Pinned Repositories
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.
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
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].
gionanide's Repositories
gionanide/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].
gionanide/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.
gionanide/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.
gionanide/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.
gionanide/ChessGame
Machine learning apply in chess for making optimal decisions, using Neural networks and Supervised learning algorithms
gionanide/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.
gionanide/python_speech_features
This library provides common speech features for ASR including MFCCs and filterbank energies.
gionanide/PyTorch-Beam-Search-Decoding
PyTorch implementation of beam search decoding for seq2seq models
gionanide/releasing-research-code
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
gionanide/scikit-learn
scikit-learn: machine learning in Python
gionanide/shap
A game theoretic approach to explain the output of any machine learning model.
gionanide/Android_Apps
My android applications made in Android Studio 2.3.3
gionanide/Blockchain_technology_initial_steps
gionanide/Euler_Project
https://projecteuler.net
gionanide/focal_loss_pytorch
A PyTorch Implementation of Focal Loss.
gionanide/format
ACM consolidated LaTeX styles
gionanide/gionanide.github.io
gionanide/infosec
repository for Information Security Assignment
gionanide/matrix-factorization-in-python
gionanide/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.
gionanide/Statistical_Signal_Processing_Timeseries
Power Spectral Density estimation, Information Theory, Hypothesis Testing
gionanide/Text_Mining
An introduction to text mining with python
gionanide/University_codingProjects
Coding projects for undergraduate courses implemented with Java,C++,C,bash