Tomasz Fajks gives short intro about Security Tests as well as guide how to start. He goes through comparison of two security scanners Burp Suite and OWASP Zed Attack Proxy (ZAP), trying to answer "which one is better".
Dokumen ini membahas perangkat keras dan lunak yang umum digunakan untuk keamanan jaringan seperti firewall, MRTG, proxy, dan antivirus. Firewall digunakan untuk menganalisis dan memfilter paket, memblokir protokol tertentu, serta melakukan otentikasi dan enkripsi koneksi menggunakan metode seperti DES, Triple DES, SSL, IPSEC, dan MD5.
Rsa archer 6.9 platform installation and upgrade guide (3)AnkurGarg165647
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The document provides information about installing and configuring RSA Archer, including:
1. RSA Archer has several main components - a web application, instance database, file repository, configuration database, and services.
2. It recommends configurations for RSA Archer and describes the system requirements.
3. It provides instructions for installing RSA Archer components, upgrading RSA Archer, activating an installation or upgrade, and validating the RSA Archer system.
4. Appendices provide additional configuration options, test environment setup instructions, qualified/supported environments, checklists, and other supplementary information.
Slides for a college course at City College San Francisco. Based on "The Shellcoder's Handbook: Discovering and Exploiting Security Holes ", by Chris Anley, John Heasman, Felix Lindner, Gerardo Richarte; ASIN: B004P5O38Q.
Instructor: Sam Bowne
Class website: https://samsclass.info/127/127_S17.shtml
Intro to some diagram auto-generation tools. For more info and sample files, head over to http://www.mbarsinai.com/blog/2014/01/12/draw-more-work-less/.
Dokumen ini membahas perangkat keras dan lunak yang umum digunakan untuk keamanan jaringan seperti firewall, MRTG, proxy, dan antivirus. Firewall digunakan untuk menganalisis dan memfilter paket, memblokir protokol tertentu, serta melakukan otentikasi dan enkripsi koneksi menggunakan metode seperti DES, Triple DES, SSL, IPSEC, dan MD5.
Rsa archer 6.9 platform installation and upgrade guide (3)AnkurGarg165647
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The document provides information about installing and configuring RSA Archer, including:
1. RSA Archer has several main components - a web application, instance database, file repository, configuration database, and services.
2. It recommends configurations for RSA Archer and describes the system requirements.
3. It provides instructions for installing RSA Archer components, upgrading RSA Archer, activating an installation or upgrade, and validating the RSA Archer system.
4. Appendices provide additional configuration options, test environment setup instructions, qualified/supported environments, checklists, and other supplementary information.
Slides for a college course at City College San Francisco. Based on "The Shellcoder's Handbook: Discovering and Exploiting Security Holes ", by Chris Anley, John Heasman, Felix Lindner, Gerardo Richarte; ASIN: B004P5O38Q.
Instructor: Sam Bowne
Class website: https://samsclass.info/127/127_S17.shtml
Intro to some diagram auto-generation tools. For more info and sample files, head over to http://www.mbarsinai.com/blog/2014/01/12/draw-more-work-less/.
Sharing Sensitive Data With Confidence: The DataTags systemMichael Bar-Sinai
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This document describes the DataTags system for sharing sensitive data with confidence. DataTags assigns standardized levels of security and access requirements to data files stored in a repository. A DataTags repository must support multiple security levels, assign one level per file, and ensure recipients satisfy the level's requirements. The document outlines several data security levels and provides examples of how DataTags could integrate with the Harvard Dataverse repository to facilitate sensitive data sharing and privacy-preserving analysis while controlling access. It also discusses other potential uses of the DataTags approach.
This document discusses Lianjia, China's largest real estate brokerage agency, looking for British partners to cooperate with to help them develop their brands in China. It introduces Lianjia's background, operations and market share. It then outlines three cooperation models for potential partners, including establishing a joint office, appointing Lianjia as an exclusive or non-exclusive sales agency. Partners would benefit from Lianjia's network and resources to promote properties and solve issues entering the Chinese market. Interested partners should contact the provided Lian
Slick (part of the Typesafe stack) is a modern database query and access library for Scala, based on functional principles. It allows you to write queries as if you are working with regular Scala collections.
In this presentation weâll have a deep dive into how you can use this library in real projects. How to map your tables and queries to structured objects, how to create more advanced queries with multiple joins, how to setup integration tests against an in-memory database and how you can integrate Slick with the Play Framework are all questions which will have been answered at the end of this presentation.
