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A comprehensive study on fault tolerance in stream processing systems

Frontiers of Computer Science 2022年第2期16卷 80-97页

作者： Xiaotong WANG Chunxi ZHANG Junhua FANG Rong ZHANG Weining QIAN Aoying ZHOU School of Data Science and Engineering East China Normal UniversityShanghai 200062China Advanced Data Analytics Laboratory of Soochow University Suzhou 215006China

Stream processing has emerged as a useful technology for applications which require continuous and low latency computation on infinite streaming *** stream processing systems(SPSs)usually require distributed deployment on clusters of servers in face of large-scale of data,it is especially common to meet with failures of processing nodes or communication networks,but should be handled seriously considering service quality.A failed system may produce wrong results or become unavailable,resulting in a decline in user experience or even significant financial ***,a large amount of fault tolerance approaches have been proposed for *** approaches often have their own priorities on specific performance concerns,e.g.,runtime overhead and recovery ***,there is a lack of a systematic overview and classification of the state-of-the-art fault tolerance approaches in SPSs,which will become an obstacle for the development of ***,we investigate the existing achievements and develop a taxonomy of the fault tolerance in ***,we propose an evaluation framework tailored for fault tolerance,demonstrate the experimental results on two representative open-sourced SPSs and exposit the possible disadvantages in current ***,we specify future research directions in this domain.

关键词： fault tolerance performance evaluation stream processing

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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights

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Multimedia Tools and Applications 2024年第41期83卷 89217-89247页

作者： Mridha, Krishna Priyok, Masrur Ahsan Shukla, Madhu Department of Computer Engineering Marwadi University Rajkot India Department of Software Engineering Tianjin University Tianjin China Department of Computer Engineering Artificial Intelligence and Big Data Analytics Marwadi University Gujarat Rajkot India

In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learning provides sophisticated tools for automated EEG analysis. We undertook a thorough investigation of EEG seizure classification in this work, with equal emphasis on statistical validation, interpretability, and model performance. Following thorough preprocessing, we used statistical significance tests in conjunction with Chernoff and Bhattacharyya Bounds to select features. Top performers were found to be the Random Forest which had accuracies of 96%. A paired t-test and bootstrapped 95% confidence intervals were used to statistically validate the performance robustness, demonstrating the large variation in model performances. Going beyond simple performance measures, we set out to decipher the nuances of model choices by utilizing methods devoted to model interpretability and shedding light on the importance of distinct aspects. This helped neurologists grasp the primary EEG indicators predictive of seizures and opened the door for future clinical insights. It also provided transparency in diagnostic predictions. Our efforts highlight the mutually reinforcing importance of predictability and model interpretability in the context of medical machine-learning applications, hence intensifying the demand for the creation and use of trustworthy and transparent neurology diagnostic instruments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurology

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THREE-DIMENSIONAL VISUALIZATION OF ENERGY CONSUMPTION data IN ADDITIVE MANUFACTURING

THREE-DIMENSIONAL VISUALIZATION OF ENERGY CONSUMPTION DATA I...

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ASME 2024 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2024

作者： Hansen, Morten Hejlskov Vernica, Teodor Mansour, Rami Ramanujan, Devarajan Department of Mechanical and Production Engineering Aarhus University Aarhus Denmark Center for Digitalisation Big Data and Data Analytics Aarhus University Aarhus Denmark

