I'm looking at the CAN layers contrib and its documentation
I want to use the SignalPacket solution to implement a few messages described in DCB's but this raises a question. How can I easily make use of the CAN layer and then bind my custom SignalPacket based on the identifier field in the CAN layer, specifically how do I handle getting the 8 byte payload data that today is included in the data field of the CAN class, into my custom SignalPacket instance?
Alternatives:
1: Modify the CAN layer, removing the payload, then binding my custom SignalPacket to the CAN layer based on the identifier?
2: Leave the CAN layer untouched and in some way access the parent layer's data using pkt.underlayer to populate the custom SignalPacket?
UPDATE:
Answered my own question after finding a solution from the maintainers, see separate reply below
Remaining question: Has anyone used the #2 type of solution in any other case and care to describe how it would look?
Polybassa has an answer to the question on the scapy github, proposing two alternatives #1 + a version of the existing solution with the CAN type and the custom type.
https://github.com/secdev/scapy/issues/2252
Related
BLUF: Can I export a .als file corresponding to a model I have created with the Alloy API?
Example: I have a module that I read in using edu.mit.csail.sdg.alloy4compiler.parser.CompUtil. I then add signatures and facts to create a modified model in memory. Can I "de-parse" that and basically invert the lexer (edu.mit.csail.sdg.alloy4compiler.parser.CompLexer) to get a .als file somehow?
It seems like there ought to be a way to decompile the model in memory and save that as code to be later altered, but I'm having trouble identifying a path to that in the Alloy API Javadocs. I'm building a translator from select behavioral aspects of UML/SysML as part of some research, so I'm trying to figure out if there is something extant I can take advantage of or if I need to create it.
It seems a similar question has been asked before: Generating .als files corresponding to model instances with Alloy API
From the attached post https://stackoverflow.com/users/2270610/lo%c3%afc-gammaitoni stated he has written a solution for this in his Lightning application. He said that he may include the source code for completing this task. I'm unsure if he has uploaded the solution yet.
I am using two different entity extraction methods (https://rasa.com/docs/nlu/entities/) while building my NLP model in the RASA framework to build a chatbot.
The bot should handle different questions which have custom entities as well as some general ones like location or organisation.
So I use both components ner_spacy and ner_crf to create the model. After that I build a small helper script in python to evaluate the model performance. There I noticed that the model struggles to choose the correct enity.
For example for a word 'X' it choosed the pre-defined enity 'ORG' from SpaCy, but it should be recogniced as a custom enity which I defined in the training data.
If I just use the ner_crf extractor I face huge problems in identifing location enities like capitals. Also one of my biggest problems are single answer enities.
Q : "What´s your favourite animal?"
A : Dog
My model is not able to extract this single entity 'animal' for this single answer. If I answer this question with two words like 'The Dog', the model has no problems to extract the animal entity with the value 'Dog'.
So my question is, is it clever to use two different components to extract entities? One for custom enities and the other one for pre-defined enities.
If I use two methods, what´s the mechanism in the model which extractor is used?
By the way, currently I´m just testing things out, so my training samples are not that huge it should be (less then 100 examples). Could the problem been solved if I have much more training examples?
You are facing 2 problems here. I am suggesting few ways that i found helpful.
1. Custom entity recognition:
To solve this you need to add more training sentences with all possible lengths of entities. ner_crf is going to predict better when there are identifiable markers around entities (e.g. prepositions)
2. Extracting entities from single word answer :
As a workaround, i suggest you to do below manipulations on client end.
When you are sending question like What´s your favorite animal?, append a marker to question to indicate to client that a single answer is expected. e.g.
You can send ##SINGLE## What´s your favorite animal? to client.
Client can remove the ##SINGLE## from question and show it to user. But when client sends user's response to server, it doesn't send Dog, it send something like User responded with single answer as Dog
You can train your model to extract entities from such an answer.
Where should I look for documentation or hints for different kinds of layers available in pycaffe? and their attributes?
I want to define an architecture which uses batch-normalization, but I'm lost, in the examples there is only convolution, fully connected, max-pooling and relu layers. nothing more.
