High-Confidence Computing最新文献

英文中文

Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security 数字孪生网络景观导航：架构、应用、隐私和安全调查

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-09-11 DOI: 10.1016/j.hcc.2024.100269

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

近年来，人工智能（AI）领域取得了巨大发展，宽带和无处不在的连接技术也在不断普及。这导致了数字孪生（DT）技术的发展和商业化。数字孪生技术的广泛应用产生了一种新的网络范式，即数字孪生网络（DTN），它通过无处不在的数字孪生网络及其相应的物理资产进行协调。DTN 通过 DT 技术创建物理对象的虚拟双胞胎，并通过数据处理、计算和 DT 建模实现物理空间和虚拟空间的共同演化。DTN 中的大量用户数据和无处不在的通信系统也带来了一系列挑战。其中最严重的问题是用户数据隐私和安全，因为大多数应用的用户并不知道他们正在与这些平台共享数据，而且对数据泄露的影响也缺乏足够的认识。此外，目前关注 DTN 应用中隐私和安全问题的文献还不够多。在本调查报告中，我们首先清楚地介绍了 DTN 的组成部分以及文献中用于评估其性能的常用指标。接下来，我们提供了一个适用于大多数 DTN 应用的标准网络模型，以便更好地理解 DTN 复杂而交错的通信和各自的组件。然后，我们阐明了 DTN 被大量采用的常见应用，以及 DTN 带来的隐私和安全问题。我们还提供了不同的隐私和安全对策来解决前面提到的 DTN 问题，并列出了一些最先进的工具来缓解这些问题。最后，我们提出了 DTN 隐私和安全领域的一些开放研究课题和问题。

{"title":"Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security","authors":"","doi":"10.1016/j.hcc.2024.100269","DOIUrl":"10.1016/j.hcc.2024.100269","url":null,"abstract":"<div><div>In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum to “An effective digital audio watermarking using a deep convolutional neural network with a search location optimization algorithm for improvement in Robustness and Imperceptibility” [High-Confid. Comput. 3 (2023) 100153] 对 "利用深度卷积神经网络和搜索位置优化算法改进鲁棒性和不可感知性的有效数字音频水印 "的勘误 [High-Confid. Comput.

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-09-01 DOI: 10.1016/j.hcc.2024.100256

引用次数: 0

On Building Automation System security 楼宇自动化系统安全

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-05-27 DOI: 10.1016/j.hcc.2024.100236

Building Automation Systems (BASs) are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control, HVAC systems, entry systems, and lighting controls. Many BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS protocol. In this paper, we provide a comprehensive, up-to-date survey on BASs and attacks against seven BAS protocols including BACnet, EnOcean, KNX, LonWorks, Modbus, ZigBee, and Z-Wave. Holistic studies of secure BAS protocols are also presented, covering BACnet Secure Connect, KNX Data Secure, KNX/IP Secure, ModBus/TCP Security, EnOcean High Security and Z-Wave Plus. LonWorks and ZigBee do not have security extensions. We point out how these security protocols improve the security of the BAS and what issues remain. A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of it. We seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.

由于楼宇自动化系统（BAS）具有气候控制自动化、暖通空调系统、入口系统和照明控制等诸多优点，因此在现代社会中的使用率越来越高。许多正在使用的楼宇自动化系统已经过时，并且存在许多源于底层楼宇自动化系统协议设计的漏洞。在本文中，我们对 BAS 以及针对七种 BAS 协议（包括 BACnet、EnOcean、KNX、LonWorks、Modbus、ZigBee 和 Z-Wave）的攻击进行了全面的最新调查。此外，还介绍了对安全 BAS 协议的全面研究，包括 BACnet Secure Connect、KNX Data Secure、KNX/IP Secure、ModBus/TCP Security、EnOcean High Security 和 Z-Wave Plus。LonWorks 和 ZigBee 没有安全扩展。我们指出了这些安全协议如何提高 BAS 的安全性，以及还存在哪些问题。我们还提供了一个案例研究，描述了现实世界中的一个 BAS，并展示了其漏洞以及改进其安全性的建议。我们力求提高学术界和工业界人士的认识，并强调 BAS 安全方面的未决问题。

