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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3578263
B. Ribeiro, M. J. Nicolau, Alexandre J. T. Santos
Intelligent Transportation Systems (ITS) are systems that consist on an complex set of technologies that are applied to road agents, aiming to provide a more efficient and safe usage of the roads. The aspect of safety is particularly important for Vulnerable Road Users (VRUs), which are entities for whose implementation of automatic safety solutions is challenging for their agility and hard to anticipate behavior. However, the usage of ML techniques on Vehicle to Anything (V2X) data has the potential to implement such systems. This paper proposes a VRUs (motorcycles) accident prediction system by using Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (pairing SUMO and ns-3). Results show that the proposed system is able to predict 96% of the accidents on Scenario A (with a 4.53s Average Prediction Time and a 41% Correct Decision Percentage (CDP) - 78 False Positives (FP)) and 95% on Scenario B (with a 4.44s Average Prediction Time and a 43% CDP - 68 FP).
智能交通系统(ITS)是由一套应用于道路代理的复杂技术组成的系统,旨在提供更有效和更安全的道路使用。对于弱势道路使用者(vru)来说,安全方面尤为重要,因为他们的灵活性和难以预测的行为,对他们来说,实施自动安全解决方案是一项挑战。然而,在V2X数据上使用机器学习技术有可能实现这样的系统。本文提出了一种基于基于vein仿真框架(配对SUMO和ns-3)生成的通信数据的长短期记忆(LSTMs)的vru(摩托车)事故预测系统。结果表明,该系统对情景A的预测准确率为96%(平均预测时间为4.53秒,正确决策百分比(CDP)为41%,误报率为78),对情景B的预测准确率为95%(平均预测时间为4.44秒,正确决策百分比为43%,误报率为68)。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577687
Mana Senuki, Ken-Ichi Ishiguro, K. Kono
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.
在多租户云环境中,Hypervisor的漏洞会导致严重的安全问题,因为Hypervisor保证了虚拟机之间的隔离。不幸的是,管理程序漏洞不断被报道,管理程序中的设备模拟由于其复杂性而成为温床之一。尽管应用补丁来修复漏洞是保护管理程序的常用方法,但是开发补丁需要时间,因为管理程序的内部知识是强制性的。在补丁发布之前,管理程序暴露在漏洞利用的威胁之下。本文提出了Nioh-PT框架来过滤非法I/O请求,减少了设备仿真的漏洞窗口。Nioh-PT的关键洞察是,恶意I/O请求包含非法I/O序列,即在正常I/O操作期间不发出的一系列I/O请求。Nioh-PT过滤掉那些非法的I/O序列,并保护设备模拟器免受利用。过滤规则为虚拟设备漏洞定义了非法I/O序列,可以在不了解管理程序和虚拟设备的内部实现的情况下指定,因为Nioh-PT与管理程序和设备模拟器解耦。我们针对设备仿真中的四个真实漏洞开发了11个过滤规则,包括CVE-2015-3456 (VENOM)和CVE-2016-7909。我们证明了Nioh-PT使用这些过滤规则可以防止虚拟设备被利用,并为文件系统和存储基准测试引入了高达8%的可忽略不计的开销。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577600
Christian Banse, Immanuel Kunz, Nico Haas, Angelika Schneider
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.
云服务的持续认证要求证据的收集和评估高度自动化。此主题的先前方法通常特定于云提供商或某个认证目录。这使得实现与多个认证方案的一致性并覆盖多云解决方案的成本高昂且复杂。本文提出了一种独立于方案和供应商的连续认证方法。利用云资源本体及其安全特性,我们将特定于供应商和方案的术语概括为所谓的语义证据的新模型。结合我们从EUCS和CCMv4的需求中得出的广义指标,我们提出了一个框架,用于跨多个云提供商收集和评估此类语义证据。这允许进行持续的云认证,同时在多个认证方案中实现指标和证据的可重用性。该框架原型实现的性能基准表明,在不到一分钟的时间内可以处理多达20万个证据,适用于短时间间隔的连续认证。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577674
Ben Crulis, Barthélémy Serres, Cyril de Runz, G. Venturini
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.
