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2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)最新文献

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Hybrid Knowledge Engineering to Build a Global Assassination Dataset 混合知识工程构建全球暗杀数据集
2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00011
Abigail Sticha, Steven Broussard, Ian Havenaar, Charles Vardeman, P. Brenner
{"title":"Hybrid Knowledge Engineering to Build a Global Assassination Dataset","authors":"Abigail Sticha, Steven Broussard, Ian Havenaar, Charles Vardeman, P. Brenner","doi":"10.1109/AIKE55402.2022.00011","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00011","url":null,"abstract":"Advances in Knowledge Engineering tools such as Named Entity Recognition, Knowledge Graphs, and Machine Learning allow researchers to generate new and more robust linked datasets from which researchers can make new discoveries. It is important for the AI research community to continue leveraging these tools for knowledge discovery while also acknowledging that each tool comes with limitations and an effective scope of use. This paper seeks to highlight the limitations of each of these tools while also uniting each of their strengths to propose a novel methodology leveraging existing databases, knowledge graphs, NER, and ML to build a knowledge repository within the constraints of a small labeled dataset and unstructured and incomplete existing databases. This methodology is implemented to build an enriched assassination dataset that will be made publicly available and assist in future political science research.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131343615","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
Exponential Moving based Features for Acoustic Scene Classification 基于指数移动特征的声学场景分类
2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00013
M. Sert
{"title":"Exponential Moving based Features for Acoustic Scene Classification","authors":"M. Sert","doi":"10.1109/AIKE55402.2022.00013","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00013","url":null,"abstract":"Acoustic scene classification (ASC) aims to classify a sound recording, which is recorded from a particular environment into a predefined category that describes the environment. In order to better model event behaviors within acoustic scenes, we investigate the use of exponential moving (EM)-based statistical representations for the ASC task. To this end, we design a convolutional neural network (CNN) based approach using statistical EM-based representations of log mel-band energies. We evaluate our proposed method on the publicly available performance dataset, DCASE 2022 Low-Complexity Acoustic Scene Classification Challenge dataset. Results show that the EM-based statistical representations achieve higher classification accuracy and better log losses compared to just using the log Mel feature.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124722400","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
RECLAIM: Reverse Engineering Classification Metrics 逆向工程分类度量
2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) Pub Date : 2022-09-01 DOI: 10.1109/AIKE55402.2022.00024
F. Giobergia, Elena Baralis
{"title":"RECLAIM: Reverse Engineering Classification Metrics","authors":"F. Giobergia, Elena Baralis","doi":"10.1109/AIKE55402.2022.00024","DOIUrl":"https://doi.org/10.1109/AIKE55402.2022.00024","url":null,"abstract":"Being able to compare machine learning models in terms of performance is a fundamental part of improving the state of the art in a field. However, there is a risk of getting locked into only using a few - possibly not ideal - performance metrics, only for comparability with earlier works. In this work, we explore the possibility of reconstructing new classification metrics starting from what little information may be available in existing works. We propose three approaches to reconstruct confusion matrices and, as a consequence, other classification metrics. We empirically verify the quality of the reconstructions, drawing conclusions on the usefulness that various classification metrics have for the reconstruction task.","PeriodicalId":441077,"journal":{"name":"2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127871905","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
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