As presented at DevDuck #5 - JavaScript meetup for developers (www.devduck.pl)
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Read more about Heuristic algorithms & Swarm intelligence
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Looking for a company to build you an electron desktop app? www.brainhub.eu
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.
Taxonomy of Swarm Intelligence
Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria.
Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.
Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance.
The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants
In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Artificial/Scientific: Clustering by a Swarm of Robots
Several ant species cluster corpses to form cemeteries.
Artificial Intelligence Today (22 June 2017)Sabri Sansoy
This was a top level presentation on some of the 30+ subcategories of Artificial Intelligence at the Hackaday LA June Meetup - Wheels, Wings, and Walkers. Sponsored by SupplyFrame Design Labs in Pasadena CA
A brief Introduction to AI and its applications in Gaming. Talk was at "Advances & Research Challenges in the Applications of AI in Gaming, Medical Imaging and Bio-Informatics"
Inspired from social behavior of insects and other creatures, artificial intelligence community has learned a series of techniques commonly known as Swarm Intelligence. Here we give a 15 minute introduction to this area.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems.
Taxonomy of Swarm Intelligence
Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria.
Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.
Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance.
The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants
In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.
Artificial/Scientific: Clustering by a Swarm of Robots
Several ant species cluster corpses to form cemeteries.
Artificial Intelligence Today (22 June 2017)Sabri Sansoy
This was a top level presentation on some of the 30+ subcategories of Artificial Intelligence at the Hackaday LA June Meetup - Wheels, Wings, and Walkers. Sponsored by SupplyFrame Design Labs in Pasadena CA
A brief Introduction to AI and its applications in Gaming. Talk was at "Advances & Research Challenges in the Applications of AI in Gaming, Medical Imaging and Bio-Informatics"
Imagine there was an app that could translate our selfies into emojis!!! Well, let’s build this app together!
Join me in this talk where we have an overview of Artificial Intelligence and Machine Learning and step by step build our app with the help of Azure Cognitive Services.
masterclass de introducción a Inteligencia Artificial utilizando las APIs de Google elaborada a partir de la de Mario Ezquerro en GDG La Rioja.
Impartida dentro de las actividades de la Agenda Digital de La Rioja por AERTIC
Brains, Data, and Machine Intelligence (2014 04 14 London Meetup)Numenta
Jeff will discuss the Brains, Data, Machine Intelligence, Cortical Learning Algorithm he developed and the Numenta Platform for Intelligent Computing (NuPIC).
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2017-alliance-vitf-samek
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Wojciech Samek of the Fraunhofer Heinrich Hertz Institute delivers the presentation "Methods for Understanding How Deep Neural Networks Work" at the Embedded Vision Alliance's September 2017 Vision Industry and Technology Forum. In his presentation, Dr. Samek covers the following topics:
▪ Unbeatable AI systems
▪ Deep neural network overview
▪ Opening the "black box"
▪ Summary
Architectural Tradeoff in Learning-Based SoftwarePooyan Jamshidi
In classical software development, developers write explicit instructions in a programming language to hardcode the explicit behavior of software systems. By writing each line of code, the programmer instructs the software to have the desirable behavior by exploring a specific point in program space.
Recently, however, software systems are adding learning components that, instead of hardcoding an explicit behavior, learn a behavior through data. The learning-intensive software systems are written in terms of models and their parameters that need to be adjusted based on data. In learning-enabled systems, we specify some constraints on the behavior of a desirable program (e.g., a data set of input–output pairs of examples) and use the computational resources to search through the program space to find a program that satisfies the constraints. In neural networks, we restrict the search to a continuous subset of the program space.
This talk provides experimental evidence of making tradeoffs for deep neural network models, using the Deep Neural Network Architecture system as a case study. Concrete experimental results are presented; also featured are additional case studies in big data (Storm, Cassandra), data analytics (configurable boosting algorithms), and robotics applications.
