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Big data and extreme-scale computing
2018
The international journal of high performance computing applications
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Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery ...
Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the ...
Acknowledgments The authors would like to acknowledge David Rogers for his work on the illustrations, Sam Crawford for editing support and the creation of the Appendix, and Piotr Luszczek for technical ...
doi:10.1177/1094342018778123
fatcat:vwrrxmad4rhtppq6ioaz4h5q7a
Efficiency in the Serverless Cloud Computing Paradigm: A Survey Study
[article]
2021
arXiv
pre-print
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reuse and approximation approaches and discussing the pros and cons of each one. ...
The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. ...
ACKNOWLEDGMENTS We would like to thank the anonymous reviewers of the paper and members of the HPCC Lab at UL Lafayette who brainstormed with us on this paper. ...
arXiv:2110.06508v1
fatcat:gp7dxqmmavfbhf7n5bssws2tje
Computational and informatics advances for reproducible data analysis in neuroimaging
[article]
2018
arXiv
pre-print
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The reproducibility of scientific research has become a point of critical concern. ...
We discuss the range of open data sharing resources that have been developed for neuroimaging data, and the role of data standards (particularly the Brain Imaging Data Structure) in enabling the automated ...
Acknowledgments: The work described here was supported by National Institutes of Health (R24MH114705 and R24MH117179), National Science Foundation (IIS-1760950), and the Laura and John Arnold Foundation ...
arXiv:1809.10024v1
fatcat:pfosfxzhfrbctb5363p7plxppy
Interdisciplinary Research in Mathematical Statistical and Computational Sciences
[article]
2024
Zenodo
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It is another thing entirely to take extreme measures to effect change, particularly in the context of an ISBN: 978-81-19746-16-3 2 Interdisciplinary Research in Mathematical Statistical and Computational ...
We provide methods for the ethical and responsible creation and application of AI-driven decarbonization solutions, with a strong emphasis on community involvement and teamwork. ...
Fitting machine learning models Various machine learning models such as KNN, SVM, etc have been performed on the training data. ...
doi:10.5281/zenodo.10755396
fatcat:t5lwm4tjsvctxjf342woossvxm
Changing Trends in Computational Drug Repositioning
2018
Pharmaceuticals
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Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning. ...
basis for the discovery of a new drug is to start with an old drug". ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ph11020057
pmid:29874824
pmcid:PMC6027196
fatcat:kd5w6uulcjgvnd6m66hwy3hmje
Evaluating the Performance Estimators via Machine Learning Supervised Learning Algorithms for Dataset Threshold
2015
International Journal of Computer Applications
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Bioinformatics uses the machine learning concepts and has attained a lot of success in this research field. ...
approaches of machine learning for supervised approaches and user modeling that is basically required for the handling of the label-data. ...
Semi-Supervised learning approach is based on Gaussian random field model. ...
doi:10.5120/21132-4059
fatcat:aozpcysea5d2hoh5aazftp6xjq
Knowledge Organization Through Statistical Computation: A New Approach
2009
Knowledge organization
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He received his bachelor's degree in engineering from Huazhong University of Science and Technology (China) in 2004, and master's degree in science from Peking University (China) in 2007. ...
Yang XU has been a Ph.D. candidate at Ecole Centrale de Nantes (France) since 2007. ...
In order to construct the model, a training set should be provided for model building and self-learning. In fact, the parameter computation process builds the model iteratively. ...
doi:10.5771/0943-7444-2009-4-227
fatcat:pe7kfweu5vhyrg455qiydibjjy
Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment
2023
Sustainability
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The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. ...
It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications ...
The second author would also like to acknowledge the fellowship at SDAIA-KFUPM Joint Research Center for Artificial Intelligence under grant number JRC-AI-RFP-04. ...
doi:10.3390/su151813951
fatcat:ymp3enfvkndhvitpmaiavihije
Randomized Algorithms for Scientific Computing (RASC)
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.11079v1 fatcat:ynz5uw4ejrfx5fqfutuvg3ybyiOther Versions
This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021. ...
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. ...
Machine learning enhancements to physical and biological models can be useful in "plugging gaps" in existing composite models, using data-driven machine learning models in those parts of the system for ...
arXiv:2104.11079v2
fatcat:qwwowtufzvbfjaiotx733eexxe
Brain-Inspired Computing: Models and Architectures
2020
IEEE Open Journal of Circuits and Systems
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With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machine learning continue to progress at a rapid pace with respect to algorithms, models, ...
This paper presents an overview of the brain-inspired computing models starting with the development of the perceptron and multi-layer perceptron followed by convolutional neural networks (CNNs) and recurrent ...
Looking back at the last decade, deep learning has pushed the limits of machine capabilities for inference in edge devices. ...
doi:10.1109/ojcas.2020.3032092
fatcat:62o3jgbxjjhs7hamsw4zvh2k7u
Table of contents
2020
2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
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Computer-Aided Ischemic Stroke Classification from EEG Data
Using a Single-Tiered Spiking Neural Network Framework
0512
17.4
1570680509 Efficiency of Different Machine Learning Algorithms on the ...
Sequence Modeling and Multitask Learning
0078
03.4
1570681010
Central Versus Distributed Statistical Computing Algorithms-A
Comparison
0087
03.5
1570675474 Energy Disaggregation Using Multilabel ...
doi:10.1109/uemcon51285.2020.9298057
fatcat:p4v3pn2m2zaaxdgobcaw5db76m
What is Cognitive Computing? An Architecture and State of The Art
[article]
2023
arXiv
pre-print
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This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. ...
Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. ...
Topic 2 is strongly related to Topic 1 represented in the concepts of "machine learn", "learn", "process" and "algorithm". ...
arXiv:2301.00882v1
fatcat:dovv3zmdubebbnibswqea7cf7y
Demonstrating Learning of Register Automata
[chapter]
2012
Lecture Notes in Computer Science
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This will not only illustrate the unique power of Register Automata, which allows one to faithfully model data independent systems, but also the ease of enhancing the LearnLib with new functionality. ...
We will demonstrate the impact of the integration of our most recently developed learning technology for inferring Register Automata into the LearnLib, our framework for active automata learning. ...
., been used to infer the behavior of a electronic passports [1] , in security research [3] , and it is a central enabler within the Connect framework. ...
doi:10.1007/978-3-642-28756-5_32
fatcat:rc4kxqzxqvabjg7pgoq4z27xs4
Adapting CRISP-DM for Idea Mining
2020
International Journal of Advanced Computer Science and Applications
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The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. ...
Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. ...
The purpose of this paper is to introduce a reusable process model for idea generation based on CRISP-DM. ...
doi:10.14569/ijacsa.2020.0110603
fatcat:odmcv6um4vdkpovljdbascecu4
Federated Learning Framework in Fogbus2-based Edge Computing Environments
[article]
2022
arXiv
pre-print
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Federated learning refers to conducting training on multiple distributed devices and collecting model weights from them to derive a shared machine-learning model. ...
It is useful in a situation where collaborative training of machine learning models is necessary while training data are highly sensitive. ...
Acknowledgements the development of a more powerful machine learning model, accessing more data is crucial and inevitable. ...
arXiv:2211.07238v1
fatcat:k5xeyfqpebhsjkshofg4p7eeli
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