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The RDF virtual machine

Marko A. Rodriguez
2011 Knowledge-Based Systems  
The Resource Description Framework (RDF) is a semantic network data model that is used to create machine-understandable descriptions of the world and is the basis of the Semantic Web.  ...  The Semantic Web is posited as not only a web of data, but also as a web of programs and processes.  ...  It is the role of the virtual machine to serve as a proxy to translate high-level instructions into the respective instruction set of the underlying CPU.  ... 
doi:10.1016/j.knosys.2011.04.004 fatcat:7tqe4vltdresxfbiavgsfsoooq

Mechanising Turing Machines and Computability Theory in Isabelle/HOL [chapter]

Jian Xu, Xingyuan Zhang, Christian Urban
2013 Lecture Notes in Computer Science  
We "tie the knot" between these three computational models by formalising a universal function and obtaining from it a universal Turing machine by our verified translation from recursive functions to abacus  ...  Following the textbook by Boolos et al, we formalise Turing machines and relate them to abacus machines and recursive functions.  ...  Acknowledgements: We are very grateful for the extremely helpful comments by the anonymous reviewers.  ... 
doi:10.1007/978-3-642-39634-2_13 fatcat:hsmb4kxqjbgi3csrx5li67qujm

Achieving human and machine accessibility of cited data in scholarly publications

Joan Starr, Eleni Castro, Mercè Crosas, Michel Dumontier, Robert R. Downs, Ruth Duerr, Laurel L. Haak, Melissa Haendel, Ivan Herman, Simon Hodson, Joe Hourclé, John Ernest Kratz (+9 others)
2015 PeerJ Computer Science  
We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier How to cite this article Starr et al. (2015) , Achieving human and machine  ...  Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP).  ...  ACKNOWLEDGEMENTS We are particularly grateful to PeerJ Academic Editor Harry Hochheiser (University of Pittsburgh), reviewer Tim Vines (University of British Columbia), and two anonymous reviewers, for  ... 
doi:10.7717/peerj-cs.1 pmid:26167542 pmcid:PMC4498574 fatcat:gjccychf7vfvrn5usawmiusm6q

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs [article]

Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec
2021 arXiv   pre-print
Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.  ...  However, existing efforts to advance large-scale graph ML have been largely limited by the lack of a suitable public benchmark.  ...  Acknowledgement We thank Michele Catasta and Larry Zitnick for helpful discussion, Shigeru Maya for motivating the project, Adrijan Bradaschia for setting up the server for the project, and Amit Bleiweiss  ... 
arXiv:2103.09430v3 fatcat:3xew2eoggfaohjzenvb7fo4ofy

Machine Learning [chapter]

2014 Encyclopedia of Social Network Analysis and Mining  
Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use.  ...  The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not  ...  A Classification Example: The Iris Dataset As an example we are going to look at another example from the UCI Machine Learning repository.  ... 
doi:10.1007/978-1-4614-6170-8_100067 fatcat:dmen3wa2gzd6jnzlxgudyscbm4

Disease Prediction Using Machine Learning and Deep Learning

Ms. Kavita Saxena, Rishabh Sharma, Rishav Kumar, Roshan Kumar
2022 International Journal for Research in Applied Science and Engineering Technology  
Keywords: Disease Detection, Artificial Intelligence, Healthcare, Machine Learning, Convolutional Neural Networks.  ...  like acquiring land for making hospital, stamp duties on it, human resource crunch which further act as roadblock for the government in providing universal good healthcare services to its citizenry .  ...  With the advancement of technology, the better computing power and availability of datasets on open-source repositories have further increased the use of machine learning.  ... 
doi:10.22214/ijraset.2022.42871 fatcat:qdsdh5qtlzbajmjg7fhdd2mj5a

A formalization of multi-tape Turing machines

Andrea Asperti, Wilmer Ricciotti
2015 Theoretical Computer Science  
We discuss the formalization, in the Matita Theorem Prover, of basic results on multi-tapes Turing machines, up to the existence of a (certified) Universal Machine, and propose it as a natural benchmark  ...  The work is meant to be a preliminary step towards the creation of a formal repository in Complexity Theory, and is a small piece in our long-term Reverse Complexity program, aiming to a comfortable, machine  ...  , this description is not directly suitable for a Universal Machine, since such a machine must work with a fixed set of states, while the size on n is unknown.  ... 
doi:10.1016/j.tcs.2015.07.013 fatcat:6dhjxrqhardlpejmkauxnj3jem

Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools

Dina Sikpa, Jérémie P. Fouquet, Réjean Lebel, Phedias Diamandis, Maxime Richer, Martin Lepage
2019 Scientific Reports  
A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs.  ...  Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists  ...  Acknowledgements The authors are grateful to the Electron Microscopy & Histology Research Core of the FMSS at the Université de Sherbrooke for their histology service.  ... 
doi:10.1038/s41598-019-53911-x pmid:31758004 pmcid:PMC6874643 fatcat:y6o5tkv4dvcwrbxqzrdufhwufu

An open source machine learning framework for efficient and transparent systematic reviews

Rens van de Schoot, Jonathan de Bruin, Raoul Schram, Parisa Zahedi, Jan de Boer, Felix Weijdema, Bianca Kramer, Martijn Huijts, Maarten Hoogerwerf, Gerbrich Ferdinands, Albert Harkema, Joukje Willemsen (+5 others)
2021 Nature Machine Intelligence  
The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text.  ...  AbstractTo help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts.  ...  This project was funded by the Innovation Fund for IT in Research Projects, Utrecht University, the Netherlands.  ... 
doi:10.1038/s42256-020-00287-7 fatcat:bdyuigxeeza45nben4c5pqrvty

Ab initio machine learning in chemical compound space [article]

Bing Huang, O. Anatole von Lilienfeld
2021 arXiv   pre-print
They rigorously reflect the underlying physics in order to reach universality and transferability across CCS.  ...  The first principles based virtual sampling of this space, for example in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest sub-sets  ...  The third category of software packages deals predominantly with data set construction, management and analysis.  ... 
arXiv:2012.07502v4 fatcat:2sfiyd5uwzdblafxjv4vmh3ule

Machine Learning, Neural, and Statistical Classification

John F. Elder IV, Donald Michie, David J. Spiegelhalter, Charles C. Taylor
1996 Journal of the American Statistical Association  
In this list we aim to include those who contributed to the Project and the Institutions at which they were primarily based at that time. G. Nakhaeizadeh, J.  ...  C Contributors This volume is based on the StatLog project, which involved many workers at over 13 institutions.  ...  It is instructive to compare the shapes that are produced by various learning systems when they partition the universe.  ... 
doi:10.2307/2291432 fatcat:mg6mr2lvjnczphrzq4t3iqjoay

Lowering the barrier to applying machine learning

Kayur Patel
2010 Adjunct proceedings of the 23nd annual ACM symposium on User interface software and technology - UIST '10  
, and understand the universe.  ...  I found that the key barrier to adoption is not a poor understanding of the machine learning algorithms themselves, but rather a poor understanding of the process for applying those algorithms and insufficient  ...  A programmer collects strokes defined as sets of ( , , ) triples, where and are 2D points and is time.  ... 
doi:10.1145/1866218.1866222 dblp:conf/uist/Patel10 fatcat:7k7ofxfstnayvgdh247ciltcgq

Lowering the barrier to applying machine learning

Kayur Patel
2010 Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems - CHI EA '10  
, and understand the universe.  ...  I found that the key barrier to adoption is not a poor understanding of the machine learning algorithms themselves, but rather a poor understanding of the process for applying those algorithms and insufficient  ...  A programmer collects strokes defined as sets of ( , , ) triples, where and are 2D points and is time.  ... 
doi:10.1145/1753846.1753882 dblp:conf/chi/Patel10 fatcat:ctphoo6owzfnpf53ihq7kjix3e

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice [article]

Bentley James Oakes and Michalis Famelis and Houari Sahraoui
2022 arXiv   pre-print
Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage  ...  Domain experts are increasingly employing machine learning to solve their domain-specific problems.  ...  Acknowledgements The authors would like to thank our colleagues Jessie Galasso-Carbonnel and István Dávid for their insightful discussions on this article.  ... 
arXiv:2203.08638v1 fatcat:lfnqklkivfby7blfupsjez4ote

Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms

Katherine Elfer, Sarah Dudgeon, Victor Garcia, Kim Blenman, Evangelos Hytopoulos, Si Wen, Xiaoxian Li, Amy Ly, Bruce Werness, Manasi S. Sheth, Mohamed Amgad, Rajarsi Gupta (+6 others)
2022 Journal of Medical Imaging  
The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.  ...  This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose.  ...  The FDA IRB determined that the research study was exempt from the requirements of 45 CFR part 46; 45 CFR 46.104(d)(2)(ii) (No. 2019-CDRH-109).  ... 
doi:10.1117/1.jmi.9.4.047501 pmid:35911208 pmcid:PMC9326105 fatcat:ujfwvvlkavhuhb445y5jdreyl4
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