Pub Date : 2023-01-01DOI: 10.1007/978-3-031-39059-3_15
Maya Trutschl, U. Cvek, M. Trutschl
{"title":"Using Artificial Intelligence to Reduce the Risk of Transfusion Hemolytic Reactions","authors":"Maya Trutschl, U. Cvek, M. Trutschl","doi":"10.1007/978-3-031-39059-3_15","DOIUrl":"https://doi.org/10.1007/978-3-031-39059-3_15","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"143 1","pages":"223-234"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74146315","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}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-39059-3_22
Lerina Aversano, M. Bernardi, Marta Cimitile, Martina Iammarino, Antonella Madau, Chiara Verdone
{"title":"An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning","authors":"Lerina Aversano, M. Bernardi, Marta Cimitile, Martina Iammarino, Antonella Madau, Chiara Verdone","doi":"10.1007/978-3-031-39059-3_22","DOIUrl":"https://doi.org/10.1007/978-3-031-39059-3_22","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"44 1","pages":"326-339"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75213494","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}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-39059-3_19
Abdallah Amine Melakhsou, M. Batton-Hubert
{"title":"Explainable Abnormal Time Series Subsequence Detection Using Random Convolutional Kernels","authors":"Abdallah Amine Melakhsou, M. Batton-Hubert","doi":"10.1007/978-3-031-39059-3_19","DOIUrl":"https://doi.org/10.1007/978-3-031-39059-3_19","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"51 1","pages":"280-294"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76030876","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}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-39059-3_21
Michael Danner, Bakir Hadzic, T. Weber, Xinjuan Zhu, Matthias Rätsch
{"title":"Towards Equitable AI in HR: Designing a Fair, Reliable, and Transparent Human Resource Management Application","authors":"Michael Danner, Bakir Hadzic, T. Weber, Xinjuan Zhu, Matthias Rätsch","doi":"10.1007/978-3-031-39059-3_21","DOIUrl":"https://doi.org/10.1007/978-3-031-39059-3_21","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"20 1","pages":"308-325"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85659001","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}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-39059-3_9
J. Feng, E. Lai, Weihua Li
{"title":"A Study of Neural Collapse for Text Classification","authors":"J. Feng, E. Lai, Weihua Li","doi":"10.1007/978-3-031-39059-3_9","DOIUrl":"https://doi.org/10.1007/978-3-031-39059-3_9","url":null,"abstract":"","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"21 1","pages":"126-142"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73882816","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}
Pub Date : 2022-08-05DOI: 10.5220/0011309100003277
Richard Marcus, Niklas Knoop, B. Egger, M. Stamminger
Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain. Most current LiDAR simulations are very simplistic and are mainly used to perform initial tests, while the majority of insights are gathered on the road. In this paper, we propose a lightweight approach for more realistic LiDAR simulation that learns a real sensor's behavior from test drive data and transforms this to the virtual domain. The central idea is to cast the simulation into an image-to-image translation problem. We train our pix2pix based architecture on two real world data sets, namely the popular KITTI data set and the Audi Autonomous Driving Dataset which provide both, RGB and LiDAR images. We apply this network on synthetic renderings and show that it generalizes sufficiently from real images to simulated images. This strategy enables to skip the sensor-specific, expensive and complex LiDAR physics simulation in our synthetic world and avoids oversimplification and a large domain-gap through the clean synthetic environment.
{"title":"A Lightweight Machine Learning Pipeline for LiDAR-simulation","authors":"Richard Marcus, Niklas Knoop, B. Egger, M. Stamminger","doi":"10.5220/0011309100003277","DOIUrl":"https://doi.org/10.5220/0011309100003277","url":null,"abstract":"Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain. Most current LiDAR simulations are very simplistic and are mainly used to perform initial tests, while the majority of insights are gathered on the road. In this paper, we propose a lightweight approach for more realistic LiDAR simulation that learns a real sensor's behavior from test drive data and transforms this to the virtual domain. The central idea is to cast the simulation into an image-to-image translation problem. We train our pix2pix based architecture on two real world data sets, namely the popular KITTI data set and the Audi Autonomous Driving Dataset which provide both, RGB and LiDAR images. We apply this network on synthetic renderings and show that it generalizes sufficiently from real images to simulated images. This strategy enables to skip the sensor-specific, expensive and complex LiDAR physics simulation in our synthetic world and avoids oversimplification and a large domain-gap through the clean synthetic environment.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"16 1","pages":"176-183"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90183862","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}
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