Applied Computing Review最新文献_第4页

MAFD: A Federated Distillation Approach with Multi-head Attention for Recommendation Tasks 基于多头关注的推荐任务联邦蒸馏方法

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577849

Aming Wu, Young-Woo Kwon

引用次数: 0

Stateful Adaptive Streams with Approximate Computing and Elastic Scaling 具有近似计算和弹性缩放的有状态自适应流

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577858

João Francisco, Miguel E. Coimbra, P. Neto, Felix Freitag, L. Veiga

{"title":"Stateful Adaptive Streams with Approximate Computing and Elastic Scaling","authors":"João Francisco, Miguel E. Coimbra, P. Neto, Felix Freitag, L. Veiga","doi":"10.1145/3555776.3577858","DOIUrl":"https://doi.org/10.1145/3555776.3577858","url":null,"abstract":"The model of approximate computing can be used to increase performance or optimize resource usage in stream and graph processing. It can be used to satisfy performance requirements (e.g., throughput, lag) in stream processing by reducing the effort that applications need to process datasets. There are currently multiple stream processing platforms, and most of them do not natively support approximate results. A recent one, Stateful Functions, is an API that uses Flink to enable developers to easily build stream and graph processing applications. It also retains Flink's features like stateful computations, fault-tolerance, scalability, control events and its graph processing library Gelly. Herein we present Approxate, an extension over this platform to support approximate results. It can also support more efficient stream and graph processing by allocating available resources adaptively, driven by user-defined requirements on throughput, lag, and latency. This extension enables flexibility in computational trade-offs such as trading accuracy for performance. The user can choose which metrics should be guaranteed at the cost of others, and/or the accuracy. Approxate incorporates approximate computing (using load shedding) with adaptive accuracy and resource manegement in state-of-the-art stream processing platforms, which are not targeted in other relevant related work. It does not require significant modifications to application code, and minimizes imbalance in data source representation when dropping events.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78035215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

G-HIN2Vec: Distributed heterogeneous graph representations for cardholder transactions G-HIN2Vec:持卡人交易的分布式异构图形表示

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577740

Farouk Damoun, H. Seba, Jean Hilger, R. State

{"title":"G-HIN2Vec: Distributed heterogeneous graph representations for cardholder transactions","authors":"Farouk Damoun, H. Seba, Jean Hilger, R. State","doi":"10.1145/3555776.3577740","DOIUrl":"https://doi.org/10.1145/3555776.3577740","url":null,"abstract":"Graph related tasks, such as graph classification and clustering, have been substantially improved with the advent of graph neural networks (GNNs). However, existing graph embedding models focus on homogeneous graphs that ignore the heterogeneity of the graphs. Therefore, using homogeneous graph embedding models on heterogeneous graphs discards the rich semantics of graphs and achieves average performance, especially by utilizing unlabeled information. However, limited work has been done on whole heterogeneous graph embedding as a supervised task. In light of this, we investigate unsupervised distributed representations learning on heterogeneous graphs and propose a novel model named G-HIN2Vec, Graph-Level Heterogeneous Information Network to Vector. Inspired by recent advances of unsupervised learning in natural language processing, G-HIN2Vec utilizes negative sampling technique as an unlabeled approach and learns graph embedding matrix from different pre-defined meta-paths. We conduct a variety of experiments on three main graph downstream applications on different socio-demographic cardholder features, graph regression, graph clustering, and graph classification, such as gender classification, age, and income prediction, which shows superior performance of our proposed GNN model on real-world financial credit card data.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78581158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Feature Selection Using an Autoencoder and Classifier: Applied to a Radiomics Case 基于自编码器和分类器的自适应特征选择:应用于放射组学案例

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577861

R. Hassanpour, Niels Netten, Tony Busker, Mortaza Shoae Bargh, Sunil Choenni

{"title":"Adaptive Feature Selection Using an Autoencoder and Classifier: Applied to a Radiomics Case","authors":"R. Hassanpour, Niels Netten, Tony Busker, Mortaza Shoae Bargh, Sunil Choenni","doi":"10.1145/3555776.3577861","DOIUrl":"https://doi.org/10.1145/3555776.3577861","url":null,"abstract":"Machine learning models have been an inevitable tool for analyzing medical images by radiologists. These models provide important information about the contents of these images using extracted radiomic features. However, the dimensionality of the feature space can cause reduction in the accuracy of prediction, a phenomenon known as the curse of dimensionality. In this study we propose a feature selection method using an autoencoder, which incorporates the performance of a classifier within the feature selection process. This is achieved by automatically adjusting a threshold value used for selecting the features fed to the classifier. The contribution of this study is twofold. The first contribution is an improvement to group lasso to include the group size as a cost parameter of the autoencoder. The second contribution is to automate the selection of the threshold value used for eliminating redundant input features. The threshold value in our proposed method is learned during training phase of the proposed model. Our experimental results indicates that the proposed model can successfully converge to appropriate feature selection parameters.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74668413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image4Assess: Automatic learning processes recognition using image processing image4evaluate:自动学习使用图像处理进行识别

