2023 10th IEEE Swiss Conference on Data Science (SDS)最新文献_第2页

Automatic Image Compositing and Snow Segmentation for Alpine Snow Cover Monitoring 高山积雪监测的自动图像合成与分割

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00018

Janik Baumer, Nando Metzger, Elisabeth D. Hafner, R. C. Daudt, J. D. Wegner, K. Schindler

{"title":"Automatic Image Compositing and Snow Segmentation for Alpine Snow Cover Monitoring","authors":"Janik Baumer, Nando Metzger, Elisabeth D. Hafner, R. C. Daudt, J. D. Wegner, K. Schindler","doi":"10.1109/SDS57534.2023.00018","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00018","url":null,"abstract":"Accurate snow cover maps are an important tool in a large variety of applications including avalanche research. The WSL Institute for Snow and Avalanche Research (SLF) has developed a snow cover mapping system for the Dischma valley in Davos, based on ground-based cameras. Their goal has been to validate snow-cover maps derived from satellite data. In the currently implemented system, several steps require manual work. The goal of this paper is to automate SLF’s approach by applying a deep learning framework. For the training of our models, we have access to data from multiple cameras mounted in the Dischma valley. In our approach, we first apply deep learning to perform fog classification and then to do pixel-wise snow segmentation. Unlike the current procedure our method is independent of image- and camera-specific thresholds. In our experiments, we compare a model that is trained on images from all cameras to camera-specific models for both tasks. For the fog classification, we aim for a higher recall to be able to detect most of the foggy images. We show that, when tuned to achieve the same precision as the baseline, our model improves recall by 17%. For the snow segmentation, we evaluate our models based on the Fl-score. When using our fully automated machine learning model without any manually selected thresholds, we achieve a higher Fl-score only for a part of the cameras in the valley, and on average, this comes at a cost of 2.3%. We also show that for the case of a new camera being mounted in the Dischma valley, our models for both tasks can still be used for that new camera and will achieve similar results compared to the currently mounted cameras.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"AES-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497124","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

Mitigating Discriminatory Biases in Success Prediction Models for Venture Capitals 减少风险投资成功预测模型中的歧视性偏见

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00011

Yiea-Funk Te, Michèle Wieland, Martin Frey, Helmut Grabner

引用次数: 0

Physics-Informed Machine Learning for Predictive Maintenance: Applied Use-Cases 基于物理的机器学习预测性维护:应用用例

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00016

L. G. Huber, Thomas Palmé, Manuel Arias Chao

引用次数: 0

Assessing polarisation in brand-related comments on three Swiss online media portals with Natural Language Processing 用自然语言处理评估瑞士三家在线媒体门户网站上品牌相关评论的两极分化

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00026

S. Griesser, Adriana Ricklin, Remo Kälin, Guang Lu

引用次数: 0

A Graph-Representation-Learning Framework for Supporting Android Malware Identification and Polymorphic Evolution 支持Android恶意软件识别和多态进化的图表示学习框架

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00012

A. Cuzzocrea, Miguel Quebrado, Abderraouf Hafsaoui, Edoardo Serra

引用次数: 1

Deep Learning for Recognizing Bat Species and Bat Behavior in Audio Recordings 深度学习识别蝙蝠种类和蝙蝠行为的录音

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00014

M. Vogelbacher, Hicham Bellafkir, Jannis Gottwald, Daniel Schneider, Markus Muhling, Bernd Freisleben

{"title":"Deep Learning for Recognizing Bat Species and Bat Behavior in Audio Recordings","authors":"M. Vogelbacher, Hicham Bellafkir, Jannis Gottwald, Daniel Schneider, Markus Muhling, Bernd Freisleben","doi":"10.1109/SDS57534.2023.00014","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00014","url":null,"abstract":"Monitoring and mitigating the continuous decline of biodiversity is a key global challenge to preserve the existential basis of human life. Bats as one of the most widespread species among terrestrial mammals are excellent indicators for biodiversity and hence for the health of an ecosystem. Typically, bats are monitored by analyzing ultrasonic sound recordings. Stateof-the-art deep learning approaches for automatic bat detection and bat species recognition commonly rely on audio spectrogram classification models based on fixed time segments, lacking exact call boundaries. While great progress has been made on bat species recognition using echolocation calls, little attention has been paid to bat behavior recognition that provides valuable additional information about bat populations. In this paper, we present a novel end-to-end approach for bat species recognition and bat behavior recognition based on a deep neural network for object detection. In contrast to state-of-the-art approaches, the presented model provides accurate call boundaries. It recognizes 19 bat species and distinguishes between three different behaviors: orientation (echolocation calls), hunting (feeding buzzes), and social behavior (social calls). Our experiments with two data sets show that our method clearly outperforms previous approaches for bat species recognition, achieving up to 86.2% mean average precision. It also performs very well for bat behavior recognition, reaching up to 98.4%, 98.3%, and 95.6% average precision for recognizing echolocation calls, feeding buzzes, and social calls, respectively.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"32 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123802568","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

