arXiv - CS - Digital Libraries最新文献_第9页

Literature Filtering for Systematic Reviews with Transformers 用转换器过滤系统性综述文献

arXiv - CS - Digital Libraries Pub Date : 2024-05-30 DOI: arxiv-2405.20354

John Hawkins, David Tivey

引用次数: 0

Detection of the papermilling behavior 检测造纸行为

arXiv - CS - Digital Libraries Pub Date : 2024-05-30 DOI: arxiv-2405.19872

Igor Podlubny

引用次数: 0

On the modification and revocation of open source licences 关于开放源代码许可证的修改和撤销

arXiv - CS - Digital Libraries Pub Date : 2024-05-29 DOI: arxiv-2407.13064

Paul Gagnon, Misha Benjamin, Justine Gauthier, Catherine Regis, Jenny Lee, Alexei Nordell-Markovits

{"title":"On the modification and revocation of open source licences","authors":"Paul Gagnon, Misha Benjamin, Justine Gauthier, Catherine Regis, Jenny Lee, Alexei Nordell-Markovits","doi":"arxiv-2407.13064","DOIUrl":"https://doi.org/arxiv-2407.13064","url":null,"abstract":"Historically, open source commitments have been deemed irrevocable once\u0000materials are released under open source licenses. In this paper, the authors\u0000argue for the creation of a subset of rights that allows open source\u0000contributors to force users to (i) update to the most recent version of a\u0000model, (ii) accept new use case restrictions, or even (iii) cease using the\u0000software entirely. While this would be a departure from the traditional open\u0000source approach, the legal, reputational and moral risks related to\u0000open-sourcing AI models could justify contributors having more control over\u0000downstream uses. Recent legislative changes have also opened the door to\u0000liability of open source contributors in certain cases. The authors believe\u0000that contributors would welcome the ability to ensure that downstream users are\u0000implementing updates that address issues like bias, guardrail workarounds or\u0000adversarial attacks on their contributions. Finally, this paper addresses how\u0000this license category would interplay with RAIL licenses, and how it should be\u0000operationalized and adopted by key stakeholders such as OSS platforms and\u0000scanning tools.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737146","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

Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models 利用知识图谱和大型语言模型生成并由人类专家评估有趣的研究想法

arXiv - CS - Digital Libraries Pub Date : 2024-05-27 DOI: arxiv-2405.17044

Xuemei Gu, Mario Krenn

{"title":"Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models","authors":"Xuemei Gu, Mario Krenn","doi":"arxiv-2405.17044","DOIUrl":"https://doi.org/arxiv-2405.17044","url":null,"abstract":"Advanced artificial intelligence (AI) systems with access to millions of\u0000research papers could inspire new research ideas that may not be conceived by\u0000humans alone. However, how interesting are these AI-generated ideas, and how\u0000can we improve their quality? Here, we introduce SciMuse, a system that uses an\u0000evolving knowledge graph built from more than 58 million scientific papers to\u0000generate personalized research ideas via an interface to GPT-4. We conducted a\u0000large-scale human evaluation with over 100 research group leaders from the Max\u0000Planck Society, who ranked more than 4,000 personalized research ideas based on\u0000their level of interest. This evaluation allows us to understand the\u0000relationships between scientific interest and the core properties of the\u0000knowledge graph. We find that data-efficient machine learning can predict\u0000research interest with high precision, allowing us to optimize the\u0000interest-level of generated research ideas. This work represents a step towards\u0000an artificial scientific muse that could catalyze unforeseen collaborations and\u0000suggest interesting avenues for scientists.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165440","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

Oil & Water? Diffusion of AI Within and Across Scientific Fields 油和水？人工智能在科学领域内和科学领域间的扩散

