Nikita Singh

1622185673

What's New In PHP 8.0? - Solace Infotech Pvt Ltd

PHP is continuously evolving and PHP 8.0 is released on November 26th, 2020. It is a mega edition as it explores a lot of features and performance improvements and deprecations to the language. The most talked about feature is the JIT compiler. Performance improving features like JIT deserve the popularity, the syntactical improvements may have more of a true impact for PHP practitioners- in the short term.

Here, we’ll discuss some of the notable features and improvements in PHP 8, including the JIT compiler and syntactical improvements that developers will surely like.

What’s New Features And Improvements In PHP 8.0?

1. JIT (Just In Time) Compiler-
One of the most exciting additions to PHP 8 is JIT compiler. As we all know that, php is an interpreted language, means it runs in real time, instead of being compiled and run at launch. JIT brings compiled code to PHP, and with it, better performance in some situations. It you’re working with web applications as most PHP developers are, JIT will not help much as these performance benchmarks show. However with the tasks like 3D rendering, data analysis, artificial intelligence and other long-running processes, it makes a huge difference. These are not common applications of PHP, but many developers are branching out, so this makes the engine more flexible. JIT makes certain to open PHP’s horizons and bring in devs interested in trying new things. For already existing projects, it might not do more. Prior to implementing JIT, ensure that you’ve tested it in an isolated environment and see whether it improves your performance or not.

2. Attributes-
Now, PHP supports attributes, or small pieces of metadata you add to parts of your code: functions, classes, parameter etc. Which means you don’t need to use docsblocks as a workaround.

You can add various attributes to any part of the code, import them with use statements and add parameters to attributes also. Add attribute to your code with signs: <>.

<>
class Foo
{
<>
public const FOO = ‘foo’;

<>
public $x;

<>
public function foo(<> $bar) { }
}

$object = new <> class () { };

<>
function f1() { }

$f2 = <> function () { };
$f3 = <> fn () => 1;
Note that attributes are not backwards compatible and will cause errors if ported into older versions of PHP. The docblock workaround is functional, however it’s always been somewhat clunky. Now, you can add attributes directly. For such a small addition, it has huge implications.

3. Union Types-
Know more at- https://solaceinfotech.com/blog/whats-new-in-php-8-0/https://solaceinfotech.com/blog/whats-new-in-php-8-0/

#php #php8 #software #development #technology

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What's New In PHP 8.0? - Solace Infotech Pvt Ltd

Semantic Similarity Framework for Knowledge Graph

Introduction

Sematch is an integrated framework for the development, evaluation, and application of semantic similarity for Knowledge Graphs (KGs). It is easy to use Sematch to compute semantic similarity scores of concepts, words and entities. Sematch focuses on specific knowledge-based semantic similarity metrics that rely on structural knowledge in taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents (corpus-IC and graph-IC). Knowledge-based approaches differ from their counterpart corpus-based approaches relying on co-occurrence (e.g. Pointwise Mutual Information) or distributional similarity (Latent Semantic Analysis, Word2Vec, GLOVE and etc). Knowledge-based approaches are usually used for structural KGs, while corpus-based approaches are normally applied in textual corpora.

In text analysis applications, a common pipeline is adopted in using semantic similarity from concept level, to word and sentence level. For example, word similarity is first computed based on similarity scores of WordNet concepts, and sentence similarity is computed by composing word similarity scores. Finally, document similarity could be computed by identifying important sentences, e.g. TextRank.

logo

KG based applications also meet similar pipeline in using semantic similarity, from concept similarity (e.g. http://dbpedia.org/class/yago/Actor109765278) to entity similarity (e.g. http://dbpedia.org/resource/Madrid). Furthermore, in computing document similarity, entities are extracted and document similarity is computed by composing entity similarity scores.

kg

In KGs, concepts usually denote ontology classes while entities refer to ontology instances. Moreover, those concepts are usually constructed into hierarchical taxonomies, such as DBpedia ontology class, thus quantifying concept similarity in KG relies on similar semantic information (e.g. path length, depth, least common subsumer, information content) and semantic similarity metrics (e.g. Path, Wu & Palmer,Li, Resnik, Lin, Jiang & Conrad and WPath). In consequence, Sematch provides an integrated framework to develop and evaluate semantic similarity metrics for concepts, words, entities and their applications.