Originally presented on the BeScala user group.
Slick: Bringing Scalaâs Powerful Features to Your Database Access Rebecca Grenier
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This talk will teach you how to use Slick in practice, based on our experience at EatingWell Media Group. Slick is a totally different (and better!) relational database mapping tool that brings Scalaâs powerful features to your database interactions, namely: static-checking, compile-time safety, and compositionality.
Here at EatingWell, we have learned quite a bit about Slick over the past two years as we transitioned from a PHP website to Scala. I will share with you tips and tricks we have learned, as well as everything you need to get started using Slick in your Scala application.
I will begin with Slick fundamentals: how to get started making your connection, the types of databases it can access, how to actually create table objects and make queries to and from them. We will using these fundamentals to demonstrate the powerful features inherited from the Scala language itself: static-checking, compile-time safety, and compositionality. And throughout I will share plenty of tips that will help you in everything from getting started to connection pooling options and configuration for use at scale.
Patterns for slick database applicationsSkills Matter
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Slick is Typesafe's open source database access library for Scala. It features a collection-style API, compact syntax, type-safe, compositional queries and explicit execution control. Community feedback helped us to identify common problems developers are facing when writing Slick applications. This talk suggests particular solutions to these problems. We will be looking at reducing boiler-plate, re-using code between queries, efficiently modeling object references and more.
Reactive Streams 1.0.0 is now live, and so are our implementations in Akka Streams 1.0 and Slick 3.0.
Reactive Streams is an engineering collaboration between heavy hitters in the area of streaming data on the JVM. With the Reactive Streams Special Interest Group, we set out to standardize a common ground for achieving statically-typed, high-performance, low latency, asynchronous streams of data with built-in non-blocking back pressureâwith the goal of creating a vibrant ecosystem of interoperating implementations, and with a vision of one day making it into a future version of Java.
Akka (recent winner of âMost Innovative Open Source Tech in 2015â) is a toolkit for building message-driven applications. With Akka Streams 1.0, Akka has incorporated a graphical DSL for composing data streams, an execution model that decouples the streamâs staged computationâitâs âblueprintââfrom its execution (allowing for actor-based, single-threaded and fully distributed and clustered execution), type safe stream composition, an implementation of the Reactive Streaming specification that enables back-pressure, and more than 20 predefined stream âprocessing stagesâ that provide common streaming transformations that developers can tap into (for splitting streams, transforming streams, merging streams, and more).
Slickâ is a relational database query and access library for Scala that enables loose-coupling, minimal configuration requirements and abstraction of the complexities of connecting with relational databases. With Slick 3.0, Slick now supports the Reactive Streams API for providing asynchronous stream processing with non-blocking back-pressure. Slick 3.0 also allows elegant mapping across multiple data types, static verification and type inference for embedded SQL statements, compile-time error discovery, and JDBC support for interoperability with all existing drivers.
The document discusses Reactive Slick, a new version of the Slick database access library for Scala that provides reactive capabilities. It allows parallel database execution and streaming of large query results using Reactive Streams. Reactive Slick is suitable for composite database tasks, combining async tasks, and processing large datasets through reactive streams.
This document discusses asynchronous I/O in Java and Scala using the Play Framework. It describes how LinkedIn uses a service-oriented architecture with hundreds of services making requests to each other. It then covers how Play supports non-blocking I/O using asynchronous code, promises, and futures to allow parallel requests without blocking threads. Key points covered include using map and flatMap to transform promises and futures, handling errors and timeouts, and the benefits of non-blocking I/O for scalability.
the evolution of data infrastructure at lianjiaæŻ ć
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Yi Lyu is the platform architect and leader of the data infrastructure team at Lianjia.com. Previously, he was a senior engineer at Baidu and an engineer at Sina. As platform architect, his mission is to build a full value chain real estate service platform that promotes industry progress and makes housing transactions easier by sharing information. He focuses on developing Lianjia.com's data assets and infrastructure, which includes housing, customer, behavioral, and transportation data. The infrastructure provides data analysis, querying, APIs, engines, ETL, management, security, and computing capabilities to support the company's products and data-driven work.
A Recovering Java Developer Learns to GoMatt Stine
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As presented at OSCON 2014.
The Go programming language has emerged as a favorite tool of DevOps and cloud practitioners alike. In many ways, Go is more famous for what it doesnât include than what it does, and co-author Rob Pike has said that Go represents a âless is moreâ approach to language design.
