Pinned Repositories
AIMS-THESIS
aims_bigdata_2_elastic_flask
airqo-test
birds-images-annotations
birds-images-data
finite_volume_project
Finite volume method for two dimensional flow in Porous media
osiastossou.github.io
PartageLivre
Un projet de partage de Livre
ProjetTD-AC
This paper presents TD-AC which is an effective algorithm for the truth discovery problem when the attributes over data are structurally correlated. We build our procedure on an abstract representation of the truth in the data, the k-means clustering technique and the silhouette measure to automatically find an optimal partitioning of the input data (or a near-optimal) maximizing the accuracy of any base truth discovery process. The intensive experiments conducted on synthetic and real datasets show that TD-AC outperforms existing partitioning approaches with a more reasonable running time. It improves on synthetic datasets the accuracy of standard truth discovery algorithms by 6% at least and by 16% at most and also significantly when the data coverage rate is high for the other types of datasets
testflask
This is the simple project called witch I made to test flask a microframework for Python. In the project I made a CRUD (Create, Read, Update, Delete) employee management web app.
osiastossou's Repositories
osiastossou/finite_volume_project
Finite volume method for two dimensional flow in Porous media
osiastossou/testflask
This is the simple project called witch I made to test flask a microframework for Python. In the project I made a CRUD (Create, Read, Update, Delete) employee management web app.
osiastossou/ProjetTD-AC
This paper presents TD-AC which is an effective algorithm for the truth discovery problem when the attributes over data are structurally correlated. We build our procedure on an abstract representation of the truth in the data, the k-means clustering technique and the silhouette measure to automatically find an optimal partitioning of the input data (or a near-optimal) maximizing the accuracy of any base truth discovery process. The intensive experiments conducted on synthetic and real datasets show that TD-AC outperforms existing partitioning approaches with a more reasonable running time. It improves on synthetic datasets the accuracy of standard truth discovery algorithms by 6% at least and by 16% at most and also significantly when the data coverage rate is high for the other types of datasets
osiastossou/aims_bigdata_2_elastic_flask
osiastossou/osiastossou.github.io
osiastossou/PartageLivre
Un projet de partage de Livre
osiastossou/AIMS-THESIS
osiastossou/airqo-test
osiastossou/birds-images-annotations
osiastossou/birds-images-data
osiastossou/bson
Independent BSON codec for Python that doesn't depend on MongoDB.
osiastossou/csplib
A benchmark library for constraints
osiastossou/mergevec
Program used to merge .vec files for use in openCV's opencv_traincascade
osiastossou/SocialNetworkRestaurantAIMSstudents2020
SocialNetworkRestaurantAIMSstudents2020
osiastossou/workshop_aims
This is the repositories of all my presentation in aims.