Getting started: 20 minutes to Sematch

Install Sematch

You need to install scientific computing libraries numpy and scipy first. An example of installing them with pip is shown below.

pip install numpy scipy

Depending on different OS, you can use different ways to install them. After sucessful installation of numpy and scipy, you can install sematch with following commands.

pip install sematch
python -m sematch.download

Alternatively, you can use the development version to clone and install Sematch with setuptools. We recommend you to update your pip and setuptools.

git clone https://github.com/gsi-upm/sematch.git
cd sematch
python setup.py install

We also provide a Sematch-Demo Server. You can use it for experimenting with main functionalities or take it as an example for using Sematch to develop applications. Please check our Documentation for more details.

Computing Word Similarity

The core module of Sematch is measuring semantic similarity between concepts that are represented as concept taxonomies. Word similarity is computed based on the maximum semantic similarity of WordNet concepts. You can use Sematch to compute multi-lingual word similarity based on WordNet with various of semantic similarity metrics.

from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()

# Computing English word similarity using Li method
wns.word_similarity('dog', 'cat', 'li') # 0.449327301063
# Computing Spanish word similarity using Lin method
wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') #0.876800984373
# Computing Chinese word similarity using  Wu & Palmer method
wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') # 0.857142857143
# Computing Spanish and English word similarity using Resnik method
wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') #7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') #0.31023804699
# Computing Chinese and English word similarity using WPath method
wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath')#0.593666388463

Computing semantic similarity of YAGO concepts.

from sematch.semantic.similarity import YagoTypeSimilarity
sim = YagoTypeSimilarity()

#Measuring YAGO concept similarity through WordNet taxonomy and corpus based information content
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Actor109765278', 'wpath') #0.642
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Singer110599806', 'wpath') #0.544
#Measuring YAGO concept similarity based on graph-based IC
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Actor109765278', 'wpath_graph') #0.423
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Singer110599806', 'wpath_graph') #0.328

Computing semantic similarity of DBpedia concepts.

from sematch.semantic.graph import DBpediaDataTransform, Taxonomy
from sematch.semantic.similarity import ConceptSimilarity
concept = ConceptSimilarity(Taxonomy(DBpediaDataTransform()),'models/dbpedia_type_ic.txt')
concept.name2concept('actor')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'path')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'wup')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'li')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'res')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'lin')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'jcn')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'wpath')

Computing semantic similarity of DBpedia entities.

from sematch.semantic.similarity import EntitySimilarity
sim = EntitySimilarity()
sim.similarity('http://dbpedia.org/resource/Madrid','http://dbpedia.org/resource/Barcelona') #0.409923677282
sim.similarity('http://dbpedia.org/resource/Apple_Inc.','http://dbpedia.org/resource/Steve_Jobs')#0.0904545454545
sim.relatedness('http://dbpedia.org/resource/Madrid','http://dbpedia.org/resource/Barcelona')#0.457984139871
sim.relatedness('http://dbpedia.org/resource/Apple_Inc.','http://dbpedia.org/resource/Steve_Jobs')#0.465991132787

Evaluate semantic similarity metrics with word similarity datasets

from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
evaluation = WordSimEvaluation()
evaluation.dataset_names()
wns = WordNetSimilarity()
# define similarity metrics
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
# evaluate similarity metrics with SimLex dataset
evaluation.evaluate_metric('wpath', wpath, 'noun_simlex')
# performa Steiger's Z significance Test
evaluation.statistical_test('wpath', 'path', 'noun_simlex')
# define similarity metrics for Spanish words
wpath_es = lambda x, y: wns.monol_word_similarity(x, y, 'spa', 'path')
# define cross-lingual similarity metrics for English-Spanish
wpath_en_es = lambda x, y: wns.crossl_word_similarity(x, y, 'eng', 'spa', 'wpath')
# evaluate metrics in multilingual word similarity datasets
evaluation.evaluate_metric('wpath_es', wpath_es, 'rg65_spanish')
evaluation.evaluate_metric('wpath_en_es', wpath_en_es, 'rg65_EN-ES')