The Cloud Foundry engineering teams have steadily increased their use of Go for building components, starting with the Router, and progressing through Loggregator, the CLI, and more recently the Health Manager. As a ârecovering-Java-developer-turned-DevOps-junkieâ focused on helping our customers and community succeed with Cloud Foundry, it became very clear to me that I needed to add Go to my knowledge portfolio.
This talk will introduce Go and its distinctives to Java developers looking to add Go to their toolkits. Weâll cover Go vs. Java in terms of:
* type systems
* modularity
* programming idioms
* object-oriented constructs
* concurrency
SQLAlchemy is an object-relational mapper (ORM) for Python. It provides patterns for mapping database tables to objects and vice versa. The document discusses several ORM patterns from the book Patterns of Enterprise Application Architecture including:
- Unit of Work pattern which maintains a set of objects to save in a transaction. SQLAlchemy implements this with the Session object.
- Identity Map pattern which avoids duplicate objects for the same database record. SQLAlchemy implements this with its identity map.
- Lazy Load pattern which loads relationships and columns on demand to improve performance. SQLAlchemy uses lazy loading by default.
- Other patterns discussed include foreign key mapping, association table mapping, single/class table inheritance, and how different architectural patterns
A presentation to introduce the Lobos project made at the Bonjure group meeing on 2011/01/21. For more information on Lobos, visit the website: http://budu.github.com/lobos/
This document discusses refactoring Java code to Clojure using macros. It provides examples of refactoring Java code that uses method chaining to equivalent Clojure code using the threading macros (->> and -<>). It also discusses other Clojure features like type hints, the doto macro, and polyglot projects using Leiningen.
This document discusses scaling R to large datasets using Scala and Akka. It describes how R has limitations for parallelism and handling large data in memory. The author demonstrates reading a large CSV file of 100 million doubles (1.7GB) in parallel using Scala, Akka actors and Rserve. Producer and Worker actors divide the file and sum parts in Rserve. This allows scaling R computations to large data beyond a single machine's memory. Potential applications mentioned include optimization, distributed linear algebra, machine learning and statistics.
These are the outline slides that I used for the Pune Clojure Course.
The slides may not be much useful standalone, but I have uploaded them for reference.
These are the slides for the seminar to have a basic overview on the GO Language, By Alessandro Sanino.
They were used on a Lesson in University of Turin (Computer Science Department) 11-06-2018
Emerging Languages: A Tour of the HorizonAlex Payne
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A tour of a number of new programming languages, organized by the job they're best suited for. Presented at Philadelphia Emerging Technology for the Enterprise 2012.
The document discusses creating an optimized algorithm in R. It covers writing functions and algorithms in R, creating R packages, and optimizing code performance using parallel computing and high performance computing. Key steps include reviewing existing algorithms, identifying gaps, testing and iterating a new algorithm, publishing the work, and making the algorithm available to others through an R package.
JavaScript is evolving with the addition of modules, platform consistency, and harmony features. Modules allow JavaScript code to be organized and avoid naming collisions. CommonJS and AMD module formats are used widely. Platform consistency is improved through polyfills that mimic future APIs for older browsers. Harmony brings language-level modules and features like destructuring assignment, default parameters, and promises to JavaScript. Traceur compiles Harmony code to existing JavaScript.
Clojure and Swing â a new productivity sweet spot? discusses how Clojure, a Lisp dialect that runs on the JVM, can be used to develop graphical user interfaces (GUIs) using Java Swing in a more productive way than plain Java. It presents several advantages of Clojure for Swing development, such as reducing boilerplate code, easier definition of actions and event bindings, and increased flexibility and reusability through functional programming techniques. Examples are provided of common Swing programming tasks implemented more concisely and readably in Clojure compared to Java. The document concludes that Clojure is a powerful and flexible language that offers opportunities to apply Lisp concepts to Swing GUI development
Juan Cruz Basso shares the real secrets of Dataweave programming using Mule, Visual Studio Code, Dataweave Playground, and the Dataweave CLI.
Video online: https://youtu.be/05Z-tJuvvVQ
This is the slide for what I shared in JS Group meetup, 2014, Taiwan. It covers what JavaScript could do for making the program more "functional", the benefits, price and the limitation.
