This dataset cointains the XML files of the inscriptions and the bibliography used in the critical edition: Dobias-Lalou, Catherine. Inscriptions of Greek Cyrenaica in collaboration with Alice Bencivenni, Hugues Berthelot, with help from Simona Antolini, Silvia Maria Marengo, and Emilio Rosamilia; Dobias-Lalou, Catherine. Greek Verse Inscriptions of Cyrenaica in collaboration with Alice Bencivenni, with help from Joyce M. Reynolds and Charlotte Roueché. Bologna: CRR-MM, Alma Mater Studiorum Università di Bologna, 2017. ISBN 9788898010684, http://doi.org/10.6092/UNIBO/IGCYRGVCYR. The inscriptions are marked up according to the principles of EpiDoc. The Inscriptions of Greek Cyrenaica (IGCyr) and the Greek Verse Inscriptions of Cyrenaica (GVCyr) are two corpora, the first collecting all the inscriptions of Greek (VII-I centuries B.C.) Cyrenaica, the second gathering the Greek metrical texts of all periods (VI B.C.-VI A.D.). These new critical editions of inscriptions from Cyrenaica are part of the international project Inscriptions of Libya (InsLib). For the first time all the inscriptions known to us in March 2018, coming from this area of the ancient Mediterranean world, are assembled in a single online and open access publication.
La collezione è composta da sei manoscritti custoditi presso la Biblioteca Augusta di Perugia, databili ai secoli XIII e XIV. Di questi, quattro sono di ambito umbro, uno è di provenienza bolognese e uno è riconducibile ad area toscana. Nel 2011 i codici sono stati oggetto di indagini diagnostiche non invasive eseguite dal Centro di Eccellenza SMAArt dell’Università di Perugia e dall’Istituto di Scienze e Tecnologie Molecolari del CNR, che hanno restituito elementi utili a ricostruire i materiali impiegati, le tecniche esecutive, la distinzione delle mani tra i diversi miniatori impegnati nella decorazione dei volumi nonché la loro localizzazione.
Dataset for training CNN built from aerial drone images of buildings in Hamburg
This dataset contains images extracted from aerial surveillance photos of the Speicherstadt and Kesselhaus buildings in Hamburg, provided by the City of Hamburg. Original 834 high resolution images (5472 x 3648 pixels) have been separated into smaller images (227 x 227 pixels) of the size that could be processed using SqueezeNet, a deep Convolutional Neural Network (CNN). This resulted in more than 350 thousand images that had to be subsequently processed automatically to retain images containing solely bricks and mortar and concrete. The final stage contained tedious manual/visual verification of images and their separation into positive (containing cracks) and negative (clear bricks and mortars) sets of images. The final set contains nearly 40 thousand images.
Since images extracted from Hamburg buildings contained only specific type of bricks and our intention was to extend the CNN to be able to deal with wider range of brick types as well as concrete surfaces, we added to our training set also images from the following Open Access databases (note that such images required resizing to 227 x 227 pixel size before use):
Concrete Crack Images for Classification (Mendeley Data)
Dataset for Crack Detection in Images of Masonry Using CNNs
Such a combined data set resulted in over 80 thousand of images.
Matlab WebApp Server application based on trained SqueezeNet CNN
The integrated database of images has been used to train the SqueezeNet CNN using a method proposed by Kenta Itakura in his article published on Matlab Central: Classify crack image using deep learning and explain "WHY", which in turn is based on the work of Lei Zhang reported in his IEEE article: Road crack detection using deep convolutional neural network published at 2016 IEEE International Conference on Image Processing (ICIP).
The "Matlab" subfolder contains the complete software to allow building the application to run under Matlab WebApps Server. The provided version of the "netTransfer.mat" file has been compiled for Matlab revision 2020b, but it should also work when compiled for other revisions from 2019a onwards. BTW, the original location of the files was "D:\Cracks (2-class)\". For instructions how to use the provided Matlab files, refer to Matlab instructions at MATLAB Web App Server and Get Started with MATLAB Web App Server.
After producing and uploading the application to the Matlab WebApps Server, the application can be found at http://localhost:9988/webapps/home/ if deployed locally. It can be also deployed on a WEB server, subject to installation of the compliant Matlab Runtime package on the custom server, whcih can be found at MATLAB Runtimes (mathworks.com).
The important function included in the package is "unscramble.m", which corrects the error that exists in all known revisions of Matlab in uploading images selected by open file function in the Matlab App Designer. The effect is that image is "scrambled beyond recognition" after uploading to the Matlab WebApps Server. Our function de-scrambles such images, converting them into their original form.
Pompeii graffiti offers a glimpse of the real people of Pompeii. Yet, there has been very few databases made of this resource and the ones that have been made are not digital, inaccessible or incomplete. My project is to make a database of graffiti from Pompeii that is the most complete and with known placement is known. For this I will use Victor Hunink’s Oh Happy Place: Pompeii in 1000 graffiti and also my personal survey of graffiti from Pompeii. The database is made within excel, as it is the most user friendly program, can be converted to numerous statistical programs and can be limited to user preferences based on categories, placement and etc., organized by columns with useful headings to organize the placement and other useful information. In order to demonstrate the utility of Pompeii graffiti I will run various statistical tests based on the qualitative graffiti data. In reviewing the database, I separated the graffiti into the categories romantic, sexual, reference, violence, civic, greeting and religious based upon modern graffiti, thus making it more easily interpreted by younger generations. I found various authors demonstrating underrepresented groups in Roman history such as women, children and foreigners. I was able to discern literacy levels of the populus on a scale of 1 to 3, to show the diversity of literacy in Pompeii. Finally, I was able to find correlations of graffiti either spatially, using the known placement of the graffiti, and socio-economically, using Miko Flohr’s database of housing structures and popularity of roadways for public structures. My database will be shared publicly, with the goal of showing the utility of graffiti as a source for examining the Romans, sparking interest in younger generations by relating ancient graffiti to modern graffiti and creating accessibility to Pompeii graffiti as a resource.
Creazione di una metodologia BIM (o ArchaeoBIM) replicabile all’interno di qualsiasi contesto archeologico contenente strutture murarie sia prive di alzato, sia conservate in elevato. Proprio per tali peculiarità il progetto ha deciso di utilizzare come caso studio le strutture romane dell’area archeologica di Massaciuccoli Romana (Massarosa – LU).
