Applied Computing Review最新文献_第8页

Machine Learning for VRUs accidents prediction using V2X data 使用V2X数据进行vru事故预测的机器学习

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3578263

B. Ribeiro, M. J. Nicolau, Alexandre J. T. Santos

引用次数: 0

Nioh-PT: Virtual I/O Filtering for Agile Protection against Vulnerability Windows Nioh-PT:针对漏洞窗口的敏捷保护的虚拟I/O过滤

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577687

Mana Senuki, Ken-Ichi Ishiguro, K. Kono

{"title":"Nioh-PT: Virtual I/O Filtering for Agile Protection against Vulnerability Windows","authors":"Mana Senuki, Ken-Ichi Ishiguro, K. Kono","doi":"10.1145/3555776.3577687","DOIUrl":"https://doi.org/10.1145/3555776.3577687","url":null,"abstract":"Hypervisor vulnerabilities cause severe security issues in multi-tenant cloud environments because hypervisors guarantee isolation among virtual machines (VMs). Unfortunately, hypervisor vulnerabilities are continuously reported, and device emulation in hypervisors is one of the hotbeds because of its complexity. Although applying patches to fix the vulnerabilities is a common way to protect hypervisors, it takes time to develop the patches because the internal knowledge on hypervisors is mandatory. The hypervisors are exposed to the threat of the vulnerabilities exploitation until the patches are released. This paper proposes Nioh-PT, a framework for filtering illegal I/O requests, which reduces the vulnerability windows of the device emulation. The key insight of Nioh-PT is that malicious I/O requests contain illegal I/O sequences, a series of I/O requests that are not issued during normal I/O operations. Nioh-PT filters out those illegal I/O sequences and protects device emulators against the exploitation. The filtering rules, which define illegal I/O sequences for virtual device exploits, can be specified without the knowledge on the internal implementation of hypervisors and virtual devices, because Nioh-PT is decoupled from hypervisors and the device emulators. We develop 11 filtering rules against four real-world vulnerabilities in device emulation, including CVE-2015-3456 (VENOM) and CVE-2016-7909. We demonstrate that Nioh-PT with these filtering rules protects against the virtual device exploits and introduces negligible overhead by up to 8% for filesystem and storage benchmarks.","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":"76072650","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

A Semantic Evidence-based Approach to Continuous Cloud Service Certification 基于语义证据的持续云服务认证方法

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577600

Christian Banse, Immanuel Kunz, Nico Haas, Angelika Schneider

{"title":"A Semantic Evidence-based Approach to Continuous Cloud Service Certification","authors":"Christian Banse, Immanuel Kunz, Nico Haas, Angelika Schneider","doi":"10.1145/3555776.3577600","DOIUrl":"https://doi.org/10.1145/3555776.3577600","url":null,"abstract":"Continuous certification of cloud services requires a high degree of automation in collecting and evaluating evidences. Prior approaches to this topic are often specific to a cloud provider or a certain certification catalog. This makes it costly and complex to achieve conformance to multiple certification schemes and covering multi-cloud solutions. In this paper, we present a novel approach to continuous certification which is scheme- and vendor-independent. Leveraging an ontology of cloud resources and their security features, we generalize vendor- and scheme-specific terminology into a new model of so-called semantic evidence. In combination with generalized metrics that we elicited out of requirements from the EUCS and the CCMv4, we present a framework for the collection and assessment of such semantic evidence across multiple cloud providers. This allows to conduct continuous cloud certification while achieving re-usability of metrics and evidences in multiple certification schemes. The performance benchmark of the framework's prototype implementation shows that up to 200,000 evidences can be processed in less than a minute, making it suitable for short time intervals used in continuous certification.","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":"79252397","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

Are alternatives to backpropagation useful for training Binary Neural Networks? An experimental study in image classification 反向传播的替代方法对训练二元神经网络有用吗?图像分类的实验研究

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577674

Ben Crulis, Barthélémy Serres, Cyril de Runz, G. Venturini

{"title":"Are alternatives to backpropagation useful for training Binary Neural Networks? An experimental study in image classification","authors":"Ben Crulis, Barthélémy Serres, Cyril de Runz, G. Venturini","doi":"10.1145/3555776.3577674","DOIUrl":"https://doi.org/10.1145/3555776.3577674","url":null,"abstract":"Current artificial neural networks are trained with parameters encoded as floating point numbers that occupy lots of memory space at inference time. Due to the increase in size of deep learning models, it is becoming very difficult to consider training and using artificial neural networks on edge devices such as smartphones. Binary neural networks promise to reduce the size of deep neural network models as well as increasing inference speed while decreasing energy consumption and so allow the deployment of more powerful models on edge devices. However, binary neural networks are still proven to be difficult to train using the backpropagation based gradient descent scheme. We propose to adapt to binary neural networks two training algorithms considered as promising alternatives to backpropagation but for continuous neural networks. We provide experimental comparative results for image classification including the backpropagation baseline on the MNIST, Fashion MNIST and CIFAR-10 datasets in both continuous and binary settings. The results demonstrate that binary neural networks can not only be trained using alternative algorithms to backpropagation but can also be shown to lead better performance and a higher tolerance to the presence or absence of batch normalization layers.","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":"77059636","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

Differences in performance, scalability, and cost of using microservice and monolithic architecture 使用微服务和单片架构在性能、可伸缩性和成本上的差异

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3578725

Przemysław Jatkiewicz, Szymon Okrój

引用次数: 1

Federated Hyperparameter Optimisation with Flower and Optuna 基于Flower和Optuna的联邦超参数优化