Scala is an alternative JVM language with both object-oriented and functional programming paradigms. Scala development with the Java EE 7 platform is definitely possible and can be a pleasant experience. If you have uncertainty about how Scala can fit around the Java EE 7 platform, then this session aims to illustrate the huge benefit that Scala adoption can bring to the platform. Many other developers are taking advantage and the challenge of the JVMâs capability of being a vessel for multi-language programming. You no longer have to write every single project using Java, even if you like Lambdas experiences. For the developer and engineering terms that feeling a little braver than usual, Scala is attractive as it is strongly typed and lets you set the gauge on how object oriented or how functional you want to be. You will learn how to reuse the annotations and creating Scala plain object safely and concisely. This session will highlight and contrast the experience I had developing Scala solutions with Java EE, and there will be plenty of advice about using the functional programming features against the Java object oriented API.
Scala language overview
Java EE 7 architecture and design
WildFly 8 application server
Using Gradle as a build tool
How to create beans in Scala with dependency injection
JAX-RS endpoints
Servlet Endpoints
JMS Messaging
Scala adoption advice and hints for sustainable team development
JavaCro 2014 Scala and Java EE 7 Development ExperiencesPeter Pilgrim
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Scala is an alternative JVM language with both object-oriented and functional programming paradigms. Scala development with the Java EE 7 platform is definitely possible and can be a pleasant experience. If you have uncertainty about how Scala can fit around the Java EE 7 platform, then this session aims to illustrate the huge benefit that Scala adoption can bring to the platform. Many other developers are taking advantage and the challenge of the JVMâs capability of being a vessel for multi-language programming. You no longer have to write every single project using Java, even if you like Lambdas experiences.
For the developer and engineering terms that feeling a little braver than usual, Scala is attractive as it is strongly typed and lets you set the gauge on how object oriented or how functional you want to be. You will learn how to reuse the annotations and creating Scala plain object safely and concisely.
This session will highlight and contrast the experience I had developing Scala solutions with Java EE, and there will be plenty of advice about using the functional programming features against the Java object oriented API.
Scala language overview
Java EE 7 architecture and design
Using Gradle as a build tool
How to create beans in Scala with dependency injection
JAX-RS endpoints
Servlet Endpoints
JMS Messaging
Scala adoption advice and hints for sustainable team development
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. It is written in Java and uses a pluggable backend. Presto is fast due to code generation and runtime compilation techniques. It provides a library and framework for building distributed services and fast Java collections. Plugins allow Presto to connect to different data sources like Hive, Cassandra, MongoDB and more.
PostgreSQL is an open source object-relational database system that has been in development since 1982. It supports Linux, Windows, Mac OS X, and Solaris and can be installed using package managers or installers. PostgreSQL provides many features including procedural languages, functions, indexes, triggers, multi-version concurrency control, and point-in-time recovery. It also has various administration and development tools.
This document introduces ClojureScript and building applications with it. It discusses how ClojureScript compiles Clojure to JavaScript and can run anywhere JavaScript runs. It covers the basics of the ClojureScript language like syntax, data structures, and functions. It also discusses tools for ClojureScript development like Leiningen, Figwheel, Shadow CLJS, and Cursive. Additionally, it covers building web applications with ClojureScript using templates like Hiccup and libraries like Reagent and Reframe.
This presentation is an introduction to Dotty / Scala 3.
It covers the features which I deem most important for Scala developers.
For detailed information see the [Dotty documentation](https://dotty.epfl.ch/docs/index.html).
This presentation summarizes new features in Scala 2.13, including improvements to the collections library, Future, standard library, language, and compiler. Key changes include overhauling collections for simplicity, performance and safety, making Future faster and more robust, adding new classes and methods to the standard library, and making the compiler 5-10% faster with deterministic output. The redesigned collections library is highlighted as a major focus of the release.
This presentation gives a pragmatic introduction to SBT - the Scala Build Tool - in 7 examples. Each example is a build in it's own root directory: ./example?? Beginning with very simple sbt examples the later examples are becoming more structured and more complex showing multi-project builds, cross version builds, packaging and publishing, custom Settings and Tasks and the integration of the Ammonite REPL into your build. We also look at InputTasks, Commands and plugin development.
With my simple implementation I wanted to demonstrate the basic ideas of th IO Monad.
My impl of the IO Monad is just a feasibility study, not production code!
When coding my impl of IO I was very much inspired by cats.effect.IO and monix.eval.Task which I studied at that time. Both are implementions of the IO Monad.
The API of my IO is very similar to the basics of Monix Task. This IO implementation also helped me to understand the IO Monad (of cats-effect) and Monix Task.