Evaluate semantic similarity metrics with category classification

Although the word similarity correlation measure is the standard way to evaluate the semantic similarity metrics, it relies on human judgements over word pairs which may not have same performance in real applications. Therefore, apart from word similarity evaluation, the Sematch evaluation framework also includes a simple aspect category classification. The task classifies noun concepts such as pasta, noodle, steak, tea into their ontological parent concept FOOD, DRINKS.

from sematch.evaluation import AspectEvaluation
from sematch.application import SimClassifier, SimSVMClassifier
from sematch.semantic.similarity import WordNetSimilarity

# create aspect classification evaluation
evaluation = AspectEvaluation()
# load the dataset
X, y = evaluation.load_dataset()
# define word similarity function
wns = WordNetSimilarity()
word_sim = lambda x, y: wns.word_similarity(x, y)
# Train and evaluate metrics with unsupervised classification model
simclassifier = SimClassifier.train(zip(X,y), word_sim)
evaluation.evaluate(X,y, simclassifier)

macro averge:  (0.65319812882333839, 0.7101245049198579, 0.66317566364913016, None)
micro average:  (0.79210167952791644, 0.79210167952791644, 0.79210167952791644, None)
weighted average:  (0.80842645056024054, 0.79210167952791644, 0.79639496616636352, None)
accuracy:  0.792101679528
             precision    recall  f1-score   support

    SERVICE       0.50      0.43      0.46       519
 RESTAURANT       0.81      0.66      0.73       228
       FOOD       0.95      0.87      0.91      2256
   LOCATION       0.26      0.67      0.37        54
   AMBIENCE       0.60      0.70      0.65       597
     DRINKS       0.81      0.93      0.87       752

avg / total       0.81      0.79      0.80      4406

Matching Entities with type using SPARQL queries

You can use Sematch to download a list of entities having a specific type using different languages. Sematch will generate SPARQL queries and execute them in DBpedia Sparql Endpoint.

from sematch.application import Matcher
matcher = Matcher()
# matching scientist entities from DBpedia
matcher.match_type('scientist')
matcher.match_type('científico', 'spa')
matcher.match_type('科学家', 'cmn')
matcher.match_entity_type('movies with Tom Cruise')

Example of automatically generated SPARQL query.

SELECT DISTINCT ?s, ?label, ?abstract WHERE {
    {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/NuclearPhysicist110364643> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Econometrician110043491> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Sociologist110620758> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Archeologist109804806> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Neurolinguist110354053> . } 
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Thing> . 
    ?s <http://www.w3.org/2000/01/rdf-schema#label> ?label . 
    FILTER( lang(?label) = "en") . 
    ?s <http://dbpedia.org/ontology/abstract> ?abstract . 
    FILTER( lang(?abstract) = "en") .
} LIMIT 5000

Entity feature extraction with Similarity Graph

Apart from semantic matching of entities from DBpedia, you can also use Sematch to extract features of entities and apply semantic similarity analysis using graph-based ranking algorithms. Given a list of objects (concepts, words, entities), Sematch compute their pairwise semantic similarity and generate similarity graph where nodes denote objects and edges denote similarity scores. An example of using similarity graph for extracting important words from an entity description.