A short talk at the iEvoBio (Informatics for Phylogenetics, Evolution, and Biodiversity) conference at the The University of Oklahoma, Embassy Suites Hotel and Conference Center, Norman, Oklahoma, USA. June 21-22, 2011.
Skynet? Really? How close are we to self aware, self replicating machines? In this fun session learn some of what computers can do and what they can’t. You think you know. You may be surprised.
The emerging focus on Cognitive computing, general AI, Computer Vision, Internet of Things, etc. signpost the way to new opportunities and new challenges for computers and humans alike. We decided to see how far we could get in building our own version of an all powerful controlling entity.
In this session we’ll cover how we did it, what we learned and answer those important questions like: “Can we build a Skynet yet?”, “Can my computer be my best friend?”, ”Will I ever able to program without a keyboard?”, ”Can a computer read my mind?” and the all important “will drones be able to deliver beer at the right temperature?”
Continuous Automated Testing - Cast conference workshop august 2014Noah Sussman
CAST 2014 New York: The Art and Science of Testing
The Association for Software Testing www.associationforsoftwaretesting.org
COURSE DESCRIPTION
Automated tools provide test professionals with the capability to make relevant observations even in the fastest-paced environments. Automated testing is also a powerful tool for improving communication between software engineers. This is important because good communication is a prerequisite for growing a great software engineering organization.
This workshop will explore the continuous testing of software systems. Special focus will be given to the situation where the engineering team is deploying code to production so frequently that it is not possible to perform deep regression testing before each release.
People who participate in this course will learn pragmatic automated testing strategies like:
* Data analysis on the command line with find, grep and wc.
* Network analysis with Chrome Inspector, Charles and netcat.
* Using code churn to predict hotspots where bugs may occur.
* Putting stack traces in context with automated SCM blame emails.
* Using statsd to instrument a whole application.
* Testing in production.
* Monitoring-as-testing.
Technical level: participants should have some familiarity with the command line and with editing code using a text editor or IDE. Familiarity with Git, SVN or another version control system is helpful but not required. Likewise some knowledge of Web servers is helpful but not required. It is desirable for participants to bring laptops.
BIO
From 2010 to 2012 Noah was a Test Architect at Etsy. He helped build Etsy's continuous integration system, and has helped countless other engineers develop successful automated testing strategies.These days Noah is an independent consultant in New York. He is passionate about helping engineers understand and use automated tools as they work to scale their applications more effectively.
Rynek cloud computingu rośnie z roku na rok. Głównymi graczami na rynku są Amazon Web Services (AWS), Microsoft Azure oraz Google Cloud Platform (GCP). Małe startupy, a także ogromne korporacje decydują się na migracje do chmury. Cloud Computing może dać ogromne możliwości rozwoju programistom i pomóc w zakresie skalowalności aplikacji i elastyczności rozwiązań. To także szereg serwisów które pozwalają przyśpieszyć wdrożenia, umożliwić automatyzacje procesów, a także - szybko zareagować na awarię.
Prelekcji odpowiada na kilka kluczowych pytań związanych z chmurą: dlaczego firmy decydują się na migracje i jak wygląda rynek chmur obliczeniowych? Do czego Formuła 1 wykorzystuje AWS? Jak rozpocząć własną przygodę z chmurą? Czy certyfikacje AWS w ogóle mają sens?
Konfiguracja GitLab CI/CD pipelines od podstawBrainhub
O prezentacji:
W trakcie prelekcji pokażę jak zaimplementować proces CI/CD dla aplikacji napisanej w JavaScript, używając GitLab CI/CD Pipelines. Będzie on zawierał kroki lint (statyczna analiza kodu), unit test, API test, Docker Build i UI end-to-end test. Pokażę też jak tworzyć, parsować i wyświetlać raporty z testów w GitLabie. Powiem też co nieco o używanych w procesie Dockerfile i docker-compose.
O prelegencie:
Przygodę z profesjonalnym IT rozpoczął ponad 10 lat temu, jako Manual Junior Tester. Od tego czasu stara się w pełni zrozumieć rolę QA w projekcie i wielopoziomowo pracować nad poprawą jakości projektu, produktu i pracy.
