The document discusses the use of symbols and rules in Rspamd for defining spam detection logic. Symbols represent metadata like rule names and scores, while rules define spam detection expressions using regular expressions and logic. Symbols can be grouped and rules can reference symbols to define dependencies. Composites allow combining multiple rules and introducing logic to remove symbols and weights conditionally. Practical examples show how to define simple regex rules, complex rules combining multiple checks, and composites modifying rule results.
[Bind DNS + Zimbra + SpamAssassin] Antispam Installation GuideMạnh Nguyễn Văn
To configure our system, I used the following software:
- DNS server: Bind DNS.
- Email server: Zimbra Collaboration Suite open source edition.
- Anti-spam: SpamAssassin.
- Mail client: Zimbra
This is the presentation I delivered on Hadoop User Group Ireland meetup in Dublin on Nov 28 2015. It covers at glance the architecture of GPDB and most important its features. Sorry for the colors - Slideshare is crappy with PDFs
Deep Dive on PostgreSQL Databases on Amazon RDS (DAT324) - AWS re:Invent 2018Amazon Web Services
In this session, we provide an overview of the PostgreSQL options available on AWS, and do a deep dive on Amazon Relational Database Service (Amazon RDS) for PostgreSQL, a fully managed PostgreSQL service, and Amazon Aurora, a PostgreSQL-compatible database with up to 3x the performance of standard PostgreSQL. Learn about the features, functionality, and many innovations in Amazon RDS and Aurora, which give you the background to choose the right service to solve different technical challenges, and the knowledge to easily move between services as your requirements change over time.
[Bind DNS + Zimbra + SpamAssassin] Antispam Installation GuideMạnh Nguyễn Văn
To configure our system, I used the following software:
- DNS server: Bind DNS.
- Email server: Zimbra Collaboration Suite open source edition.
- Anti-spam: SpamAssassin.
- Mail client: Zimbra
This is the presentation I delivered on Hadoop User Group Ireland meetup in Dublin on Nov 28 2015. It covers at glance the architecture of GPDB and most important its features. Sorry for the colors - Slideshare is crappy with PDFs
Deep Dive on PostgreSQL Databases on Amazon RDS (DAT324) - AWS re:Invent 2018Amazon Web Services
In this session, we provide an overview of the PostgreSQL options available on AWS, and do a deep dive on Amazon Relational Database Service (Amazon RDS) for PostgreSQL, a fully managed PostgreSQL service, and Amazon Aurora, a PostgreSQL-compatible database with up to 3x the performance of standard PostgreSQL. Learn about the features, functionality, and many innovations in Amazon RDS and Aurora, which give you the background to choose the right service to solve different technical challenges, and the knowledge to easily move between services as your requirements change over time.
Solving Data Discovery Challenges at Lyft with Amundsen, an Open-source Metad...Databricks
Amundsen is the data discovery metadata platform that originated from Lyft which is recently donated to Linux Foundation AI. Since its open-sourced, Amundsen has been used and extended by many different companies within our community.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
한국어를 위한 AWS 인공지능(AI) 서비스 소개 및 활용 방법 - 강정희 솔루션즈 아키텍트, AWS :: AWS Innovate 2019Amazon Web Services Korea
한국어를 위한 AWS 인공지능(AI) 서비스 소개 및 활용 방법 - 강정희 솔루션즈 아키텍트, AWS :: AWS Innovate 2019
언어와 문자에 대한 이해는 인공지능 기술의 대표적인 주제입니다. AWS는 인공지능에 대한 깊은 이해나 투자 없이도 손쉽게 이를 활용할 수 있도록, 2017년 다양한 AI 언어 서비스들을 발표하였습니다. 여기에 최근 한국어 지원이 추가된 번역 서비스 Amazon Translate와 re:invent 2018에서 발표된 문서 분석 서비스 Amazon Textract을 활용하면 보다 다양한 시나리오에서 애플리케이션에 인텔리전스를 적용하여 비즈니스에 필요한 인사이트를 얻을 수 있습니다. 본 세션에서는 AI 언어 서비스와 Textract의 신규 기능과 다양한 사용 사례를 예제와 함께 알아봅니다.