Pycaffe is just a wrapper/interface for caffe.
Check here for the list of layers available in caffe.
Find the layer you are interested in (e.g. batch_norm_layer.hpp). Open the header file and check the method type(). The return value of this method (e.g. "BatchNorm") is what you are looking for and is used in pycaffe.
Read the comments at the top of each header file. It explains layer details and their input parameters.
As a supplementary approach, take a look at layer implementations. Check the LayerSetUp method and look for param or this->layer_param_. You can find the exact parameters and their names.
The parameters' names are the same as those in the network description (.prototxt) files. Find an example and you will figure out the parameters.
I Use a CQRS thin read layer to provide denormalized lists/reporting data for the UI.
In some parts of my application I want to provide a search box so the user can filter through the data.
Lucene.NET is my full text search engine of choice at the moment, as I've implemented it before and am very happy with it.
But where does the searching side of things fit in with CQRS?
I see two options but there are probably more.
1] My Controller can pass the search string to a search layer (Lucene.NET) which returns a list of ID that I can then pass to the CQRS read layer. The read layer will take these IDs and assemble them into a WHERE ID IN (1,2,3) clause, ultimately returning a DataTable or IEnumerable back to the controller.
List<int> ids = searchLayer.SearchCustomers("searchString");
result = readLayer.GetCustomers(ids);
2] My thin read layer can have searching coded directly into it, so I just call
readLayer.GetListOfCustomers("search string", page, page1);
Remember that using CQRS doesn't mean that you use it in every part of your application. Slicing an application into smaller components allows using various architectural principles and patterns as they make sense. A full text search API might be one of these components.
Without knowing all the details of your application, I would lean towards a single entry point from your controller into your search layer (your option #2 above). I think your controller shouldn't know that it needs to call layer #1 for full-text enabled searching and layer #2 for just regular WHERE clause type searching.
I would assume you would have two different 'contexts' (e.g. SQLContext and LuceneContext), and these would be dependencies injected into your ReadLayer. Your read layer logic should then make the decision on when to use LuceneContext and when to use SQLContext; your controller won't know and shouldn't know.
This also allows you to swap out LuceneContext for MongoContext if there's a compelling reason in the future without your controller knowing or having to change.
And if you use interfaces for everything (e.g. ISearchContext is implemented by LuceneContext and MongoContext), then really the only change for swapping out contexts is in your IoC container's initialization/rules.
So, go with option #2, inject in your dependencies, have your controller just work through your read layer, and you should be good to go. Hopefully this helps!
I have a requirement to add fields onto a form based on data from another set of entities. Is this possible using an event script or does it require a plugin?
Given that I understand your assignment correctly, it can be done using JavaScript as well as a plugin. There is a significant difference that you need to take into consideration.
Is the change to the other entities to be made only when an actual user loads a form? If so, JS is the right way.
Or perhaps you need to ensure that those values are written even if a console client or system process retrieves the value of the primary entity? In that case, C# is your only option.
EDIT:
Simply accessing the values from any entity in the onload event can be done using a call to oData. I believe someone else asked a similar question recently. The basic format will look like this.
http://Server:Port/Organization
/XrmServices/2011/OrganizationData.svc
/TheEntityLogicalNameOfYoursSet()?$filter=FieldName eq 'ValueOfIt'
Some extra remarks.
If you're targeting on-line installation, the syntax will differ, of course, because the Schema-Server-Port-Organization are provided in a different pattern (https, orgName.crm4.something.something.com etc.). You can look it up on Settings.
Perhaps it should go without saying and I'm sure you realize it but for completeness' sake, TheEntityLogicalNameOfYours needs to be substituted for the actual name (unless that is your actual name, in which case I'll be worried, haha).
If you're new to this whole oData thingy, keep asking. I got the impression that the info I'm giving you is appreciated but not really producing "aha!" experience for you. You might want to ask separate questions, though. Some examples right off the top of my head.
a. "How do I perform oData call in JavaScript?"
b. "How do I access the fetched data?"
c. "How do I add/remove/hide a field programmatically on a form?"
d. "How do I combine data from...?"