations. By decoding neural signals and elucidating error-related processes,this aims to bridge the gap between neuroscience and practical applications, ultimately improving the quality of life for individuals with diverse cognitive profiles. These findings have profound implications for real-time interventions, human-computer interaction, and the development of assistive technologies, opening new pathways for improving adaptive behavior and cognitive function in daily life. © 2024 IEEE.

关键词： Assistive technology

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Enhancing Misinformation Detection through Semantic Analysis and knowledge Graphs

Enhancing Misinformation Detection through Semantic Analysis...

引用

data engineering and Communication systems (ICDECS), international conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai School of Electrical Engineering and Computer Science University Of North Dakota Grand Forks USA

Introducing improvements in technology has caused an exponential increase in the availability and consumption of virtual data. This has also given rise to the spread of misinformation, a problem that has significant consequences for both individuals and society. The ability to correctly locate and combat misinformation is vital in preventing its dangerous outcomes. Traditional methods of detecting misinformation, including truth-checking and source evaluation, have proven to be inadequate in the contemporary digital landscape. To address this problem, researchers have turned to advanced technology, including natural language processing and machine learning, to improve the accuracy and performance of misinformation detection. However, these techniques often depend on surface-level features, such as language patterns and metadata, which can be easily manipulated by those creating and disseminating misinformation. This quandary requires a deeper understanding of the underlying semantic meaning of the facts. Semantic analysis, a branch of natural language processing, focuses on understanding the meaning and context of language. By incorporating this method into misinformation detection, it is possible to go beyond surface-level features and analyze the underlying intent and meaning of the information. This could offer a much better and accurate approach for detecting misinformation. Moreover, the use of knowledge graphs can further enhance this approach by capturing and representing the relationships between entities, ideas, and information.

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RevI-Ve: A Comprehensive Software Interface for Easy ReRAM Device Characterization 12

RevI-Ve: A Comprehensive Software Interface for Easy ReRAM D...

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12th international conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Riquelme, Juan Melivilu, Matias Vourkas, Ioannis Cirera, Albert Dept. of Electronic Engineering Valparaiso Chile Barcelona Spain

ISBN: (纸本)9798350321074

Introducing the uninitiated students & researchers to ReRAM technology and its applications via true device characterization and practical laboratory work, will certainly motivate them further to implement and test their application ideas in hardware. However, the lack of knowledge of the necessary experiments and strategies to follow for the adequate treatment of such devices, is a limiting factor in this direction. This work bridges the gap introducing RevI-Ve, a comprehensive characterization software interface developed for laboratory work on ReRAM devices. RevI-Ve is compatible with the Digilent Analog Discovery instrument series and is suitable both for educational and for research purposes. RevIVe supports different voltage-/current-based circuit topologies to access the ReRAM devices and enables the quick realization of a variety of complex, yet fully-customizable, experiments. We present the principal advantages offered by the current version of this tool, a brief summary of the experiments it facilitates, and show some results with data collected from measurements on commercial Self-Directed-Channel devices sold by Knowm Inc. © 2023 IEEE.

关键词： RRAM

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Combinatorial Explosion Problem of Big data Analytics by Morphological Approach

Combinatorial Explosion Problem of Big Data Analytics by Mor...

引用

international Russian Smart Industry conference (SmartIndustryCon)

作者： Dmitry Rakov Department of technological processes and systems control Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) Moscow Russia

Decision making by extracting the knowledge underlying Big data is considered. Big data and morphological synthesis of engineering systems can be interrelated in the context of using Big data to improve and develop innovative systems. The analysis and use of Big data can help to identify patterns, trends and relationships between different components of technical systems and optimize their performance. Through the use of Big data, morphological synthesis of engineering solutions can be more accurate and efficient, contributing to the development of new technologies and innovations in various fields. However, one of the major challenges in morphological systems analysis is the curse of dimensionality or combinatorial explosion. The paper discusses possible approaches to reduce the dimensionality of the problem. In particular, it is shown that the application of clustering and variant selection can reduce the number of analyzed variants and select the best ones. The proposed approach allows to increase the efficiency of search for rational engineering solutions. Unlike AI on neural networks, the solutions based on the morphological approach are transparent and controllable. The approach used can improve the validity and visibility of the information analyzed and increase the automation of processes in design and production.

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GPR B-scan data Classification through Deep Learning Approach 9

GPR B-scan Data Classification through Deep Learning Approac...