When it comes time to select database software for your project, there are a bewildering number of choices. How do you know if your project is a good fit for a relational database, or whether one of the many NoSQL options is a better choice?
In this webinar you will learn when to use MongoDB and how to evaluate if MongoDB is a fit for your project. You will see how MongoDB's flexible document model is solving business problems in ways that were not previously possible, and how MongoDB's built-in features allow running at scale.
Topics covered include:
Performance and Scalability
MongoDB's Data Model
Popular MongoDB Use Cases
Customer Stories
Deep Dive Amazon Redshift for Big Data Analytics - September Webinar SeriesAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and tune query and database performance.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Building Robust Production Data Pipelines with Databricks DeltaDatabricks
"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets.
Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain.
This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT
YOU’LL LEARN:
– Understand the key data reliability and performance data pipelines challenges
– How Databricks Delta helps build robust pipelines at scale
– Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements
– How to deliver performance gains using Delta
PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Pre-register for Databricks Community Edition"
Speakers: Steven Yu, Burak Yavuz
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
This talk cover various advanced topics in the area of backups:
- incremental backups;
- archive management;
- backup validation;
- retention policies;
etc.
Based on these features, we'll compare various backup/recovery solutions for PostgreSQL.
This information will help you to choose the most appropriate tool for your system.
In the context of package management, the solver is an algorithm (or set of algorithms) to resolve dependencies and conflicts. The solver must handle options, upgrades, multiple repos, locally installed software, as well as other factors. The upcoming 1.3 release of pkg will have the new solver that has some important consequences.
This talk is dedicated to the design concepts of the new solver in pkg management system (pkg-ng initially). In this talk, I describe the basic architecture of the solver, ideas used and the consequences of using this algorithm. Moreover, this talk describes the proposed pkg and ports architecture to simplify binary packages and ports using for all FreeBSD users.
Solving Data Discovery Challenges at Lyft with Amundsen, an Open-source Metad...Databricks
Amundsen is the data discovery metadata platform that originated from Lyft which is recently donated to Linux Foundation AI. Since its open-sourced, Amundsen has been used and extended by many different companies within our community.
Welcome to my post on ‘Architecting Modern Data Platforms’, here I will be discussing how to design cutting edge data analytics platforms which meet the ever-evolving data & analytics needs for the business.
https://www.ankitrathi.com
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
한국어를 위한 AWS 인공지능(AI) 서비스 소개 및 활용 방법 - 강정희 솔루션즈 아키텍트, AWS :: AWS Innovate 2019Amazon Web Services Korea
한국어를 위한 AWS 인공지능(AI) 서비스 소개 및 활용 방법 - 강정희 솔루션즈 아키텍트, AWS :: AWS Innovate 2019
언어와 문자에 대한 이해는 인공지능 기술의 대표적인 주제입니다. AWS는 인공지능에 대한 깊은 이해나 투자 없이도 손쉽게 이를 활용할 수 있도록, 2017년 다양한 AI 언어 서비스들을 발표하였습니다. 여기에 최근 한국어 지원이 추가된 번역 서비스 Amazon Translate와 re:invent 2018에서 발표된 문서 분석 서비스 Amazon Textract을 활용하면 보다 다양한 시나리오에서 애플리케이션에 인텔리전스를 적용하여 비즈니스에 필요한 인사이트를 얻을 수 있습니다. 본 세션에서는 AI 언어 서비스와 Textract의 신규 기능과 다양한 사용 사례를 예제와 함께 알아봅니다.
When it comes time to select database software for your project, there are a bewildering number of choices. How do you know if your project is a good fit for a relational database, or whether one of the many NoSQL options is a better choice?
In this webinar you will learn when to use MongoDB and how to evaluate if MongoDB is a fit for your project. You will see how MongoDB's flexible document model is solving business problems in ways that were not previously possible, and how MongoDB's built-in features allow running at scale.
Topics covered include:
Performance and Scalability
MongoDB's Data Model
Popular MongoDB Use Cases
Customer Stories
Deep Dive Amazon Redshift for Big Data Analytics - September Webinar SeriesAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and tune query and database performance.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
Building Robust Production Data Pipelines with Databricks DeltaDatabricks
"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets.
Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain.
This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT
YOU’LL LEARN:
– Understand the key data reliability and performance data pipelines challenges
– How Databricks Delta helps build robust pipelines at scale
– Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements
– How to deliver performance gains using Delta
PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Pre-register for Databricks Community Edition"
Speakers: Steven Yu, Burak Yavuz
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
This talk cover various advanced topics in the area of backups:
- incremental backups;
- archive management;
- backup validation;
- retention policies;
etc.
Based on these features, we'll compare various backup/recovery solutions for PostgreSQL.
This information will help you to choose the most appropriate tool for your system.
In the context of package management, the solver is an algorithm (or set of algorithms) to resolve dependencies and conflicts. The solver must handle options, upgrades, multiple repos, locally installed software, as well as other factors. The upcoming 1.3 release of pkg will have the new solver that has some important consequences.
This talk is dedicated to the design concepts of the new solver in pkg management system (pkg-ng initially). In this talk, I describe the basic architecture of the solver, ideas used and the consequences of using this algorithm. Moreover, this talk describes the proposed pkg and ports architecture to simplify binary packages and ports using for all FreeBSD users.
These slides are from ruBSD conf held in Moscow, Russia on 14 Dec 2013. In my presentation, I've described the new SAT solver I've developed for FreeBSD package management system and the important consequences of this change.
Shows some advanced REXX techniques to make your programs more efficient and more readable for easier debugging. Also describes some tips for creating file and program structures not discussed in a typical REXX class.
Regular Expressions: JavaScript And BeyondMax Shirshin
Regular Expressions is a powerful tool for text and data processing. What kind of support do browsers provide for that? What are those little misconceptions that prevent people from using RE effectively?
The talk gives an overview of the regular expression syntax and typical usage examples.
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
Expression, Index Expression, Reshape Elements (reshape), Is Element an Index (isindex) Arithmetic Operators Addition & Subtraction, Multiplication, Division Power, Unary Operations Left Division (ldivide) Matrix Left Division (mldivide), Subtraction (minus), Matrix Power (mpower), Matrix Right Division (mrdivide) Recursive Product (mtimes), Element-wise Recursive Product (times) Element-wise Right Division (rdivide), Addition of Elements (plus), Power (power) Unary Subtraction (uminus), Unary Addition (uplus), Comparison Operator Equals (eq) Greater Than or Equal (ge), Greater Than (gt) Is Arguments are Equal (isequal) Less Than or Equal (le), Less Than (lt) Not Equals (ne), Evaluation, Arithmetic, Absolute Value (abs), Ceiling (ceil), Truncate Fraction (fix) Geometry, Cartesian to Polar Conversion (cartpol), Polar to Cartesian Conversion (polcart), Spherical to Cartesian Conversion (sphcart), Cartesian to Spherical Conversion (cartsph), Logarithm, Natural Logarithm (log), Logarithm Base Ten (log), Unit Increment Logarithm (logp), Binary Base Logarithm (log) Exponential Base (e), Matrix, Transpose of Matrix (transpose) Complex Conjugate Transpose of Matrix (ctranspose) Dot Product (dot) Cross Product (cross) Determinant (det) Identity Matrix (eye), Eigenvalues (eig), Eigens (eigs), Inverse of Matrix (inv) Linear Equation Solver (linsolve) Type of Matrix (matrix type) Normalized Matrix (norm), Null Space Matrix (null), Orthogonal Basis (orth), Rank of Matrix (rank) Trace of Matrix (trace), Cholesky Matrix (chol), Inverse Cholesky Matrix (cholinv), Matrix Exponential (expm), Logarithmic Matrix (logm), Square Root of Matrix (sqrtm), Kronecker Product (kron) Diagonal Matrix (diag), Single Value Decomposition (svd) Lower Upper Decomposition (lu) Lower Upper Composition (qr), Length of Matrix (length) Special Functions, Bessel Function of First Kind (besselj), Bessel Function of Second Kind (bessely), Hyperbolic Bessel Function of First Kind (besseli) Hyperbolic Bessel Function of Second Kind (besselk), Bessel Function as Hankel Function (besselh), Beta Function (beta), Gamma Function (gamma), Error Function (erf), Complementary Error Function (erfc), Inverse Error Function (erfinv), Legendre Function (legendre) Differentiation Derivative (diff), Linear ODE Solver (lsode) Options For Linear ODE Solver (lsode options) Differential Algebraic System Solver (dassl) Differential Algebraic System Solver Options (dassl options) Differential Algebraic Equations (daspk) Differential Algebraic Equations Options (daspk oot Solver Options (dasrt options) Integration Quadratic Integration (quad) Vectorized Quadratic Integration (quadv), Quadratic Lobatto’s Integration (quadl) Quadratic Gauss-Kronrod Integration (quadgk) Quadratic Clenshaw-Curtis Integration
JavaScript - An Introduction is a beginner's guide to JavaScript. It starts with very basic level and goes to intermediate level. You'll be introduced with every language constructs, Event handling, Form handling and AJAX which is supported by JavaScript with XMLHttpRequest object. This XHR object is discussed in enough detail so that you can understand how the underlying AJAX functionality works in jQuery. At the end it discusses advance concepts and library build on/around JavaScript.
Perl, a cross-platform, open-source computer programming language used widely in the commercial and private computing sectors. Perl is a favourite among Web developers for its flexible, continually evolving text-processing and problem-solving capabilities.
This is the seventh set of slightly updated slides from a Perl programming course that I held some years ago.
I want to share it with everyone looking for intransitive Perl-knowledge.
A table of content for all presentations can be found at i-can.eu.
The source code for the examples and the presentations in ODP format are on https://github.com/kberov/PerlProgrammingCourse
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
2. WHAT ARE SYMBOLS AND RULES
DEFINITIONS
RULE SYMBOL
SCORE
GROUP
DESCRIPTION
WEIGHT
*
true/false
OPTIONS
Dynamic part
Static part
∑ Results
FLAGS
3. WHAT ARE SYMBOLS AND RULES
WHY DO WE NEED SYMBOLS
RULE
SYMBOL_ALLOW
SYMBOL_DENY
SYMBOL_UNKNOWN
Either of symbols
4. WHAT ARE SYMBOLS AND RULES
WHY DO WE NEED SYMBOLS
RULE
MAP1
MAP2
MAP3
Multiple symbols
5. WHAT ARE SYMBOLS AND RULES
WHY DO WE NEED SYMBOLS
RULE1 SYMBOL1
RULE2
Dependency
6. WHAT ARE SYMBOLS AND RULES
RULES
▸ Rules define what is executed:
▸ Regexps expression
▸ Lua code
▸ Plugin logic
▸ Each rule can be associated with one or many symbols
▸ Rule can depend on other rules identified by associated symbols
▸ Each rule can define the current dynamic weight (usually from 0 to 1)
7. WHAT ARE SYMBOLS AND RULES
SYMBOLS
▸ Symbols define meta-information of a rule:
▸ Name
▸ Static score
▸ Other data (description, group, flags, etc)
▸ Symbols can be:
▸ Normal: associated with exactly one rule
▸ Virtual: are not associated with rules but grouped with normal symbol)
▸ Callback: do not have name or score, just define common rule
▸ Special: have special purpose (e.g. composite symbols)
9. SYMBOLS
SYMBOLS GROUPS
▸ Groups join common symbols logically
▸ Groups can set joint limit for symbols scores enclosed
▸ Groups can be used in composite rules:
▸ SYMBOL5 && G:GROUP1
▸ SYMBOL5 && (G:GROUP1 || !G:GROUP2)
10. RULES
EXPRESSIONS IN RULES
▸ Expressions are used in:
▸ Regexp rules
▸ Composite symbols
▸ Expressions have common syntax:
▸ Logic operations: AND (&&), OR (||), NOT (!)