ISBN: (纸本)9780791888346

This paper addresses the growing interest in design for sustainable manufacturing, focusing particularly on Additive Manufacturing (AM). While AM offers advantages like customized geometries and reduced material usage, there’s a need to address energy consumption in 3D printing beyond the conventional emphasis on material reduction. While previous research has explored various techniques for predicting and reducing the energy consumption of AM processes, there is limited work on guiding designers to generate more energy-efficient designs via localized geometric changes. This paper introduces a method to visualize energy consumption directly on 3D objects, aiming to guide designers in optimizing object shapes for sustainability. The methodology presented in the paper involves several steps: data collection, interpolation of positional points, classification of objects, support structures, and rapid movements. Following this, support and rapid movement energy are allocated to the printed object. Finally, the energy required for printing the object is directly visualized on a 3D model of the object using a mesh interpolation scheme. A use case demonstrating the potential application of the visualization method for reducing printing energy consumption is presented, through introducing a local shape modification to an axle bracket based on the visualization information. This modification resulted in a 7.5% energy reduction in this analyzed region compared to the original design. Thus, results showcase the ability of the proposed visualization method to support sustainability-oriented design for AM, through incorporating local shape modifications. © 2024 by ASME.

关键词： Smart manufacturing

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2D T1-Weighted CE MRI Images Classification with Hybrid Channel Attention Block enhanced VGG-16 and MobileNet 1

2D T1-Weighted CE MRI Images Classification with Hybrid Chan...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Fareed, Mohammad Jawad Pluempitiwiriyawej, Charnchai Murtaza, Ghulam Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electircal Engineering Bangkok Thailand

ISBN: (纸本)9798350373042

Motivated by the imperative need for precise and swift classification of brain tumors depicted in MRI images, this study unveils a novel classification network tailored explicitly for this task. Existing lightweight methodologies, although explored extensively, have fallen short of achieving the accuracy benchmarks attained by the model. The approach underwent rigorous testing on a challenging 2D T1-weighted CE-MRI dataset housing three distinct types of brain tumors: Meningioma, Glioma, and Pituitary. Recognizing the susceptibility of models to overfitting during training, introduced was a normalized combined channel and spatial attention mechanism, strategically incorporated to serve as an effective regularizer. Comparative analysis against state-of-the-art methodologies on this dataset highlights a notable performance boost of 2.12 percentage points achieved by integrating this attention mechanism into a base network. Furthermore, the fusion of the model with the pre-trained VGG16 network and GoogleNet into an ensemble demonstrates superior accuracy;however, this amalgamation comes at the expense of execution speed. © 2024 IEEE.

关键词： Image classification

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A Case Study on Economic Impact Assessment of Developing Single Drill Level in Sublevel Stoping Method

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Journal of The Institution of Engineers (India): Series D 2024年第1期105卷 119-125页

作者： Samanta, Gopinath Sinha, Suranjan Dey, Tapan Process Technology Group Tata Steel Ltd. Jamshedpur831001 India Department of Mining Engineering Bengal Engineering and Science University Shibpur711103 India Data and Analytics TCS Kolkata700156 India

Sublevel stoping is one of the most successful mining methods in respect of higher production, mechanization and safety. The method is a cost-effective and safe mining solution for steeply dipping vein deposits where high-level mechanization can be adopted. Progress of stope development and rate of production drilling are vital aspects in mine economics and profitability. Any change in the stope design stage effect total stope development, production drilling and explosive consumption. Hence, to optimize the mining cost and to maintain sustainable ore production by preparing stope ready for production in lesser time, an innovative stope design solution and drilling and blasting design can be applied in the sublevel stoping method. In this paper, a case study has been discussed and analyzed from an Indian copper mine where stope design practice was changed from two sub-level to a single sub-level. This modification leads to a change in production drilling design with a 115 mm diameter hole in place of a 57 mm diameter hole. The explosive consumption is also varying with this change in mining method. From the case study in five stopes, it has been observed that the development and drilling costs have been reduced by 32% each. Although the explosive cost is slightly increasing, the overall cost is reduced by 24% in the modified design with the single sublevel stoping method. The stope preparation time is reduced by five months on average for each stope. Hence, the modified stope design, as per results available from the case study area are encouraging and this modified method can be widely applied in different mines. © The Institution of Engineers (India) 2023.