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577643

Hsin-Yu Lee, Maral Hooshyar, Chia-Ju Lin, Wei-Sheng Wang, Yueh-Min Huang

{"title":"Image4Assess: Automatic learning processes recognition using image processing","authors":"Hsin-Yu Lee, Maral Hooshyar, Chia-Ju Lin, Wei-Sheng Wang, Yueh-Min Huang","doi":"10.1145/3555776.3577643","DOIUrl":"https://doi.org/10.1145/3555776.3577643","url":null,"abstract":"Recently, there has been a growing interest in improving students' competitiveness in STEM education. Self-reporting and observation are the most used tools for the assessment of STEM education. Despite their effectiveness, such assessment tools face several challenges, such as being labor-intensive and time-consuming, prone to subjective awareness, depending on memory limitations, and being influenced due to social expectations. To address these challenges, in this research, we propose an approach called Image4Assess that---by benefiting from state-of-the-art machine learning like convolutional neural networks and transfer learning---automatically and uninterruptedly assesses students' learning processes during STEM activities using image processing. Our findings reveal that the Image4Assess approach can achieve accuracy, precision, and recall higher than 85% in the learning process recognition of students. This implies that it is feasible to accurately measure the learning process of students in STEM education using their imagery data. We also found that there is a significant correlation between the learning processes automatically identified by our proposed approach and students' post-test, confirming the effectiveness of the proposed approach in real-world classrooms.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Acala: Aggregate Monitoring for Geo-Distributed Cluster Federations Acala:地理分布式集群联合的聚合监控

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577716

Chih-Kai Huang, G. Pierre

引用次数: 2

NFT Trust Survey 信托调查

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577824

Jean-Marc Seigneur, Suzana Moreno

引用次数: 0

An Extensible Framework for Implementing Byzantine Fault-Tolerant Protocols 实现拜占庭容错协议的可扩展框架

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3578614

Hanish Gogada, J. Olsen, H. Meling, Leander Jehl

引用次数: 0

The not-so-easy task of taking heavy-lift ML models to the edge: a performance-watt perspective 将重型ML模型带到边缘的不太容易的任务:性能瓦特的角度

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577742

Lucas Pereira, B. Guterres, Kauê Sbrissa, Amanda Mendes, Francisca Vermeulen, L. Lain, Marié Smith, Javier Martinez, Paulo L. J. Drews-Jr, Nelson Duarte, Vinicus Oliveira, S. Botelho, M. Pias

{"title":"The not-so-easy task of taking heavy-lift ML models to the edge: a performance-watt perspective","authors":"Lucas Pereira, B. Guterres, Kauê Sbrissa, Amanda Mendes, Francisca Vermeulen, L. Lain, Marié Smith, Javier Martinez, Paulo L. J. Drews-Jr, Nelson Duarte, Vinicus Oliveira, S. Botelho, M. Pias","doi":"10.1145/3555776.3577742","DOIUrl":"https://doi.org/10.1145/3555776.3577742","url":null,"abstract":"Edge computing is a new development paradigm that brings computational power to the network edge through novel intelligent end-user services. It allows latency-sensitive applications to be placed where the data is created, thus reducing communication overhead and improving security, mobility and power consumption. There is a plethora of applications benefiting from this type of processing. Of particular interest is emerging edge-based image classification at the microscopic level. The scale and magnitude of the objects to segment, detect and classify are very challenging, with data collected using order of magnitude in magnification. The required data processing is intense, and the wish list of end-users in this space includes tools and solutions that fit into a desk-based device. Taking heavy-lift classification models initially built in the cloud to desk-based image analysis devices is a hard job for application developers. This work looks at the performance limitations and energy consumption footprint in embedding deep learning classification models in a representative edge computing device. Particularly, the dataset and heavy-lift models explored in the case study are phytoplankton images to detect Harmful Algae Blooms (HAB) in aquaculture at early stages. The work takes a deep learning model trained for phytoplankton classification and deploys it at the edge. The embedded model, deployed in a base form alongside optimised options, is submitted to a series of system stress experiments. The performance and power consumption profiling help understand system limitations and their impact on the microscopic grade image classification task.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75864732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Study on Fuchsia and Linux Device Driver Architecture Fuchsia与Linux设备驱动架构的比较研究

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577828

Taejoon Song, Youngjin Kim

引用次数: 0