This article is consistent with my recent experience with academics being in a frenzy to produce papers riding the AI/ML hype, yet producing bunch of practicall

This article is consistent with my recent experience with academics being in a frenzy to produce papers riding the AI/ML hype, yet producing bunch of practically useless papers. The following quote from the article is striking: "Qiu et al. introduce a “novel anomaly detector for time-series KPIs based on supervised deep-learning models with convolution and long short-term memory (LSTM) neural networks, and a variational auto-encoder (VAE) oversampling model.” This description sounds like it has many “moving parts”, and indeed, the dozen or so explicitly listed parameters include: convolution filter, activation, kernel size, strides, padding, LSTM input size, dense input size, softmax loss function, window size, learning rate and batch size. All of this is to demonstrate “accuracy exceeding 0.90.” However, as we will show, much of the results of this complex approach can be duplicated with a single line of code and a few minutes of effort."