2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)最新文献_第4页

Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media 基于社交媒体实时知识聚合的疫情信息快速传播的挑战与机遇

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00026

C. Pu, Abhijit Suprem, Rodrigo Alves Lima

{"title":"Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media","authors":"C. Pu, Abhijit Suprem, Rodrigo Alves Lima","doi":"10.1109/CogMI50398.2020.00026","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00026","url":null,"abstract":"A rapidly evolving situation such as the COVID-19 pandemic is a significant challenge for AI/ML models because of its unpredictability. The most reliable indicator of the pandemic spreading has been the number of test positive cases. However, the tests are both incomplete (due to untested asymptomatic cases) and late (due the lag from the initial contact event, worsening symptoms, and test results). Social media can complement physical test data due to faster and higher coverage, but they present a different challenge: significant amounts of noise, misinformation and disinformation. We believe that social media can become good indicators of pandemic, provided two conditions are met. The first (True Novelty) is the capture of new, previously unknown, information from unpredictably evolving situations. The second (Fact vs. Fiction) is the distinction of verifiable facts from misinformation and disinformation. Social media information that satisfy those two conditions are called live knowledge. We apply evidence-based knowledge acquisition (EBKA) approach to collect, filter, and update live knowledge through the integration of social media sources with authoritative sources. Although limited in quantity, the reliable training data from authoritative sources enable the filtering of misinformation as well as capturing truly new information. We describe the EDNA/LITMUS tools that implement EBKA, integrating social media such as Twitter and Facebook with authoritative sources such as WHO and CDC, creating and updating live knowledge on the COVID-19 pandemic.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132954647","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: 5

Towards Distributed Edge-based Systems 走向分布式边缘系统

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00021

S. Dustdar, Ilir Murturi

{"title":"Towards Distributed Edge-based Systems","authors":"S. Dustdar, Ilir Murturi","doi":"10.1109/CogMI50398.2020.00021","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00021","url":null,"abstract":"In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud and the IoT domain. Thus, processing IoT data streams closer to the end-users and IoT domain can solve several operational challenges. Since then, a plethora of application-specific IoT systems are introduced, mainly hard-coded, inflexible, and limited extensibility for future changes. Additionally, most IoT systems maintain a centralized design to operate without considering the dynamic nature of edge networks. In this paper, we discuss some of the research issues, challenges, and potential solutions to enable: i) deploying edge functions on edge resources in a distributed manner and ii) deploying and scaling edge applications on-premises of Edge-Cloud infrastructure. Additionally, we discuss in detail the three-tier Edge-Cloud architecture. Finally, we introduce a conceptual framework that aims to enable easy configuration and deployment of edge-based systems on top of heterogeneous edge infrastructure and present our vision within a smart city scenario.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620313","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: 10

On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems 大规模智能数据融合系统的不确定性与鲁棒性

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00020

Benjamin M. Marlin, T. Abdelzaher, G. Ciocarlie, Adam D. Cobb, Mark S. Dennison, Brian Jalaian, Lance M. Kaplan, Tiffany R. Raber, A. Raglin, P. Sharma, M. Srivastava, T. Trout, Meet P. Vadera, Maggie B. Wigness

引用次数: 5

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) CogMI 2020 2020 IEEE第二届认知机器智能国际会议(CogMI)

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00004

引用次数: 0

Conference Keynote: Synthesizing Interpretable Behavior for Human-Aware AI Systems 会议主题:为人类感知的人工智能系统综合可解释行为

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00010

S. Kambhampati

引用次数: 0

Social Media and Ubiquitous Technologies for Remote Worker Wellbeing and Productivity in a Post-Pandemic World 大流行后世界中远程工作者福利和生产力的社交媒体和无处不在的技术

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-07-16 DOI: 10.1109/CogMI50398.2020.00025

V. D. Swain, Koustuv Saha, G. Abowd, M. Choudhury

引用次数: 16