gionanide Goto Github PK

android_apps

My android applications made in Android Studio 2.3.3

blockchain_technology_initial_steps

chessgame

Machine learning apply in chess for making optimal decisions, using Neural networks and Supervised learning algorithms

cryptography

Crypto projects in python, e.g. Attacks to Vigenere, RSA, Telnet Protocol, Hip Replacement , Vernam cipher, Crack Zip Files, Encryptions RC4, Steganography, Feistel cipher, Superincreasing Knapsac, Elliptic Curve Cryptography, Diffie Hellman & EDF.

euler_project

https://projecteuler.net

focal_loss_pytorch

A PyTorch Implementation of Focal Loss.

format

ACM consolidated LaTeX styles

gionanide.github.io

infosec

repository for Information Security Assignment

matrix-factorization-in-python

memory_card_game

Memory game to check and train your memory . This game is about matching cards in different modes such as combact or solo mode. You can play vs PC in tree difficulty levels, PC player using AI in order to remember.

nerd-game

Nerd Game is an app game which tests you in very nerd questions of computer science, maths etc. You have three lives and you can modify your game, if you want to play against the clock. All the questions are in a SQL server which is connected to the GUI part.

neural_machine_translation

Neural Machine Translation using LSTMs and Attention mechanism. Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture and attention mechanism.

opirec

Opinion recommendation is a task, recently introduced, for consistently generating a text review and a rating score that a certain user would give to a certain product, which has never seen before. Input information driving recommendation is text reviews and ratings for this product contributed by other users and text reviews submitted by the user under consideration for other products. The aforementioned task faces the same problems emerging in text generation using neural networks, namely repetition and specificity. In this paper, it is experi- mentally demonstrated that by employing coverage loss during training, repetition is reduced without adding extra parameters. Furthermore, the amount of repetition in the generated text review is defined as a measure of the captured information. Such measure is used to improve rating score prediction significantly during testing.

python_speech_features

This library provides common speech features for ASR including MFCCs and filterbank energies.

pytorch-beam-search-decoding

PyTorch implementation of beam search decoding for seq2seq models

releasing-research-code

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

scikit-learn

scikit-learn: machine learning in Python

shap

A game theoretic approach to explain the output of any machine learning model.

speech_signal_processing_and_classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

statistical_signal_processing_timeseries

Power Spectral Density estimation, Information Theory, Hypothesis Testing

text_mining

An introduction to text mining with python

university_codingprojects

Coding projects for undergraduate courses implemented with Java,C++,C,bash