It's a powerful version of linebot

Overview

CTPS-FINAL

Linbot-sever.py

主程式

Algorithm.py

推薦演算法,媒合餐廳端資料與顧客端資料

config.ini

儲存 channel-access-token、channel-secret 資料

Preface

生活在成大將近4年,我們每天的午餐時間看著形形色色的店家,看似玲瑯滿目卻都吃膩了,中午覓食已經從期待變成壓力,每天問著「待會吃什麼?」,然後花費大量時間和心力,還是不知道要午餐吃什麼。因此我們希望運用Computational Thinking and Problem Solving 的思維,幫助大家解決這個困擾已久的問題。

Problem Definition

My target problem - 解決成大師生不知道午餐吃什麼的困擾?

Problem Decomposition

  • :成大師生
  • :午餐煩惱
  • :週一到週五 11點 ~ 14點
  • :成大周遭 1.5km 以內距離
  • constrain : 交通限制(交通工具)、店家營業時間限制、用戶人數(餐廳是否能容納)、預計等待及用餐時間

Pattern Recognition

  1. 大家通常到正餐時間才會想要吃甚麼
  2. 大家移動的距離有限,如果下午1點還有課,就會在學校附近用餐
  3. 同類型食物太頻繁吃會吃膩
  4. 學生會考慮cp值(有價格區間考量)
  5. 如果店家以人潮眾多就傾向換一間店家
  6. 會因為天氣而影響選擇(例如很熱,就會找有冷氣的餐廳)
  7. 朋友或認識的同學會一起用餐

Abstraction

(把Problem Decomposition的細項問題化)

  • 店家資料
      1. 如何取得店家資料?
      1. 如何確保店家資料即時性?
  • 用戶資料
      1. 如何取得用戶資料?
      1. 如何做到使用者優化?
  • 演算法
      1. 如何根據實際狀況設計演算法
      1. 怎麼測試演算法結果是否符合用戶需求
  • 訊息回推
      1. 用什麼管道回送推薦清單
      1. 介面如何優化
      1. 怎麼得知用戶實際使用情況

Algorithm

  • 店家資料
    • 如何取得店家資料?
      • 利用 google maps 爬蟲
      • 實地探索(地點限制在成大周圍,所以有一定可行性)
    • 如何確保店家資料即時性?
      • 設計用戶回報機制
      • 定期網路爬蟲
  • 用戶資料
    • 如何取得用戶資料?
      • 利用 linbot 與使用者溝通,取得使用者需求
    • 如何做到使用者優化?
      • 利用 richmenus 串接 linbot,藉由圖文選單輸入
  • 演算法
    • 如何根據實際狀況設計演算法
      • 找外在生活條件(例如 : 天氣很熱,那冷氣的需求權重就提高一點)
    • 怎麼測試演算法結果是否符合用戶需求
      • 請朋友實際使用,並根據意見做出修改
  • 訊息回推
    • 用什麼管道回送推薦清單
      • Linebot
    • 介面如何優化
      • 建置模板按鈕,讓畫面看起來乾淨一點
    • 怎麼得知用戶實際使用情況
      • 設計用戶評分機制
      • 根據用戶評分或意見,進行修正

Solution Proposal

final report ppt & demo

References

Paper: Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification

Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification T M Feroz Ali, Subhasis Chaudhuri, ICVGIP-20-21

T M Feroz Ali 3 Jun 17, 2022
Code for Boundary-Aware Segmentation Network for Mobile and Web Applications

BASNet Boundary-Aware Segmentation Network for Mobile and Web Applications This repository contain implementation of BASNet in tensorflow/keras. comme

Hamid Ali 8 Nov 24, 2022
Build and run Docker containers leveraging NVIDIA GPUs

NVIDIA Container Toolkit Introduction The NVIDIA Container Toolkit allows users to build and run GPU accelerated Docker containers. The toolkit includ

NVIDIA Corporation 15.6k Jan 01, 2023
The repository contain code for building compiler using puthon.

Building Compiler This is a python implementation of JamieBuild's "Super Tiny Compiler" Overview JamieBuilds developed a wonderfully educative compile

Shyam Das Shrestha 1 Nov 21, 2021
Algo-burn - Script to configure an Algorand address as a "burn" address for one or more ASA tokens

Algorand Burn Address This is a simple script to illustrate how a "burn address"

GSD 5 May 10, 2022
Code for Discriminative Sounding Objects Localization (NeurIPS 2020)

Discriminative Sounding Objects Localization Code for our NeurIPS 2020 paper Discriminative Sounding Objects Localization via Self-supervised Audiovis

51 Dec 11, 2022
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening Introduction This is an implementation of the model used for breast

757 Dec 30, 2022
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

CRF - Conditional Random Fields A library for dense conditional random fields (CRFs). This is the official accompanying code for the paper Regularized

Đ.Khuê Lê-Huu 21 Nov 26, 2022
A model to classify a piece of news as REAL or FAKE

Fake_news_classification A model to classify a piece of news as REAL or FAKE. This python project of detecting fake news deals with fake and real news

Gokul Stark 1 Jan 29, 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod

20.5k Jan 08, 2023
The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

VAENAR-TTS This repo contains code accompanying the paper "VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis". Sa

THUHCSI 138 Oct 28, 2022
Py-faster-rcnn - Faster R-CNN (Python implementation)

py-faster-rcnn has been deprecated. Please see Detectron, which includes an implementation of Mask R-CNN. Disclaimer The official Faster R-CNN code (w

Ross Girshick 7.8k Jan 03, 2023
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow. YOLOv4 is a state of the art algorithm that uses deep convolutional neural networks to perform object detections. We can take the ou

The AI Guy 1.1k Dec 29, 2022
PyTorch implementation of DreamerV2 model-based RL algorithm

PyDreamer Reimplementation of DreamerV2 model-based RL algorithm in PyTorch. The official DreamerV2 implementation can be found here. Features ... Run

118 Dec 15, 2022
GraPE is a Rust/Python library for high-performance Graph Processing and Embedding.

GraPE GraPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both of

AnacletoLab 194 Dec 29, 2022
OOD Generalization and Detection (ACL 2020)

Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark

littleRound 57 Jan 09, 2023
Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore

[AI6122] Computer Vision is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instructor of this course

HT. Li 5 Sep 12, 2022
Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods”

Uncertainty Estimation Methods Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods” Reference If you use this code,

EPFL Machine Learning and Optimization Laboratory 4 Apr 05, 2022
CNNs for Sentence Classification in PyTorch

Introduction This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch. Kim's implementation of t

Shawn Ng 956 Dec 19, 2022
PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It

Meta Research 49 Dec 21, 2022