Every human being in this world produces waste. South Africa is a developing
country with many townships that have limited waste resources. Over-increasing
population growth overpowers the volume of most municipal authorities to
provide even the most essential services. Waste in townships is produced via
littering, dumping of bins, cutting of trees, dumping of waste near rivers, and
overrunning of waste bins. Waste increases diseases, air pollution, and
environmental pollution, and lastly increases gas emissions that contribute to
the release of greenhouse gases. The ungathered waste is dumped widely in the
streets and drains contributing to flooding, breeding of insects, rodent
vectors, and spreading of diseases. Therefore, the aim of this paper is to
design a smart waste management system for the city of Johannesburg. The city
of Johannesburg contains waste municipality workers and has provided some areas
with waste resources such as waste bins and trucks for collecting waste. But
the problem is that the resources only are not enough to solve the problem of
waste in the city. The waste municipality uses traditional ways of collecting
waste such as going to each street and picking up waste bins. The traditional
way has worked for years but as the population is increasing more waste is
produced which causes various problems for the waste municipalities and the
public at large. The proposed system consists of sensors, user applications,
and a real-time monitoring system. This paper adopts the experimental
methodology.

设计并测试了一个智能废物管理系统，该系统包含传感器、用户应用程序和实时监控系统，可有效解决南非约翰内斯堡市乡镇废物处理不足的问题。

约翰内斯堡智能废物管理系统设计

Design of a Smart Waste Management System for the City of Johannesburg

Deep metric learning papers from the past four years have consistently
claimed great advances in accuracy, often more than doubling the performance of
decade-old methods. In this paper, we take a closer look at the field to see if
this is actually true. We find flaws in the experimental methodology of
numerous metric learning papers, and show that the actual improvements over
time have been marginal at best.

本文研究过去四年深度度量学习领域的论文，发现其中许多论文实验方法存在缺陷，显示出时间上的改进最好只是微不足道的。