Applied Computing Review最新文献_第4页

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":"27 1","pages":""},"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

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

Graph Convolutional Neural Network for Multimodal Movie Recommendation 多模态电影推荐的图卷积神经网络

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577853

Prabir Mondal, Daipayan Chakder, Subham Raj, S. Saha, N. Onoe

引用次数: 2

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

Student Research Abstract: A Hybrid Approach to Design Embedded Software Using JavaScript's Non-blocking Principle 学生研究摘要:利用JavaScript的非阻塞原理设计嵌入式软件的混合方法

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577210

Fernando L. Oliveira

{"title":"Student Research Abstract: A Hybrid Approach to Design Embedded Software Using JavaScript's Non-blocking Principle","authors":"Fernando L. Oliveira","doi":"10.1145/3555776.3577210","DOIUrl":"https://doi.org/10.1145/3555776.3577210","url":null,"abstract":"Embedded Systems (ES) are present in several domains like automotive, smart homes, smart cities, industry, and healthcare, to name but a few. ES brings new challenges to designing embedded software that requires a high level of abstraction and being aware of resource consumption, mainly on resource-constrained devices. Modern programming languages like JavaScript (JS) can help solve these issues. However, JS is an interpreted language that demands attention to develop applications considering the balance between performance and resource consumption. In this scenario, this paper introduces an architecture design that proposes to model software for embedded systems as event-driven applications. Our design combines traditional architectures traits of Time-triggered (TT) and Event-triggered (ET) into a framework named JSEVAsync, promoting a hybrid system that explores JavaScript's non-blocking concept as a development interface to structure the algorithms into asynchronous units. As a result, we aid the development of applications with high abstraction levels and better resource consumption. To validate it, we compare C- and JavaScript-based applications, analyze the source code (static code analysis) to extract software quality metrics, and explore the results from the energy consumption perspective. We found that writing code through JSEVAsync can be up to 21% more energy efficient than the traditional method and can improve design-time metrics.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"44 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79933869","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 Context Caching for Efficient Distributed Context Management Systems 高效分布式上下文管理系统的自适应上下文缓存

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577602

Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev

{"title":"Adaptive Context Caching for Efficient Distributed Context Management Systems","authors":"Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev","doi":"10.1145/3555776.3577602","DOIUrl":"https://doi.org/10.1145/3555776.3577602","url":null,"abstract":"We contend that performance metrics-driven adaptive context caching has a profound impact on performance efficiency in distributed context management systems (CMS). This paper proposes an adaptive context caching approach based on (i) a model of economics-inspired expected returns of caching particular items, and (ii) learning from historical context caching performance, i.e., our approach adaptively (with respect to statistics on historical performance) caches \"context\" with the objective of minimizing the cost incurred by a CMS in responding to context queries. Our novel algorithm enables context queries and sub-queries to reuse and repurpose cached context in an efficient manner, different from traditional data caching. The paper also proposes heuristics and adaptive policies such as eviction and context cache memory scaling. The method is evaluated using a synthetically generated load of sub-queries inspired by a real-world scenario. We further investigate optimal adaptive caching configurations under different settings. This paper presents and discusses our findings that the proposed statistical selective caching method reaches short-term cost optimality fast under massively volatile queries. The proposed method outperforms related algorithms by up to 47.9% in cost efficiency.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"22 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90098562","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: 5

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