2020 7th Swiss Conference on Data Science (SDS)最新文献

{"title":"SDS 2020 Breaker Page","authors":"","doi":"10.1109/sds49233.2020.00003","DOIUrl":"https://doi.org/10.1109/sds49233.2020.00003","url":null,"abstract":"","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186739","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}

{"title":"High-resolution snow depth monitoring for entire mountain ranges","authors":"H. Wulf, Bernhard Sassik, G. Milani, R. Leiterer","doi":"10.1109/SDS49233.2020.00008","DOIUrl":"https://doi.org/10.1109/SDS49233.2020.00008","url":null,"abstract":"Reliable information on the spatial distribution of snow in mountain ranges are critical for risk assessments, outdoor activities, water resource management and alpine ecology. Despite it’s importance, we lack an accurate monitoring of the continuous distribution of snow in space and time, which currently constitutes one of the most important unsolved problems in mountain hydrology. In this study, we present a novel approach for snow depth mapping based on satellite data and ground station networks, that takes topographical and meteorological differences into account. The resulting maps provide snow depth quantities for entire mountain ranges in a 20 m spatial resolution on a daily basis. We validate our geo-statistical model based on 61 airborne snow depth surveys from the European Alps and Rocky Mountains. Overall, the average correlation coefficients and RMSE is 0.32 and 0.85 m in the US and 0.22 and 0.53 m in Switzerland, respectively. Given the highly variable nature of snow depths, these numbers indicate an important milestone towards improved snow monitoring in alpine regions with versatile applications.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121623237","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}

{"title":"SDS 2020 Committees","authors":"Gundula Heinatz Bürki, Melanie Geiger, Kurt Stockinger, Kesheng Wu, Marco Zaffalon, Tayebeh Razmi","doi":"10.1109/sds49233.2020.00006","DOIUrl":"https://doi.org/10.1109/sds49233.2020.00006","url":null,"abstract":"Academic Program Committee Sihem Amer-Yahia, French National Centre for Scientific Research, France Ali Bigdeli, Aston University, UK Stefano Bromuri, Open Universiteit, Netherlands André Csillaghy, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland Markus Christen, University of Zurich, Switzerland Philippe Cudré-Mauroux, University of Fribourg, Switzerland Oliver Dürr, University of Technology, Business and Design Konstanz, Germany Andrea Ferrario, Swiss Federal Institute of Technology in Zurich, Switzerland Andreas Fischer, University of Fribourg, Switzerland Klaus Frick, Interstate University of Technology Buchs, Switzerland Samuel Fricker, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland Christoph Friedrich, University of Applied Sciences and Arts in Dortmund, Germany Reinhard Furrer, University of Zürich, Switzerland Luca Gambardella, University of Applied Sciences and Arts of Italian Switzerland, Switzerland David Garcia, Computational Social Science Vienna Research Group, Austria Alba García Seco de Herrera, University of Essex, UK Besma Glaa, University in Karlstad, Sweden Christoph Heitz, Zürich University of Applied Sciences, Switzerland Anne Herrmann, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland Jens Jelitto, IBM Research, Switzerland Stefan Keller, University of Applied Sciences Rapperswil, Switzerland Georgia Koutrika, Athena Research, Greece Christian Kowalkowski, Linköping University, Sweden Mathias Kraus, Swiss Federal Institute of Technology in Zurich, Switzerland Michele Loi, University of Zurich, Switzerland Jürg Meierhofer, Zürich University of Applied Sciences, Switzerland Benedikt Moser, Institute for Industrial Management RWTH Aachen University, Germany Elena Mugellini, University of Applied Sciences and Arts of Western Switzerland, Switzerland Henning Müller, University of Applied Sciences and Arts of Western Switzerland, Switzerland Miguel Prada, IBM Research Switzerland, Switzerland Alessandro Puiatti, University of Applied Sciences and Arts of Italian Switzerland, Switzerland Mario Rapaccini, Università degli Studi di Firenze, Italy Jacques Savoy, University of Neuchatel, Switzerland Dorothea Schaffner, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland René Schumann, University of Applied Sciences and Arts of Western Switzerland, Switzerland Philipp Schütz, Lucerne University of Applied Sciences and Arts, Switzerland Fabio Sigrist, Lucerne University of Applied Sciences and Arts, Switzerland Josef Spillner, Zürich University of Applied Sciences, Switzerland Ingo Steinwart, University of Stuttgart, Germany","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116900658","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}

{"title":"Exploration of Scenario-based Simulations for Stress Benchmarking in Swiss Public Service","authors":"M. Vadym","doi":"10.1109/SDS49233.2020.00012","DOIUrl":"https://doi.org/10.1109/SDS49233.2020.00012","url":null,"abstract":"Statistical interpretation of stress-related indicators collected through wearable biosensors often relies on benchmarking, especially in the context of stress management interventions. However, it remains unclear how to construct stress level benchmarks for group stress-related indicators using limited historical data. This study examines whether the method of numerical simulation of stress-related responses could contribute to constructing benchmark curves. Experimental data consists of physiological and non-physiological signals of 18 Swiss public servants collected through wearable biosensors. This study draws upon Stress Pattern Recognition algorithm and Markov Chain modeling for simulating emotional responses according to specified data-driven scenarios of high and low stress. Proposed method allows constructing benchmark curves for an Overarousal Index. Results demonstrate that numerical simulation based on small datasets can be used effectively for constructing stress level benchmarks. The findings contribute to methodological knowledge in statistical learning on Stress Pattern Recognition algorithms and Markov Chains modeling by expanding their application to a new field of emotional response simulation according to scenarios.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114072951","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}

