GeoTourNet

# 🌍 GeoTourNet: Feature-Wise Attention Mapping for Tourism Emotion Understanding --- ## 🧠 Overview **GeoTourNet** is a deep learning framework that integrates **spatial-temporal reasoning**, **attention-guided feature mapping**, and **embedding-layer emotion alignment** to model tourist emotions and behaviors across heterogeneous data sources. It bridges **geospatial analytics** with **artificial intelligence** to deliver interpretable, data-driven insights for smart tourism systems. --- ## 🔑 Key Features - **Feature-Wise Attention Mapping (FAM):** Aligns emotion indicators with feature embeddings to enhance interpretability and precision. - **Dual-Encoding Mechanism:** Integrates environmental cues and user intent across varying temporal resolutions. - **Intent-Guided Spatial Attention (IGSA):** Dynamically focuses on emotionally salient spatial regions based on user intent. - **Density-Aware Output Control (DAOC):** Adapts predictions according to data sparsity and observation entropy. - **STRATEM Strategy Layer:** Adds spatial-temporal correction and cross-city transfer learning using semantic anchors. --- ## 🧩 Model Architecture GeoTourNet consists of three primary components: 1. **Multimodal Feature Integration** Encodes images, texts, and metadata into unified latent embeddings. 2. **Intent-Guided Spatial Attention** Learns spatial relevance weighted by user intent. 3. **Density-Aware Output Control** Regulates prediction confidence based on regional data density and entropy. An auxiliary **STRATEM** layer further refines predictions through: - Spatiotemporal residual correction - Multi-resolution spatial aggregation - Semantic anchor transfer learning --- ## ⚙️ Methodology GeoTourNet processes multimodal tourism data and generates predictions \[ \hat{y}(l, t) \] using: - Transformer-based attention and GRU decoders - Gaussian kernel density modulation - Spatial embeddings and adaptive gating functions - Anchor-based domain transfer between cities --- ## 🗃️ Datasets Experiments were conducted on four major datasets: | Dataset | Description | |----------|--------------| | TripAdvisor Reviews | Tourist reviews with geolocation and sentiment data | | Yelp Open Dataset | Restaurant and service reviews with location/time metadata | | Airbnb Reviews | Accommodation reviews with spatial-temporal context | | Hotel Reviews | Service and emotion ratings across regions | Each dataset includes **textual reviews**, **spatial coordinates**, **temporal metadata**, and **service ratings**, enabling fine-grained emotional and behavioral analysis. --- ## 📈 Performance GeoTourNet outperformed state-of-the-art baselines (LSTM, Transformer, N-BEATS, Autoformer) with: - Lower **MAE** and **RMSE** - Higher **interpretability** via attention visualizations - **Robust generalization** in sparse spatial settings --- ## 🧮 Implementation Details | Parameter | Setting | |------------|----------| | **Framework** | PyTorch 2.0 | | **Optimizer** | Adam (lr = 1e-4, weight decay = 1e-5) | | **Regularization** | Dropout (0.3), Gradient Clipping (norm=5) | | **Evaluation Metrics** | MAE, RMSE, MAPE, R² | | **GPU** | NVIDIA A100 (80GB) | --- **GeoTourNet** demonstrates that combining spatial-temporal learning with feature-wise attention can yield interpretable and accurate emotion understanding in tourism data.
import torch
import torch.nn as nn
import torch.nn.functional as F

class FeatureWiseAttention(nn.Module):
    def __init__(self, embed_dim):
        super().__init__()
        self.proj_q = nn.Linear(embed_dim, embed_dim)
        self.proj_k = nn.Linear(embed_dim, embed_dim)
        self.proj_v = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        q = self.proj_q(x)
        k = self.proj_k(x)
        v = self.proj_v(x)
        attn = F.softmax(torch.bmm(q, k.transp

Claude Code - Skills & Context

# Guide de Gestion du Contexte et des Skills

**Version:** 1.0  
**Date:** Octobre 2024  
**Pour:** Claude Code avec Skills personnalisés

## Vue d'ensemble

Ce guide explique comment gérer efficacement le contexte dans Claude Code quand tu utilises des Skills personnalisés, pour maintenir des performances optimales tout au long de tes sessions de développement.

---

## Comprendre le Contexte

### Fenêtre de contexte totale

**Limite technique:** 200,000 tokens (200k)

**Mais 

10.6: New Rest Calls

ContactListActivity.java

 public void getContactsFromAPI()
    {
        RestClient.execGetRequest(CONTACTSAPI,
                this,
                new VolleyCallback() {
                    @Override
                    public <T> void onSuccess(ArrayList<T> result) {
                        Log.d(TAG, "onSuccess: Contacts : " + result.size());
                        contacts = (ArrayList<Contact>) result;
                        if (!contacts.isEmpty()) {
                            Recycl

10.5: Contact

package edu.lakeland.mycontactlist;

import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.util.Base64;
import android.util.Log;

import org.json.JSONException;
import org.json.JSONObject;

import java.io.ByteArrayOutputStream;
import java.io.Serializable;
import java.util.Calendar;

public class Contact implements Serializable {

    private static final String TAG = "ContactEntity";
    private static final String DELIM = "|";

