references.json

[
  {"id":"dr.linda-rabeaheydenOPENNEXTD34Report2022","abstract":"Report for the D3.3 deliverable","accessed":{"date-parts":[[2022,8,1]]},"citation-key":"dr.linda-rabeaheydenOPENNEXTD34Report2022","contributor":[{"family":"Dr. Linda-Rabea Heyden","given":""},{"family":"Aleynikova","given":"Elena"},{"family":"Häuer","given":"Martin"}],"DOI":"10.5281/ZENODO.6950056","issued":{"date-parts":[[2022,8,1]]},"language":"en","license":"Creative Commons Attribution 4.0 International, Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"OPEN-NEXT/D3.4-Report: v1.0.0","title-short":"OPEN-NEXT/D3.4-Report","type":"report","URL":"https://zenodo.org/record/6950056"},
  {"id":"gogineniDeliverableUserStories2020","abstract":"This document is a deliverable (3.1) of project OPEN_NEXT. This deliverable report describes and summarizes the activities carried out and their results in Task 3.1: Assessing the needs, which belongs to work package (WP) 3. The work package aims at developing information and communication technology (ICT) infrastructure to support open source hardware (OSH) and collaborative engineering in company-community collaboration (C3). Hence, the activities mentioned in this deliverable report is the first step to find and understand the needs for developing the required ICT infrastructure. This deliverable document delivers the following results, which are further detailed in later sections:  Collecting and accessing needs: An outline of twenty in-depth interviews conducted to assess community needs.  Development of user stories: A summary of the user stories generated from the interviews. The user stories act as the first step to translate the needs into solutions for filling the current ICT infrastructure gaps.  Data flow architecture development: Analysis and development of data flow architecture to understand the activities and processes of OSH development in C3.","accessed":{"date-parts":[[2022,8,9]]},"author":[{"family":"Gogineni","given":"Sonika"}],"citation-key":"gogineniDeliverableUserStories2020","contributor":[{"family":"Technische Universität Berlin","given":""},{"family":"Technische Universität Berlin","given":""}],"DOI":"10.14279/DEPOSITONCE-9852","issued":{"date-parts":[[2020,3,1]]},"language":"en","publisher":"Technische Universität Berlin","source":"DOI.org (Datacite)","title":"Deliverable 3.1 - User stories of collaborative engineering needs","type":"report","URL":"https://depositonce.tu-berlin.de/handle/11303/10962"},
  {"id":"hauerOPENNEXTD33Report2021","abstract":"changelog: minor format changes applied <strong>Find the complete export package in the ZIP file here ↓</strong>","accessed":{"date-parts":[[2022,6,7]]},"author":[{"family":"Häuer","given":"Martin"},{"family":"Sonika Gogineni","given":""}],"citation-key":"hauerOPENNEXTD33Report2021","DOI":"10.5281/ZENODO.5638687","issued":{"date-parts":[[2021,11,2]]},"license":"Creative Commons Attribution 4.0 International, Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"OPEN-NEXT/D3.3-Report: v1.0.2","title-short":"OPEN-NEXT/D3.3-Report","type":"report","URL":"https://zenodo.org/record/5638687","version":"v1.0.2"},
  {"id":"miesDEVELOPMENTOPENSOURCE2020b","abstract":"Abstract\n            Open source hardware is hardware whose design is shared online so that anyone can study, modify, distribute, make, and sell it. In spite of the increasing popularity of this alternative IP management approach, the field of OSH remains fragmented of diverse practices seeking for settlement. This challenges providers of groupware solutions to capture the specific needs of open source product development practitioners. This contribution therefore delivers a list of basic requirements and verifies them by comparing offered functions of existing groupware solutions.","accessed":{"date-parts":[[2022,6,21]]},"author":[{"family":"Mies","given":"R."},{"family":"Bonvoisin","given":"J."},{"family":"Stark","given":"R."}],"citation-key":"miesDEVELOPMENTOPENSOURCE2020b","container-title":"Proceedings of the Design Society: DESIGN Conference","container-title-short":"Proc. Des. Soc.: Des. Conf.","DOI":"10.1017/dsd.2020.38","ISSN":"2633-7762","issued":{"date-parts":[[2020,5]]},"language":"en","page":"997-1006","source":"DOI.org (Crossref)","title":"DEVELOPMENT OF OPEN SOURCE HARDWARE IN ONLINE COMMUNITIES: INVESTIGATING REQUIREMENTS FOR GROUPWARE","title-short":"DEVELOPMENT OF OPEN SOURCE HARDWARE IN ONLINE COMMUNITIES","type":"article-journal","URL":"https://www.cambridge.org/core/product/identifier/S2633776220000382/type/journal_article","volume":"1"},
