active_stream | Active learning support for targeted Twitter stream
kandi X-RAY | active_stream Summary
kandi X-RAY | active_stream Summary
The Twitter streaming API allows to track tweets about a specific topic by tracking user defined keywords. All tweets that contain a keyword can be accessed (as long as the volume is lower than 1% of total stream). However, tracking a topic via a keyword has two major disadvantages:. This system is aimed to build a streaming interface that allows the user to obtain a fine tuned stream that maximizes the number of relevant tweets from the stream. Given a set of user selected seed keywords, an initial stream is produced. The active learning component classifies tweets as relevant or not and concurrently presents tweets to the user for manual annotation. Only tweets that the system is most uncertain about are selected for manual annotation. A second component proposes new keywords based on co-occurence in the tweet text.
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Top functions reviewed by kandi - BETA
- Run loop
- Convert a batch to a dense matrix
- Predict probability
- Main thread
- Process a Tweet
- Removes the text from the given indices
- Run the main thread
- Calculate missed tweets
- Gets the performance metrics for the annotator
- The main loop
- Evaluate guess
- Stop the annotator
- Wait for an annotation
- Train the model
active_stream Key Features
active_stream Examples and Code Snippets
Community Discussions
Trending Discussions on active_stream
QUESTION
We are developing a project using Angular in the front and Spring at the backend. Nothing new. But we have set-up the backend to use HTTP2 and from time to time we find weird problems.
Today I started playing with "Network Log Export" from chrome and I found this interesting piece of information in the HTTP2_SESSION line of the log.
...ANSWER
Answered 2020-Apr-27 at 08:20The overhead protection was put in place in response to a collection of CVE's reported against HTTP/2 in the middle of 2019. While Tomcat wasn't directly affected (the malicious input didn't trigger excessive load) we did take steps to block input that matched the malicious profile.
From your GitHub comment, you see issues with POSTs. That strongly suggests that the client is sending the POST data in multiple small packets rather than a smaller number of larger packets. Some clients (e.g. Chrome) are know to do this occasionally due to they way they buffer data.
A number of the HTTP/2 DoS attacks could be summarized as sending more overhead than data. While Tomcat wasn't directly affected, we took the decision to monitor for clients operating in this way and drop connections if any were found on the grounds that the client was likely to be malicious.
Generally, data packets reduce the overhead count, non-data packets increase the overhead count and (potentially) malicious packets increase the overhead count significantly. The idea is that an established, generally well-behaved, connection should be able to survive the occasional 'suspect' packet but any more than that will quickly trigger the connection to be closed.
In terms of small POST packets the key configuration setting is:
overheadCountFactor
overheadDataThreshold
The overhead count starts at -10. For every DATA frame received it is reduced by 1. For every SETTINGS, PRIORITY and PING frame it is increased by overheadCountFactor
.If the overhead count goes above 0, the connection is closed.
In addition, if the average size of a received non-final DATA frame and the previously received DATA frame (on that same stream) is less than overheadDataThreshold
then the overhead count is increased by overheadDataThreshold/(average size of current and previous DATA frames)
. In this way, the smaller the DATA frame, the greater the increase in the overhead. A small number of small non-final DATA frames should be enough to trigger connection closure.
The averaging is there so buffering such as exhibited by Chrome does not trigger the overhead protection.
To diagnose this problem you need to look at the logs to see what size non-final DATA frames are being sent by the client. I suspect that will show a series of non-final DATA frames with size less than 1024 (the default for overheadDataThreshold
).
To fix the issue my recommendation is to look at the client first. Why is it sending small non-final DATA frames and what can be done to stop it?
If you need an immediate mitigation then you can reduce overheadDataThreshold
. The information you get on DATA frame sizes sent by the client should guide you as to what to set this to. It needs to be smaller than DATA frames being sent by the client. In extremis you can set overheadDataThreshold
to zero to disable the protection.
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No vulnerabilities reported
Install active_stream
You can use active_stream like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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