Applied Computing Review最新文献_第5页

Exploring Candlesticks and Multi-Time Windows for Forecasting Stock-Index Movements 探索烛台和多时间窗口预测股指走势

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577604

Kanghyeon Seo, Jihoon Yang

引用次数: 0

A Multi-layered Collaborative Framework for Evidence-driven Data Requirements Engineering for Machine Learning-based Safety-critical Systems 基于机器学习的安全关键系统证据驱动数据需求工程的多层协作框架

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577647

Sangeeta Dey, Seok-Won Lee

{"title":"A Multi-layered Collaborative Framework for Evidence-driven Data Requirements Engineering for Machine Learning-based Safety-critical Systems","authors":"Sangeeta Dey, Seok-Won Lee","doi":"10.1145/3555776.3577647","DOIUrl":"https://doi.org/10.1145/3555776.3577647","url":null,"abstract":"In the days of AI, data-centric machine learning (ML) models are increasingly used in various complex systems. While many researchers are focusing on specifying ML-specific performance requirements, not enough guideline is provided to engineer the data requirements systematically involving diverse stakeholders. Lack of written agreement about the training data, collaboration bottlenecks, lack of data validation framework, etc. are posing new challenges to ensuring training data fitness for safety-critical ML components. To reduce these gaps, we propose a multi-layered framework that helps to perceive and elicit data requirements. We provide a template for verifiable data requirements specifications. Moreover, we show how such requirements can facilitate an evidence-driven assessment of the training data quality based on the experts' judgments about the satisfaction of the requirements. We use Dempster Shafer's theory to combine experts' subjective opinions in the process. A preliminary case study on the CityPersons dataset for the pedestrian detection feature of autonomous cars shows the usefulness of the proposed framework for data requirements understanding and the confidence assessment of the dataset.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"35 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89390161","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

Exploiting Machine-learning Prediction for Enabling Real-time Pixel-scaling Techniques in Mobile Camera Applications 利用机器学习预测在移动相机应用中实现实时像素缩放技术

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577770

S. Wei, Sheng-Da Tsai, Chun-Han Lin

引用次数: 0

The EVIL Machine: Encode, Visualize and Interpret the Leakage 邪恶的机器:编码，可视化和解释泄漏

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577688

Valence Cristiani, Maxime Lecomte, P. Maurine

{"title":"The EVIL Machine: Encode, Visualize and Interpret the Leakage","authors":"Valence Cristiani, Maxime Lecomte, P. Maurine","doi":"10.1145/3555776.3577688","DOIUrl":"https://doi.org/10.1145/3555776.3577688","url":null,"abstract":"Unsupervised side-channel attacks allow extracting secret keys manipulated by cryptographic primitives through leakages of their physical implementations. As opposed to supervised attacks, they do not require a preliminary profiling of the target, constituting a broader threat since they imply weaker assumptions on the adversary model. Their downside is their requirement for some a priori knowledge on the leakage model of the device. On one hand, stochastic attacks such as the Linear Regression Analysis (LRA) allow for a flexible a priori, but are mostly limited to a univariate treatment of the traces. On the other hand, model-based attacks require an explicit formulation of the leakage model but have recently been extended to multidimensional versions allowing to benefit from the potential of Deep Learning (DL) techniques. The EVIL Machine Attack (EMA), introduced in this paper, aims at taking the best of both worlds. Inspired by generative adversarial networks, its architecture is able to recover a representation of the leakage model, which is then turned into a key distinguisher allowing flexible a priori. In addition, state-of-the-art DL techniques require 256 network trainings to conduct the attack. EMA requires only one, scaling down the time complexity of such attacks by a considerable factor. Simulations and real experiments show that EMA is applicable in cases where the adversary has very low knowledge on the leakage model, while significantly reducing the required number of traces compared to a classical LRA. Eventually, a generalization of EMA, able to deal with masked implementation is introduced.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"144 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77580079","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: 2

Dynamic Controllability of Parameterized CSTNUs 参数化CSTNUs的动态可控性

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577618

M. Franceschetti, Roberto Posenato, Carlo Combi, Johann Eder

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":"9 1","pages":""},"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

Detecting and Measuring the Polarization Effects of Adversarial Botnets on Twitter Twitter上对抗性僵尸网络极化效应的检测和测量

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577730

Yeonjung Lee, M. Ozer, S. Corman, H. Davulcu

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":"129 1","pages":""},"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

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

CEM: an Ontology for Crime Events in Newspaper Articles 报纸文章中犯罪事件的本体

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577862

Federica Rollo, Laura Po, Alessandro Castellucci

引用次数: 1