[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118701-en":3,"doc-seo-118701-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118701,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","BEAT - An Open-Science Web Platform","BEAT is an open platform for computational-science research in machine learning and pattern recognition, created to address the frequent lack of result reproducibility in research publications, competitions, and benchmarks. It targets core barriers including sensitive data distribution, hard-to-install software frameworks, and complex test protocols. The system enables academic, governmental, and industrial organizations to collaboratively develop toolchains, reuse datasets and algorithms, and compare results across methods with minimal effort, while presenting key features, uses, and limitations.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk) brought to you by CORE  \nprovided by Infoscience- École polytechnique fédérale de Lausanne  \nBEAT: An Open-Science Web Platform  \nAndr Anjos, Laurent El-Shafey and Sbastien Marcel  \nIdiap Research Institute  \nrue Marconi, 19 Centre du Parc  \n1920, Martigny, VS, Switzerland  \nandre.anjos,laurent.el-shafey,[sebastien.marcel@idiap.ch](sebastien.marcel@idiap.ch)  \nAbstract  \nWith the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle.  \nTo bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.  \n1 Introduction  \nOne of the key aspects of modern computer science research lies in the use of personal computers (PCs) either for the simulation of known phenomena or for the evaluation of data collected from natural observations. Mashups of these data, organized in tables and figures are attached to textual descriptions leading to scientific publications. Frequently, data sets, code and actionable software leading to results are excluded upon recording and preservation of articles. This situation slows down potential scientific development in at least two major aspects: (1) re-using ideas from different sources normally implies the re-development of software leading to original results and (2) thereviewing process of candidate ideas is based on trust rather than on hard, verifiable evidence [12] .  \nThe need and benefits for reproducibility in computational science was already recognized by academia [5, 8, 13] and industry [1], though concrete actions to overcome inherent difficulties are yet to appear into de facto standards in this field. For example, it has been shown by MIT researchers [4] that the reviewing process that determines article acceptance in some conferences may by tricked by publications with machine generated content.  \nWhile scientific articles normally incorporate a stage of certification referred as peer-reviewing, the very same software frameworks and data leading to the stated conclusions, when available, are considered as a bonus and dismissed unreviewed. Even if knowledgable reviewers can predict when  \n31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.  \nwritten material is insufficiently discussed or poorly argued, one must also consider the hypothesis of rich arguments being coupled to poorly executed software implementations and data quality ending up in misleading conclusions, which do not translate in scientific development. It is a fact that correct and repeatable execution of software over data does not guarantee a good trend either, but less so does just an article. Onl","cbCaig2Mtj7xJVn9","https://ap.wps.com/l/cbCaig2Mtj7xJVn9","pdf",347516,1,7,"English","en",105,"# Abstract\n# 1 Introduction\n## Need for reproducibility in computational science\n## Platforms for evaluation and web-based approaches","[{\"question\":\"Why is result reproducibility often overlooked in computational science publications?\",\"answer\":\"The document attributes the gap to complications such as sensitive data distribution, difficult software installation and maintenance, and intricate test protocols that are hard to manage.\"},{\"question\":\"How does the BEAT platform help improve reproducibility and certification?\",\"answer\":\"BEAT supports development and comparison of processing toolchains by enabling reuse of data, algorithms, and workflows, allowing users to evaluate results with reduced effort.\"},{\"question\":\"What issues do other machine-learning and pattern-recognition platforms struggle with?\",\"answer\":\"The text highlights key issues such as data sharing, and contrasts web-service platforms with run-yourself software solutions.\"}]","BEAT - 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