Interop with Future is also supported. You can convert IO to a Future. Vice versa you can convert a Future to an IO.
The development of my impl can be followed step by step in the code files in package iomonad.
scala.concurrent.Future is familiar to nearly all Scala devs.
This presentation first talks about referential transparency and the IO Monad in general. (Monix Task is an impl of the IO Monad.)
Then it compares Future Monix 3.x Task with their Pros and Cons.
Interop with Future: As Scala's Future is used in many environments and libraries, we look at the conversion from Task to Future and - vice versa - from Future to Task.
I will also take a look at Task evaluation, cancelation and memoization as well as tail recursive loops and asynchronous boundaries.
The presentation will include a comparative discussion on ExecutionContext (required for Future) and Scheduler (required for Task, but only to run it).
Often recurring on the valuable Monix Task doumentation at https://monix.io/docs/3x/eval/task.html the presentation can also be seen as an introduction to Monix Task.
Most Scala developers are familiar with monadic precessing. Monads provide flatMap and hence for-comprehensions (syntactic sugar for map and flatMap).
Often we don't need Monads. Applicatives are sufficient in many cases.
In this talk I examine the differences between monadic and applicative processing and give some guide lines when to use which.
After a closer look to the Applicative trait I will contrast the gist of Either and cats.data.Validated (the latter being an Applicative but not a Monad).
I will also look at traversing and sequencing which harness Applicatives as well.
Different Ways of Function Composition in Scala:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Fine-grained composability of functions is one of the core advantages of FP.
Treating "Functions as Data" means that we can store, manipulate, pass functions around and compose them in much the same way we do with data.
This talk demonstrates different ways of function composition in Scala.
The focus lies on scala.Function1, because due to tupling and currying we can regard any FunctionN (except Function0) as a Function1. Curried functions are easier to compose.
Starting with the composition methods of scala.Function1: apply, compose and andThen, we will investigate folding a Seq of functions.
We can also define a pipe operator |> as in F# in order to 'pipe' values through a pipeline of functions.
Defining a Monoid for Function1 allows us to combine two or more functions into a new one.
A function can also be seen as a Functor and a Monad. That means: Functions can be mapped and flatMapped over. And we can write for-comprehensions in a Function1 context just as we do with List, Option, Future, Either etc.
Being Monads, we can use functions in any monadic context. We will see that Function1 is the Reader Monad.
The most powerful way of function composition is Kleisli (also known as ReaderT). We will see that Kleisli (defined with the Id context) is the Reader Monad again.
Composing an App with Free Monads (using Cats)Hermann Hueck
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In this talk I will explain what Free Monads are and how to use them (using the Cats implementation).
After having shown the basics I build a small app by composing several
Free Monads to a small program.
I discuss the pros and cons of this technique.
Finally I will demonstrate how to avoid some boilerplate with the FreeK library.
From Functor Composition to Monad TransformersHermann Hueck
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In a List[Option[A]] or Future[Option[A]] we want to access the value of type A conveniently without nested mapping and flatMapping.
We can avoid nested mapping with composed Functors. Functors compose, but Monads do not! What can we do?
Monad transformers to the rescue!
After going through Functor composition I show how 2 Monads are bolted together with a Monad transformer and how to use this construct. I demonstrate this with the Option transformer OptionT and will end up with best practices.
This document discusses type classes in Scala and Haskell. It provides a recap of implicits in Scala, including implicit parameters, conversions, and views. It then introduces type class concepts and examples from the Scala standard library like Ordering and Numeric. It explains how to define a Printable type class and instances for types like Int and Cat. It discusses improved designs using companion objects and implicit classes. Finally, it covers where to define type class instances, such as for standard types in the companion object and domain types in their package.
Adobe After Effects Crack FREE FRESH version 2025kashifyounis067
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Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
.
After Effects is powerful for creating animated titles, transitions, and other visual elements to enhance the look of videos and presentations.
Visual Effects:
.
It's used extensively in film and television for creating special effects like green screen compositing, object manipulation, and other visual enhancements.
Video Compositing:
.
After Effects allows users to combine multiple video clips, images, and graphics to create a final, cohesive visual.
Animation:
.
It uses keyframes to create smooth, animated sequences, allowing for precise control over the movement and appearance of objects.
Integration with Adobe Creative Cloud:
.
After Effects is part of the Adobe Creative Cloud, a suite of software that includes other popular applications like Photoshop and Premiere Pro.
Post-Production Tool:
.
