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8bitmixtape.github.io

Public page for the 8-Bit MixTape

8v2

homework website

8var

901540-g1-trimestres2

ADSI grupo 901540 Segundo Trimestre

901540-g1-trimestres3

Trimestre 3 ADSI 901540 G1

90_operations_research_mathematical_programming

91_game_theory_economics_social_and_behavioral_sciences

92_biology_and_other_natural_sciences

92jling.github.io

93_systems_theory_control

94_information_and_communication_circuits

955.wlb

955 不加班的公司名单

97_mathematics_education

99-ml-learning-projects

A list of 99 machine learning projects for anyone interested to learn from coding and building projects

990-scraper

Grab nonprofit tax information from the ProPublica API and put it in a Google spreadsheet!

9gag

Android unofficial REST Client of 9gag.

9rules

Small Object Programming Checker

9to12

The club for developing new things from 9PM to 12AM daily

a

Library for annotating ES5

a-1

a-baseline-for-front-end-developers

A Baseline for Front-End Developers

a-coin

母體概念智能合約

a-editor

Accessible Rich Internet Applications Text Editor

a-editorsync

A-Editor Synchronizer

a-file-icon

Sublime file icons for improved visual grepping

a-game-of-light-and-shadows

Updated prototype of Augmented Tactics in 2D with F#

a-haas.github.io

My github website

a-metamodels

AUTOSAR MetaModels

a-novel-framework-for-generating-handwritten-datasets

The performance of deep learning algorithms is highly dependent on the size and diversity of data. However, for handwritten character recognition, dataset creation, segmentation, and labeling are time consuming and laborious tasks and not much researched. This work proposes a novel and generic framework which automates the segmentation and labeling processes for handwritten datasets. First, a user collects handwritten glyphs on the proposed form. Next, based on a priori knowledge, local peaks from horizontal and vertical projection functions are computed. This helps in locating and segmenting individual samples automatically. To show the effectiveness of the proposed framework, a dataset of 160,000 samples is collected for an oriental language. We profile the segmentation of samples from one sheet with three approaches: manual, semi-automatic, and the proposed fully automatic approach. Compared to the manual and semi-automatic processes, the proposed approach is 120× and 65× faster, respectively. Further, we also present the classification of this dataset by traditional and state-of-the-art machine learning algorithms.

a-pensive-berry

My personal website along with utilities.