fix: update redirect URLs for cifar and china-robot-industry-alliance (#84)

- cifar.json: www.cs.toronto.edu → web.cs.toronto.edu (302 domain migration)
  data_url unchanged — resolves directly without redirect.

- china-robot-industry-alliance.json: http → https (website + data_url)
  Site supports HTTPS with valid cert, old HTTP triggers 301 redirect.

Verification:
  curl -sI https://web.cs.toronto.edu → 200 OK
  curl -sI https://cria.mei.net.cn/ → 200 OK
  curl -sI https://cria.mei.net.cn/gzpt.asp?lm=/1310 → 200 OK

Co-authored-by: 明察 <mingcha@mininglamp.com>

Author: mingcha-dev

firstdata/sources/sectors/C-manufacturing/robotics/china-robot-industry-alliance.json

@@ -8,8 +8,8 @@
     "en": "The Robot Branch of China Machinery Industry Federation (formerly China Robot Industry Alliance, established in April 2013) is a national industry organization led by China Machinery Industry Federation. With over 520 member units including domestic and international robot companies, research institutes, and universities, it serves as a collaborative platform for industry-academia-research-application cooperation. The branch provides industry statistical reports, organizes conferences and competitions, promotes standardization, and coordinates resources to enhance the competitiveness of China's robot industry.",
     "zh": "中国机械工业联合会机器人分会(前身为2013年4月成立的中国机器人产业联盟)是由中国机械工业联合会领导的全国性行业组织。拥有520余家会员单位,包括国内外机器人企业、科研院所和高校,是我国机器人产业全国性产、学、研、用行业协同工作平台。分会发布行业统计报告,组织会议和竞赛活动,推进标准化工作,协调产业资源,提升中国机器人产业竞争力。"
   },
-  "website": "http://cria.mei.net.cn/",
-  "data_url": "http://cria.mei.net.cn/gzpt.asp?lm=/1310",
+  "website": "https://cria.mei.net.cn/",
+  "data_url": "https://cria.mei.net.cn/gzpt.asp?lm=/1310",
   "api_url": null,
   "country": "CN",
   "domains": [

firstdata/sources/sectors/J-information-communication/cifar.json

@@ -8,7 +8,7 @@
     "en": "CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset, widely used as benchmark datasets for machine learning and computer vision research. CIFAR-10 consists of 60,000 32x32 color images in 10 classes (6,000 images per class), with 50,000 training images and 10,000 test images. CIFAR-100 contains 100 classes with 600 images each (500 training, 100 testing per class), organized into 20 superclasses. Created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton, these datasets have become foundational benchmarks for evaluating image classification algorithms.",
     "zh": "CIFAR-10 和 CIFAR-100 是 8000 万微小图像数据集的标注子集，广泛用作机器学习和计算机视觉研究的基准数据集。CIFAR-10 包含 60,000 张 32x32 彩色图像，分为 10 个类别（每类 6,000 张图像），其中 50,000 张训练图像和 10,000 张测试图像。CIFAR-100 包含 100 个类别，每类 600 张图像（每类 500 张训练图像，100 张测试图像），组织成 20 个超类。这些数据集由 Alex Krizhevsky、Vinod Nair 和 Geoffrey Hinton 创建，已成为评估图像分类算法的基础性基准。"
   },
-  "website": "https://www.cs.toronto.edu",
+  "website": "https://web.cs.toronto.edu",
   "data_url": "https://www.cs.toronto.edu/~kriz/cifar.html",
   "api_url": null,
   "country": null,