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Exploring alternatives of Complex Event Processing execution engines in demanding cases 探索复杂事件处理执行引擎在苛刻情况下的替代方案
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577734
Styliani Kyrama, A. Gounaris
Complex Event Processing (CEP) is a mature technology providing particularly efficient solutions for pattern detection in streaming settings. Nevertheless, even the most advanced CEP engines struggle to deal with cases when the number of pattern matches grows exponentially, e.g., when the queries involve Kleene operators to detect trends. In this work, we present an overview of state-of-the-art CEP engines used for pattern detection, focusing also on systems that discover demanding event trends. The main contribution lies in the comparison of existing CEP engine alternatives and the proposal of a novel hash-endowed automata-based lazy hybrid execution engine, called SASEXT, that undertakes the processing of pattern queries involving Kleene patterns. Our proposal is orders of magnitude faster than existing solutions.
复杂事件处理(CEP)是一种成熟的技术,为流设置中的模式检测提供了特别有效的解决方案。然而,即使是最先进的CEP引擎也难以处理模式匹配数量呈指数增长的情况,例如,当查询涉及Kleene操作符来检测趋势时。在这项工作中,我们概述了用于模式检测的最先进的CEP引擎,还关注了发现苛刻事件趋势的系统。本文的主要贡献在于比较了现有的CEP引擎替代方案,并提出了一种新的基于哈希的自动机的惰性混合执行引擎(称为SASEXT),该引擎负责处理涉及Kleene模式的模式查询。我们的建议比现有的解决方案快几个数量级。
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引用次数: 0
DEDACS: Decentralized and dynamic access control for smart contracts in a policy-based manner dedecs:基于策略的智能合约去中心化动态访问控制
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577676
Kristof Jannes, Vincent Reniers, Wouter Lenaerts, B. Lagaisse, W. Joosen
Distributed Ledger Technology (DLTs) or blockchains have been steadily emerging and providing innovation in the past decade for several use cases, ranging from financial networks, to notarization, or trustworthy execution via smart contracts. DLTs are enticing due to their properties of decentralization, non-repudiation, and auditability (transparency). These properties are of high potential to access control systems that can be implemented on-chain, and are executed without infringement and full transparency. While it remains uncertain which use cases will truly turn out to be viable, many use cases such as financial transactions can benefit from integrating certain restrictions via access control on the blockchain. In addition, smart contracts may in the future present security risks that are currently yet unknown. As a solution, access control policies can provide flexibility in the execution flow when adopted by smart contracts. In this paper, we present our DEDACS architecture which provides decentralized and dynamic access control for smart contracts in a policy-based manner. Our access control is expressive as it features policies, and dynamic as the environment or users can be changed, or alternative policies can be assigned to smart contracts. DEDACS ensures that our access control preserves the desired properties of decentralization and transparency, while aiming to keep the costs involved as minimal as possible. We have evaluated DEDACS in the context of a Uniswap token-exchange platform, in which we evaluated the costs related to (i) the introduced overhead at deployment time and (ii) the operational overhead cost. DEDACS introduces a relative overhead of on average 52% at deployment time, and an operational overhead between 52% and 80% depending on the chosen policy and its complexity.
