2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)最新文献

Technical Program Committee CogMI 2020 技术项目委员会CogMI 2020

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00007

引用次数: 0

Organizing Committee CogMI 2020 组委会CogMI 2020

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00006

引用次数: 0

Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners 使用LightGBM和XGBoost学习器检测使用不同网络特征的网络安全攻击

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00032

Joffrey L. Leevy, John T. Hancock, R. Zuech, T. Khoshgoftaar

{"title":"Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners","authors":"Joffrey L. Leevy, John T. Hancock, R. Zuech, T. Khoshgoftaar","doi":"10.1109/CogMI50398.2020.00032","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00032","url":null,"abstract":"CSE-CIC-IDS2018 is an intrusion detection dataset containing roughly 16,000,000 normal and anomalous instances, with about 17% of these instances representing attack traffic. Our big data study has two parts, ensemble feature selection and model comparison. In the first part, we select features from the dataset for input to two classifiers that we employ in the second part. In the second part, we evaluate the performance of the classifiers with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and Fl-score. The outcome of our experiments enables us to answer three research questions. The first question is, “Does feature selection impact performance of classifiers in terms of AUC and Fl-score?” The second question is, “Does including the Destination_Port categorical feature significantly impact performance of LightGBM in terms of AUC and Fl-score?” And, our third question is, “Does the choice of classifier: LightGBM or XGBoost, significantly impact performance in terms of AUC and Fl-score?” For CSE-CIC-IDS2018, we conclude that feature selection and classifier choice impact performance score, and Destination_Port is a significant feature for LightGBM. In our case study, we present the application and analysis of the impact of an ensemble feature selection technique. To the best of our knowledge, we are the first to apply this technique to the CSE-CIC-IDS2018 dataset.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"186 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":"131578283","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: 18

Plenary Panel Cognitive Machine Intelligence and Killer Applications 认知机器智能和杀手级应用

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00009

引用次数: 0

Next - Location Prediction Using Federated Learning on a Blockchain 下一个-在bb0上使用联邦学习的位置预测

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00038

S. M. D. Halim, L. Khan, B. Thuraisingham

引用次数: 4

Steering Committee CogMI 2020 CogMI 2020指导委员会

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00008

引用次数: 0

Global Pandemic: Business Model Impact on Enterprises reTHINK, reIMAGINE, reINVENT Businesses 全球流行病:商业模式对企业的影响重新思考、重新构想、重塑企业

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00024

Sandeep Gopisetty

{"title":"Global Pandemic: Business Model Impact on Enterprises reTHINK, reIMAGINE, reINVENT Businesses","authors":"Sandeep Gopisetty","doi":"10.1109/CogMI50398.2020.00024","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00024","url":null,"abstract":"The COVID-19 outbreak is a sharp reminder that pandemics, like other rarely occurring catastrophes, have happened in the past and will continue to happen in the future. Even if we cannot prevent dangerous catastrophes, businesses need to manage risks, reduce costs, improve efficiency while digitizing products and offerings, streamlining processes to retain existing customers. As industry leaders around the globe are driving their teams forward from their attics, basements and spare rooms, the need for business continuity is vital more than ever. It is important for Enterprises to have the right tools, technology and skills to deal with this crisis. One can make use of expert advice through consulting services to understand the level of preparedness and to respond quickly with what needs to be done. It is time to quickly move beyond manual, people-dependent processes and switch to automation and orchestration to reduce the complexity. Our solution takes advantage of modeling in over 80 different types of industries and addresses both industry standard as well as custom priorities to help enterprises focus on the right investment to improve the performance of their business components.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"1 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":"128651631","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

A Decentralized Approach for Determining Configurator Placement in Dynamic Edge Networks 动态边缘网络中配置器位置的分散确定方法

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00027

Ilir Murturi, M. Barzegaran, S. Dustdar

{"title":"A Decentralized Approach for Determining Configurator Placement in Dynamic Edge Networks","authors":"Ilir Murturi, M. Barzegaran, S. Dustdar","doi":"10.1109/CogMI50398.2020.00027","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00027","url":null,"abstract":"In today's IoT infrastructures, increasingly newly added computational resources at the edge of a network are added to acquire faster response and increased privacy. Such edge networks bring an opportunity for deploying edge application services in proximity to IoT domains and the end-users. In this paper, we consider the problem of utilizing various computational resources established by multiple heterogeneous edge devices in dynamic edge networks. A new lightweight decentralized mechanism (i.e., configurator) is required to monitor an edge infrastructure to enable deploying, orchestrating, and monitoring edge applications at the edge. In this setting, one critical task is to determine the node where the configurator should be placed (deployed) and run (executed) at the edge. In this paper, we propose an efficient approach that solves the configurator's placement problem on the most suited edge device in a given dynamic edge network. Our approach supports the system coping with the dynamicity and uncertainty of the environment and adapts based on the configurator's service quality. We discuss the architecture, processes of the approach, and the simulations we conducted to validate its feasibility.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"133 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":"127364750","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

Interpretable Next Basket Prediction Boosted with Representative Recipes 可解释的下一个篮子预测与代表性食谱增强

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00018

Riccardo Guidotti, Stefano Viotto

{"title":"Interpretable Next Basket Prediction Boosted with Representative Recipes","authors":"Riccardo Guidotti, Stefano Viotto","doi":"10.1109/CogMI50398.2020.00018","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00018","url":null,"abstract":"Food is an essential element of our lives, cultures, and a crucial part of human experience. The study of food purchases can drive the design of practical services such as next basket predictor and shopping list reminder. Current approaches aimed at realizing these services do not exploit a contextual dimension involving food, i.e., recipes. To this aim, we design a next basket predictor based on representative recipes able to exploit the interest of customers towards certain ingredients when making the recommendation. The proposed method first identifies the representative recipes of a customer by analyzing her purchases and then estimates the rating of the items for the prediction. The ratings are based on both the purchases and the ingredients of the representative recipes. In addition, through our method, it is easy to justify why a specific set of items is predicted while such explanations are often not easily available in many other effective but opaque recommenders. Experimentation on a real-world dataset shows that the usage of recipes leverages the performance of existing next basket predictors.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"13 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":"126418188","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

Causality and Uncertainty of Information for Content Understanding 内容理解中的信息因果关系和不确定性

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00023

A. Raglin, Raha Moraffah, Huan Liu

引用次数: 0