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The Queensland Cloud Seeding Research Program

Sarah A. Tessendorf, Roelof T. Bruintjes, Courtney Weeks, James W. Wilson, Charles A. Knight, Rita D. Roberts, Justin R. Peter, Scott Collis, Peter R. Buseck, Evelyn Freney, Michael Dixon, Matthew Pocernich (+17 others)
2012 Bulletin of The American Meteorological Society - (BAMS)  
The early experiments treated the physical chain of events from seeding to rain at the surface as a "black box" (i.e., one does not attempt to learn what is happening in the "box").  ...  ; and • the power and limitations of existing radar systems as integral experimental tools and as possible means of verification of seeding results.  ... 
doi:10.1175/bams-d-11-00060.1 fatcat:mju56xtgzfavvilexrqrexvknq

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

Seppo Pulkkinen, Daniele Nerini, Andrés A. Pérez Hortal, Carlos Velasco-Forero, Alan Seed, Urs Germann, Loris Foresti
2019 Geoscientific Model Development  
In this sense, pysteps has the potential to become an important component for integrated early warning systems for severe weather.  ...  Pysteps is an open-source and community-driven Python library for probabilistic precipitation nowcasting, that is, very-short-range forecasting (0–6 h).  ...  The authors express their gratitude to Weather Decision Technologies for providing access to the United States radar composites.  ... 
doi:10.5194/gmd-12-4185-2019 fatcat:ue6nkk6iwvg23jevcyyjeiyyxm

Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes

Elisabeth A. Karuza, Lauren L. Emberson, Matthew E. Roser, Daniel Cole, Richard N. Aslin, Jozsef Fiser
2017 Journal of Cognitive Neuroscience  
learning from scenes with a structured spatial layout.  ...  In addition, bilateral medial temporal activation was linked to participants' behavioral performance, suggesting that spatial statistical learning recruits additional resources from the limbic system.  ...  The authors would like to acknowledge Daphne Bavelier and Elissa Newport for helpful comments on this work and Merry Mani for assistance with preliminary analyses.  ... 
doi:10.1162/jocn_a_01182 pmid:28850297 pmcid:PMC5886020 fatcat:bdwc4meknngotmvuplfl77v4e4

Oscillatory EEG Signatures of Affective Processes during Interaction with Adaptive Computer Systems

Mathias Vukelić, Katharina Lingelbach, Kathrin Pollmann, Matthias Peissner
2020 Brain Sciences  
Electroencephalography (EEG) was used to examine the reactivity of the cortical system during the interaction by studying both event-related (de-)synchronization (ERD/ERS) and event-related functional  ...  Sixteen participants interacted with a simulated assistance system which deliberately evoked positive (supporting goal-achievement) and negative (impeding goal-achievement) affective reactions.  ...  Acknowledgments: We would like to thank Fabian Ries who contributed significantly with his patient and flexible support during data acquisition.  ... 
doi:10.3390/brainsci11010035 pmid:33396330 fatcat:gsmzxa4ip5d7nftkpipz2qyeay

Survey on Technique and User Profiling in Unsupervised Machine Learning Method

Andri M Kristijansson, Tyr Aegisson
2022 Journal of Machine and Computing  
In order to generate precise behavioural patterns or user segmentation, organisations often struggle with pulling information from data and choosing suitable Machine Learning (ML) techniques.  ...  The goal of this research is to provide a framework that outlines the Unsupervised Machine Learning (UML) methods for User-Profiling (UP) based on essential data attributes.  ...  This research intends to participate in an answer by establishing a method and paradigm of UML techniques with regard to essential data attributes, based on the second classification technique.  ... 
doi:10.53759/7669/jmc202202002 fatcat:kznjwlyygbgw7dhzrwyzbp5ahm

Damage mechanism identification in composites via machine learning and acoustic emission

C. Muir, B. Swaminathan, A. S. Almansour, K. Sevener, C. Smith, M. Presby, J. D. Kiser, T. M. Pollock, S. Daly
2021 npj Computational Materials  
This review evaluates the state of the field, beginning with a physics-based understanding of acoustic emission waveform feature extraction, followed by a detailed overview of waveform clustering, labeling  ...  AbstractDamage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems.  ...  The authors additionally thank Aaron Engel for the suggestion for this project, and Dr Neal Brodnik for an introduction to t-SNE.  ... 
doi:10.1038/s41524-021-00565-x fatcat:hna6ank7vbcktm2jqsxxcloama

Lung Nodule Segmentation with a Region-Based Fast Marching Method

Marko Savic, Yanhe Ma, Giovanni Ramponi, Weiwei Du, Yahui Peng
2021 Sensors  
systems.  ...  An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data,  ...  A more practical drawback of deep learning is the need for powerful computers.  ... 
doi:10.3390/s21051908 pmid:33803297 pmcid:PMC7967233 fatcat:iahlhzxsuffcpfp2e5cvhi2wt4

Search-based test and improvement of machine-learning-based anomaly detection systems