{"title":"On Building Automation System security","authors":"","doi":"10.1016/j.hcc.2024.100236","DOIUrl":"10.1016/j.hcc.2024.100236","url":null,"abstract":"<div><p>Building Automation Systems (BASs) are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control, HVAC systems, entry systems, and lighting controls. Many BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS protocol. In this paper, we provide a comprehensive, up-to-date survey on BASs and attacks against seven BAS protocols including BACnet, EnOcean, KNX, LonWorks, Modbus, ZigBee, and Z-Wave. Holistic studies of secure BAS protocols are also presented, covering BACnet Secure Connect, KNX Data Secure, KNX/IP Secure, ModBus/TCP Security, EnOcean High Security and Z-Wave Plus. LonWorks and ZigBee do not have security extensions. We point out how these security protocols improve the security of the BAS and what issues remain. A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of it. We seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295224000394/pdfft?md5=5f78ccec6343d24a81a3bf545e6ddec0&pid=1-s2.0-S2667295224000394-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SoK: Decentralized Storage Network SoK：去中心化存储网络

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-05-22 DOI: 10.1016/j.hcc.2024.100239

Decentralized Storage Networks (DSNs) represent a paradigm shift in data storage methodology, distributing and housing data across multiple network nodes rather than relying on a centralized server or data center architecture. The fundamental objective of DSNs is to enhance security, reinforce reliability, and mitigate censorship risks by eliminating a single point of failure. Leveraging blockchain technology for functions such as access control, ownership validation, and transaction facilitation, DSN initiatives aim to provide users with a robust and secure alternative to traditional centralized storage solutions. This paper conducts a comprehensive analysis of the developmental trajectory of DSNs, focusing on key components such as Proof of Storage protocols, consensus algorithms, and incentive mechanisms. Additionally, the study explores recent optimization tactics, encountered challenges, and potential avenues for future research, thereby offering insights into the ongoing evolution and advancement within the DSN domain.

分散式存储网络（DSN）代表了数据存储方法的一种模式转变，它将数据分布和存储在多个网络节点上，而不是依赖于集中式服务器或数据中心架构。DSN 的基本目标是通过消除单点故障来提高安全性、加强可靠性和降低审查风险。利用区块链技术实现访问控制、所有权验证和交易促进等功能，DSN 计划旨在为用户提供一个强大而安全的替代方案，以取代传统的集中式存储解决方案。本文全面分析了 DSN 的发展轨迹，重点关注存储证明协议、共识算法和激励机制等关键组件。此外，本研究还探讨了最近的优化策略、遇到的挑战以及未来研究的潜在途径，从而为 DSN 领域的持续发展和进步提供了深入见解。

引用次数: 0

Exploring Personalized Internet of Things (PIoT), social connectivity, and Artificial Social Intelligence (ASI): A survey 探索个性化物联网（PIoT）、社交连接和人工智能（ASI）：调查

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-05-20 DOI: 10.1016/j.hcc.2024.100242

Pervasive Computing has become more personal with the widespread adoption of the Internet of Things (IoT) in our day-to-day lives. The emerging domain that encompasses devices, sensors, storage, and computing of personal use and surroundings leads to Personal IoT (PIoT). PIoT offers users high levels of personalization, automation, and convenience. This proliferation of PIoT technology has extended into society, social engagement, and the interconnectivity of PIoT objects, resulting in the emergence of the Social Internet of Things (SIoT). The combination of PIoT and SIoT has spurred the need for autonomous learning, comprehension, and understanding of both the physical and social worlds. Current research on PIoT is dedicated to enabling seamless communication among devices, striking a balance between observation, sensing, and perceiving the extended physical and social environment, and facilitating information exchange. Furthermore, the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence (ASI) in PIoT systems. However, autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration. This paper provides a comprehensive review of the evolving domains of PIoT, SIoT, and ASI. Moreover, the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios. This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions, paving the way for further advancements in this transformative field.