Mastering Fencing Techniques with Machine Learning: A Video-Based Classification and Correction System 掌握击剑技术与机器学习:基于视频的分类和校正系统

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00025

Solange Emmenegger, Matthias Egli, M. Pouly

引用次数: 0

Comparative Deep Learning Architectures to Detect Tiny Features in Ophthalmic Imaging 比较深度学习架构检测眼科成像中的微小特征

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-06-01 DOI: 10.1109/SDS57534.2023.00024

Julia Hartmann, Peter M. Maloca, C. Huwyler, Martin Melchior, Susanne Suter

{"title":"Comparative Deep Learning Architectures to Detect Tiny Features in Ophthalmic Imaging","authors":"Julia Hartmann, Peter M. Maloca, C. Huwyler, Martin Melchior, Susanne Suter","doi":"10.1109/SDS57534.2023.00024","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00024","url":null,"abstract":"In ophthalmology, the analysis of optical coherence tomography (OCT) images has brought important insights for early detection of eye diseases. This task requires a high amount of experience and training. Moreover, the detection is challenging due to the small size of the pathologies. An early detection is especially relevant for diseases, which cause permanent damage and if left untreated lead to blindness, such as wet age-related macular degeneration (wetAMD). In this work, six deep learning architectures trained to segment small and tiny pathological structures of wetAMD in OCT images were compared analytically and visually. We used a dataset of 2016 annotated OCT images from Augenspital University of Basel. The U-Net and our proposed variants N-Net and U-Net-M-Dec performed best for pixel-wise segmentation of these pathologies. Cropping input images into regions of interest and tiles improved the model training notably. Moreover, augmenting the data by brightness and rotation variations regularized the model training best. The proposed U-Net-M-Dec represents a middle ground between the evaluated binary and multiclass model approaches. The executed inter-observer variability of human annotators reached a Dice score of 0.74. The best multiclass segmentation U-Net reached a Dice score of 0.748 and U-Net-M-Dec achieved Dice scores per pathologies [IRF, SRF, HF, SHRM] of [0.845,0.808,0.488,0.862]. The segmentation models are intended to be used for ophthalmic training and an assistive tool in ophthalmic practices.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128988591","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

Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States 用基于报价的模型预测调查结果:对美国好感度的个案研究

2023 10th IEEE Swiss Conference on Data Science (SDS) Pub Date : 2023-05-23 DOI: 10.1109/SDS57534.2023.00008

A. Amirshahi, Nicolas kirsch, Jonathan Reymond, Saleh Baghersalimi

{"title":"Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States","authors":"A. Amirshahi, Nicolas kirsch, Jonathan Reymond, Saleh Baghersalimi","doi":"10.1109/SDS57534.2023.00008","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00008","url":null,"abstract":"The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this paper, we propose a pioneering approach for predicting survey responses by examining quotations using machine learning. Our investigation focuses on evaluating the degree of favorability towards the United States, a topic of interest to many organizations and governments. We leverage a vast corpus of quotations from individuals across different nationalities and time periods to extract their level of favorability. We employ a combination of natural language processing techniques and machine learning algorithms to construct a predictive model for survey responses. We investigate two scenarios: first, when no surveys have been conducted in a country, and second when surveys have been conducted but in specific years and do not cover all the years. Our experimental results demonstrate that our proposed approach can predict survey responses with high accuracy. Furthermore, we provide an exhaustive analysis of the crucial features that contributed to the model’s performance. This study has the potential to impact survey research in the field of data science by substantially decreasing the cost and time required to conduct surveys while simultaneously providing accurate predictions of public opinion.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"35 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116488745","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