text:
We show that there is an optimal solution to this problem in terms of the average information retrieved and the degree of the home nodes and design an adaptive strategy based on the behavior of the random walker. Finally, we compare different strategies that emerge from the model in the context of network reconstruction. Our results could be useful for the discovery of unknown connections in large-scale networks.},
	number = {6},
	urldate = {2019-03-27},
	journal = {Physical Review E},
	author = {Prignano, Luce and Moreno, Yamir and Díaz-Guilera, Albert},
	month = dec,
	year = {2012},
	pages = {066116},
	file = {APS Snapshot:H\:\\BackUp\\Dokumente\\Archäologie\\Zotero\\storage\\226D6G5P\\PhysRevE.86.html:text/html;Prignano et al_2012_Exploring complex networks by means of adaptive walkers.pdf:H\:\\BackUp\\Dokumente\\Archäologie\\Zotero\\storage\\PWCGH5HW\\Prignano et al_2012_Exploring complex networks by means of adaptive walkers.pdf:application/pdf}
}

@article{baxter_reinventing_2016,
	title = {Reinventing the wheel? {Modelling} temporal uncertainty with applications to brooch distributions in {Roman} {Britain}},
	volume = {66},
	issn = {0305-4403},
	shorttitle = {Reinventing the wheel?},
	url = {http://www.sciencedirect.com/science/article/pii/S0305440315003234},
	doi = {10.1016/j.jas.2015.12.007},
	abstract = {A simple, simulation-based model of temporal uncertainty is presented that embraces other approaches recently proposed in the literature, including those more usually involving mathematical calculation rather than simulation. More specifically, it is shown how the random generation of dates for events, conditioned by uncertain temporal knowledge of the true date, can be adapted to what has been called the chronological apportioning of artefact assemblages and aoristic analysis (as a temporal rather than spatio-temporal method). The methodology is in the same spirit – though there are differences – as that underpinning the use of summed radiocarbon dates. A possibly novel approach to representing temporal change is suggested. Ideas are illustrated using data extracted from a large corpus of late Iron Age and Roman brooches, where the focus of interest was on their temporal distribution over a period of about 450 years.},
	urldate = {2019-04-14},
	journal = {Journal of Archaeological Science},
	author = {Baxter, M. J. and Cool, H. E. M.},
	month = feb,
	year = {2016},
	keywords = {Roman, Simulation, Modelling, Aoristic, Brooches, Temporal change, Temporal uncertainty},
	pages = {120--127},
	file = {ScienceDirect Full Text PDF:H\:\\BackUp\\Dokumente\\Archäologie\\Zotero\\storage\\G8AUQLHC\\Baxter und Cool - 2016 - Reinventing the wheel Modelling temporal uncertai.pdf:application/pdf;ScienceDirect Snapshot:H\:\\BackUp\\Dokumente\\Archäologie\\Zotero\\storage\\DAWBFJRI\\S0305440315003234.html:text/html}
}

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib


text:
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extension: css


text:
@article{cui_scale_2020,
 abstract = {Purpose This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data? Design/methodology/approach Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing. Findings The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks. Originality/value The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.},
 author = {Cui, Ying and Chen, Fu and Shiri, Ali},
 doi = {10.1108/ILS-05-2019-0041},
 file = {Snapshot:/Users/fu/Zotero/storage/8MXAU8DM/html.html:text/html},
 issn = {2398-5348},
 journal = {Information and Learning Sciences},
 keywords = {Higher education, Learning analytics, Learning management system, Predictive models, Student activity data, Student course success},
 month = {January},
 note = {Publisher: Emerald Publishing Limited},
 number = {3/4},
 pages = {97--116},
 shorttitle = {Scale up predictive models for early detection of at-risk students},
 title = {Scale up predictive models for early detection of at-risk students: A feasibility study},
 url = {https://doi.org/10.1108/ILS-05-2019-0041},
 urldate = {2020-05-30},
 volume = {121},
 year = {2020}
}


text:
/* LESS Variable */
/*
 Less function for applying transition animations
 @prop - the element property for animation (e.g color)
 @t - time: the durtation of the animation in seconds
*/
body,
html {
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}

repository: borisKarastanev/JavaScript-Weather-App

repo_name: borisKarastanev/JavaScript-Weather-App


text:
FILESEXTRAPATHS_append := "${THISDIR}/${PN}:"
require recipes-bsp/u-boot/u-boot-common.inc
require recipes-bsp/u-boot/u-boot.inc

DEPENDS += "bc-native dtc-native"
SRC_URI += "file://0001-imx-variscite-Fix-regression-of-SPL-and-U-boot-booti.patch \
	    file://0001-spl-imx6-Let-spl_boot_device-return-USDHC1-or-USDHC2.patch \
	"
SRCREV = "e5aee22e4be75e75a854ab64503fc80598bc2004"

repository: parthitce/meta-variscite

repo_name: parthitce/meta-variscite

extension: bb