FFM | Freedom Fighting Mode : open source hacking harness | Hacking library

To extract and print all the Agent Name, Phone and Email using Selenium you can use List Comprehension inducing WebDriverWait for visibility_of_all_elements_located() and you can use either of the following Locator Strategies:

• Code Block:

Better let the library does the work for you. Use NameValueCollection returned by HttpUtility.ParseQueryString (note that they return a different derived type HttpValueCollection which you cannot initialize by yourself). See more here.

Since you didn't give the class definition, I switched the type to object so the test code can run:

The simple answer is that you cannot cleanly use dependency injection with functions, only with methods. Technically, you could make the functions global vars instead (ex. var WriteToFile = func(content []byte) (bool, error) { [...] }), but this is rather brittle code.

The more proper solution, from an idiomatic perspective, is to make any behavior you want to replace, inject, or wrap into a method that is then wrapped in an interface.

For example:

Yes you can use embeddings and that approach does work.

The attribute will not be equal to one element in the embedding but that combination of elements will equal to that attribute. The size of the embedding is something that you will have to select yourself. A good formula to follow is embedding_size = min(50, m+1// 2). Where m is the number of categories so if you have m=10 you will have an embedding size of 5.

A higher embedding size means it will capture more details on the relationship between the categorical variables.

In my experience embeddings do help especially when you have 100's of categories(if you have a small number of categories i.e. sex of a person, then one-hot encoding is sufficient) within a certain category.

On which is better I find embeddings do perform better in general when there are 100's of unique values in a category. Why this is so I do not have any concrete reasons but some intuitions for it.

For example, representing categories as 300-dimensional dense vectors(word embeddings) requires classifiers to learn far fewer weights than if the categories were represented as 50,000-dimensional vectors(one-hot encoding), and the smaller parameter space possibly helps with generalization and avoiding overfitting.

Before pull queries were introduced, ksqlDB nodes did not talk to each other. The 'clustering' of ksqlNodes leveraged a shared command topic and the Kafka consumer groups protocol of Kafka to share out work. This is why you're not seeing any messages about trying to join the cluster: the node doesn't join the cluster.

Pull queries using Kafka Streams' interactive queries under the hood. This works by each ksqlDB node advertising an endpoint other nodes can reach it. These advertised endpoints are shared between ksqlDB nodes using the Kafka consumer group protocol, i.e. the nodes become aware of each other through their communication with Kafka.

When you issue a pull query to a node that doesn't host the information you require it will attempt to forward the request to the node that does by contacting it on the endpoint it advertised. In your case, what is happening is that the endpoint your nodes from one machine are advertising is not accessible from the other machine.

KsqlDB v0.8 - 0.9 takes the first listener in the listeners config and uses this as its advertised listener / endpoint. A common problem is people use a wildcard or loopback address in their listener config, e.g.

One approach may be to use strsplit and unlist, checking that all characters are equal to type2 (in addition to checking that type2 equals type3).

Starting with the 'reproducible' part and the information that the final aim is to summarize another column, it is possible to exploit the fact that the intervals are non-overlapping and simply use the cut function.

Since df[1,6] is still a tibble using as.character doesn't work on it. You need to pass a vector to as.character which can be done with