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577847

J. Parra-Ullauri, Xunzheng Zhang, A. Bravalheri, R. Nejabati, D. Simeonidou

{"title":"Federated Hyperparameter Optimisation with Flower and Optuna","authors":"J. Parra-Ullauri, Xunzheng Zhang, A. Bravalheri, R. Nejabati, D. Simeonidou","doi":"10.1145/3555776.3577847","DOIUrl":"https://doi.org/10.1145/3555776.3577847","url":null,"abstract":"Federated learning (FL) is an emerging distributed machine learning technique in which multiple clients collaborate to learn a model under the management of a central server. An FL system depends on a set of initial conditions (i.e., hyperparameters) that affect the system's performance. However, defining a good choice of hyperparameters for the central server and clients is a challenging problem. Hyperparameter tuning in FL often requires manual or automated searches to find optimal values. Nonetheless, a noticeable limitation is the high cost of algorithm evaluation for server and client models, making the tuning process computationally expensive and time-consuming. We propose an implementation based on integrating the FL framework Flower, and the prime optimisation software Optuna for automated and efficient hyperparameter optimisation (HPO) in FL. Through this combination, it is possible to tune hyperparameters in both clients and server online, aiming to find the optimal values at runtime. We introduce the HPO factor to describe the number of rounds that the HPO will take place, and the HPO rate that defines the frequency for updating the hyperparameters and can be used for pruning. The HPO is managed by the FL server which updates clients' hyperparameters, with an HPO rate, using state-of-the-art optimisation algorithms enabled by Optuna. We tested our approach by updating multiple client models simultaneously in popular image recognition datasets which produced promising results compared to baselines.","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":"72646118","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

A formal analysis of Dutch Generic Integral Tunnel Design models 荷兰通用整体隧道设计模型的形式化分析

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577786

Kevin H. J. Jilissen, P. Dieleman, J. F. Groote

引用次数: 0

Topic Aware Influential Member Detection in Meetup 主题感知的Meetup中有影响力的成员检测

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577684

Arpan Dam, Surya Kumar, Debjyoti Bhattacharjee, Sayan D. Pathak, Bivas Mitra

引用次数: 0

Towards a Recommender System-based Process for Managing Risks in Scrum Projects 基于推荐系统的Scrum项目风险管理流程

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577748

Ademar França de Sousa Neto, F. Ramos, D. Albuquerque, Emanuel Dantas, M. Perkusich, H. Almeida, A. Perkusich

{"title":"Towards a Recommender System-based Process for Managing Risks in Scrum Projects","authors":"Ademar França de Sousa Neto, F. Ramos, D. Albuquerque, Emanuel Dantas, M. Perkusich, H. Almeida, A. Perkusich","doi":"10.1145/3555776.3577748","DOIUrl":"https://doi.org/10.1145/3555776.3577748","url":null,"abstract":"Agile Software Development (ASD) implicitly manages risks through, for example, its short development cycles (i.e., iterations). The absence of explicit risk management activities in ASD might be problematic since this approach cannot handle all types of risks, might cause risks (e.g., technical debt), and does not promote knowledge reuse throughout an organization. Thus, there is a need to bring discipline to agile risk management. This study focuses on bringing such discipline to organizations that conduct multiple projects to develop software products using ASD, specifically, the Scrum framework, which is the most popular way of adopting ASD. For this purpose, we developed a novel solution that was articulated in partnership with an industry partner. It is a process to complement the Scrum framework to use a recommender system that recommends risks and response plans for a target project, given the risks registered for similar projects in an organization's risk memory (i.e., database). We evaluated the feasibility of the proposed recommender system solution using pre-collected datasets from 17 projects from our industry partner. Since we used the KNN algorithm, we focused on finding the best configuration of k (i.e., the number of neighbors) and the similarity measure. As a result, the configuration with the best results had k = 6 (i.e., six neighbors) and used the Manhattan similarity measure, achieving precision = 45%; recall = 90%; and F1-score = 58%. The results show that the proposed recommender system can assist Scrum Teams in identifying risks and response plans, and it is promising to aid decision-making in Scrum-based projects. Thus, we concluded that our proposed recommender system-based risk management process is promising for helping Scrum Teams address risks more efficiently.","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":"73470263","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

Towards the support of Industrial IoT applications with TSCH 通过TSCH支持工业物联网应用

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Applied Computing Review Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577752

Ivanilson F. Vieira Júnior, M. Curado, J. Granjal

{"title":"Towards the support of Industrial IoT applications with TSCH","authors":"Ivanilson F. Vieira Júnior, M. Curado, J. Granjal","doi":"10.1145/3555776.3577752","DOIUrl":"https://doi.org/10.1145/3555776.3577752","url":null,"abstract":"Low-power and Lossy Networks (LLN) are utilised for numerous Internet of Things (IoT) applications. IEEE has specified the Time-slotted Channel Hopping (TSCH) Media Access Control (MAC) to target the needs of Industrial IoT. TSCH supports deterministic communications over unreliable wireless environments and balances energy, bandwidth and latency. Furthermore, the Minimal 6TiSCH configuration defined Routing Protocol for Low power and Lossy networks (RPL) with the Objective Function 0 (OF0). Inherent factors from RPL operation, such as joining procedure, parent switching, and trickle timer fluctuations, may introduce overhead and overload the network with control messages. The application and RPL control data may lead to an unpredicted networking bottleneck, potentially causing network instability. Hence, a stable RPL operation contributes to a healthy TSCH operation. In this paper, we explore TSCH MAC and RPL metrics to identify factors that lead to performance degradation and specify indicators to anticipate network disorders towards increasing Industrial IoT reliability. A TSCH Schedule Function might employ the identified aspects to foresee disturbances, proactively allocate the proper amount of cells, and avoid networking congestion.","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":"87524582","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