分布式账本技术(dlt)或区块链在过去十年中稳步出现,并为几个用例提供了创新,从金融网络到公证,或通过智能合约进行可信执行。分布式账本技术由于其去中心化、不可否认性和可审计性(透明度)的特性而具有吸引力。这些属性对于可以在链上实现的访问控制系统具有很高的潜力,并且执行时不会侵权且完全透明。虽然仍不确定哪些用例将真正可行,但许多用例(如金融事务)可以通过在区块链上的访问控制集成某些限制而受益。此外,智能合约未来可能会出现目前未知的安全风险。作为一种解决方案,当智能合约采用访问控制策略时,可以在执行流中提供灵活性。在本文中,我们提出了我们的dedecs架构,它以一种基于策略的方式为智能合约提供去中心化和动态的访问控制。我们的访问控制具有表现力,因为它具有策略,并且是动态的,因为环境或用户可以更改,或者可以将替代策略分配给智能合约。dedecs确保我们的访问控制保留了去中心化和透明度的期望属性,同时旨在尽可能降低所涉及的成本。我们在Uniswap令牌交换平台的背景下评估了DEDACS,其中我们评估了与(i)部署时引入的开销和(ii)运营开销相关的成本。DEDACS在部署时的相对开销平均为52%,根据所选择的策略及其复杂性,操作开销在52%到80%之间。
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引用次数: 1
Generic Privacy Preserving Private Permissioned Blockchains 通用隐私保护私有许可区块链
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577735
Frederic A. Hayek, Mirko Koscina, P. Lafourcade, Charles Olivier-Anclin
Private permissioned blockchains are becoming gradually more sought-after. Such systems are reachable by authorized users, and tend to be completely transparent to whoever interacts with the blockchain. In this paper, we mitigate the latter. Authorized users can now stay unlinked to the transaction they propose in the blockchain while being authenticated before being allowed to interact. As a first contribution, we developed a consensus algorithm for private permissioned blockchains based on Hyperledger Fabric and the Practical Byzantine Fault Tolerance consensus. Building on this blockchain, five additional variations achieving various client-wise privacy preserving levels are proposed. These different protocols allow for different use cases and levels of privacy control and sometimes its revocation by an authority. All our protocols guarantee the unlinkability of transactions to their issuers achieving anonymity or pseudonymity. Miners can also inherit some of the above privacy preserving setting. Naturally, we maintain liveness and safety of the system and its data.
私人许可的区块链正逐渐变得越来越受欢迎。这样的系统可以被授权用户访问,并且对于与区块链交互的任何人来说都是完全透明的。在本文中,我们减轻了后者。授权用户现在可以不链接到他们在区块链中提出的交易,同时在被允许进行交互之前进行身份验证。作为第一个贡献,我们基于超级账本结构和实用拜占庭容错共识为私有许可区块链开发了共识算法。在此区块链的基础上,提出了实现各种客户端隐私保护级别的五个附加变体。这些不同的协议支持不同的用例和级别的隐私控制,有时还允许权威机构撤销隐私控制。我们所有的协议都保证交易与发行者的不可链接性,实现匿名或假名。矿工也可以继承上面的一些隐私保护设置。当然,我们要维护系统及其数据的活力和安全性。
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引用次数: 1
Real-life Performance of Fairness Interventions - Introducing A New Benchmarking Dataset for Fair ML 公平干预的现实表现——为公平ML引入一个新的基准数据集
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577634
Daphne Lenders, T. Calders
Some researchers evaluate their fair Machine Learning (ML) algorithms by simulating data with a fair and biased version of its labels. The fair labels reflect what labels individuals deserve, while the biased labels reflect labels obtained through a biased decision process. Given such data, fair algorithms are evaluated by measuring how well they can predict the fair labels, after being trained on the biased ones. The big problem with these approaches is, that they are based on simulated data, which is unlikely to capture the full complexity and noise of real-life decision problems. In this paper, we show how we created a new, more realistic dataset with both fair and biased labels. For this purpose, we started with an existing dataset containing information about high school students and whether they passed an exam or not. Through a human experiment, where participants estimated the school performance given some description of these students, we collect a biased version of these labels. We show how this new dataset can be used to evaluate fair ML algorithms, and how some fairness interventions, that perform well in the traditional evaluation schemes, do not necessarily perform well with respect to the unbiased labels in our dataset, leading to new insights into the performance of debiasing techniques.
