Pinned Repositories
Image-Forgery-Detection-CNN
Image forgery detection using convolutional neural networks. Group 10's final project for TU Delft's course CS4180 Deep Learning 2019.
AutoComments
Description: We want to create a deep Neural Network that can automatically generate comments for code snippets passed to it. The motivation behind this is that in software development and maintenance, developers spend around 59% of their time on program comprehension activities. Having comments that are generated automatically will hopefully cut this time down. In order to do this we will combine the recent paper Code2Vec: Learning Distributed Representations of Code by Alon et al. with the paper Deep Code Comment Generation in order to make a better performing model using the newer Code2Vec encoding that was not used in the Deep Code Comment Generation paper. Dataset: The dataset that we will use is the same dataset used by the Deep Code Comment Generation paper, this is a dataset of more than 500,000 code snippets including comments. This also gives us a baseline against which to compare. Papers: Deep Code: https://xin-xia.github.io/publication/icpc182.pdf Code2Vec: https://arxiv.org/abs/1803.09473
complex_networks
cyber_data_analytics
Implementations for the lab assignments of Cyber Data Analytics (CS4035), a MSc course in TU Delft. The topics that we worked on are credit card fraud detection, anomaly detection in SCADA networks and streams and malware detection.
Data_Visualization
Developed two visualization projects with two fellow classmates. The first project is related to Information Visualization (InfoVis), where and we chose our own topic (NBA statistics) and conducted exploratory visual analysis. The second project is about Volume Visualization (VolVis) and we developed a volume renderer based on the raycasting approach. For the former, we used the D3 library of JavaScript, while the latter was fully implemented in Java.
Handwritten_Digits_Classification
Implemented in Matlab, in a group of 3, a Handwritten Digits identification system by exploiting several machine learning techniques.
malicious_PHP_detection
Build a system which is able predict if a given php file is malicious or benign, by using machine learning methods. To achieve this I extracted several lexical features and we chose the best among of them. Also I experimented with several different classifiers found in literature, namely Support-Vector Machine(SVM), Logistic Regression and Decision Tree.
netflow_classification
clickbait-challenge
Project aims to detect clickbaits by extracting various features from each part of article/post. Code for Clickbait Challenge Competition.
mathoverflow-network-analysis
Analysis of the mathoverflow user interaction data using networks
RafailSkoulos17's Repositories
RafailSkoulos17/cyber_data_analytics
Implementations for the lab assignments of Cyber Data Analytics (CS4035), a MSc course in TU Delft. The topics that we worked on are credit card fraud detection, anomaly detection in SCADA networks and streams and malware detection.
RafailSkoulos17/netflow_classification
RafailSkoulos17/complex_networks
RafailSkoulos17/Data_Visualization
Developed two visualization projects with two fellow classmates. The first project is related to Information Visualization (InfoVis), where and we chose our own topic (NBA statistics) and conducted exploratory visual analysis. The second project is about Volume Visualization (VolVis) and we developed a volume renderer based on the raycasting approach. For the former, we used the D3 library of JavaScript, while the latter was fully implemented in Java.
RafailSkoulos17/Handwritten_Digits_Classification
Implemented in Matlab, in a group of 3, a Handwritten Digits identification system by exploiting several machine learning techniques.
RafailSkoulos17/IR_core_project
RafailSkoulos17/malicious_PHP_detection
Build a system which is able predict if a given php file is malicious or benign, by using machine learning methods. To achieve this I extracted several lexical features and we chose the best among of them. Also I experimented with several different classifiers found in literature, namely Support-Vector Machine(SVM), Logistic Regression and Decision Tree.
RafailSkoulos17/web_data_management_group_9
Implementing Microservices with different database Backends: Cassandra vs. Postgres
RafailSkoulos17/netflow_embeddings
Experimenting with applying word embeddings in netflow data
RafailSkoulos17/Supercomputing-for-Big-Data
Developed a big data pipeline for identifying important events from the GDELT Global Knowledge Graph, as a series of 3 assignments. In the first two assignments, a Spark application for processing the GDELT dataset was written in Scala and deployed in a big data cluster on AWS. In the final assignment, the application was modified to work in a streaming data context using Apache Kafka.
RafailSkoulos17/TemporalOpponentModeling