2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)最新文献_第3页

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

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00001

引用次数: 0

Fine-grained, aspect-based semantic sentiment analysis within the economic and financial domains 在经济和金融领域进行细粒度的、基于方面的语义情感分析

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00017

S. Consoli, Luca Barbaglia, S. Manzan

引用次数: 2

Explaining Any Time Series Classifier 解释任何时间序列分类器

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00029

Riccardo Guidotti, A. Monreale, Francesco Spinnato, D. Pedreschi, F. Giannotti

引用次数: 23

Learning Latent Perception Graphs for Personalized Unknowns Recommendation 个性化未知推荐的潜在感知图学习

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00015

Lo Pang-Yun Ting, Shan-Yun Teng, Suhang Wang, Kun-Ta Chuang, Huan Liu

{"title":"Learning Latent Perception Graphs for Personalized Unknowns Recommendation","authors":"Lo Pang-Yun Ting, Shan-Yun Teng, Suhang Wang, Kun-Ta Chuang, Huan Liu","doi":"10.1109/CogMI50398.2020.00015","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00015","url":null,"abstract":"The fast-growing online-learning platforms, which are very convenient and contain rich course resources, have attracted many users to explore new knowledge online. However, the learning quality of online-learning is generally not as effective as offline classes. In offline studies in classrooms, teachers can interact with students and teach students in accordance with personal aptitude from students' feedback in classes. Without such real-time interaction, it is difficult for users to be aware of personal unknowns. In this paper, we consider an important issue to discover “user unknowns” from the question-giving process in online-learning platforms. A novel personalized learning framework, called PagBay, is devised to recommend user unknowns in the iterative round-by-round strategy, which contributes to applications such as a conversational bot. The flow enables users to progressively discover their weakness and to help them progress. However, discovering personal unknowns is quite challenging in online-learning platforms. Even though solving the problem with previous recommender algorithms provides solutions, they often lead to suboptimal results for unknowns recommendation as they simply rely on the user ratings and contextual features of questions. Generally, questions are associated with perceptions, and mining the relationships among users, questions, and perceptions potentially provide the clue to the better unknowns recommendation. Therefore, in this paper, we develop a novel recommender framework by borrowing strengths from perception-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in online learning services.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"30 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":"117217778","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

No Classifier Left Behind: An In-depth Study of the RBF SVM Classifier's Vulnerability to Image Extraction Attacks via Confidence Information Exploitation 不落下一个分类器:基于置信度信息的RBF SVM分类器易受图像提取攻击的深入研究

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00037

Michael R. Clark, Peter Swartz, Andrew Alten, Raed M. Salih

{"title":"No Classifier Left Behind: An In-depth Study of the RBF SVM Classifier's Vulnerability to Image Extraction Attacks via Confidence Information Exploitation","authors":"Michael R. Clark, Peter Swartz, Andrew Alten, Raed M. Salih","doi":"10.1109/CogMI50398.2020.00037","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00037","url":null,"abstract":"Training image extraction attacks attempt to reverse engineer training images from an already trained machine learning model. Such attacks are concerning because training data can often be sensitive in nature. Recent research has shown that extracting training images is generally much harder than model inversion, which attempts to duplicate the functionality of the model. In this paper, we correct common misperceptions about image extraction attacks and develop a deep understanding ofwhy some trained models are vulnerable to ourattack while others are not. In particular, we use the RBFSVMclassifier to show that we can extract individual training images from models trained on thousands of images., which refutes the notion that these attacks can only extract an “average” of each class. We also show that increasing diversity of the training data set leads to more successful attacks. To the best of our knowledge, our work is the first to show semantically meaningful images extracted from the RBF SVM classifier.","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":"131047155","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

Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence 物联网中的机器学习系统:边缘智能的可信度权衡

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00030

Wiebke Toussaint, A. Ding

{"title":"Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence","authors":"Wiebke Toussaint, A. Ding","doi":"10.1109/CogMI50398.2020.00030","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00030","url":null,"abstract":"Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the tradeoffs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"18 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":"130086550","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: 6

Artificial Dendrites: an Algorithm 人工树突:一个算法

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00033

Zachary S. Hutchinson

引用次数: 0

Cortically-Coupled Generative Adversarial Network for Target Image Retrieval in Rapid Image Search 快速图像搜索中目标图像检索的皮质耦合生成对抗网络

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00036

Ruchi Bagwe, K. George

引用次数: 1

Real-Time Data Quality Analysis 实时数据质量分析

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00022

A. Iyengar, Dhaval Patel, Shrey Shrivastava, N. Zhou, A. Bhamidipaty

引用次数: 0

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