After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi
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A secure test infrastructure ensures that the testing process doesnât become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
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As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and contextâoften triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup
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In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoftâs AI assistant, Copilot, to achieve their goals.
This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft.
What Youâll Learn in Part 2:
Explore real-world nonprofit use cases and success stories.
Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!
Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot
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Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
đđ±đCOPY LINK & PASTE ON GOOGLE http://drfiles.net/ đđ
Adobe Illustrator is a powerful, professional-grade vector graphics software used for creating a wide range of designs, including logos, icons, illustrations, and more. Unlike raster graphics (like photos), which are made of pixels, vector graphics in Illustrator are defined by mathematical equations, allowing them to be scaled up or down infinitely without losing quality.
Here's a more detailed explanation:
Key Features and Capabilities:
Vector-Based Design:
Illustrator's foundation is its use of vector graphics, meaning designs are created using paths, lines, shapes, and curves defined mathematically.
Scalability:
This vector-based approach allows for designs to be resized without any loss of resolution or quality, making it suitable for various print and digital applications.
Design Creation:
Illustrator is used for a wide variety of design purposes, including:
Logos and Brand Identity: Creating logos, icons, and other brand assets.
Illustrations: Designing detailed illustrations for books, magazines, web pages, and more.
Marketing Materials: Creating posters, flyers, banners, and other marketing visuals.
Web Design: Designing web graphics, including icons, buttons, and layouts.
Text Handling:
Illustrator offers sophisticated typography tools for manipulating and designing text within your graphics.
Brushes and Effects:
It provides a range of brushes and effects for adding artistic touches and visual styles to your designs.
Integration with Other Adobe Software:
Illustrator integrates seamlessly with other Adobe Creative Cloud apps like Photoshop, InDesign, and Dreamweaver, facilitating a smooth workflow.
Why Use Illustrator?
Professional-Grade Features:
Illustrator offers a comprehensive set of tools and features for professional design work.
Versatility:
It can be used for a wide range of design tasks and applications, making it a versatile tool for designers.
Industry Standard:
Illustrator is a widely used and recognized software in the graphic design industry.
Creative Freedom:
It empowers designers to create detailed, high-quality graphics with a high degree of control and precision.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev
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If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most canât. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and canât handle full dataset retrieval.
GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.
Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185
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GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developersâ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
https://arxiv.org/abs/2501.16495
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik
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This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 usersâwhile meeting strict HIPAA compliance requirements.
Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldnât keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows.
We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testimâs self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure.
The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issueâunlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.
Why Orangescrum Is a Game Changer for Construction Companies in 2025Orangescrum
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Orangescrum revolutionizes construction project management in 2025 with real-time collaboration, resource planning, task tracking, and workflow automation, boosting efficiency, transparency, and on-time project delivery.
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Implementing a many-to-many Relationship with Slick
1. Implementing a many-to-many
Relationship with Slick
Implementing a many-to-many
Relationship with Slick
Author: Hermann Hueck
Source code and Slides available at:
https://github.com/hermannhueck/MusicService/tree/master/Services/MusicService-Play-Scala-Slick-NoAuth
http://de.slideshare.net/hermannhueck/implementing-a-manytomany-relationship-with-slick
2. Who am I?Who am I?
Hermann Hueck
Software Developer â Scala, Java, Akka, Play
https://www.xing.com/profile/Hermann_Hueck
4. Part 1: Intro to SlickPart 1: Intro to Slick
â What is Slick?
â What is FRM (Functional Relational Mapping)?
â How to execute a database action?
â Why is it reactive?
â Short Reminder of Scala Futures
â Simple Slick Example (Activator Template: hello-slick-3.1)
â Table Definitions without and with Case Class Mapping
5. What is Slick?What is Slick?
The Slick manual says:
Slick (âScala Language-Integrated Connection Kitâ) is
Typesafeâs Functional Relational Mapping (FRM) library for
Scala that makes it easy to work with relational databases. It
allows you to work with stored data almost as if you were
using Scala collections while at the same time giving you full
control over when a database access happens and which data
is transferred. You can also use SQL directly. Execution of
database actions is done asynchronously, making Slick a
perfect fit for your reactive applications based on Play and Akka.
6. What is FRM?What is FRM?
â FRM = Functional Relational Mapping (opposed to ORM)
â Slick allows you to process persistent relational data stored in
DB tables the same (functional) way as you do with in-memory
data, i.e. Scala collections.
