2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)最新文献

Performance Evaluation of Text Augmentation Methods with BERT on Small-sized, Imbalanced Datasets 基于BERT的文本增强方法在小型不平衡数据集上的性能评价

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00027

Lingshu Hu, Can Li, Wenbo Wang, Bin Pang, Yi Shang

{"title":"Performance Evaluation of Text Augmentation Methods with BERT on Small-sized, Imbalanced Datasets","authors":"Lingshu Hu, Can Li, Wenbo Wang, Bin Pang, Yi Shang","doi":"10.1109/CogMI56440.2022.00027","DOIUrl":"https://doi.org/10.1109/CogMI56440.2022.00027","url":null,"abstract":"Recently deep learning methods have achieved great success in understanding and analyzing text messages. In real-world applications, however, labeled text data are often small-sized and imbalanced in classes due to the high cost of data collection and human annotation, limiting the performance of deep learning classifiers. Therefore, this study explores an understudied area—how sample sizes and imbalance ratios influence the performance of deep learning models and augmentation methods—and provides a solution to this problem. Specifically, this study examines the performance of BERT, Word2Vec, and WordNet augmentation methods with BERT fine-tuning on datasets of sizes 500, 1,000, and 2,000 and imbalance ratios of 4:1 and 9:1. Experimental results show that BERT augmentation improves the performance of BERT in detecting the minority class, and the improvement is most significantly (15.6–40.4% F1 increase compared to the base model and 2.8%–10.4% F1 increase compared to the model with the oversampling method) when the data size is small (e.g., 500 training documents) and highly imbalanced (e.g., 9:1). When the data size increases or the imbalance ratio decreases, the improvement generated by the BERT augmentation becomes smaller or insignificant. Moreover, BERT augmentation plus BERT fine-tuning achieves the best performance compared to other models and methods, demonstrating a promising solution for small-sized, highly imbalanced text classification tasks.","PeriodicalId":211430,"journal":{"name":"2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126747571","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

Evaluating the Privacy Exposure of Interpretable Global Explainers 评估可解释全局解释器的隐私暴露

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00012

Francesca Naretto, A. Monreale, F. Giannotti

引用次数: 1

Thoughts on Non-IID Data Impact in Healthcare with Federated Learning Medical Blockchain 联邦学习医疗区块链对医疗保健中非iid数据影响的思考

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00013

Zonyin Shae, Kun-Yi Chen, Chi-Yu Chang, Yuan-Yu Tsai, C. Chou, William I. Baskett, Chi-Ren Shyu, J. J. Tsai

{"title":"Thoughts on Non-IID Data Impact in Healthcare with Federated Learning Medical Blockchain","authors":"Zonyin Shae, Kun-Yi Chen, Chi-Yu Chang, Yuan-Yu Tsai, C. Chou, William I. Baskett, Chi-Ren Shyu, J. J. Tsai","doi":"10.1109/CogMI56440.2022.00013","DOIUrl":"https://doi.org/10.1109/CogMI56440.2022.00013","url":null,"abstract":"We share the common hypothesis/belief that the more aggregated good quality training data, the better the performance that can be attained by the resulting Artificial Intelligence (AI) model. However, this common belief, in general, is not true in the medical area, since healthcare data sets sourced from different hospitals are often not identically distributed (Non-IID). This imposes severe technical challenges for effectively aggregating the individual hospital data sets together. In this vision paper, instead of offering complete solutions, we will discuss some questions and food for thought with the goal of aiding effective data aggregation and improving federated learning (FL) AI model performance: (1) benchmark and measure the Non-IID degree of medical data sets. (2) include the Non-IID degree metrics in the FL data aggregation mechanism. (3) search for the optimal global model creation strategy among a group of many medical data sets. (4) investigate FL performance better than the centralized learning. This paper will discuss these questions by outlining a visionary approach for exploring a medical blockchain FL mechanism to effectively aggregate medical data across multiple healthcare systems to serve large populations with broad demographics.","PeriodicalId":211430,"journal":{"name":"2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128495506","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 Multilingual Virtual Guide for Self-Attachment Technique 自我依恋技术的多语言虚拟指南

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00025

Alicia Jiayun Law, Ruoyu Hu, Lisa Alazraki, A. Gopalan, Neophytos Polydorou, A. Edalat

引用次数: 1

PSLotto: A Privacy-Enhanced COVID Lottery System PSLotto:增强隐私的COVID彩票系统

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00019

Stacey Truex, Giorgi Alavidze

{"title":"PSLotto: A Privacy-Enhanced COVID Lottery System","authors":"Stacey Truex, Giorgi Alavidze","doi":"10.1109/CogMI56440.2022.00019","DOIUrl":"https://doi.org/10.1109/CogMI56440.2022.00019","url":null,"abstract":"In March 2020, the World Health Organization (WHO) declared the novel coronavirus (COVID-19) a global pandemic. Globally the rapid spread of COVID-19 ground economies to a halt with stay at home orders and took the lives of millions of people. Therefore when vaccines became available as a tool to slow the spread of the COVID-19 virus, governments world-wide were looking to incentivize their populations to get vaccinated. Included in this effort, the government of Georgia created a lottery initiative to monetarily reward citizens who were vaccinated and encourage participation from those who were hesitant to get vaccinated. The Georgian lottery system that developed out of this initiative included a website displaying lottery winner data leading to serious privacy leakage. In this paper, we develop of an attack framework that allows adversaries with minimal background knowledge to re-identify STOPCOV Lottery winners and deploying our system against a subpopulation vulnerable to attack. We then propose our privacy-enhanced alternative, PSLotto, which simultaneously preserves the functionalities of the existing STOPCOV Lottery system and protects the privacy of lottery winners.","PeriodicalId":211430,"journal":{"name":"2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115579790","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

