Java and SHACL code commented in the paper "Towards compliance checking in reified I/O logic via SHACL" submitted to ICAIL 2021

Related tags

Deep LearningshRIOL
Overview

shRIOL

The subfolder shRIOL contains Java files to execute the SHACL files on the OWL ontology.

To compile the Java files: "javac -cp ./src/;./lib/* -d ./class ./src/DetectViolations.java"
To run compiled class files: "java -cp ./class;./lib/* DetectViolations"

By executing the Java files, the following messages are printed on screen. See the paper for more details and explanations.

The model is not GDPR-compliant. The following violations have been detected:
----------------------------------------------------------------------------------------------
Personal Data Processing: http://w3.org/ns/shRIOL#pdpHans
MESSAGE: The personal data processing is not transparent, as required/defined by Article 12 of the GDPR
EXPLANATION: Specifically, these legal authorities judged one or more communications related to pdpHans as follows:
	- courtA does NOT deem the communication c2Hans enough readable.
	- courtB deems the communication c2Hans enough readable.
----------------------------------------------------------------------------------------------
Personal Data Processing: http://w3.org/ns/shRIOL#pdpLuca
MESSAGE: The personal data processing is not lawful, as required by Art.5(1)(a) and defined by Art.6 of the GDPR.
EXPLANATION: The age of the data subject is below the minimal age for consent in his/her Member State. See Art.8(1) of the GDPR.
----------------------------------------------------------------------------------------------
Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning".

ERICA Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive L

THUNLP 75 Nov 02, 2022
A solution to the 2D Ising model of ferromagnetism, implemented using the Metropolis algorithm

Solving the Ising model on a 2D lattice using the Metropolis Algorithm Introduction The Ising model is a simplified model of ferromagnetism, the pheno

Rohit Prabhu 5 Nov 13, 2022
9th place solution in "Santa 2020 - The Candy Cane Contest"

Santa 2020 - The Candy Cane Contest My solution in this Kaggle competition "Santa 2020 - The Candy Cane Contest", 9th place. Basic Strategy In this co

toshi_k 22 Nov 26, 2021
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES)

Non-Imaging Transient Reconstruction And TEmporal Search (NITRATES) This repo contains the full NITRATES pipeline for maximum likelihood-driven discov

13 Nov 08, 2022
[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

CKDN The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment" O

Multimedia Research 50 Dec 13, 2022
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (SDPoint) This repository contains the cod

Jason Kuen 17 Jul 04, 2022
Automatically replace ONNX's RandomNormal node with Constant node.

onnx-remove-random-normal This is a script to replace RandomNormal node with Constant node. Example Imagine that we have something ONNX model like the

Masashi Shibata 1 Dec 11, 2021
Efficient Online Bayesian Inference for Neural Bandits

Efficient Online Bayesian Inference for Neural Bandits By Gerardo Durán-Martín, Aleyna Kara, and Kevin Murphy AISTATS 2022.

Probabilistic machine learning 49 Dec 27, 2022
Explaining Hyperparameter Optimization via PDPs

Explaining Hyperparameter Optimization via PDPs This repository gives access to an implementation of the methods presented in the paper submission “Ex

2 Nov 16, 2022
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Benedek Rozemberczki 490 Dec 16, 2022
(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR (CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic Paper Authors: Xiangtao Kong, Hengyuan

Xiangtao Kong 308 Jan 05, 2023
Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals

Federico Lopez 2 Jan 14, 2022
Totally Versatile Miscellanea for Pytorch

Totally Versatile Miscellania for PyTorch Thomas Viehmann [email protected] Thi

Thomas Viehmann 428 Dec 28, 2022
Permeability Prediction Via Multi Scale 3D CNN

Permeability-Prediction-Via-Multi-Scale-3D-CNN Data: The raw CT rock cores are obtained from the Imperial Colloge portal. The CT rock cores are sub-sa

Mohamed Elmorsy 2 Jul 06, 2022
Deep Learning (with PyTorch)

Deep Learning (with PyTorch) This notebook repository now has a companion website, where all the course material can be found in video and textual for

Alfredo Canziani 6.2k Jan 07, 2023
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021)

Code for HDR Video Reconstruction HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021) Guanying Chen, Cha

Guanying Chen 64 Nov 19, 2022
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network Paddle-PANet 目录 结果对比 论文介绍 快速安装 结果对比 CTW1500 Method Backbone Fine

7 Aug 08, 2022
A Python package for time series augmentation

tsaug tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to conn

Arundo Analytics 278 Jan 01, 2023
Orchestrating Distributed Materials Acceleration Platform Tutorial

Orchestrating Distributed Materials Acceleration Platform Tutorial This tutorial for orchestrating distributed materials acceleration platform was pre

BIG-MAP 1 Jan 25, 2022