Back in high school, I was already obsessed with computer hardware. Whenever manufacturers released new products, I would look them up immediately. I knew the differences between models inside out, and I even loved the exaggerated promotional images used by sellers. My Taobao shopping cart was a training ground for balancing cost and performance, while the PC-building requests of friends and relatives were my bugle call to put that knowledge to use. But the PC market had long since passed its era of rapid growth. High technical barriers and ecosystem monopolies led to product homogenization and very little real innovation, and over time I lost interest. Recently I built a small project with an 8051 microcontroller and, along the way, learned more about the many kinds of microcontrollers now on the market. Their low technical barriers and diverse use cases have produced a wild yet flourishing world of chips, memory, and peripherals, and that let me rediscover the old fun. That, in turn, made me want to build a small project that combines microcontrollers with machine learning.

Logistic regression is a classification algorithm widely used in industry. Its structure is simple, with the following main advantages and disadvantages:

pros

1. Training and inference are both fast
2. Easy to implement
3. Low memory usage
4. Good interpretability

cons

1. Because it cannot fit nonlinear relationships, it places higher demands on feature engineering.
2. It is relatively sensitive to multicollinearity

HDFS, short for Hadoop Distributed File System, is the distributed operating system within Hadoop and is now widely used for big-data storage. HDFS was originally designed for traditional disks, but with the rapid development of hardware devices, optimization and support for new storage media have become challenging topics for HDFS. This article summarizes HDFS optimization techniques for external storage media such as traditional disks, shingled magnetic recording disks, and solid-state drives, as well as for in-memory media such as conventional memory and non-volatile memory. It argues that future HDFS systems will necessarily be heterogeneous clusters that balance performance and cost, and it proposes four possible research directions for discussion.

In recent years, mobile internet and big data have developed rapidly, and the collection and use of personal information by mobile apps has expanded in a wild and disorderly way. Xiaomi’s latest mobile operating system, MIUI 12, was officially released and its new privacy features were widely praised. MIUI 12 protects users’ personal privacy rights and information security, but it also faces problems such as the difficulty of defining data ownership and the overlap between personal privacy rights and the scientific value of data. This article discusses these three issues from the perspectives of developers, regulators, and executors, respectively.

Ten years ago, the golden age of personal blogs had already passed. So why am I still writing a preface for one in 2020?