随着物联网（IoT）在日常生活中的广泛应用，普适计算变得更加个性化。个人物联网（PIoT）是一个新兴领域，包括个人使用和周围环境的设备、传感器、存储和计算。PIoT 为用户提供了高度的个性化、自动化和便利性。PIoT 技术的扩散已延伸到社会、社会参与和 PIoT 物体的互联性，从而导致了社会物联网（SIoT）的出现。PIoT 和 SIoT 的结合激发了人们对自主学习、理解和认识物理世界和社会世界的需求。目前有关 PIoT 的研究致力于实现设备之间的无缝通信，在观察、感知和感知扩展物理和社会环境之间取得平衡，并促进信息交流。此外，从社会环境中自主学习的虚拟化技术也催生了 PIoT 系统中的人工社会智能（ASI）。然而，社交环境中不同节点之间的自主数据通信带来了各种资源管理挑战，需要仔细考虑。本文对不断发展的 PIoT、SIoT 和 ASI 领域进行了全面回顾。此外，本文还提供了深入的建模和案例研究，探讨了 PIoT 在后 COVID 场景中的作用。本研究有助于加深对 PIoT 复杂性及其各个层面的理解，为这一变革性领域的进一步发展铺平道路。

{"title":"Exploring Personalized Internet of Things (PIoT), social connectivity, and Artificial Social Intelligence (ASI): A survey","authors":"","doi":"10.1016/j.hcc.2024.100242","DOIUrl":"10.1016/j.hcc.2024.100242","url":null,"abstract":"<div><p>Pervasive Computing has become more personal with the widespread adoption of the Internet of Things (IoT) in our day-to-day lives. The emerging domain that encompasses devices, sensors, storage, and computing of personal use and surroundings leads to Personal IoT (PIoT). PIoT offers users high levels of personalization, automation, and convenience. This proliferation of PIoT technology has extended into society, social engagement, and the interconnectivity of PIoT objects, resulting in the emergence of the Social Internet of Things (SIoT). The combination of PIoT and SIoT has spurred the need for autonomous learning, comprehension, and understanding of both the physical and social worlds. Current research on PIoT is dedicated to enabling seamless communication among devices, striking a balance between observation, sensing, and perceiving the extended physical and social environment, and facilitating information exchange. Furthermore, the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence (ASI) in PIoT systems. However, autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration. This paper provides a comprehensive review of the evolving domains of PIoT, SIoT, and ASI. Moreover, the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios. This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions, paving the way for further advancements in this transformative field.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266729522400045X/pdfft?md5=b97b12cf0359158d875f2dc4cb6bbd8d&pid=1-s2.0-S266729522400045X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of acoustic eavesdropping attacks: Principle, methods, and progress 声学窃听攻击调查：原理、方法和进展

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-05-18 DOI: 10.1016/j.hcc.2024.100241

In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.

在当今的信息时代，窃听已成为信息安全领域最严重的隐私威胁之一，如exodus间谍软件（Rudie等人，2021年）和pegasus间谍软件（Anatolyevich，2020年）。其中最主要的是声学窃听。声学窃听（George 和 Sagayarajan，2023 年）是一种利用麦克风、传感器或其他设备收集和处理声音信号并将其转换为可读信息的技术。尽管在这一领域已经做了很多研究，但鉴于技术的不断进步和窃听方法的快速发展，对这一技术的时效性仍然缺乏全面的调查。在本文中，我们对声学窃听进行了选择性概述，重点介绍了声学窃听的方法。具体来说，我们将声学窃听分为三类：基于运动传感器的声学窃听、基于光学传感器的声学窃听和基于射频的声学窃听。在这三个具有代表性的框架内，我们将根据它们使用的设备类型和各自的物理原理回顾声学窃听的成果。其次，我们还介绍了这些声学窃听方法的几个重要但具有挑战性的应用。此外，我们还从是否具有非侵入性、是否能实现无约束的文字窃听、是否使用了机器学习等多个角度，比较了符合实际场景中声学窃听要求的系统。我们文章的总体模板如下：首先，我们对现有的窃听技术进行了系统的回顾和分类，阐述了它们的工作机制，并给出了相应的公式。然后，对这些窃听方法进行对比分析，从多个维度评价每种方法的有效性和技术难度。除了对该领域的现状进行评估外，我们还讨论了当前的不足和挑战，并为声学窃听研究的未来发展指明了富有成效的方向。我们希望能继续激励研究人员朝这个方向努力。

{"title":"A survey of acoustic eavesdropping attacks: Principle, methods, and progress","authors":"","doi":"10.1016/j.hcc.2024.100241","DOIUrl":"10.1016/j.hcc.2024.100241","url":null,"abstract":"<div><div>In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AIDCT: An AI service development and composition tool for constructing trustworthy intelligent systems AIDCT：用于构建可信智能系统的人工智能服务开发和组合工具