▸ Braces
▸ Limit operation: A + B + C > 2
▸ Elements are called atoms
11. RULES
REGEXP EXPRESSIONS
▸ Atoms are regular expressions (/re/flags):
▸ Header: Header=/re/H
▸ Mime (/P): scan text parts
▸ Body (/B): scan full undecoded body
▸ URL (/U): scan URLs found
▸ There is no order of regexps execution within an expression
▸ Same expressions are cached and executed once
14. COMPOSITE EXPRESSIONS
COMPOSITES STRUCTURE
▸ Composite atoms can include:
▸ Other symbols
▸ Groups (gr:)
▸ Other composites (with recursive references check)
▸ Composite operations can be the following:
▸ Remove symbol and weight (SYMBOL)
▸ Remove weight only (~SYMBOL)
▸ Remove symbol but preserve weight (-SYMBOL)
▸ Always remove symbol and weight (^SYMBOL)
15. COMPOSITE EXPRESSIONS
COMPOSITES OPERATION
▸ If any composite proposes that a symbol should NOT be
removed, then it is NOT removed:
▸ A & ~B and C & B: B will NOT be removed because of the
first rule, but its weight will be removed
▸ A & -B and C & ~B: neither weight, nor symbol B will be
removed
▸ Removal could be forced by “^” symbol:
▸ A & ^B and C & -B: weight and symbol B are both removed
16. PRACTICAL EXAMPLES
A SIMPLE REGEXP EXPRESSION
local reconf = config['regexp'] -- Define alias for regexp module
-- Define a single regexp rule
reconf['PRECEDENCE_BULK'] = {
-- Header regexp that detects bulk email
re = 'Precedence=/bulk/Hi',
-- Default score
score = 0.1,
description = "Message marked as bulk",
group = 'upstream_spam_filters'
}
rspamd.local.lua:
17. PRACTICAL EXAMPLES
A MORE COMPLEX EXAMPLE
rspamd.local.lua:
local reconf = config['regexp'] -- Define alias for regexp module
-- Define encodings types
-- /X is undecoded header
local subject_encoded_b64 = 'Subject=/=?S+?B?/iX'
local subject_encoded_qp = 'Subject=/=?S+?Q?/iX'
-- Define whether subject must be encoded (contains non-7bit characters)
local subject_needs_mime = 'Subject=/[x00-x08x0bx0cx0e-x1fx7f-xff]/X'
-- Final rule
reconf['SUBJECT_NEEDS_ENCODING'] = {
-- Combine regexps
re = string.format('!(%s) & !(%s) & (%s)', subject_encoded_b64,
subject_encoded_qp, subject_needs_mime),
score = 3.5,
description = "Subject contains non-ASCII chars but it is not encoded",
group = 'headers'
}
18. PRACTICAL EXAMPLES
A MORE COMPLEX EXAMPLE
rspamd.local.lua:
local reconf = config['regexp'] -- Define alias for regexp module
-- Define encodings types
-- /X is undecoded header
local subject_encoded_b64 = 'Subject=/=?S+?B?/iX'
local subject_encoded_qp = 'Subject=/=?S+?Q?/iX'
-- Define whether subject must be encoded (contains non-7bit characters)
local subject_needs_mime = 'Subject=/[x00-x08x0bx0cx0e-x1fx7f-xff]/X'
-- Final rule
reconf['SUBJECT_NEEDS_ENCODING'] = {
-- Combine regexps
re = string.format('!(%s) & !(%s) & (%s)', subject_encoded_b64,
subject_encoded_qp, subject_needs_mime),
score = 3.5,
description = "Subject contains non-ASCII chars but it is not encoded",
group = 'headers'
}
19. PRACTICAL EXAMPLES
A MORE COMPLEX EXAMPLE
rspamd.local.lua:
local reconf = config['regexp'] -- Define alias for regexp module
-- Define encodings types
-- /X is undecoded header
local subject_encoded_b64 = 'Subject=/=?S+?B?/iX'
local subject_encoded_qp = 'Subject=/=?S+?Q?/iX'
-- Define whether subject must be encoded (contains non-7bit characters)
local subject_needs_mime = 'Subject=/[x00-x08x0bx0cx0e-x1fx7f-xff]/X'
-- Final rule