{"title":"Same Same but Different: Augmentation of Tiny Industrial Datasets using Generative Adversarial Networks","authors":"Evangelos Ntavelis, I. Kastanis, L. Gool, R. Timofte","doi":"10.1109/SDS49233.2020.00011","DOIUrl":"https://doi.org/10.1109/SDS49233.2020.00011","url":null,"abstract":"The evolution of generative adversarial networks has permitted the generation of realistic fake images which, in some cases, are indistinguishable from the real ones. Many recent works in image generation focus on learning internal image statistics via training only on a single natural image. While natural images exhibit a variability in their attributes, industrial images are often acquired in a controlled environment following a specific structure. In this work we utilize the cutting-edge results of single image generation on the structured case of industrial images. Deep Learning plays an important role in Industry 4.0 manufacturing lines and multiple ML-based image processing products are currently on the market. To be able to tackle a variety of problems where image acquisition is costly and time-consuming data generation is a promising approach. The proposed method only requires a handful of images for training, making it an ideal candidate for industrial application where data is scarce and confidential. It provides the foundation for a variety of use cases in the field of industrial Deep Learning.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122754395","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}

{"title":"Message from the General Chairs: SDS 2020","authors":"","doi":"10.1109/sds49233.2020.00005","DOIUrl":"https://doi.org/10.1109/sds49233.2020.00005","url":null,"abstract":"","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727077","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}

{"title":"Annotating Web Tables through Knowledge Bases: A Context-Based Approach","authors":"Yasamin Eslahi, Akansha Bhardwaj, Paolo Rosso, Kurt Stockinger, P. Cudré-Mauroux","doi":"10.1109/SDS49233.2020.00013","DOIUrl":"https://doi.org/10.1109/SDS49233.2020.00013","url":null,"abstract":"The Web has a collection of over 150 million tables, which as a whole represents an invaluable source of semi-structured knowledge. Such tables are commonly referred to as Web tables, and are considerably easier to leverage in automated processes than completely unstructured, free-format text. Understanding the semantics of Web tables is important since they are used in various applications like knowledge base augmentation, information retrieval or natural language interfaces for databases. The task of understanding the semantics of a given Web table is known as Web table annotation. In recent years, it has been tackled through methods where the table is enriched using existing knowledge bases containing valuable information on the domain at hand, its entities and their mutual relationships.In this paper, we present two novel and unsupervised Web table annotation methods, which leverage the context of the tables to better capture their semantics. Our first method is lookup-based and exploits text similarity to find reference entities in the knowledge base. The second method uses distributional vector representations – a.k.a. embeddings – of the Web tables to elicit their context and disambiguate their semantics. Experiments show that our proposed approach outperforms the state of the art in Web table annotation by up to 18%. Another contribution of this work is a manually corrected version of one of the popular gold standard datasets, Limaye, with annotations from DBpedia. Our dataset and code are publicly available1.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131624257","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}

{"title":"Data-driven Decision Support by Digital Twins in Manufacturing","authors":"J. Meierhofer, S. West","doi":"10.1109/sds49233.2020.00019","DOIUrl":"https://doi.org/10.1109/sds49233.2020.00019","url":null,"abstract":"This conceptual paper addresses the implementation of digital twins to provide decision support services in manufacturing environments. It differentiates the twin among different layers and phases of the equipment lifecycle and integrates different modelling techniques into a systematic decision chain.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132456523","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}

{"title":"Data Resources to Create Digital Twins","authors":"Lukas Schweiger, L. Barth, J. Meierhofer","doi":"10.1109/SDS49233.2020.00020","DOIUrl":"https://doi.org/10.1109/SDS49233.2020.00020","url":null,"abstract":"Data resources are crucial to develop and maintain digital twins, which are seen as one of the key technologies within the paradigm shift called servitization in which organizations move from producing goods to offering services. Our hypothesis is that a large part of this data resources are already generated during the beginning of life (BOL) phases of the product lifecycle, but are not used in the middle of life (MOL) phases. This paper describes a framework and outlines a methodology to research the use of data resources from BOL phases in MOL phases. Projected results are an ontology of product data resources and an enhanced understanding of their usage in creating and maintaining digital twins.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117300776","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}

{"title":"Efficient Blind-Spot Neural Network Architecture for Image Denoising","authors":"David Honzátko, S. Bigdeli, Engin Türetken, Andrea L. Dunbar","doi":"10.1109/SDS49233.2020.00022","DOIUrl":"https://doi.org/10.1109/SDS49233.2020.00022","url":null,"abstract":"Image denoising is an essential tool in computational photography. Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy images for its training. If we do not possess the clean samples, we can use blind-spot neural network architectures, which estimate the pixel value based on the neighbouring pixels only. These networks thus allow training on noisy images directly, as they by-design avoid trivial solutions. Nowadays, the blind-spot is mostly achieved using shifted convolutions or serialization. We propose a novel fully convolutional network architecture that uses dilations to achieve the blind-spot property. Our network improves the performance over the prior work and achieves state-of-the-art results on established datasets.","PeriodicalId":136287,"journal":{"name":"2020 7th Swiss Conference on Data Science (SDS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128328943","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}