  {"id":"miesIntroducingReadinessScales2022","accessed":{"date-parts":[[2022,6,22]]},"author":[{"family":"Mies","given":"Robert"},{"family":"Häuer","given":"Martin"},{"family":"Hassan","given":"Mehera"}],"citation-key":"miesIntroducingReadinessScales2022","container-title":"Procedia CIRP","container-title-short":"Procedia CIRP","DOI":"10.1016/j.procir.2022.05.306","ISSN":"22128271","issued":{"date-parts":[[2022]]},"language":"en","page":"635-640","source":"DOI.org (Crossref)","title":"Introducing readiness scales for effective reuse of open source hardware","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2212827122007557","volume":"109"},
  {"id":"PDFProgrammaticallyIdentifying","abstract":"Multiple natural language processing models are developed to identify and classify the presence of bias in text originating from software development artifacts: anchoring bias, availability bias, confirmation bias, and hyperbolic discounting. Mitigating bias in AI-enabled systems is a topic of great concern within the research community. While efforts are underway to increase model interpretability and de-bias datasets, little attention has been given to identifying biases that are introduced by developers as part of the software engineering process. To address this, we began developing an approach to identify a subset of cognitive biases that may be present in development artifacts: anchoring bias, availability bias, confirmation bias, and hyperbolic discounting. We developed multiple natural language processing (NLP) models to identify and classify the presence of bias in text originating from software development artifacts.","accessed":{"date-parts":[[2022,9,16]]},"citation-key":"PDFProgrammaticallyIdentifying","language":"en","source":"www.semanticscholar.org","title":"[PDF] Programmatically Identifying Cognitive Biases Present in Software Development | Semantic Scholar","type":"article-journal","URL":"https://www.semanticscholar.org/paper/Programmatically-Identifying-Cognitive-Biases-in-Kraft-Widjaja/42001e44b83f9714c46f929c32e3151c6a4dc852"},
  {"id":"penyuanOPENNEXTWp2Dev2021","abstract":"This is largely identical to the initial release for the OPENNEXT project work package 2 task 2.2's month 18 deliverable, but with additional documentation in the form of a step-by-step walkthrough of setting up the data-mining scripts and demo dashboard on your server. It will be simultaneously archived on Zenodo.org.","accessed":{"date-parts":[[2022,8,9]]},"author":[{"family":"Penyuan","given":""},{"family":"Mkampik","given":""},{"family":"Bonvoisin","given":"Jérémy"},{"family":"Jean-Francois Boujut","given":""}],"citation-key":"penyuanOPENNEXTWp2Dev2021","DOI":"10.5281/ZENODO.4560580","issued":{"date-parts":[[2021,6,11]]},"language":"en","license":"Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"OPEN-NEXT/wp2.2_dev: Add step-by-step walkthrough","title-short":"OPEN-NEXT/wp2.2_dev","type":"software","URL":"https://zenodo.org/record/4560580","version":"v0.2.0"},
  {"id":"sonikagogineniOPENNEXTD32Report2022","abstract":"Report of the D3.2 deliverable","accessed":{"date-parts":[[2022,8,1]]},"author":[{"family":"Sonika Gogineni","given":""},{"family":"Häuer","given":"Martin"},{"family":"Konietzko","given":"Erik Paul"},{"family":"Tanrikulu","given":"Cansu"},{"family":"Kampik","given":"Max"}],"citation-key":"sonikagogineniOPENNEXTD32Report2022","DOI":"10.5281/ZENODO.6950103","issued":{"date-parts":[[2022,8,1]]},"language":"en","license":"Creative Commons Attribution 4.0 International, Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"OPEN-NEXT/D3.2-Report: v1.0.0-copy","title-short":"OPEN-NEXT/D3.2-Report","type":"article-journal","URL":"https://zenodo.org/record/6950103","version":"v1.0.0"},
  {"id":"TheoreticalEmpiricalAnalysis","abstract":"Tory and empirical analysis of learning rate in neural network modeling for its application in stock price prediction is presented, and an increasing learning rate approach is suggested for practice. Neural Network training requires a large number of learning epochs. An appropriate learning rate is important to the overall performance of the training. Under a weight-update algorithm, a low learning rate would make the network learning slowly, and a high learning rate would make the weights and error function diverge. To optimize the model parameters, this paper presents theoretical and empirical analysis of learning rate in neural network modeling for its application in stock price prediction, an increasing learning rate approach is suggested for practice. The effect of momentum factor is also investigated to speed up the convergence for network training.","accessed":{"date-parts":[[2022,9,16]]},"citation-key":"TheoreticalEmpiricalAnalysis","language":"en","source":"www.semanticscholar.org","title":"Theoretical and Empirical Analysis of the Learning Rate and Momentum Factor in Neural Network Modeling for Stock Prediction | Semantic Scholar","type":"article-journal","URL":"https://www.semanticscholar.org/paper/Theoretical-and-Empirical-Analysis-of-the-Learning-Ke-Liu/21d6cdba6403273c2cc6e1b78c71e5f954d58ef1"}
]