bidirectional | Write Arabic text on apps that do n't support | Frontend Framework library

Using older word processing software applications there seems to be problems when LTR characters and RTL characters gets mixed in one text run. Then using special BiDi character types might be the solution. See https://en.wikipedia.org/wiki/Bidirectional_text#Table_of_possible_BiDi_character_types.

See also bidirectional with word document using Aphace POI.

Using this the following works:

Service Broker requires a service for both initiator and target. Your trigger is the from initiator service so you should create a service and queue for that. However, the trigger does not need to implement receiving messages from the initiator queue.

You can have a separate process (perhaps a scheduled batch process) to monitor the initiator queue for unexpected error and end dialog messages, and perhaps start a new long-running conversation when needed.

Can I try to read if it no data available?

Yep, and it's going to be case more often than not. Read() will do nothing until data is available, and only then put its passed tag into the completion queue. (see below for details)

Is it blocking call?

Nope. Read() and Write() return immediately. However, you can only have one of each in flight at any given moment. If you try to send a second one before the previous has completed, it (the second one) will fail.

What is the proper way to async reading?

Each time a Read() is done, start a new one. For that, you need to be able to tell when a Read() is done. This is where tags come in!

When you call Read(&msg, tag), or Write(request, tag),you are telling grpc to put tag in the completion queue associated with that responder once that operation has completed. grpc doesn't care what the tag is, it just hands it off.

So the general strategy you will want to go for is:

• As soon as you are ready to start receiving messages:
  • call responder->Read() once with some tag that you will recognize as a "read done".
• Whenever cq_.Next() gives you back that tag, and ok == true:
  • consume the message
  • Queue up a new responder->Read() with that same tag.

Obviously, you'll also want to do something similar for your calls to Write().

But since you still want to be able to lookup the handler instance from a given tag, you'll need a way to pack a reference to the handler as well as information about which operation is being finished in a single tag.

Lookup the handler instance from a given tag? Why?

The true raison d'être of completion queues is unfortunately not evident from the examples. They allow multiple asynchronous rpcs to share the same thread. Unless your application only ever makes a single rpc call, the handling thread should not be associated with a specific responder. Instead, that thread should be a general-purpose worker that dispatches events to the correct handler based on the content of the tag.

The official examples tend to do that by using pointer to the handler object as the tag. That works when there's a specific sequence of events to expect since you can easily predict what a handler is reacting to. You often can't do that with async bidirectional streams, since any given completion event could be a Read() or a Write() finishing.

Here's a general outline of what I personally consider to be a clean way to go about all that:

I found the problem and so I'm answering my own question.

There is a setting in Keras that specifies the way of working with (and supossedly affecting only) image data.

• Channels Last. Image data is represented in a three-dimensional array where the last channel represents the color channels, e.g. [rows][cols][channels].

• Channels First. Image data is represented in a three-dimensional array where the first channel represents the color channels, e.g. [channels][rows][cols].

Keras keeps this setting differently for different backends, and this is supossedly set as Channels Last for Tensorflow, BUT it looks like in our planets it is set as Channels First.

Thankfully, this can be set manually and I managed to fix it with:

Instead of trying to pick one model that would be the superior at anytime, it's typically better to do an average of the models, in order to include as many individual views as possible. In the experiments I've been involved in, where we tried to pick one model that would outperform, based on historical performance, it's typically shown that a simple average was as good or better. Which is in line with the typical results on this problem: https://otexts.com/fpp2/combinations.html

So, before you try to go more advanced at it by using trying to pick a specific model based on previous performance, or by using an weighted average, consider doing a simple average of the two models.

If you want to continue with a sort of selection/weighted averaging, try to have a look at the FFORMA package in R: https://github.com/pmontman/fforma I've not tried the specific package (yet), but have seen promising results in my test using the original m4metalearning package.

first of all try to use the MapStruct version

When trying to save the model as a .h5 file the following error occurred:

You have wrapped your LSTM layer with a Bidirectional layer. Thus, you should pass the input_shape parameter to the Bidirectional and not the LSTM layer. Change according to the following:

There's no general-purpose way to find a bijection from std::string to int for the simple but mundane reason that there are more possible std::strings than there are ints. (Specifically, there's effectively an unbounded number of possible std::strings, and there are only 2³² or 2⁶⁴ distinct possible integers).

There are ways to construct perfect hash functions from strings to integers if you have a fixed set of strings you want to work with, but in your case if the goal is just to label all the strings with distinct values your initial idea of just having a counter and assigning each string the next available number is probably just fine.