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canboy123/README.md

πŸ“‘ Introduction

  • πŸ‘‹ Hi, I’m Ho Jiacang.
  • πŸ‘€ I’m interested in machine learning / deep learning / AI and web application developement.
  • πŸ’ͺ I love to challenge new problems.
  • πŸ“§ Contact me here.

πŸ’» Programming Languages

  • Web programming
    • PHP
    • HTML
    • CSS
    • Javascript
    • Ajax
    • Jquery
    • Python with Jinja2 + Flask
  • Python
    • Tensorflow
    • Keras
    • Jinja2
    • Flask
    • PyTorch
  • R
  • Java
  • Etc.

πŸ–₯️ Databases

  • MYSQL
  • PostgreSQL
  • MongoDB

πŸ–±οΈ Frameworks

  • Web Application
    • CodeIgniter
    • PrestaShop
    • WordPress
    • Laravel
  • Machine Learning / Deep Learning / AI
    • Tensorflow
    • Keras
    • Pytorch

πŸ‘… Speaking Languages

  • Mandarin
  • English
  • Cantonese
  • Malaysian Language
  • Korean (TOPIK I - Level 2)

πŸ† Portfolio

ML/DL/AI Web based System

Adversarial Rank System, Dongseo University λ™μ„œλŒ€

This is a web application based adversarial ranking system which allows a user to upload an image and view the adversarial image after the image is being distorted with an attack technique. It is also used for showing the ranking between several of attack and defense techniques.

Port Code Prediction, Ecomarine (μ£Ό)μ—μ½”λ§ˆλ¦°

This is a text classification which predicts a port code from an input (i.e. destination name).

Engine Prediction, Ecomarine (μ£Ό)μ—μ½”λ§ˆλ¦°

This is a script to predict a ship engine's consumption (i.e. torque and fuel) from the speed value of the ship.

AutoML-Zero, Dongseo University λ™μ„œλŒ€

This is a web application based automated machine learning which allows a user to upload a dataset, and then train the dataset by searching the best network architecture for it and train the dataset for the classification task.

ML/DL/AI Work

Attack-less Adversarial Training (ALAT)
  • Ho, J., Lee, B. G., & Kang, D. K. (2022). Attack-less adversarial training for a robust adversarial defense. Applied Intelligence, 52(4), 4364-4381. [Paper]
Universal Image (Uni-Image) Construction
  • Ho, J., Lee, B. G., & Kang, D. K. (2020). Uni-image: universal image construction for robust neural model. Neural Networks, 128, 279-287. [Paper]
Brick Assembly Network (BAN)
  • Ho, J., & Kang, D. K. (2020). Brick Assembly Networks: An Effective Network for Incremental Learning Problems. Electronics, 9(11), 1929. [Paper]
GLNAS: Greedy Layer-wise Network Architecture Search for Low Cost and Fast Network Generation
  • In press

Web Application

This is an expenses management system that is created for my own purpose. This system can record your own daily activity, daily expenses, debts, wallets and many more. It can also show the report with the graph.

Wimple Management System, Seoreu Co., Ltd. (μ£Ό)μ„œλ₯΄

This is a web management system which allows a user to monitor, edit, upload, create a new ticket for the device he/she has purchased or rented.

Casareno - Sales System, Matrix Centric Solutions

This is a web sales system which allows a user to insert a customer's information, set installation dates, view reports, make a floor plan, quotation, etc.

WordPress - Library Modification, Matrix Centric Solutions

This is a modification of booknetic library used in WordPress.

OGProtect - Management System, Matrix Centric Solutions

This is a web based management system.

Government Project - Management System, Matrix Centric Solutions

This is a web based management system from the government project.

Execsearch - Job Searching Application, Matrix Centric Solutions

This is a job searching web application.

Embedded System Related Work

WimpleBin, Seoreu Co., Ltd. (μ£Ό)μ„œλ₯΄

This is a web application that control the wimplebin which is a recycle bin with several features.

πŸ“š Publication

  • Ho, J., Lee, B. G., & Kang, D. K. (2022). Attack-less adversarial training for a robust adversarial defense. Applied Intelligence, 52(4), 4364-4381. [Paper] (SCIE, IF: 5.3 (2022))
  • Ho, J., & Kang, D. K. (2020). Brick Assembly Networks: An Effective Network for Incremental Learning Problems. Electronics, 9(11), 1929. [Paper] (SCIE, IF: 2.9 (2022))
  • Ho, J., Lee, B. G., & Kang, D. K. (2020). Uni-image: universal image construction for robust neural model. Neural Networks, 128, 279-287. [Paper] (SCIE, IF: 7.8 (2022))
  • Ho, J., & Kang, D. K. (2018). One-class naΓ―ve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics. Applied Intelligence, 48, 1547-1564. [Paper] (SCIE, IF: 5.3 (2022))
  • Ho, J., & Kang, D. K. (2017). Mini-batch bagging and attribute ranking for accurate user authentication in keystroke dynamics. Pattern Recognition, 70, 139-151. [Paper] (SCIE, IF: 8.0 (2022))
  • Ho, J., & Kang, D. K. (2017). Improvement of the Convergence Rate of Deep Learning by Using Scaling Method. International journal of advanced smart convergence, 6(4), 67-72. [Paper]
  • Ho, J., & Kang, D. K. (2016). Ensemble-By-Session Method on Keystroke Dynamics based User Authentication. International Journal of Internet, Broadcasting and Communication, 8(4), 19-25. [Paper]
  • Ho, J., & Kang, D. K. (2016). Mini-Batch Ensemble Method on Keystroke Dynamics based User Authentication. International journal of advanced smart convergence, 5(3), 40-46. [Paper]
  • Ho, J., & Kang, D. K. (2015). Sequence alignment with dynamic divisor generation for keystroke dynamics based user authentication. Journal of Sensors, 2015. [Paper] (SCIE, IF: 1.9 (2022))
  • Ho, J., & Kang, D. K. (2014, December). Sequence alignment of dynamic intervals for keystroke dynamics based user authentication. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS) (pp. 1433-1438). IEEE. [Paper]

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