arXiv - CS - Digital Libraries Pub Date : 2024-05-24 DOI: arxiv-2405.15828

Eamon Duede, William Dolan, André Bauer, Ian Foster, Karim Lakhani

{"title":"Oil & Water? Diffusion of AI Within and Across Scientific Fields","authors":"Eamon Duede, William Dolan, André Bauer, Ian Foster, Karim Lakhani","doi":"arxiv-2405.15828","DOIUrl":"https://doi.org/arxiv-2405.15828","url":null,"abstract":"This study empirically investigates claims of the increasing ubiquity of\u0000artificial intelligence (AI) within roughly 80 million research publications\u0000across 20 diverse scientific fields, by examining the change in scholarly\u0000engagement with AI from 1985 through 2022. We observe exponential growth, with\u0000AI-engaged publications increasing approximately thirteenfold (13x) across all\u0000fields, suggesting a dramatic shift from niche to mainstream. Moreover, we\u0000provide the first empirical examination of the distribution of AI-engaged\u0000publications across publication venues within individual fields, with results\u0000that reveal a broadening of AI engagement within disciplines. While this\u0000broadening engagement suggests a move toward greater disciplinary integration\u0000in every field, increased ubiquity is associated with a semantic tension\u0000between AI-engaged research and more traditional disciplinary research. Through\u0000an analysis of tens of millions of document embeddings, we observe a complex\u0000interplay between AI-engaged and non-AI-engaged research within and across\u0000fields, suggesting that increasing ubiquity is something of an oil-and-water\u0000phenomenon -- AI-engaged work is spreading out over fields, but not mixing well\u0000with non-AI-engaged work.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165434","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

Rethinking the production and publication of machine-reusable expressions of research findings 重新思考制作和出版可通过机器重复使用的研究成果表达方式

arXiv - CS - Digital Libraries Pub Date : 2024-05-21 DOI: arxiv-2405.13129

Markus Stocker, Lauren Snyder, Matthew Anfuso, Oliver Ludwig, Freya Thießen, Kheir Eddine Farfar, Muhammad Haris, Allard Oelen, Mohamad Yaser Jaradeh

{"title":"Rethinking the production and publication of machine-reusable expressions of research findings","authors":"Markus Stocker, Lauren Snyder, Matthew Anfuso, Oliver Ludwig, Freya Thießen, Kheir Eddine Farfar, Muhammad Haris, Allard Oelen, Mohamad Yaser Jaradeh","doi":"arxiv-2405.13129","DOIUrl":"https://doi.org/arxiv-2405.13129","url":null,"abstract":"Literature is the primary expression of scientific knowledge and an important\u0000source of research data. However, scientific knowledge expressed in narrative\u0000text documents is not inherently machine reusable. To facilitate knowledge\u0000reuse, e.g. for synthesis research, scientific knowledge must be extracted from\u0000articles and organized into databases post-publication. The high time costs and\u0000inaccuracies associated with completing these activities manually has driven\u0000the development of techniques that automate knowledge extraction. Tackling the\u0000problem with a different mindset, we propose a pre-publication approach, known\u0000as reborn, that ensures scientific knowledge is born reusable, i.e. produced in\u0000a machine-reusable format during knowledge production. We implement the\u0000approach using the Open Research Knowledge Graph infrastructure for FAIR\u0000scientific knowledge organization. We test the approach with three use cases,\u0000and discuss the role of publishers and editors in scaling the approach. Our\u0000results suggest that the proposed approach is superior compared to classical\u0000manual and semi-automated post-publication extraction techniques in terms of\u0000knowledge richness and accuracy as well as technological simplicity.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149818","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

Transfer Learning Approach for Railway Technical Map (RTM) Component Identification 铁路技术图 (RTM) 组件识别的迁移学习方法

arXiv - CS - Digital Libraries Pub Date : 2024-05-21 DOI: arxiv-2405.13229

Obadage Rochana Rumalshan, Pramuka Weerasinghe, Mohamed Shaheer, Prabhath Gunathilake, Erunika Dayaratna

引用次数: 0

Scientific discourse on YouTube: Motivations for citing research in comments YouTube 上的科学讨论：在评论中引用研究成果的动机

arXiv - CS - Digital Libraries Pub Date : 2024-05-21 DOI: arxiv-2405.12798

Sören Striewski, Olga Zagovora, Isabella Peters

引用次数: 0

Amplifying Academic Research through YouTube: Engagement Metrics as Predictors of Citation Impact 通过 YouTube 放大学术研究：作为引文影响力预测指标的参与度指标

arXiv - CS - Digital Libraries Pub Date : 2024-05-21 DOI: arxiv-2405.12734

Olga Zagovora, Talisa Schwal, Katrin Weller