reconf['SUBJECT_NEEDS_ENCODING'] = {
-- Combine regexps
re = string.format('!(%s) & !(%s) & (%s)', subject_encoded_b64,
subject_encoded_qp, subject_needs_mime),
score = 3.5,
description = "Subject contains non-ASCII chars but it is not encoded",
group = 'headers'
}
20. PRACTICAL EXAMPLES
COMPOSITES EXAMPLE
local.d/composites.conf:
# Ignore forged recipients in case of mailing list
composite "FORGED_RECIPIENTS_MAILLIST" {
# MALLIST symbol is preserved
expression = "FORGED_RECIPIENTS & -MAILLIST";
}
# Ignore forged sender if a message has been forwarded
composite "FORGED_SENDER_FORWARDING" {
# Symbols from `forwarding` group are removed
expression = "FORGED_SENDER & g:forwarding";
}
# Ignore forged sender if a message has been from the mailing list
composite "FORGED_SENDER_MAILLIST" {
# Symbol 'FORGED_SENDER' is forced to be removed
expression = "^FORGED_SENDER & -MAILLIST";
}
21. PRACTICAL EXAMPLES
COMPOSITES EXAMPLE
local.d/composites.conf:
# Ignore forged recipients in case of mailing list
composite "FORGED_RECIPIENTS_MAILLIST" {
# MALLIST symbol is preserved
expression = "FORGED_RECIPIENTS & -MAILLIST";
}
# Ignore forged sender if a message has been forwarded
composite "FORGED_SENDER_FORWARDING" {
# Symbols from `forwarding` group are removed
expression = "FORGED_SENDER & g:forwarding";
}
# Ignore forged sender if a message has been from the mailing list
composite "FORGED_SENDER_MAILLIST" {
# Symbol 'FORGED_SENDER' is forced to be removed
expression = "^FORGED_SENDER & -MAILLIST";
}
22. PRACTICAL EXAMPLES
COMPOSITES EXAMPLE
local.d/composites.conf:
# Ignore forged recipients in case of mailing list
composite "FORGED_RECIPIENTS_MAILLIST" {
# MALLIST symbol is preserved
expression = "FORGED_RECIPIENTS & -MAILLIST";
}
# Ignore forged sender if a message has been forwarded
composite "FORGED_SENDER_FORWARDING" {
# Symbols from `forwarding` group are removed
expression = "FORGED_SENDER & g:forwarding";
}
# Ignore forged sender if a message has been from the mailing list
composite "FORGED_SENDER_MAILLIST" {
# Symbol 'FORGED_SENDER' is forced to be removed
expression = "^FORGED_SENDER & -MAILLIST";
}
23. PRACTICAL EXAMPLES
COMPOSITES EXAMPLE
local.d/composites.conf:
# Ignore forged recipients in case of mailing list
composite "FORGED_RECIPIENTS_MAILLIST" {
# MALLIST symbol is preserved
expression = "FORGED_RECIPIENTS & -MAILLIST";
}
# Ignore forged sender if a message has been forwarded
composite "FORGED_SENDER_FORWARDING" {
# Symbols from `forwarding` group are removed
expression = "FORGED_SENDER & g:forwarding";
}
# Ignore forged sender if a message has been from the mailing list
composite "FORGED_SENDER_MAILLIST" {
# Symbol 'FORGED_SENDER' is forced to be removed
expression = "^FORGED_SENDER & -MAILLIST";
}
24. PRACTICAL EXAMPLES
COMPOSITES EXAMPLE
local.d/composites.conf:
# Ignore forged recipients in case of mailing list
composite "FORGED_RECIPIENTS_MAILLIST" {
# MALLIST symbol is preserved
expression = "FORGED_RECIPIENTS & -MAILLIST";
}
# Ignore forged sender if a message has been forwarded
composite "FORGED_SENDER_FORWARDING" {
# Symbols from `forwarding` group are removed
expression = "FORGED_SENDER & g:forwarding";
}
# Ignore forged sender if a message has been from the mailing list
composite "FORGED_SENDER_MAILLIST" {
# Symbol 'FORGED_SENDER' is forced to be removed
expression = "^FORGED_SENDER & -MAILLIST";
}