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9th international conference on Big data and Information Analytics, BigDIA 2023

作者： Zhang, Yaohong Xiao, Kaiming Liu, Kehui Ge, Bin Chen, Haiwen Li, Donghui National University of Defense Technology Laboratory for Big Data and Decision Changsha China Beijing Academy of Science and Technology Institute of Urban Systems Engineering Beijing China Hunan Agricultural University College of Information and Intelligence Changsha China

ISBN: (纸本)9798350330076

Ground Penetrating Radar (GPR) has proven indispensable in non-destructive geophysical investigations, offering a window into subsurface structures using electromagnetic waves. Its prowess in identifying underground utilities and hazards, like cavities, has been universally recognized. However, the intricate B-scan data generated by GPR poses significant interpretation challenges. Traditional methodologies, especially those reliant on dielectric constants and earlier machine learning models, often necessitate prior subsurface knowledge, inhibiting their universal applicability. Enter deep learning, which, in recent years, has changed the landscape of data interpretation across domains. Among the various architectures, Convolutional Neural Networks (CNNs) have shown immense potential in deciphering GPR data. This paper champions a novel methodology that harnesses the power of the ResNet18 architecture. Uniquely, our approach begins with a model pre-trained on the WiderFace dataset, renowned for facial recognition, and subsequently fine-tuned for GPR data. This merger of facial pattern recognition with geophysical data interpretation creates a synergy that promises unprecedented accuracy in subsurface detection. In summation, our research showcases how deep learning, especially the fusion of facial recognition insights with geophysical data, can redefine the boundaries of GPR data interpretation, offering enhanced accuracy and adaptability over conventional methods. © 2023 IEEE.

关键词： Deep learning

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Research on the Application of Artificial Intelligence Technology in Network Security 6

Research on the Application of Artificial Intelligence Techn...

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6th international conference on Computer Network, Electronic and Automation, ICCNEA 2023

作者： Xiao, Bozhi Yang, Shengquan Huang, Shujuan Xi'an Technological University School of Computer Science and Engineering Xi'an710032 China

ISBN: (纸本)9798350305388

Health Management (PHM) is a comprehensive method and strategy that aims to monitor, diagnose and optimize the health status and performance of systems, equipment or components. It includes health monitoring, fault diagnosis, and remaining service life (URL) forecast. Among them, URL is a more important field in PHM. Because the physical knowledge used by precise modeling machines is complicated, data-based driving methods based on learning data have become a promising alternative method for model prediction methods. This article proposes a new time convolutional neural network (TCN) with a soft threshold and attention mechanism for mechanical prediction. The method used in this paper avoids the process of complicated artificial extraction, and uses bearings directly as the input of the network model. A soft threshold mechanism is added to the TCN network, so that more useful information is retained in the activation function ReLu. The threshold is self-Adaptive learning by the sub-network trained by the channel attention mechanism, not the certainty value of the threshold distribution. Therefore, under the influence of the attention mechanism, each feature diagram has its own threshold, which retains some key features. On behalf of verify the generalization of the method proposed, the three benchmark data sets related to rolling bearings were tested, and the performance of the development method was compared with several advanced prediction methods. The results show that the development of all three cases has produced accurate RUL predictions, and has good robustness and generalization capabilities. © 2023 IEEE.

关键词： Roller bearings

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Fuzzy Inference Engine in Condition Monitoring of Industrial Equipment: An Overview 14

Fuzzy Inference Engine in Condition Monitoring of Industrial...

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14th international conference on Mechanical and Intelligent manufacturing Technologies, ICMIMT 2023

作者： Kgatwe, Calvinia K. Olatunji, Obafemi O. Adedeji, Paul A. Madushele, Nkosinathi University of Johannesburg Department of Mechanical Engineering Science South Africa University of Johannesburg Process Energy and Environmental Technology Station South Africa

ISBN: (纸本)9798350313093

Fuzzy logic systems are intelligent systems applied in equipment condition monitoring. Fuzzy logic systems are well known for their ability to deal with data complexity for better analysis and interpretation. A basic fuzzy logic system consists of four essential parts, among which is the fuzzy inference engine. A fuzzy inference engine is a framework that describes the actual process of converting inputs into outputs using fuzzy logic. However, some challenges have been recognizable in the fuzzy inference engine. Given this, the study aims to systematically review existing literature trends on approaches used for determining parameters of the fuzzy inference system. Specifically, the study examines the methods of assigning membership functions and rules. The study further discusses the contributions of membership functions and rules on fuzzy inference and fuzzy logic systems. Then the study concludes that the system's parameters are configured based on the problem size and type. Also, there is no standard method of defining or improving parameters to advance the system's performance. Furthermore, a substantial interrelation between parameters affects the inference system results. Therefore, the study also proposes new ideas for expanding the literature on inference system parameters to improve the overall system's effectiveness. Moreover, the study recommended future research to be on studies that investigate the contribution of individual parameters on the inference engine. This will enable monitoring of the parameter's level of impact and the shortcomings of these effects on the inference engine performance. Lastly, a study focusing on the rule reduction methods and the acceptable conditions for carrying out rule reduction is recommended. This study is of importance since indistinct information has been provided on rule reduction, even though it proves to be effective in altering the performance and accuracy of the inference system. © 2023 IEEE.

关键词： Condition monitoring

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