2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)最新文献

PerfDB: A Data Management System for Fine-Grained Performance Anomaly Detection PerfDB:一个用于细粒度性能异常检测的数据管理系统

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00021

Joshua Kimball, Rodrigo Alves Lima, Yasuhiko Kanemasa, C. Pu

引用次数: 0

Assessing the provision of public-facing cybersecurity guidance for end-users 评估为最终用户提供面向公众的网络安全指导

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00028

Nirosha Holton, S. Furnell

{"title":"Assessing the provision of public-facing cybersecurity guidance for end-users","authors":"Nirosha Holton, S. Furnell","doi":"10.1109/CIC50333.2020.00028","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00028","url":null,"abstract":"The increased use of digital technologies and services brings with it a similarly increasing requirement for their end-users to have the awareness and ability to protect the security and privacy of their devices and data. However, this raises the related questions of what they need to know and from where they may obtain related guidance. The discussion begins by considering the knowledge and skills that could usefully characterize a cybersecurity literate user. The paper then proceeds to examine the provision of end-user guidance from 17 public-facing sources, spanning national sources, operating system provides, training providers and popular online services. The findings illustrate that, while some topics are commonly covered, none of them are found to be uniformly addressed across all sources. As such, the level of awareness that users may have, and the level of good practice that they may follow, is likely to be highly dependent upon where they have looked for guidance. This points toward the need for a consistent and more harmonized approach if we wish to ensure that a common baseline of cybersecurity literacy can be achieved across an increasingly digital society.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"70 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132738601","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

Discovering Localized Information for Heterogeneous Graph Node Representation Learning 异构图节点表示学习中的局部信息发现

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00020

Lin Meng, Ning Yan, Masood S. Mortazavi, Jiawei Zhang

{"title":"Discovering Localized Information for Heterogeneous Graph Node Representation Learning","authors":"Lin Meng, Ning Yan, Masood S. Mortazavi, Jiawei Zhang","doi":"10.1109/CIC50333.2020.00020","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00020","url":null,"abstract":"Representation learning for heterogeneous graphs aims at learning meaningful node (or edge) representations to facilitate downstream tasks such as node classification, node clustering, and link prediction. While graph neural networks (GNNs) have recently proven to be effective in representation learning, one of the limitations is that most investigations focus on homogeneous graphs. Existing investigations on heterogeneous graphs often make direct use of meta-path type structures. Meta-path-based approaches often require a priori designation of meta-paths based on heuristic foreknowledge regarding the characteristics of heterogeneous graphs under investigation. In this paper, we propose a model without any a priori selection of meta-paths. We utilize locally-sampled (heterogeneous) context graphs “centered” at a target node in order to extract relevant representational information for that target node. To deal with the heterogeneity in the graph, given the different types of nodes, we use different linear transformations to map the features in different domains into a unified feature space. We use the classical Graph Convolution Network (GCN) model as a tool to aggregate node features and then aggregate the context graph feature vectors to produce the target node's feature representation. We evaluate our model on three real-world datasets. The results show that the proposed model has better performance when compared with four baseline models.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116372876","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

Design and Implementation of a Platform for Managing Time-Sensitive IoT Applications 时间敏感物联网应用管理平台的设计与实现

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00016

H. Korala, Ali Yavari, Dimitrios Georgakopoulos, P. Jayaraman

引用次数: 3

2020 IEEE 6th International Conference on Collaboration and Internet Computing CIC 2020 2020 IEEE第六届协同与互联网计算国际会议

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/cic50333.2020.00001

引用次数: 0

Designing User-Centric Information Security Management Systems in Financial Services Organisations 在金融服务机构设计以用户为中心的信息安全管理系统

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.9492732

Ivano Bongiovanni

引用次数: 2

Cybersecurity in Medical Private Practice: Results of a Survey in Audiology 医疗私人实践中的网络安全:听力学调查的结果

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00029

J. Dykstra, Rohan Mathur, A. Spoor

{"title":"Cybersecurity in Medical Private Practice: Results of a Survey in Audiology","authors":"J. Dykstra, Rohan Mathur, A. Spoor","doi":"10.1109/CIC50333.2020.00029","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00029","url":null,"abstract":"Despite well-documented cyber threats to patients' protected health information (PHI), sparse evidence exists about the state of cybersecurity behavior of health care workers and medical private practices. There is evidence of insecure behavior in hospital settings, even though specific insights about private practice are still absent. In addition to mandatory standards for securing PHI, such as the Health Insurance Portability & Accountability Act (HIPAA), small business viability and their patients' security and privacy are critically dependent upon technology availability and reliability. In this survey of 131 clinical audiologists we show that many lack time, staff expertise, or funds to deploy adequate cybersecurity that prevents and mitigates threats to security and privacy. We find widespread deployment of HIPAA-compliant cybersecurity, including antivirus software and individual logins. Only 9.9% of participants reported at least one data breach in 2019, significantly less than the average for small businesses and health care providers, and only 24.4% reported having cyber insurance. Practice owners view patient data as well protected and unlikely victims for cyber attacks and breaches. These results have important implications for cybersecurity products and services, and to medical professionals who must acknowledge the acute importance of cybersecurity in securing protected health information and mitigating risks. Small business private practice health care providers who are particularly sensitive to the impacts of cyber attacks and must prioritize and adopt countermeasures that decrease the risks to patients and their own businesses.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130382561","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: 3