Maxime Cordy, Steve Muller, Mike Papadakis, Yves Le Traon
2019 Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis - ISSTA 2019  
Machine-learning-based anomaly detection systems can be vulnerable to new kinds of deceptions, known as training attacks, which exploit the live learning mechanism of these systems by progressively injecting  ...  Going a step further, we also propose searching for countermeasures, learning from the successful attacks and thereby increasing the resilience of the tested IDS.  ...  [15] use k-means and fuzzy cognitive maps to cluster security events in a power system and detect anomalies. Hendry et al.  ... 
doi:10.1145/3293882.3330580 dblp:conf/issta/CordyMPT19 fatcat:tlbufms4l5amjls7bcuze33af4

UNSUPERVISED DISCOVERY OF VISUAL FACE CATEGORIES

SHICAI YANG, GEORGE BEBIS, MUHAMMAD HUSSAIN, GHULAM MUHAMMAD, ANWAR M. MIRZA
2013 International journal on artificial intelligence tools  
To address the issue of face category discovery, we represent faces using local features and apply unsupervised learning (i.e., clustering).  ...  Yang et al. 1250029-2 or learn the separating boundaries between face categories using supervised learning (i.e., classification).  ...  Acknowledgments This work was supported by the National Plan for Science and Technology, King Saud University, Riyadh, Saudi Arabia under project number 10-INF1044-02.  ... 
doi:10.1142/s0218213012500297 fatcat:tvjyxnqwrnb4daleplhpyv5wju

Image Steganography Using HBC and RDH Technique

Hemalatha M, Prasanna A, Dinesh Kumar R, Vinothkumar D
2014 International Journal of Computer Applications Technology and Research  
There are algorithms in existence for hiding data within an image. The proposed scheme treats the image as a whole.  ...  With these methods the performance of the stegnographic technique is improved in terms of PSNR value.  ...  ACKNOWLEDGEMENTS Our thanks to the colleague Lecturers of Computer Science department Federal College of Education (Technical) Potiskum for their contributions towards development of the paper.  ... 
doi:10.7753/ijcatr0303.1001 fatcat:4i6tujs4oje2tnxf5c25eh26x4

Event Prediction in the Big Data Era: A Systematic Survey [article]

Liang Zhao
2020 arXiv   pre-print
Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics.  ...  First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research  ...  [110] proposed a greedy-based heuristic tailored for the grid-based data formulation, which extends the original "seed" grid containing statistically-large future event densities to four directions  ... 
arXiv:2007.09815v3 fatcat:ypmjm3n3xjbcjbzdlhowdkaona

Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome [article]

John Booth, Ben Margetts, William Bryant, Richard Issitt, John Ciaran Hutchinson, Nigel Martin, Neil Sebire
2020 biorxiv/medrxiv   pre-print
In order to aid counselling and understand how to improve the investigation, we explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome.  ...  Methods: A paediatric autopsy database containing >7,000 cases in total with >300 variables per case, was analysed with cases categorised both by stage of examination (external, internal and internal with  ...  In order to optimise the data for machine learning we used one-hot encoding for categorical features (rather than each feature having a single column of data with the appropriate category; each category  ... 
doi:10.1101/2020.05.21.20105221 fatcat:buwm2rh64jgdtm757agedtaolm

What's Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

Zafar Saeed, Rabeeh Ayaz Abbasi, Onaiza Maqbool, Abida Sadaf, Imran Razzak, Ali Daud, Naif Radi Aljohani, Guandong Xu
2019 Journal of Grid Computing  
Twitter presents an interesting opportunity for detecting events happening around the world.  ...  EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area.  ...  learning technique for event detection.  ... 
doi:10.1007/s10723-019-09482-2 fatcat:lypwnpnmonb3laa3koftllchby

LATTICE: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

Somali Chaterji, Nathan Delay, John Evans, Nathan Mosier, Bernie Engel, Dennis Buckmaster, Michael Ladisch, Ranveer Chandra
2021 IEEE Open Journal of the Computer Society  
Through LATTICE, we present an integrated vision for IoT solutions, data processing, and actionable analytics, with economics and policy considerations for digital agriculture.  ...  It then goes on to describe the most prevalent and promising aspects of machine learning and cloud computing for digital agriculture.  ...  More progressive producers use yield data to determine variable rate fertilizer application and to compare seed varieties.  ... 
doi:10.1109/ojcs.2021.3085846 fatcat:5ru7iw3nhrh6lcz6ffi4cthqhe

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

Ferhat Ucar, Jose Cordova, Omer F. Alcin, Besir Dandil, Fikret Ata, Reza Arghandeh
2019 Energies  
Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis.  ...  This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier.  ...  Acknowledgments: The authors would like to thank TEIAS and the National Power Quality Monitoring Center engineer team members for their kind cooperation in sharing real-world data with a formal bilateral  ... 
doi:10.3390/en12081449 fatcat:fkorqoidgbglbmhb3dw44no2ta
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