from sematch.semantic.graph import SimGraph
from sematch.semantic.similarity import WordNetSimilarity
from sematch.nlp import Extraction, word_process
from sematch.semantic.sparql import EntityFeatures
from collections import Counter
tom = EntityFeatures().features('http://dbpedia.org/resource/Tom_Cruise')
words = Extraction().extract_nouns(tom['abstract'])
words = word_process(words)
wns = WordNetSimilarity()
word_graph = SimGraph(words, wns.word_similarity)
word_scores = word_graph.page_rank()
words, scores =zip(*Counter(word_scores).most_common(10))
print words
(u'picture', u'action', u'number', u'film', u'post', u'sport', 
u'program', u'men', u'performance', u'motion')

Publications

Ganggao Zhu, and Carlos A. Iglesias. "Computing Semantic Similarity of Concepts in Knowledge Graphs." IEEE Transactions on Knowledge and Data Engineering 29.1 (2017): 72-85.

Oscar Araque, Ganggao Zhu, Manuel Garcia-Amado and Carlos A. Iglesias Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis, ICDM sentire, 2016.

Ganggao Zhu, and Carlos Angel Iglesias. "Sematch: Semantic Entity Search from Knowledge Graph." SumPre-HSWI@ ESWC. 2015.


Support

You can post bug reports and feature requests in Github issues. Make sure to read our guidelines first. This project is still under active development approaching to its goals. The project is mainly maintained by Ganggao Zhu. You can contact him via gzhu [at] dit.upm.es


Why this name, Sematch and Logo?

The name of Sematch is composed based on Spanish "se" and English "match". It is also the abbreviation of semantic matching because semantic similarity metrics helps to determine semantic distance of concepts, words, entities, instead of exact matching.

The logo of Sematch is based on Chinese Yin and Yang which is written in I Ching. Somehow, it correlates to 0 and 1 in computer science.

Author: Gsi-upm
Source Code: https://github.com/gsi-upm/sematch 
License: View license

#python #jupyternotebook #graph 

What's New In PHP 8.0? - Solace Infotech Pvt Ltd

PHP is continuously evolving and PHP 8.0 is released on November 26th, 2020. It is a mega edition as it explores a lot of features and performance improvements and deprecations to the language. The most talked about feature is the JIT compiler. Performance improving features like JIT deserve the popularity, the syntactical improvements may have more of a true impact for PHP practitioners- in the short term.

Here, we’ll discuss some of the notable features and improvements in PHP 8, including the JIT compiler and syntactical improvements that developers will surely like.

What’s New Features And Improvements In PHP 8.0?

1. JIT (Just In Time) Compiler-
One of the most exciting additions to PHP 8 is JIT compiler. As we all know that, php is an interpreted language, means it runs in real time, instead of being compiled and run at launch. JIT brings compiled code to PHP, and with it, better performance in some situations. It you’re working with web applications as most PHP developers are, JIT will not help much as these performance benchmarks show. However with the tasks like 3D rendering, data analysis, artificial intelligence and other long-running processes, it makes a huge difference. These are not common applications of PHP, but many developers are branching out, so this makes the engine more flexible. JIT makes certain to open PHP’s horizons and bring in devs interested in trying new things. For already existing projects, it might not do more. Prior to implementing JIT, ensure that you’ve tested it in an isolated environment and see whether it improves your performance or not.

2. Attributes-
Know more at- https://solaceinfotech.com/blog/whats-new-in-php-8-0/

#php #php8.0 #php_features #cordova #tech

I am Developer

1599536794

Laravel 8 New Features | Release Notes - Tuts Make

In this post, i will show you what’s new in laravel 8 version.

#What’s new in Laravel 8?