A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data 一种高度不平衡数据的无监督学习新方法

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00018

Robert K. L. Kennedy, Zahra Salekshahrezaee, T. Khoshgoftaar

引用次数: 2

An approach to dealing with incremental concept drift in personalized learning systems 个性化学习系统中增量概念漂移的处理方法

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00029

Bander Allogmany, D. Josyula

{"title":"An approach to dealing with incremental concept drift in personalized learning systems","authors":"Bander Allogmany, D. Josyula","doi":"10.1109/CogMI56440.2022.00029","DOIUrl":"https://doi.org/10.1109/CogMI56440.2022.00029","url":null,"abstract":"In recent years, personalized learning systems have garnered significant academic research attention in the field of education. In a personalized learning system, learners receive a customized learning style that is tailored to their unique needs, goals, and abilities. Thus, students can achieve their objectives faster than with the traditional method of learning. Rapid advancements in artificial intelligence technologies enable tracking and influencing each student’s learning process. Machine learning algorithms facilitate the determination of students’ learning styles, abilities, and progress throughout the learning process. One of the major challenges to effective personalization is the resistance of machine learning models to adapt to non-stationary data streams. Machine learning models for personalized learning systems are susceptible to the concept drift phenomenon, in which the models’ performance deteriorates over time due to changes in data distribution. For successful personalization, it is critical for the underlying predictive and classification models to adapt to data distribution changes. In this paper, we propose an approach to address concept drifts in personalized learning systems, and evaluate the approach on the OULAD dataset infused with concept drift. The proposed method comprises training utilizing sequential features extracted automatically.","PeriodicalId":211430,"journal":{"name":"2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116664090","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

Development of New Aerial Image Datasets and Deep Learning Methods for Waterfowl Detection and Classification 新型航空图像数据集的开发和水鸟检测与分类的深度学习方法

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00026

Yang Zhang, Shiqi Wang, Zhenduo Zhai, Y. Shang, Reid Viegut, Elisabeth Webb, A. Raedeke, J. Sartwell

{"title":"Development of New Aerial Image Datasets and Deep Learning Methods for Waterfowl Detection and Classification","authors":"Yang Zhang, Shiqi Wang, Zhenduo Zhai, Y. Shang, Reid Viegut, Elisabeth Webb, A. Raedeke, J. Sartwell","doi":"10.1109/CogMI56440.2022.00026","DOIUrl":"https://doi.org/10.1109/CogMI56440.2022.00026","url":null,"abstract":"Monitoring waterfowl populations and distribution is important for conservation. This paper presents our recent work on creating new aerial image datasets collected by drones and applying and evaluating state-of-the-art deep learning models for waterfowl detection and classification. We collected thousands of aerial images from 10 conservation areas in Missouri, labeled around 600 images with close to 300,000 bird labels, and created 9 datasets with different properties for training and evaluating deep neural network models. Among the models, YOLOv5 performed the best, outperforming Faster R-CNN and RetinaNet. To reduce the amount of labeled data needed for model training, we applied Soft Teacher, a semi-supervised learning method, and obtained slightly better detection performance than supervised learning methods, with just half of the labeled training examples. We trained generic detection models using all datasets containing diverse images and obtained accurate detection results in most cases. For waterfowl classification, we created a dataset of images containing individual waterfowl by cropping them from raw aerial images. We applied several deep learning models to the dataset and obtained promising results.","PeriodicalId":211430,"journal":{"name":"2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123355984","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

Deep Learning Methods for Tree Detection and Classification 树检测和分类的深度学习方法

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00030

Yang Zhang, Yizhen Wang, Zhicheng Tang, Zhenduo Zhai, Y. Shang, Reid Viegut

{"title":"Deep Learning Methods for Tree Detection and Classification","authors":"Yang Zhang, Yizhen Wang, Zhicheng Tang, Zhenduo Zhai, Y. Shang, Reid Viegut","doi":"10.1109/CogMI56440.2022.00030","DOIUrl":"https://doi.org/10.1109/CogMI56440.2022.00030","url":null,"abstract":"This paper presents the results of our deep learning methods for tree detection and classification on aerial images in the Plant Recognition University Challenge sponsored by Ameren in 2021–2022. The task was to locate the trees in an aerial image and predict their family, genus, and species. For tree detection, we applied various supervised learning methods with labeled training data as well as semi-supervised learning methods with the addition of unlabeled data. Our experimental results show that the semi-supervised learning method outperformed the supervised learning methods, improving the f1-score by an average of three percent on the set of images used in the final Plant Challenge competition. For tree classification, We applied various machine learning methods and deep learning models for image classification to predict family, genus and species on the portions of images detected of trees by the detection models. By considering the relationships between family, genus and species, we developed a multi-head ResNet18-based neural network and increased mean accuracy by two percent over the baseline ResNet18. Finally, our team ranked first among all teams in the Plant Challenge competition.","PeriodicalId":211430,"journal":{"name":"2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132599943","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

Adversarial Promotion for Video based Recommender Systems 基于视频的推荐系统的对抗性推广

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2022-12-01 DOI: 10.1109/CogMI56440.2022.00028

DeMarcus Edwards, D. Rawat, Brian M. Sadler

引用次数: 1