What Information is Required for Explainable AI? : A Provenance-based Research Agenda and Future Challenges 可解释的AI需要哪些信息?:基于来源的研究议程和未来的挑战

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00030

Fariha Tasmin Jaigirdar, C. Rudolph, G. Oliver, David Watts, Chris Bain

{"title":"What Information is Required for Explainable AI? : A Provenance-based Research Agenda and Future Challenges","authors":"Fariha Tasmin Jaigirdar, C. Rudolph, G. Oliver, David Watts, Chris Bain","doi":"10.1109/CIC50333.2020.00030","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00030","url":null,"abstract":"Deriving explanations of an Artificial Intelligence-based system's decision making is becoming increasingly essential to address requirements that meet quality standards and operate in a transparent, comprehensive, understandable, and explainable manner. Furthermore, more security issues as well as concerns from human perspectives emerge in describing the explainability properties of AI. A full system view is required to enable humans to properly estimate risks when dealing with such systems. This paper introduces open issues in this research area to present the overall picture of explainability and the required information needed for the explanation to make a decision-oriented AI system transparent to humans. It illustrates the potential contribution of proper provenance data to AI-based systems by describing a provenance graph-based design. This paper proposes a six-Ws framework to demonstrate how a security-aware provenance graph-based design can build the basis for providing end-users with sufficient meta-information on AI-based decision systems. An example scenario is then presented that highlights the required information for better explainability both from human and security-aware aspects. Finally, associated challenges are discussed to provoke further research and commentary.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130984405","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: 3

Sustainability-aware Resource Provisioning in Data Centers 数据中心的可持续性资源分配

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00018

Jingzhe Wang, Balaji Palanisamy, Jinlai Xu

{"title":"Sustainability-aware Resource Provisioning in Data Centers","authors":"Jingzhe Wang, Balaji Palanisamy, Jinlai Xu","doi":"10.1109/CIC50333.2020.00018","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00018","url":null,"abstract":"In the big data era, cloud computing provides an effective usage model for providing computing services to handle diverse data-intensive workloads. Data center capacity planning and resource provisioning policies play a vital role in longterm life cycle management of datacenters. Effective design and management of data center infrastructures while ensuring good performance is critical to minimizing the carbon footprint of the datacenter. Traditional solutions have primarily focused on optimizing data center operational phase impacts including reducing energy cost during the resource management phase. In this paper, we propose a two-phase sustainability-aware resource allocation and management framework for data center life-cycle management that jointly optimizes the data center manufacturing phase and operational phase impact without impacting the performance and service quality for the jobs. Phase 1 of the proposed approach minimizes data center building phase carbon footprint through a novel manufacturing cost-aware server provisioning plan. In phase 2, the approach minimizes the operational phase carbon footprint using a server lifetime-aware resource allocation scheme and a manufacturing cost-aware replacement plan. The proposed techniques are evaluated through extensive experiments using realistic workloads generated in a data center. The evaluation results show that the proposed framework significantly reduces the carbon footprint in the data center without impacting the performance of the jobs in the workload.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133255519","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

Challenges in developing collaborative IoT systems 开发协作物联网系统的挑战

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC) Pub Date : 2020-12-01 DOI: 10.1109/CIC50333.2020.00014

Flávia Coimbra Delicato, Paulo F. Pires

{"title":"Challenges in developing collaborative IoT systems","authors":"Flávia Coimbra Delicato, Paulo F. Pires","doi":"10.1109/CIC50333.2020.00014","DOIUrl":"https://doi.org/10.1109/CIC50333.2020.00014","url":null,"abstract":"The Internet of Things (IoT) represents the next significant step in the evolution of the Internet. It will allow “things” to be connected anytime, anywhere, with anything and anyone, providing a myriad of novel applications and augmented services to citizens, governments, and enterprises. We believe that for the IoT to reach its full potential, it will be necessary to advance the investigation of techniques and technologies to build systems in such a brand-new scenario. In the IoT, the collaborating entities encompass both physical and virtual resources, interactions occur both in an active and programmed way as well as by chance, and therefore it is necessary to deal with expected but also emerging behaviors of the collaborating parties. In this paper, we first discuss the main features that make IoT a unique ecosystem, and as such, calls for new software development solutions. We claim that there is a need to rethinking the techniques and methodologies for developing IoT systems and applications. Novel models, architectural approaches and techniques should be proposed, or existing ones should be adapted to deal with the high heterogeneity, dynamism, serendipity and interdependencies that are typical of the IoT ecosystem. We then analyze and discuss potential key design solutions that deserve a deeper understanding in order to pave the way for the building of this new generation of systems. Our discussion is presented from a bottom-up perspective: from the modeling of devices that make up the $mathrm{IoT}$ to the representation of requirements that must be addressed when engineering IoT systems.","PeriodicalId":265435,"journal":{"name":"2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128538882","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: 3