â Table Queries are monads.
â Table Queries are pre-optimized.
â Table Queries are type-safe.
â The Scala compiler complains, if you specify your table query
incorrectly.
7. TableQuery s are Monads.TableQuery s are Monads.
â filter() for the selection of data
â map() for the projection
â flatMap() to pass the output of your 1st DB operation as input to the
2nd DB operation.
â With the provision of these three monadical functions TableQuery s
are monads.
â Hence you can also use the syntactic sugar of for-comprehensions.
â There is more. E.g. the sortBy() function allows you to define the
sort order of your query result.
8. How to execute a Slick DB Action?How to execute a Slick DB Action?
â Define a TableQuery
val query: Query[âŠ] = TableQuery(âŠ)âŠ
â For this TableQuery, define a database action which might be a query,
insert, bulk insert, update or a delete action or even a DDL action.
val dbAction: DBIO[âŠ] = query += record // insert
val dbAction: DBIO[âŠ] = query ++= Seq(row0, row1, âŠ, rowN) // bulk insert
val dbAction: DBIO[âŠ] = query.update(valuesToUpdate) // update
val dbAction: DBIO[âŠ] = query.delete // delete
val dbAction: DBIO[âŠ] = query.result // insert
â Run the database action by calling db.run(dbAction). db.run never returns
the Result. You always get a Future[Result].
val dbAction: DBIO[âŠ] = TableQuery(âŠ).result
val future: Future[âŠ] = db.run(dbAction)
9. Why is Slick reactive?Why is Slick reactive?
â It is asynchronous and non-blocking.
â It provides its own configurable thread pool.
â If you run a database action you never get the Result directly. You
always get a Future[Result].
val dbAction: DBIOAction[Seq[String]] = TableQuery(âŠ).result
val future: Future[Seq[String]] = db.run( dbAction )
â Slick supports Reactive Streams. Hence it can easily be used together
with Akka-Streams (which is not subject of this talk).
val dbAction: StreamingDBIO[Seq[String]] = TableQuery(âŠ).result
val publisher: DatabasePublisher[String] = db.stream( dbAction )
val source: Source = Source.fromPublisher( publisher )
// Now use the Source to construct a RunnableGraph. Then run the graph.
10. Short Reminder of Scala FuturesShort Reminder of Scala Futures
In Slick every database access returns a Future.
How can one async (DB) function process the result of another async
(DB) function?
This scenario happens very often when querying and manipulating
database records.
A very informative and understandable blog on Futures can be found
here:
http://danielwestheide.com/blog/2013/01/09/the-neophytes-guide-to-scala
-part-8-welcome-to-the-future.html
11. How to process the Result of an Async
Function by another Async Function
How to process the Result of an Async
Function by another Async Function
Using Future.flatMap:
def doAAsync(input: String): Future[A] = Future { val a = f(input); a }
def doBAsync(a: A): Future[B] = Future { val b = g(a); b }
val input = âsome inputâ
val futureA: Future[A] = doAAsync(input)
val futureB: Future[B] = futureA flatMap { a => doBAsync(a) }
futureB.foreach { b => println(b) }
12. Async Function processing the
Result of another Async Function
Async Function processing the
Result of another Async Function
Using a for-comprehension:
def doAAsync(input: String): Future[A] = Future { val a = f(input); a }
def doBAsync(a: A): Future[B] = Future { val b = g(a); b }
val input = âsome inputâ
val futureB: Future[B] = for {
a <- doAAsync(input)
b <- doBAsync(a)
} yield b
futureB.foreach { b => println(b) }
13. A Simple Slick ExampleA Simple Slick Example
Activator Template: hello-slick-3.1
14. Table Definition with a TupleTable Definition with a Tuple
class Users(tag: Tag) extends Table[(String, Option[Int])](tag, "USERS") {
// Auto Increment the id primary key column
def id = column[Int]("ID", O.PrimaryKey, O.AutoInc)
// The name can't be null
def name = column[String]("NAME", O.NotNull)
// the * projection (e.g. select * ...)
def * = (name, id.?)