    // Private 

10.4: execRequest

 private static <T> void executeRequest(T entity,
                                           String url,
                                           Context context,
                                           VolleyCallback volleyCallback,
                                           int method,
                                           Class<T> entityType)

    {

        Log.d(TAG, "executeRequest: Start : " + method + " : " + url);
        ArrayList<T> entities = new ArrayList<T>();

10.3: execPostRequest

 public static <T> void execPostRequest(T entity,
                                           String url,
                                           Context context,
                                           VolleyCallback volleyCallback,
                                           Class<T> entityType)
    {
        try
        {
            executeRequest(entity, url, context, volleyCallback, Request.Method.POST, entityType);
        }
        catch(Exception e)
        {
           

10.2: execGetOneRequest

public static <T> void execGetOneRequest(String url,
                                             Context context,
                                             VolleyCallback volleyCallback,
                                             Class<T> entityType)
    {
        Log.d(TAG, "execGeOneRequest: Start");
        RequestQueue requestQueue = Volley.newRequestQueue(context);
        ArrayList<T> entities = new ArrayList<T>();
        Log.d(TAG, "execGeOneRequest: " + url);

        tr

10.1: executeGetRequest

public static <T> void execGetRequest(String url,
                                          Context context,
                                          VolleyCallback volleyCallback,
                                          Class<T> entityType)
    {
        Log.d(TAG, "execGetRequest: Start");
        RequestQueue requestQueue = Volley.newRequestQueue(context);
        ArrayList<T> entities = new ArrayList<T>();
        Log.d(TAG, "execGetRequest: " + url);

        try
        {
  

10.1: executeGetRequest

public static <T> void execGetRequest(String url,
                                          Context context,
                                          VolleyCallback volleyCallback,
                                          Class<T> entityType)
    {
        Log.d(TAG, "execGetRequest: Start");
        RequestQueue requestQueue = Volley.newRequestQueue(context);
        ArrayList<T> entities = new ArrayList<T>();
        Log.d(TAG, "execGetRequest: " + url);

        try
        {
  

10.1: executeGetRequest

public static <T> void execGetRequest(String url,
                                          Context context,
                                          VolleyCallback volleyCallback,
                                          Class<T> entityType)
    {
        Log.d(TAG, "execGetRequest: Start");
        RequestQueue requestQueue = Volley.newRequestQueue(context);
        ArrayList<T> entities = new ArrayList<T>();
        Log.d(TAG, "execGetRequest: " + url);

        try
        {
  

Prompt Systemowy - Master Generator

## Prompt Systemowy - Master Generator

```xml
<generator_asystentow>

<misja_glowna>
Jesteś Uniwersalnym Generatorem Asystentów AI - ekspertem systemem zaprojektowanym do tworzenia wysoce wyspecjalizowanych, gotowych do wdrożenia asystentów AI dla każdej domeny profesjonalnej. Twoim zadaniem jest generowanie kompletnych, gotowych do użycia promptów systemowych, które definiują kompetentnych, niezawodnych asystentów AI zdolnych zarówno do ekspertyzy domenowej, jak i współpracy technicznej.
</mis

5. Sharing

# Sharing

Every snippet in Cacher has a corresponding page on [Snippets](https://snippets.cacher.io/), our community hub for sharing code. The page's visibility is dependent on whether the snippet is **public or private** and the **Privacy Setting** for the page.

Here are a few examples of snippet pages:
- [Markdown Task Lists in Cacher](https://snippets.cacher.io/snippet/108f62b6b8d8dd18610c)
- [Svelte is really fast](https://snippets.cacher.io/snippet/37dc3da63ce006296521)
- [DigitalOcean Ra

workflow asistant

Tu es un assistant de développement rigoureux.  
Ta mission est d’exécuter et corriger le projet jusqu’à ce qu’il soit entièrement propre et fonctionnel.

Objectif :
Exécuter successivement :
1️⃣ `npm run lint`
2️⃣ `npm run typecheck`
3️⃣ `npm run build`
4️⃣ `npm run test`

Règles :
- Corriger **toutes** les erreurs ou warnings jusqu’à obtenir **0 erreur** sur les 4 étapes.
- **Ne jamais** désactiver une règle ESLint (`// eslint-disable` ou équivalent).
- Corriger les causes racines (imports, ty

See Shopify product change history

```
https://admin.shopify.com/store/{your-store-link}/products/9954787197241/events.json
```

GitHub actions query by title

#!/usr/bin/env nu

def main [title_query: string, wf: string = actions.yml, limit: int = 1000] {
    gh run list --workflow=$"($wf)" --limit $limit --json displayTitle,url,headBranch,status | from json | where displayTitle =~ $title_query
}

K.E.C.W.A

<!DOCTYPE html> <!-- This file was downloaded from https://eaglercraft.ir/ --> <!-- Visit our website for more updates, tools, and support: https://eaglercraft.ir/ --> <html style="width:100%;height:100%;background-color:black;"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, minimum-scale=1.0, maximum-scale=1.0" /> <meta name="description" content="Eaglercraft 1.12 WASM-GC Offline" /> <meta name="keywords" content="eaglercraft, eaglercraftx