ERCIM NewsPub Date : 2016-10-01DOI: 10.21256/ZHAW-3785
Jan A. Stampfli, Kurt Stockinger
{"title":"Applied Data Science: Using Machine Learning for Alarm Verification","authors":"Jan A. Stampfli, Kurt Stockinger","doi":"10.21256/ZHAW-3785","DOIUrl":"https://doi.org/10.21256/ZHAW-3785","url":null,"abstract":"","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68015453","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}
ERCIM NewsPub Date : 2015-10-08DOI: 10.1007/978-3-319-25141-7_4
M. Reniers, S. Engell, H. Thompson
{"title":"Core Research and Innovation Areas in Cyber-Physical Systems of Systems","authors":"M. Reniers, S. Engell, H. Thompson","doi":"10.1007/978-3-319-25141-7_4","DOIUrl":"https://doi.org/10.1007/978-3-319-25141-7_4","url":null,"abstract":"","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2015-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-25141-7_4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72379910","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}
ERCIM NewsPub Date : 2015-04-01DOI: 10.14337/XMLLONDON15.PEMBERTON01
S. Pemberton
{"title":"Interfaces to the Internet of Things with XForms","authors":"S. Pemberton","doi":"10.14337/XMLLONDON15.PEMBERTON01","DOIUrl":"https://doi.org/10.14337/XMLLONDON15.PEMBERTON01","url":null,"abstract":"htmlabstractXForms is a language for describing interfaces to data, designed at W3C by researchers from industry and academia. It is a declarative language, meaning it describes what has to be done, but largely not how. The interface it describes does not have to run locally on the machine producing the data, but can be run remotely over the network. Since Internet of Things (IoT) computers typically have little memory and are low-powered, this makes XForms ideally suited for the task. \u0000 \u0000One of the unexpected successes of HTML was its adoption for controlling devices with embedded computers, such as home Wi-Fi routers. To make an adjustment to such a device, the user directs the browser to the IP address from which it is running and a small web server on the device serves up web pages that allow the user to fill in and submit values to change the working of the device. \u0000 \u0000However, the tiny embedded computers that form part of the IoT typically have memory in kilobytes, not megabytes, and lack the power to run a web server that can serve and interpret web pages. This calls for a different approach. \u0000 \u0000One approach is for the devices to serve up only the data of the parameters, so that those values can then be injected into an interface served from elsewhere. XForms [1], a standard that we have helped develop at W3C, is designed for exactly this type of scenario: although it is a technology originally designed for improving the handling of forms on the web, it has since been generalised to more general applications; version 2.0 is currently in preparation [2].","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67042572","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}
ERCIM NewsPub Date : 2015-04-01DOI: 10.5167/uzh-116554
C. Schmitt, B. Stiller
{"title":"Secure and Efficient Wireless Sensor Networks","authors":"C. Schmitt, B. Stiller","doi":"10.5167/uzh-116554","DOIUrl":"https://doi.org/10.5167/uzh-116554","url":null,"abstract":"There exists a multitude of implemented, as well as envisioned, use cases for the Internet of Things (IoT) and Wireless Sensor Networks (WSN). Some of these use cases would benefit from the collected data being globally accessible to: (a) authorized users only; and (b) data processing units through the Internet. Much of the data collected, such as location or personal identifiers, are of a highly sensitive nature. Even seemingly innocuous data (e.g., energy consumption) can lead to potential infringements of user privacy.","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70635662","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}
ERCIM NewsPub Date : 2015-02-19DOI: 10.1145/2700406
Andrea Esuli, F. Sebastiani
{"title":"Optimizing Text Quantifiers for Multivariate Loss Functions","authors":"Andrea Esuli, F. Sebastiani","doi":"10.1145/2700406","DOIUrl":"https://doi.org/10.1145/2700406","url":null,"abstract":"We address the problem of quantification, a supervised learning task whose goal is, given a class, to estimate the relative frequency (or prevalence) of the class in a dataset of unlabeled items. Quantification has several applications in data and text mining, such as estimating the prevalence of positive reviews in a set of reviews of a given product or estimating the prevalence of a given support issue in a dataset of transcripts of phone calls to tech support. So far, quantification has been addressed by learning a general-purpose classifier, counting the unlabeled items that have been assigned the class, and tuning the obtained counts according to some heuristics. In this article, we depart from the tradition of using general-purpose classifiers and use instead a supervised learning model for structured prediction, capable of generating classifiers directly optimized for the (multivariate and nonlinear) function used for evaluating quantification accuracy. The experiments that we have run on 5,500 binary high-dimensional datasets (averaging more than 14,000 documents each) show that this method is more accurate, more stable, and more efficient than existing state-of-the-art quantification methods.","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2015-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2700406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64165178","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}
ERCIM NewsPub Date : 2015-01-01DOI: 10.21256/zhaw-3643
Thilo Stadelmann, Mark Cieliebak, Kurt Stockinger
{"title":"Toward Automatic Data Curation for Open Data","authors":"Thilo Stadelmann, Mark Cieliebak, Kurt Stockinger","doi":"10.21256/zhaw-3643","DOIUrl":"https://doi.org/10.21256/zhaw-3643","url":null,"abstract":"","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68015330","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}
ERCIM NewsPub Date : 2014-09-30DOI: 10.55630/dipp.2014.4.4
C. Meghini, Annalisa Molino, Francesca Borri, Giulio Galesi
{"title":"Preserving Linked Data","authors":"C. Meghini, Annalisa Molino, Francesca Borri, Giulio Galesi","doi":"10.55630/dipp.2014.4.4","DOIUrl":"https://doi.org/10.55630/dipp.2014.4.4","url":null,"abstract":"PRELIDA (PREserving LInked DAta) is an FP7 Coordination Action funded by the European Commission under the Digital Preservation Theme.\u0000PRELIDA targets the particular stakeholders of the Linked Data community, including data providers, service providers, technology providers and end user communities. These stakeholders have not been traditionally targeted by the Digital Preservation community, and are typically not aware of the digital preservation solutions already available. So an important task of PRELIDA is to raise awareness of existing preservation solutions and to facilitate their uptake.\u0000At the same time, the Linked Data cloud has specific characteristics in terms of structuring, interlinkage, dynamicity and distribution that pose new challenges to the preservation community. PRELIDA organises in-depth discussions among the two communities to identify which of these characteristics require novel solutions, and to develop road maps for addressing the new challenges.\u0000PRELIDA will complete its lifecycle at the end of this year, and the talk will report about the major findings.","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2014-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71015531","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}
ERCIM NewsPub Date : 2013-10-02DOI: 10.1007/978-3-642-41062-8_25
Giuseppe Amato, Paolo Bolettieri, F. Falchi, C. Gennaro
{"title":"Large Scale Image Retrieval Using Vectors of Locally Aggregated Descriptors","authors":"Giuseppe Amato, Paolo Bolettieri, F. Falchi, C. Gennaro","doi":"10.1007/978-3-642-41062-8_25","DOIUrl":"https://doi.org/10.1007/978-3-642-41062-8_25","url":null,"abstract":"","PeriodicalId":44543,"journal":{"name":"ERCIM News","volume":null,"pages":null},"PeriodicalIF":0.1,"publicationDate":"2013-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77067162","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}