目前的人工神经网络是用浮点数编码的参数来训练的,在推理时占用了大量的内存空间。由于深度学习模型规模的增加,考虑在智能手机等边缘设备上训练和使用人工神经网络变得非常困难。二元神经网络有望减少深度神经网络模型的大小,提高推理速度,同时降低能耗,从而允许在边缘设备上部署更强大的模型。然而,使用基于反向传播的梯度下降方案训练二元神经网络仍然被证明是困难的。我们提出了适合于二元神经网络的两种训练算法,这两种算法被认为是有前途的反向传播替代方案,但适用于连续神经网络。在连续和二进制设置下,我们提供了包括MNIST、Fashion MNIST和CIFAR-10数据集上的反向传播基线图像分类的实验比较结果。结果表明,二元神经网络不仅可以使用反向传播的替代算法进行训练,而且还可以显示出更好的性能和对批处理归一化层存在或不存在的更高容忍度。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3578725
Przemysław Jatkiewicz, Szymon Okrój
A microservices-based architecture is a set of small components that communicate with each other using a programming language-independent API [1]. It has been gaining popularity for more than a decade. One of its advantages is greater agility in software development and following modern, agile software development practices [2]. The article presents an experimental study. Two applications with the same business logic and different architecture were developed. Both applications were tested using prepared test cases on the local computer of one of the authors and the Microsoft Azure platform. The results were collected and compared using the JMeter tool. In almost all cases, the monolithic architecture proved to be more efficient. The comparable performance of both architectures occurred when queries were handled by the business logic layer for a relatively long time.
基于微服务的架构是一组使用独立于编程语言的API相互通信的小组件[1]。十多年来,它一直越来越受欢迎。它的优点之一是在软件开发中具有更高的敏捷性,并遵循现代的敏捷软件开发实践[2]。本文进行了实验研究。开发了两个具有相同业务逻辑和不同架构的应用程序。这两个应用程序都在一位作者的本地计算机和Microsoft Azure平台上使用准备好的测试用例进行了测试。使用JMeter工具收集和比较结果。在几乎所有的情况下,单片架构被证明是更高效的。当查询由业务逻辑层处理较长时间时,两种体系结构的性能可比较。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577847
J. Parra-Ullauri, Xunzheng Zhang, A. Bravalheri, R. Nejabati, D. Simeonidou
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.
联邦学习(FL)是一种新兴的分布式机器学习技术,其中多个客户端在中央服务器的管理下协作学习模型。FL系统依赖于一组影响系统性能的初始条件(即超参数)。然而,为中心服务器和客户机定义一个好的超参数选择是一个具有挑战性的问题。FL中的超参数调优通常需要手动或自动搜索以找到最优值。尽管如此,一个明显的限制是服务器和客户机模型的算法评估的高成本,使得优化过程在计算上昂贵且耗时。我们提出了一种基于集成FL框架Flower和主要优化软件Optuna的实现,用于FL中自动化和高效的超参数优化(HPO)。通过这种组合,可以在线调整客户端和服务器中的超参数,旨在在运行时找到最优值。我们引入了HPO因子来描述HPO将发生的轮数,以及HPO率,它定义了更新超参数的频率,并可用于修剪。HPO由FL服务器管理,该服务器使用Optuna启用的最先进的优化算法,以HPO率更新客户端的超参数。我们通过在流行的图像识别数据集中同时更新多个客户端模型来测试我们的方法,与基线相比,产生了有希望的结果。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577786
Kevin H. J. Jilissen, P. Dieleman, J. F. Groote
The Generic Integral Tunnel Design (GITO) contains generic models for the tunnel control systems of Rijkswaterstaat, part of the Dutch Ministry of Infrastructure and Water Management. A formal verification of these models advances the safety and reliability of GITO derived tunnel control systems. In this paper, the first known large-scale formalisation of tunnel control systems is presented which transforms GITO models to the formal specification language mCRL2. This transformation is applied to two sub-systems of the GITO to analyse the correctness of the supplied models. In this formal analysis, several deficiencies in the specifications and faults in the existing models are revealed and verified solutions are proposed. Some of the presented faults even find their origin in the legally required standards.