一些研究人员通过用公平和有偏见的标签版本模拟数据来评估他们公平的机器学习(ML)算法。公平的标签反映了个人应得的标签,而有偏见的标签反映了通过有偏见的决策过程获得的标签。给定这样的数据,公平算法是通过测量它们在对有偏见的标签进行训练后预测公平标签的程度来评估的。这些方法的一个大问题是,它们基于模拟数据,不太可能捕捉到现实生活中决策问题的全部复杂性和噪音。在本文中,我们展示了如何创建一个新的、更真实的数据集,其中包含公平和有偏见的标签。为此,我们从一个现有的数据集开始,其中包含有关高中生的信息,以及他们是否通过了考试。通过一项人体实验,参与者根据对这些学生的一些描述来估计他们在学校的表现,我们收集了这些标签的偏见版本。我们展示了这个新数据集如何用于评估公平的机器学习算法,以及一些在传统评估方案中表现良好的公平干预措施如何不一定在我们的数据集中表现良好,从而对去偏技术的性能产生了新的见解。
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引用次数: 1
A Synthetic Dataset Generation for the Uveitis Pathology Based on MedWGAN Model 基于MedWGAN模型的葡萄膜炎病理合成数据集生成
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577648
Heithem Sliman, I. Megdiche, Sami Yangui, Aida Drira, Ines Drira, E. Lamine
Artificial Intelligence (AI) has undergone considerable development in recent years in the field of medicine and in particular in decision support diagnostic. However, the development of such algorithms depends on the presence of a sufficiently large amount of data to provide reliable results. Unfortunately in medicine, it is not always possible to provide so much data on all pathologies. This problem is particularly true for rare diseases. In this paper we focus on uveitis, a rare disease in ophthalmology which is the third cause of blindness worldwide. This pathology is difficult to diagnose because of the disparity in prevalence of its etiologies. In order to provide physicians with a diagnostic aid system, it would be necessary to have a representative dataset of epidemiological profiles that have been studied for a long time in this domain. This work proposes a breakthrough in this field by suggesting a methodological framework for the generation of an open source dataset based on the crossing of several epidemiological profiles and using data augmentation techniques. The results of these generated synthetic data have been qualitatively validated by specialist physicians in ophthalmology. Our results are very promising and consist in a first brick to promote research in AI on Uveitis disease.
人工智能(AI)近年来在医学领域,特别是在决策支持诊断方面取得了长足的发展。然而,这种算法的发展依赖于足够大的数据量来提供可靠的结果。不幸的是,在医学上,不可能总是提供所有病理的如此多的数据。这个问题在罕见疾病中尤为明显。葡萄膜炎是一种罕见的眼科疾病,是世界上致盲的第三大原因。这种病理很难诊断,因为其病因的患病率存在差异。为了给医生提供一个诊断辅助系统,有必要拥有一个在这一领域已经研究了很长时间的具有代表性的流行病学概况数据集。这项工作提出了一个突破,提出了一个基于几种流行病学概况交叉和使用数据增强技术生成开源数据集的方法框架。这些合成数据的结果已由眼科专科医生进行了定性验证。我们的结果非常有希望,并且是推动人工智能在葡萄膜炎疾病研究中的第一块砖。
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引用次数: 0
Unsupervised Forecasting and Anomaly Detection of ADLs in single-resident elderly smart homes 单居老年人智能家居中ADLs的无监督预测与异常检测
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577822
Zahraa Khais Shahid, S. Saguna, C. Åhlund
As the aging population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we develop and evaluate a method to forecast activities of daily living (ADL) and detect anomalous behaviour using motion sensor data from smart homes. We build a predictive Multivariate long short term memory (LSTM) model for forecasting activities and evaluate it using data from six real-world smart homes. Further, we use Mahalanobis distance to identify anomalies in user behaviors based on predictions and actual values. In all of the datasets used for forecasting both duration of stay and level of activities using duration of activeness/stillness features, the max NMAE error was about 6%, the values show that the performance of LSTM for predicting the direct next activity versus the seven coming activities are close.
