Basically, I'm creating an ML model for reCaptcha images to identify those I need to load a data set of objects that frequently used in the reCaptcha model
If I get a list of objects used in ReCAPTCHA it will be easy for me to build a proper dataset and train my ML model
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I'm new on computer vision field , I work on a project to detect and track trashs.
I use YOLO v5 to detect objects now I want to count each object that is present on the Video . I want some suggestion if there are some models that we can use on my own dataset.
This repo contains all you need for tracking and counting objects: https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet. If you already have a set of weights you could start tracking by:
python track.py --yolo-weights /path/to/your/weights.pt
Adding the counting functionality should be straight forward
Using the Dynatrace SaaS GUI in Multidimensional analysis menu I am able to split and filter metrics with request attributes, but I can't find any document on how is the syntax to do the same with the environment v2 API (/metrics/query).
Thanks!
The documentation of the metric query API describes the metricSelector parameter, which is used to select the metric to query and to perform operations on it, e.g. splitting by dimension or filtering based on some values.
You can develop and test the metric-selector in the UI via menu item "Metrics" -> then search for the metric and in the data-explorer build your metric query via "split by" and "filter", then the tab "Code" will show the corresponding metricSelector that you can use also in the query-API.
E.g. a possible metric-query looks like follows:
sample.metric:filter(and(in("dt.entity.process_group_instance",entitySelector("type(process_group_instance),tag(~"Prod~")")))):splitBy("dt.entity.process_group_instance",rx_pid):avg:auto:sort(value(avg,descending)):limit(10)
The documentation for the metric selector explains details and contains many more examples.
I am working on a project to detect anomalies in web users activity in real-time. Any ill intention or malicious activity of the user has to be detected in real-time. Input data is clickstream data of users. Click data contains user-id ( Unique user ID), click URL ( URL of web page), Click text (Text/function in the website on which user has clicked) and Information (Any information typed by user). This project is similar to an Intrusion detection system (IDS). I am using python 3.6 and I have the following queries,
Which is the best approach to carry out the data preprocessing, Considering all the attributes in the dataset are categorical values.
Encoding methods like hot encoding or label encoding could be applied but data has to be processed in real-time which makes it difficult to apply
As per the requirement of the project 3 columns(click URL, Click Text and Typed information) considered as feature columns.
I am really confused about how to approach data preprocessing. Any insight or suggestions would be appreciated
In some recent personal and professional projects when faced with the challenge of applying ML on streaming data I have had success with the python library River https://github.com/online-ml/river.
Some online algorithms can handle labelled values (like hoeffding trees) so depending on what you want to achieve you may not need to conduct preprocessing.
If you do need to conduct preprocessing, label encoding and one hot encoding could be applied in an incremental fashion. Below is some code to get you started. River also has a number of classes to help out with feature extraction and feature selection e.g: TF-IDF, bag of words or frequency aggregations.
online_label_enc = {}
for click in click_stream:
try:
label_enc = click[click__feature_label_of_interest]
except KeyError:
click[click__feature_label_of_interest] = len(online_label_enc)
label_enc = click[click__feature_label_of_interest]
I am not sure what you are asking - but if you are approaching the problem online/incrementally then extract the features you want and pass them to your online algorithm of choice - which should then be updating and learning at every data increment.
Prediction using NLP/ML/word2vec/tensorflow.
I have 1000's of records from service request utility.
I have list of sevice requests & corresponding action taken by executive for each service request which is in natural english langauge(text could be different for same type of request from different user).
Sample record-
request: addition to xxx mail alias for the user id yyy
action: ask user to get approval from manager and then manually add the user id to mail alias group.
My understanding is that this data can be used as training data for my model(NLP/ML/word2vec), in such a way that if any new request of same type given as input to model, model should be able to suggest the required actions that executive should take. This is what I want to achieve but not sure how to and from where to start. I have executed samples from
https://github.com/tmikolov/word2vec
https://github.com/tensorflow/tensorflow/tree/r0.12/tensorflow/models/embedding
Any pointers.
I think you can approach this as a translation problem. Just instead of translating between languages you translate from request to action. The TensorFlow page has a translation tutorial you could try: https://www.tensorflow.org/tutorials/seq2seq/
For an even simpler go at it you could also just train a character-prediction RNN on the whole text file that contains the requests and answers. This is described well with example code at http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
The RNN will then be able to generate or auto-complete requests as well :).
Can somebody bring the light on the following issue - I can't figure out the difference between alfresco workflow model and repository model. Are they different ? Can I use them together, or one inside the other ?
Workflow model deals with the Business Process Modeling(BPM), where you would manage flow of different tasks to user by user. On the other hand there is a 'Content Model' which is independent of workflow model, and it deals with your content types, properties and constraints within the content model. Both are different and independent, however you can use both of them together.