}
Tuple
15. Table Definition with Case Class
Mapping
Table Definition with Case Class
Mapping
case class User(name: String, id: Option[Int] = None)
class Users(tag: Tag) extends Table[User](tag, "USERS") {
// Auto Increment the id primary key column
def id = column[Int]("ID", O.PrimaryKey, O.AutoInc)
// The name can't be null
def name = column[String]("NAME", O.NotNull)
// the * projection (e.g. select * ...) auto-transforms the tupled
// column values to / from a User
def * = (name, id.?) <> (User.tupled, User.unapply)
}
Map Tuple to User Map User to Tuple
16. Part 2: Many-to-many with SlickPart 2: Many-to-many with Slick
â The Demo App: MusicService
â Many-to-many Relationship (DB Schema)
â Web Application Demo
â Many-to-many in Slick Table Definitions
â Ensuring Referential Integrity
â Adding and Deleting Relationships
â Traversing the many-to-many Relationship in Queries
17. The Demo App: MusicServiceThe Demo App: MusicService
â The MusicService manages Music Recordings
and Performers.
â A Recording is performedBy many (0 ⊠n)
Performers.
â A Performer is performingIn many (0 ⊠n)
Recordings.
18. Many-to-many in the DB SchemaMany-to-many in the DB Schema
ID NAME PERFORMER_TYPE
1 Arthur Rubinstein Soloist
2 London Phil. Orch. Ensemble
3 Herbert von Karajan Conductor
4 Christopher Park Soloist
...
PERFORMERS
ID TITLE COMPOSER YEAR
1 Beethoven's Symphony No. 5 Ludwig v. Beethoven 2005
2 Forellenquintett Franz Schubert 2006
3 Eine kleine Nachtmusik Wolfgang Amadeus Mozart 2005
4 EntfĂŒhrung aus dem Serail Wolfgang Amadeus Mozart 2008
...
RECORDINGS
REC_ID PER_ID
1 1
1 2
1 3
3 1
... ...
RECORDINGS_PERFORMERS
19. Web Application DemoWeb Application Demo
MusicService is a Play Application with a rather primitive
UI. This interface allows the user to âŠ
â Create, delete, update Performers and Recordings
â Assign Performers to Recordings or Recordings to
Performers and delete these Assignments
â Query / Search for Performers and Recordings
â Play Recordings
20. Many-to-many Relationship in the
Slick Table Definitions
Many-to-many Relationship in the
Slick Table Definitions
case class RecordingPerformer(recId: Long, perId: Long)
// Table 'RecordingsPerformers' mapped to case class 'RecordingPerformer' as join table to map
// the many-to-many relationship between Performers and Recordings
//
class RecordingsPerformers(tag: Tag)
extends Table[RecordingPerformer](tag, "RECORDINGS_PERFORMERS") {
def recId: Rep[Long] = column[Long]("REC_ID")
def perId: Rep[Long] = column[Long]("PER_ID")
def * = (recId, perId) <> (RecordingPerformer.tupled, RecordingPerformer.unapply)
def pk = primaryKey("primaryKey", (recId, perId))
def recFK = foreignKey("FK_RECORDINGS", recId, TableQuery[Recordings])(recording =>
recording.id, onDelete=ForeignKeyAction.Cascade)
def perFK = foreignKey("FK_PERFORMERS", perId, TableQuery[Performers])(performer =>
performer.id)
// onUpdate=ForeignKeyAction.Restrict is omitted as this is the default
}
21. Ensuring Referential IntegrityEnsuring Referential Integrity
â Referential Integrity is guaranteed by the definition of a foreignKey()
function in the referring table, which allows to navigate to the referred
table.
â You can optionally specify an onDelete action and an onUpdate
action, which has one of the following values:
î ForeignKeyAction.NoAction
î ForeignKeyAction.Restrict
î ForeignKeyAction.Cascade
î ForeignKeyAction.SetNull
î ForeignKeyAction.SetDefault
22. Adding and Deleting RelationshipsAdding and Deleting Relationships
â Adding a concrete relationship == Adding an
entry into the Mapping Table, if it doesn't
already exist.
â Deleting a concrete relationship == Deleting an
entry from the Mapping Table, if it exists.
â Updates in the Mapping Table do not make
much sense. Hence I do not support them im
my implementation.
23. Traversing many-to-many the
Relationship in Queries
Traversing many-to-many the
Relationship in Queries
â The Query.join() function allows you to perform an inner
join on tables.
â The Query.on() function allows you to perform an inner
join on tables.
â Example:
val query = TableQuery[Performers] join TableQuery[RecordingsPerformers] on (_.id === _.perId)
val future: Future[Seq[(Performer, RecordingPerformer)]] = db.run { query.result }
// Now postprocess this future with filter, map, flatMap etc.
// Especially filter the result for a specific recording id.