随着人口老龄化的加剧,针对老年人的预测性健康应用可以为老年人提供更加独立生活的机会,提高成本效益,改善老年人的健康服务质量。基于物联网的智能家居中的人类活动识别可以通过向老年人和支持性医疗保健提供者提供主动测量和干预措施,实现与轻度认知障碍相关的早期健康风险检测。在本文中,我们开发并评估了一种方法来预测日常生活活动(ADL),并使用来自智能家居的运动传感器数据检测异常行为。我们建立了一个预测的多元长短期记忆(LSTM)模型,用于预测活动,并使用来自六个现实世界智能家居的数据对其进行评估。此外,我们使用马氏距离来识别基于预测和实际值的用户行为异常。在使用活跃/静止特征的持续时间预测停留时间和活动水平的所有数据集中,最大NMAE误差约为6%,这些值表明LSTM预测直接下一个活动与七个即将到来的活动的性能接近。
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引用次数: 1
POI types characterization based on geographic feature embeddings 基于地理特征嵌入的POI类型表征
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577659
Salatiel Dantas Silva, C. E. Campelo, Maxwell Guimarães De Oliveira
Representing Points of Interest (POI) types, such as restaurants and shopping malls, is crucial to develop computational mechanisms that may assist in tasks such as urban planning and POI recommendation. The POI co-occurrences in different spatial regions have been used to represent POI types in high-dimensional vectors. However, such representations do not consider the geographic features (e.g. streets, buildings, rivers, parks) in the vicinity of POIs which may contribute to characterize such types. In this context, we propose the Geographic Context to Vector (GeoContext2Vec), an approach that relies on geographic features in the POIs' vicinity to generate POI types representation based on embeddings. We carried out an experiment to evaluate the GeoContext2Vec by using a POI type representation from the state-of-the-art that it does not consider geographic features. The promising results show that the geographic information provided by the GeoContext2Vec outperforms the state-of-the-art and demonstrates the relevance of surrouding geographic features on representing POI type more precisely.
表示兴趣点(POI)类型,如餐馆和购物中心,对于开发可能有助于城市规划和POI推荐等任务的计算机制至关重要。利用不同空间区域的POI共现来表示高维向量上的POI类型。然而,这种表述没有考虑到poi附近的地理特征(例如街道、建筑物、河流、公园),这些特征可能有助于描述这类类型。在这种情况下,我们提出了地理上下文到向量(GeoContext2Vec),这是一种依赖于POI附近的地理特征来基于嵌入生成POI类型表示的方法。我们进行了一项实验,通过使用不考虑地理特征的最先进的POI类型表示来评估GeoContext2Vec。结果表明,GeoContext2Vec提供的地理信息优于目前最先进的地理信息,并证明了周围地理特征与更精确地表示POI类型的相关性。
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引用次数: 0
Parallel construction of RNA databases for extensive lncRNA-RNA interaction prediction 并行构建RNA数据库,广泛预测lncRNA-RNA相互作用
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577772
Iñaki Amatria-Barral, J. González-Domínguez, J. Touriño
Long non-coding RNA sequences (lncRNAs) have completely changed how scientists approach genetics. While some believe that many lncRNAs are results of spurious transcriptions, recent evidence suggests that there exist thousands of them and that they have functions and regulate key biological processes. For the experimental characterization of lncRNAs, many tools that try to predict their interactions with other RNAs have been developed. Some of the fastest and more accurate tools, however, require a slow database construction step prior to the identification of interaction partners for each lncRNA. This paper presents a novel and efficient parallel database construction procedure. Benchmarking results on a 16-node multicore cluster show that our parallel algorithm can build databases up to 318 times faster than other tools in the market using just 256 CPU cores. All the code developed in this work is available to download at GitHub under the MIT License (https://github.com/UDC-GAC/pRIblast).
长链非编码RNA序列(lncRNAs)已经完全改变了科学家研究遗传学的方式。虽然一些人认为许多lncrna是虚假转录的结果,但最近的证据表明,它们存在数千个,并且它们具有功能并调节关键的生物过程。对于lncrna的实验表征,已经开发了许多工具,试图预测它们与其他rna的相互作用。然而,一些最快和更准确的工具需要在确定每个lncRNA的相互作用伙伴之前进行缓慢的数据库构建步骤。本文提出了一种新的、高效的并行数据库构建方法。在16节点多核集群上的基准测试结果表明,我们的并行算法构建数据库的速度比市场上仅使用256个CPU内核的其他工具快318倍。本工作中开发的所有代码都可以根据MIT许可证(https://github.com/UDC-GAC/pRIblast)在GitHub上下载。
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引用次数: 0
Security Verification Software Platform of Data-efficient Image Transformer Based on Fast Gradient Sign Method 基于快速梯度符号法的数据高效图像转换器安全验证软件平台
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577731
In-pyo Hong, Gyu-ho Choi, Pan-koo Kim, Chang Choi
Recently, research using knowledge distillation in artificial intelligence (AI) has been actively conducted. In particular, data-efficient image transformer (DeiT) is a representative transformer model using knowledge distillation in image classification. However, DeiT's safety against the patch unit's adversarial attacks was not verified. Furthermore, existing DeiT research did not prove security robustness against adversarial attacks. In order to verify the vulnerability of adversarial attacks, we conducted an attack using the fast gradient sign method (FGSM) targeting the DeiT model based on knowledge distillation. As a result of the experiment, an accuracy of 93.99% was shown in DeiT verification based on Normal data (Cifar-10). In contrast, when verified with abnormal data based on FGSM (adversarial examples), the accuracy decreased by 83.49% to 10.50%. By analyzing the vulnerability pattern related to adversarial attacks, we confirmed that FGSM showed successful attack performance through weight control of DeiT. Moreover, we verified that DeiT has security limitations for practical application.