通用整体隧道设计(GITO)包含荷兰基础设施和水资源管理部Rijkswaterstaat隧道控制系统的通用模型。对这些模型的形式化验证提高了GITO导出的隧道控制系统的安全性和可靠性。本文提出了第一个已知的隧道控制系统的大规模形式化,它将GITO模型转换为形式化规范语言mCRL2。将此转换应用于GITO的两个子系统,以分析所提供模型的正确性。在此形式化分析中,揭示了规范中的一些不足和现有模型中的缺陷,并提出了验证的解决方案。一些出现的缺陷甚至可以在法律要求的标准中找到它们的根源。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577684
Arpan Dam, Surya Kumar, Debjyoti Bhattacharjee, Sayan D. Pathak, Bivas Mitra
Hosting popular Meetup events is one of the primary objectives of the Meetup organizers. This paper explores the possibility of inviting a few key influential members to attend Meetup events, who may further influence their followers to attend and boost the popularity of those Meetup events. Importantly, our pilot study reveals that topics of the Meetup events play a key role behind the effectiveness of the influential members. Leveraging this observation, in this paper, we develop Topic Aware Influencer Detection (TAID) heuristics, which recommends (i) top-k influential members Ik, and (ii) top-b influence badges Rb based on the topical interest of a Meetup group. This indicates that Ik. will be most effective in influencing the Meetup members to attend the events hosted on topic Rb. TAID heuristics contains two major blocks (a) influence propagation graph construction, and (b) recommendation generation. Rigorous evaluation of TAID on 1447 Meetup groups with three different topics reveals that TAID comfortably outperforms the baselines by influencing (on average) 15% more Meetup members.
举办受欢迎的Meetup活动是Meetup组织者的主要目标之一。本文探讨了邀请一些关键的有影响力的成员参加Meetup活动的可能性,这些成员可能会进一步影响他们的追随者参加Meetup活动,从而提高这些Meetup活动的知名度。重要的是,我们的试点研究表明,Meetup活动的主题在有影响力的成员的有效性背后起着关键作用。利用这一观察结果,在本文中,我们开发了话题感知影响者检测(TAID)启发式方法,该方法根据Meetup小组的主题兴趣推荐(i) top-k有影响力的成员Ik和(ii) top-b有影响力的徽章Rb。这表明Ik。将最有效地影响Meetup成员参加主题Rb举办的活动。TAID启发式包含两个主要块(a)影响传播图构建和(b)推荐生成。对1447个有三个不同主题的Meetup小组的TAID进行的严格评估显示,TAID对Meetup成员的影响(平均)增加了15%,远远超过了基线。
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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
IF 1 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
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.
例如,敏捷软件开发(ASD)通过其短的开发周期(即迭代)隐含地管理风险。在ASD中缺乏明确的风险管理活动可能是有问题的,因为这种方法不能处理所有类型的风险,可能导致风险(例如,技术债务),并且不能促进整个组织中的知识重用。因此,有必要给敏捷风险管理带来纪律。本研究的重点是将这样的规则引入到使用ASD开发软件产品的多个项目的组织中,特别是Scrum框架,这是采用ASD的最流行的方式。为此,我们开发了一种新颖的解决方案,该解决方案是与行业合作伙伴合作提出的。这是一个补充Scrum框架的过程,使用推荐系统,根据组织风险记忆(即数据库)中类似项目的风险,为目标项目推荐风险和响应计划。我们使用我们的行业合作伙伴从17个项目中预先收集的数据集评估了建议的推荐系统解决方案的可行性。由于我们使用了KNN算法,我们专注于寻找k的最佳配置(即邻居的数量)和相似性度量。结果表明,k = 6(即6个邻居)的最佳配置使用曼哈顿相似性度量,精度为45%;召回率= 90%;F1-score = 58%。结果表明,建议的推荐系统可以帮助Scrum团队识别风险和响应计划,并且有望在基于Scrum的项目中帮助决策。因此,我们得出结论,我们建议的基于系统的风险管理流程有望帮助Scrum团队更有效地处理风险。
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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
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577752
Ivanilson F. Vieira Júnior, M. Curado, J. Granjal
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.
低功耗和有损网络(LLN)用于许多物联网(IoT)应用。针对工业物联网的需求,IEEE指定了时隙信道跳频(TSCH)媒体访问控制(MAC)。TSCH支持不可靠无线环境下的确定性通信,并平衡能量、带宽和延迟。此外,最小6TiSCH配置定义了低功耗和有损网络(RPL)的路由协议,目标函数为0 (OF0)。来自RPL操作的固有因素,如加入过程、父节点交换和涓流定时器波动,可能会引入开销并使控制消息使网络过载。应用程序和RPL控制数据可能会导致不可预测的网络瓶颈,从而可能导致网络不稳定。因此,稳定的RPL操作有助于健康的TSCH操作。在本文中,我们探讨了TSCH MAC和RPL指标,以确定导致性能下降的因素,并指定指标来预测网络紊乱,以提高工业物联网的可靠性。TSCH调度函数可以使用识别的方面来预测干扰,主动分配适当数量的单元,并避免网络拥塞。
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引用次数: 0
期刊
Applied Computing Review
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