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

High-Confidence Computing

Pub Date : 2024-05-10 DOI: 10.1016/j.hcc.2024.100227

The growing prevalence of AI services on cloud platforms is driving the demand for technologies and tools which enable the integration of multiple AI services to handle intricate tasks. Traditional methods of evaluating intelligent systems focus mainly on the performance of AI components, without providing comprehensive metrics for the system as a whole. Additionally, as these AI components are often sourced from third-party providers, users may face challenges due to inconsistent quality assurance and limitations in further developing AI models, and dealing with third-party service providers’ limitations. These limitations often involve quality assurance and a lack of capability for secondary development and training of services. To address these issues, we have developed a tool based on our previous work. It can autonomously build Intelligent systems from AI services while tackling the issues mentioned above. This tool not only creates service composition solutions that align with user-defined functional requirements and performance metrics but also executes these solutions to verify if the metrics meet user requirements. We have demonstrated the effectiveness of this tool in constructing trustworthy intelligent systems through a series of case studies.

云平台上的人工智能服务日益普及，推动了对能够整合多种人工智能服务以处理复杂任务的技术和工具的需求。传统的智能系统评估方法主要关注人工智能组件的性能，而不提供系统整体的综合指标。此外，由于这些人工智能组件通常来自第三方提供商，用户在进一步开发人工智能模型和处理第三方服务提供商的限制时，可能会面临质量保证不一致和限制等挑战。这些限制往往涉及质量保证以及缺乏二次开发和培训服务的能力。为了解决这些问题，我们在以往工作的基础上开发了一种工具。它可以自主地从人工智能服务中构建智能系统，同时解决上述问题。该工具不仅能创建符合用户定义的功能要求和性能指标的服务组成解决方案，还能执行这些解决方案，以验证指标是否符合用户要求。我们通过一系列案例研究证明了该工具在构建值得信赖的智能系统方面的有效性。

{"title":"AIDCT: An AI service development and composition tool for constructing trustworthy intelligent systems","authors":"","doi":"10.1016/j.hcc.2024.100227","DOIUrl":"10.1016/j.hcc.2024.100227","url":null,"abstract":"<div><div>The growing prevalence of AI services on cloud platforms is driving the demand for technologies and tools which enable the integration of multiple AI services to handle intricate tasks. Traditional methods of evaluating intelligent systems focus mainly on the performance of AI components, without providing comprehensive metrics for the system as a whole. Additionally, as these AI components are often sourced from third-party providers, users may face challenges due to inconsistent quality assurance and limitations in further developing AI models, and dealing with third-party service providers’ limitations. These limitations often involve quality assurance and a lack of capability for secondary development and training of services. To address these issues, we have developed a tool based on our previous work. It can autonomously build Intelligent systems from AI services while tackling the issues mentioned above. This tool not only creates service composition solutions that align with user-defined functional requirements and performance metrics but also executes these solutions to verify if the metrics meet user requirements. We have demonstrated the effectiveness of this tool in constructing trustworthy intelligent systems through a series of case studies.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141028150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud data security with deep maxout assisted data sanitization and restoration process 通过深度最大化辅助数据清理和恢复流程保障云数据安全

High-Confidence Computing

Pub Date : 2024-05-01 DOI: 10.1016/j.hcc.2024.100238

Shrikant D. Dhamdhere, M. Sivakkumar, V. Subramanian

引用次数: 0

EBIAS: ECC-enabled blockchain-based identity authentication scheme for IoT device EBIAS：基于 ECC 的物联网设备区块链身份验证方案

High-Confidence Computing

Pub Date : 2024-05-01 DOI: 10.1016/j.hcc.2024.100240

Wenyue Wang, Biwei Yan, Baobao Chai, Ruiyao Shen, Anming Dong, Jiguo Yu

引用次数: 0

Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners 均衡的 ID-OOD 权衡转移使基于查询的检测器成为少数几个镜头的学习器

High-Confidence Computing

Pub Date : 2024-05-01 DOI: 10.1016/j.hcc.2024.100237

Yuantao Yin, Ping Yin, Xue Xiao, Liang Yan, Siqing Sun, Xiaobo An

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

High-Confidence Computing

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