知鸦日报2021-08-27

2021-08-26 16:30:00 ~ 2021-08-27 16:30:00

技术

360技术：音频质量检测模型中标准数据集的构建方法

摘要

音频质量检测模型训练中，纯净高质量的音频数据集比较好获得，但是损伤音频的数据集比较少，而且损伤音频的质量得分也很难评估。我们采用了一种只依靠纯净高质量的语音数据集来制作低质量音频并打分的方法。

爱奇艺技术：APP DIFF自动化解决方案

摘要

APP DIFF方案利用deeplink、mock提升了UI自动化稳定率和效率，并结合AUI图像匹配算法，达到接近人工的验证效果，在收藏、card等业务推进落地，以较低成本持续产生收益，在移动端UI自动化方向实现了突破与创新。

爱奇艺技术：如何支持亿级用户分流实验？AB实验平台在爱奇艺的实践

摘要

随着互联网公司的产品和业务越来越多样，利用数据来驱动业务决策成为必然，而AB实验正是以数据指标来判断产品功能和运营策略迭代效果的方法和工具，其可以在保证样本同时性和同质性基础上，对比两个或多个实验组在同一应用场景下的效果。

京东技术：京东到家商品系统架构演进

摘要

本文讲述了京东到家商品系统创建至今，系统架构三个阶段的演进，以及如何通过设计来解决业务快速发展滋生的系统复杂度问题。

pinterest技术：Pinterest’s Analytics as a Platform on Druid (Part 2 of 3)

摘要

In this blog post series, we’ll discuss Pinterest’s Analytics as a Platform on Druid and share some learnings on using Druid. This is the second of the blog post series, and will discuss learnings on optimizing Druid for batch use cases.

slack技术：Building Android Conversation Bubbles

摘要

We recently rolled out support for Conversation Bubbles for DMs and Group DMs on Android 11.

In case you’re not familiar with Conversation Bubbles, take a look at the video below. Basically, they are a way to pop out a conversation from a notification into a Bubble that will draw over other apps, making multitasking while chatting easy and comfortable.

This post will describe how we built support for these bubbles, the challenges we faced along the way, and how we overcame them. Some details were simplified where it made sense to, but stayed relatively close to our actual implementation overall.

uber技术：How Data Shapes the Uber Rider App

摘要

Data is crucial for our products. Data analytics help us provide a frictionless experience to the people that use our services. It also enables our engineers, product managers, data analysts, and data scientists to make informed decisions. The impact of data analysis can be seen in every screen of our app: what is displayed on the home screen, the order in which products are shown, what relevant messages are shown to users, what is stopping users from taking rides or signing up, and so on.

With such a huge user base and wide range of features, support across all geographic regions is a complicated problem to solve. Furthermore, our app keeps expanding with new products, which mandates that the underlying tech also be flexible enough to evolve and support them.

Data is the primary tool enabling this. The following article will focus on rider data in particular: how we collect and process it, and how that has informed concrete improvements to the Rider app.