2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)最新文献_第2页

A Tipping Point? Heightened self-disclosure during the Coronavirus pandemic 引爆点?在冠状病毒大流行期间加强自我披露

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00040

A. Squicciarini, Sarah Rajtmaier, Prasanna Umar, Taylor Blose

引用次数: 4

A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports 荷兰乳腺癌放射学报告自动摘要的混合文本分类和语言生成模型

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00019

Elisa Nguyen, Daphne Theodorakopoulos, Shreyasi Pathak, Jeroen Geerdink, O. Vijlbrief, M. V. Keulen, C. Seifert

{"title":"A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports","authors":"Elisa Nguyen, Daphne Theodorakopoulos, Shreyasi Pathak, Jeroen Geerdink, O. Vijlbrief, M. V. Keulen, C. Seifert","doi":"10.1109/CogMI50398.2020.00019","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00019","url":null,"abstract":"Breast cancer diagnosis is based on radiology reports describing observations made from medical imagery, such as X-rays obtained during mammography. The reports are written by radiologists and contain a conclusion summarizing the observations. Manually summarizing the reports is time-consuming and leads to high text variability. This paper investigates the automated summarization of Dutch radiology reports. We propose a hybrid model consisting of a language model (encoder-decoder with attention) and a separate BI-RADS score classifier. The summarization model achieved a ROUGE-L F1 score of 51.5% on the Dutch reports, which is comparable to results in other languages and other domains. For the BI-RADS classification, the language model (accuracy 79.1 %) was outperformed by a separate classifier (accuracy 83.3 %), leading us to propose a hybrid approach for radiology report summarization. Our qualitative evaluation with experts found the generated conclusions to be comprehensible and to cover mostly relevant content, and the main focus for improvement should be their factual correctness. While the current model is not accurate enough to be employed in clinical practice, our results indicate that hybrid models might be a worthwhile direction for future research.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"48 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":"126806735","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: 2

Low-shot Learning in Natural Language Processing 自然语言处理中的低概率学习

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00031

Congying Xia, Chenwei Zhang, Jiawei Zhang, Tingting Liang, Hao Peng, Philip S. Yu

引用次数: 4

Functional Perceptron using Multi-dimensional Activation Functions 基于多维激活函数的函数感知器

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00012

Chungheon Yi, Wonik Choi, Youngjun Jeon, Ling Liu

引用次数: 0

Promoting High Diversity Ensemble Learning with EnsembleBench 使用EnsembleBench促进高多样性集成学习

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00034

Yanzhao Wu, Ling Liu, Zhongwei Xie, Juhyun Bae, Ka-Ho Chow, Wenqi Wei

{"title":"Promoting High Diversity Ensemble Learning with EnsembleBench","authors":"Yanzhao Wu, Ling Liu, Zhongwei Xie, Juhyun Bae, Ka-Ho Chow, Wenqi Wei","doi":"10.1109/CogMI50398.2020.00034","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00034","url":null,"abstract":"Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accuracy ensembles. The design of EnsembleBench offers three novel features: (1) EnsembleBench introduces a set of quantitative metrics for assessing the quality of ensembles and for comparing alternative ensembles constructed for the same learning tasks. (2) EnsembleBench implements a suite of baseline diversity metrics and optimized diversity metrics for identifying and selecting ensembles with high diversity and high quality, making it an effective framework for benchmarking, evaluating and recommending high diversity model ensembles. (3) Four representative ensemble consensus methods are provided in the first release of EnsembleBench, enabling empirical study on the impact of consensus methods on ensemble accuracy. A comprehensive experimental evaluation on popular benchmark datasets demonstrates the utility and effectiveness of EnsembleBench for promoting high diversity ensembles and boosting the overall performance of selected ensembles.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"55 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":"125690633","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: 7

Online Intelligent Music Recommendation: The Opportunity and Challenge for People Well-Being Improvement 在线智能音乐推荐:提升人们幸福感的机遇与挑战

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00014

Jialie Shen, K. Rafferty, Jia Jia

引用次数: 1

Unseen Filler Generalization In Attention-based Natural Language Reasoning Models 基于注意的自然语言推理模型中看不见的填充物泛化

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00016

Chin-Hui Chen, Yi-Fu Fu, Hsiao-Hua Cheng, Shou-de Lin

引用次数: 1

Message from the IEEE CogMI 2020 General Chairs and PC Chairs CogMI 2020 来自IEEE CogMI 2020通用主席和PC主席的消息

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/cogmi50398.2020.00005

引用次数: 0

Deep Learning Mechanism for Pervasive Internet Addiction Prediction 深度学习机制对普遍网络成瘾的预测

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00011

Zonyin Shae, J. Tsai

引用次数: 2

End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models 从嘈杂人群到监督机器学习模型的端到端学习

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2020-10-01 DOI: 10.1109/CogMI50398.2020.00013

Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi, R. Birke, L. Chen

{"title":"End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models","authors":"Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi, R. Birke, L. Chen","doi":"10.1109/CogMI50398.2020.00013","DOIUrl":"https://doi.org/10.1109/CogMI50398.2020.00013","url":null,"abstract":"Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number of non-expert workers via crowd-sourcing. Due to the varying background and experience of crowd workers, the obtained labels are highly prone to errors and even detrimental to the learning models. In this paper, we advocate using hybrid intelligence, i.e., combining deep models and human experts, to design an end-to-end learning framework from noisy crowd-sourced data, especially in an on-line scenario. We first summarize the state-of-the-art solutions that address the challenges of noisy labels from non-expert crowd and learn from multiple annotators. We show how label aggregation can benefit from estimating the annotators' confusion matrices to improve the learning process. Moreover, with the help of an expert labeler as well as classifiers, we cleanse aggregated labels of highly informative samples to enhance the final classification accuracy. We demonstrate the effectiveness of our strategies on several image datasets, i.e. UCI and CIFAR-10, using SVM and deep neural networks. Our evaluation shows that our on-line label aggregation with confusion matrix estimation reduces the error rate of labels by over 30%. Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"67 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":"130743660","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