Context: Telecommunication Networks are complex multi-vendor/technology (Huawei, Ericsson, Nokia, …) which NOC (Network Operation Center) continually monitor and respond to faults/failures on equipment & sites usually concentrating all alarms from different vendors.

Challenge: Use historical data to predict if a site will go down on the next hour (or so) based on the events their NMS (Network Management Software) are collecting from all elements – this is a time window-based analysis that chances all the time.

Anyone on the community that have faced this (or a similar problem) would like to share any thoughts / papers / links to solution/model/approach ?

Hi, I am one of the authors of this EMNLP 2019 paper.

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We create Universal Adversarial Triggers:

Phrases that cause a specific model prediction when concatenated to 𝘢𝘯𝘺 input.

Triggers cause:

– GPT-2 to turn racist

– SQuAD models to predict “to kill american people” for 72% of “why” questions

– Text classifier accuracy 90%->1%.

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Paper: https://arxiv.org/abs/1908.07125

Twitter: https://twitter.com/Eric_Wallace_/status/1168907518623571974

Blog: http://ericswallace.com/triggers

In CMT

I’m a beginner in machine learning and I want to classify high dimensional sequences without using an LSTM or other deep learning methods, but using something simpler, to learn more about feature extraction.

I’m trying gesture recognition using OpenPose, with a couple of gestures (wave, swipe left, swipe right, circle) of varying length. Because of OpenPose, I have 67 features (body and hands) times 3 (x, y, c). The sequences go from 4 to 43 frames, so there is a lot of variation there.

First of all, how do I google this? Time series classification is all about long, usually one dimensional sequences, while these are shorter and highly multi dimensional sequences. I don’t know if this has been done before without deep learning?

Second of all, here is what I tried:

• Padding and clipping the sequences to the same length, then using an RBF SVC from sklearn.
• Extracting mean, median, std, max, min, and other statistics to obtain a fixed representation
• Performing Fourier transformations per keypoint/feature pair, so 67*3 transforms

All three of these overfit a lot (100 to 50 train/test accuracy), the Fourier one the most because of the high amount of features. PCA does not help a lot. Are Fourier transformations even useful here because there is no real periodicity in the gestures and the transformations look weird (normally you have these peaks in frequencies, but for me the Fourier transform just looks like a choppy sine wave for most cases)

I looked at DTW but I have way too much features???

How do I classify OpenPose sequences using machine learning and not deep learning? I can’t find a lot of info on google

I have just written an article about a benchmark I made of labeling tools for computer vision.

https://blog.sicara.com/best-open-source-annotation-tools-in-computer-vision-4b9f6a18f911

Do you know other tools which I should talk about?

Crosspost r/datascience

Suppose you’re working on a machine learning/coding contest and through your own research come up with a technique that is say, 4% better than the best thing anyone has tried (just pulling numbers out of the air here).

At what point is it better to not claim the prize on said contest and just keep the method secret? At what point should you publish it? Does it ever make sense to just use it in your own capacity analyzing data for companies as a 3rd party?

I mean I’m sure it’s all dependent on the money involved but one has to wonder where the breaking point is. What can you make as an independent 3rd party willing to do analysis with proprietary software you aren’t releasing?

In such a case, how could you ever provide confidence enough that the methods work? Also, how would you bill if essentially the time spent is mainly runtime? I’d love to hear any speculation, stories, industry standard behavior or history around this sort of thing.

A while ago I’ve seen a video of a talk by Ali Rahimi. It’s worth the 20 minutes for people who haven’t seen it!

I recently rewatched it and tried to look around if I could find papers which go into the direction of focusing more onto smaller ML experiments to build fundamental knowledge. But I’ve not found much aside from stuff on interpretability.

As a side note, I’m not an ML researcher but a physicist who uses ML in various aspects for work. I’d not consider myself an expert on any ML specific field but have an interest and try to keep up to date on things. From my personal experience, I’ve certainly felt (not sure how to describe it but let’s go with annoyed/demotivated) to use ML when it’s not clear to me why certain things work.

So a lot of times it seems more like Alchemy, in that I mix and match certain things until I get a result I like.

But why does it work? No clue.

Will it work for the next problem that is sufficiently different? No clue!

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I’d be very interested to hear what other people think or how they feel about this topic.

Also if you know of any paper seeking to build a more fundamental understanding, I’d appreciate a link 🙂

I was looking to extract if any news related to a company was positive or negative from news using python.

Which libraries would work good for such a project?