  • 1 - Change Path Of Default Models Directory
  • 2 - Removed Controllers Namespace Prefix
  • 3 - Enhancements on php artisan serve
  • 4 - Enhanced Rate Limiting
  • 5 - Enhanced on Route Caching
  • 6 - Update on Pagination Design
  • 8 - Dynamic Blade Componenets
  • 7 - Update Syntax for Closure Based Event Listeners
  • 8 - Queueable Model Event Listeners
  • 9 - Maintenance mode: secret access
  • 10 - Maintenance mode: pre-rendered page
  • 11 - Queued job batching
  • 12 - Queue backoff()
  • 13 - Laravel Factory

https://www.tutsmake.com/laravel-8-new-features-release-notes/

#laravel 8 features #laravel 8 release date #laravel 8 tutorial #news - laravel 8 new features #what's new in laravel 8 #laravel 8 release notes

Hunter  Krajcik

Hunter Krajcik

1617466260

How to Install PHP 8 on Ubuntu 20.04

PHP is an acronym of “Hypertext Preprocessor” is a scripting language widely used for developing web applications. It is the server side scripting language that is embedded in HTML. Which is used to manage dynamic content, databases, session tracking, even build entire e-commerce websites.

PHP 8.0 is the latest stable version available for production use. The debian packages for PHP 8 is available under  ppa:ondrej/php PPA for Ubuntu systems.

This tutorial will help you to install PHP 8.0 on Ubuntu 20.04 LTS Focal system.

Step 1 – Installing PHP 8 on Ubuntu

Use the following commands to add PPA and install PHP 8.0 on Ubuntu 20.04 system.

  1. Enable PPA – Use the following command
sudo add-apt-repository ppa:ondrej/php 
  1. Update cache – After enabling apt repository, update cache on your system.
sudo apt update 
  1. Installing PHP – Next, install the PHP 8.0 on Ubuntu system.
sudo apt install php8.0 
  1. Press ‘Y’ for any confirmation asked by the installer.

Once completed above commands, you have successfully installed PHP 8.0 on Ubuntu system.

#php #php 8 #php 8.0

Nikita Singh

1622185673

What's New In PHP 8.0? - Solace Infotech Pvt Ltd

PHP is continuously evolving and PHP 8.0 is released on November 26th, 2020. It is a mega edition as it explores a lot of features and performance improvements and deprecations to the language. The most talked about feature is the JIT compiler. Performance improving features like JIT deserve the popularity, the syntactical improvements may have more of a true impact for PHP practitioners- in the short term.

Here, we’ll discuss some of the notable features and improvements in PHP 8, including the JIT compiler and syntactical improvements that developers will surely like.

What’s New Features And Improvements In PHP 8.0?

1. JIT (Just In Time) Compiler-
One of the most exciting additions to PHP 8 is JIT compiler. As we all know that, php is an interpreted language, means it runs in real time, instead of being compiled and run at launch. JIT brings compiled code to PHP, and with it, better performance in some situations. It you’re working with web applications as most PHP developers are, JIT will not help much as these performance benchmarks show. However with the tasks like 3D rendering, data analysis, artificial intelligence and other long-running processes, it makes a huge difference. These are not common applications of PHP, but many developers are branching out, so this makes the engine more flexible. JIT makes certain to open PHP’s horizons and bring in devs interested in trying new things. For already existing projects, it might not do more. Prior to implementing JIT, ensure that you’ve tested it in an isolated environment and see whether it improves your performance or not.

2. Attributes-
Now, PHP supports attributes, or small pieces of metadata you add to parts of your code: functions, classes, parameter etc. Which means you don’t need to use docsblocks as a workaround.

You can add various attributes to any part of the code, import them with use statements and add parameters to attributes also. Add attribute to your code with signs: <>.

<>
class Foo
{
<>
public const FOO = ‘foo’;

<>
public $x;

<>
public function foo(<> $bar) { }
}

$object = new <> class () { };

<>
function f1() { }

$f2 = <> function () { };
$f3 = <> fn () => 1;
Note that attributes are not backwards compatible and will cause errors if ported into older versions of PHP. The docblock workaround is functional, however it’s always been somewhat clunky. Now, you can add attributes directly. For such a small addition, it has huge implications.

3. Union Types-
Know more at- https://solaceinfotech.com/blog/whats-new-in-php-8-0/https://solaceinfotech.com/blog/whats-new-in-php-8-0/

#php #php8 #software #development #technology