近年来,将知识蒸馏应用于人工智能(AI)的研究得到了积极的开展。其中,数据高效图像变压器(DeiT)是一种利用知识升华进行图像分类的典型变压器模型。然而,DeiT对补丁单元对抗性攻击的安全性尚未得到验证。此外,现有的DeiT研究并没有证明对对抗性攻击的安全性稳健性。为了验证对抗性攻击的脆弱性,我们利用快速梯度符号方法(FGSM)对基于知识蒸馏的DeiT模型进行了攻击。实验结果表明,基于Normal数据(Cifar-10)的DeiT验证准确率为93.99%。相比之下,当使用基于FGSM(对抗性示例)的异常数据进行验证时,准确率下降了83.49%至10.50%。通过分析对抗性攻击相关的漏洞模式,我们证实了FGSM通过DeiT的权重控制实现了成功的攻击性能。此外,我们验证了DeiT在实际应用中存在安全限制。
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引用次数: 1
Software Aging in a Real-Time Object Detection System on an Edge Server 边缘服务器实时目标检测系统中的软件老化问题
IF 1 Pub Date : 2023-03-27 DOI: 10.1145/3555776.3577717
Kengo Watanabe, F. Machida, E. Andrade, R. Pietrantuono, Domenico Cotroneo
Real-time object detection systems are rapidly adopted in many edge computing systems for IoT applications. Since the computational resources on edge devices are often limited, continuous real-time object detection may suffer from the degradation of performance and reliability due to software aging. To provide a reliable IoT applications, it is crucial to understand how software aging can manifest in object detection systems under resource-constrained environment. In this paper, we investigate the software aging issue in a real-time object detection system using YOLOv5 running on a Raspberry Pi-based edge server. By performing statistical analysis on the measurement data, we detected a suspicious trend of software aging in the memory usage, which is induced by real-time object detection workloads. We also observe that a system monitoring process is halted due to the shortage of free storage space as a result of YOLOv5's resource dissipation. The monitoring process fails after 24.11, 44.56, and 115.36 hours (on average), when we set the sizes of input images to 160px, 320px, and 640px, respectively, in our system. Our experimental results can be used to plan countermeasures such as software rejuvenation and task offloading.
在物联网应用的许多边缘计算系统中,实时对象检测系统被迅速采用。由于边缘设备上的计算资源往往是有限的,持续的实时目标检测可能会由于软件老化而导致性能和可靠性下降。为了提供可靠的物联网应用,了解软件老化如何在资源受限环境下的目标检测系统中表现出来是至关重要的。在本文中,我们研究了在基于Raspberry pi的边缘服务器上使用YOLOv5运行的实时目标检测系统中的软件老化问题。通过对测量数据进行统计分析,我们发现内存使用中存在软件老化的可疑趋势,这是由实时对象检测工作负载引起的。我们还观察到,由于YOLOv5的资源耗散导致可用存储空间不足,导致系统监控进程停止。当我们在系统中分别将输入图像的大小设置为160px、320px和640px时,监控过程在24.11、44.56和115.36小时(平均)后失败。我们的实验结果可用于规划诸如软件复兴和任务卸载等对策。
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引用次数: 2
期刊
Applied Computing Review
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