关键词： Cost effectiveness

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Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

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IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

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Cost-Optimal Preventive Maintenance and Parts Replacement Schedule Using Mixed Integer Linear Programming

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Journal of The Institution of Engineers (India): Series D 2024年第3期105卷 1463-1471页

作者： Dey, Tapan Samanta, Gopinath Sinha, Suranjan Data and Analytics TCS Kolkata700156 India Process Technology Group Tata Steel Ltd. Jamshedpur831001 India Department of Mining Engineering Bengal Engineering and Science University Shibpur711103 India

A proper maintenance program is a key to achieving maximum profit by minimizing the total maintenance cost and number of shutdowns. This paper presents a framework for preventive maintenance scheduling for the equipment and highlights for mining industries. The present study describes the normal practice of equipment maintenance and the limitations of the practice. It is based on either no loss in equipment life or minimum loss in equipment life through combining a few maintenances at a time. Here, the authors present, a mixed integer linear programming-based optimization model to determine the optimal maintenance schedule and the model also minimized maintenance costs. The proposed optimization model is efficient to provide much lesser maintenance cost and reduced number of shutdowns than the present practice. When the maintenance schedule is compared with the present practice, the proposed model provides 14% lesser maintenance cost and 41% lesser number of shutdowns. It has been observed that the model is suitable for generating an optimal maintenance schedule of complex mining equipment like continuous miner, road header, dragline, etc. Therefore, the proposed model may be a guide to the maintenance Engineer to take decisions easily to make the optimized maintenance plan for the greater performance of the equipment. © The Institution of Engineers (India) 2023.

关键词： Preventive maintenance

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Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Chowdam, Jayanth Panigrahi, Sibarama National Institute of Technology Intelligent Data Analytics Laboratory Department of Computer Science and Engineering Odisha Rourkela India

ISBN: (纸本)9798350369175

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL. © 2024 IEEE.

关键词： Dimensionality reduction

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LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning 50th

LightDiC: A Simple yet Effective Approach for Large-scale Di...

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Li, Xunkai Liao, Meihao Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology China Peking University National Engineering Laboratory for Big Data Analytics and Applications China

Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployment. Compared with undirected graphs, directed graphs (digraphs) fit the demand for modeling more complex topological systems by capturing more intricate relationships between nodes. While some directed GNNs have been introduced, their inspiration mainly comes from deep learning architectures, which lead to redundant complexity and computation, making them inapplicable to large-scale databases. To address these issues, we propose LightDiC, a scalable variant of the digraph convolution based on the magnetic Laplacian. Since topology-related computations are conducted solely during offline pre-processing, LightDiC achieves exceptional scalability, enabling downstream predictions to be trained separately without incurring recursive computational costs. Theoretical analysis shows that LightDiC achieves message passing based on the complex field, which corresponds to the proximal gradient descent process of the Dirichlet energy optimization function from the perspective of digraph signal denoising, ensuring its expressiveness. Experimental results demonstrate that LightDiC performs comparably well or even outperforms other SOTA methods in various downstream tasks, with fewer learnable parameters and higher efficiency. © 2024, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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VRLLab at HSD-2Lang 2024: Turkish Hate Speech Detection Online with TurkishBERTweet 7

VRLLab at HSD-2Lang 2024: Turkish Hate Speech Detection Onli...

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7th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2024

作者： Najafi, Ali Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Turkey Center of Excellence in Data Analytics Sabanci University Turkey

ISBN: (纸本)9798891760707

Social media platforms like Twitter - recently rebranded as X - produce nearly half a billion tweets daily and host a significant number of users that can be affected by content that is not properly moderated. In this work, we present an approach that ranked third at the HSD-2Lang 2024 competition's subtask-A, along with additional methodology developed for this task and evaluation of different approaches. We utilize three different models, and the best-performing approach uses the publicly available TurkishBERTweet model with low-rank adaptation (LoRA) for fine-tuning. We also experiment with another publicly available model and a novel methodology to ensemble different hand-crafted features and outcomes of different models. Finally, we report the experimental results, competition scores, and discussion to improve this effort further. © 2024 Association for Computational Linguistics.

关键词： Social networking (online)

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