O prezentacji:
Chcąc uzyskać type safety w projekcie możemy zdecydować się na samodzielne tworzenie, utrzymywane oraz współdzielenie typów. Inną możliwością jest skorzystanie z gotowego rozwiązania (np. generatora typów), które stworzy typy za pomocą komendy. Obie te opcje wymagają jednak dodatkowego nakładu pracy. tRPC niweluje ten problem pozwalając na natychmiastową synchronizację zmian między backendem a frontendem.
Podczas prelekcji opowiem o obecnych możliwościach i ograniczeniach tRPC, a także kiedy warto z tego narzędzia skorzystać. Dodatkowo podczas live codingu pokażę jak szybko i wygodnie można stworzyć API za pomocą tRPC i frameworku Next.js.
O prelegencie:
Karierę w IT zaczęła niecałe 3 lata temu jako programistka React Native. Szybko jednak zaciekawił ją także web dev i backend, co rozpoczęło jej drogę jako programistka full-stack. Uwielbia śledzić i wykorzystywać w projektach nowinki ze świata JavaScriptu. Poza pracą spędza czas uprawiając przeróżne sporty - od treningu siłowego i roweru, poprzez jogę, aż po narty.
Solid.js - czy rzeczywiście został tak solidnie stworzony? Na najbliższym meetupie weźmiemy na warsztat prostą apkę napisaną w React i w Solid, omówimy różnice między nimi i spróbujemy zagłębić się w szczegóły. Odpowiemy sobie też na dwa pytania: czy Solid będzie w stanie zdetronizować Reacta mając JSX i observability? Czy warto było szaleć tak? Przekonamy się na DevDucku.
Struktury algebraiczne do programowania mają się tak, jak fizyka molekularna ma się do gotowania - można się bez nich obejść, ale to nie znaczy, że ich tam nie ma. Podczas najbliższego DevDucka przyjrzymy się kilku z nich i sprawdzimy, jak mogą się przydać do pisania czystego kodu i rozwiązywania problemów w praktyce.
WebAssembly - czy dzisiaj mi się to przyda do pracy?Brainhub
Rust, Go, AssemblyScript - wszystko co chcesz wiedzieć o WebAssembly, a o co boisz się zapytać. WebAssembly jest bardzo młodą technologią i jeszcze wiele pracy czeka programistów stojących za projektem. Benedykt opowiadał już na ten temat podczas dev.js Summit 2021, ale postanowił zgłębić niektóre wątki i uzupełnić o nowości ze świata WebAssembley.
We współpracy z Mateuszem Koniecznym opowiedzą o WASM i pokażą kilka przykładów podczas live-codingu.
Ewoluowanie neuronowych mózgów w JavaScript, wielowątkowo!Brainhub
JavaScript nie słynie z wydajności. Wielowątkowy on też za bardzo nie jest i zupełnie nie nadaje się ani do symulowania wirtualnego świata z ewoluującymi "organizmami", ani do liczenia sieci neuronowych. Cooooo? Nie nadaje się? Potrzymaj mi piwo!
Prezentacja Łukasza pozwoli na obserwację tego, co wyewoluuje w zależności od stworzonych warunków z wykorzystaniem algorytmu ewolucyjnego, odpowiadającego jak najbardziej biologicznej ewolucji. Będzie również o tym, jak różni się on od algorytmów uczenia maszynowego, zazwyczaj używanego do trenowania sieci neuronowych. Spróbujemy też sprawić, by symulacja była wydajna i może nawet wielowątkowa. Pogadamy także o sieciach neuronowych oraz biologii ewolucyjnej.
As presented at DevDuck #4 - JavaScript meetup for developers (www.devduck.pl)
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Looking for a company to build your app? - Check us out at www.brainhub.eu
As presented at DevDuck #6 - JavaScript meetup for developers (www.devduck.pl)
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Looking for a company to build your app? - Check us out at www.brainhub.eu
As presented at DevDuck #6 - JavaScript meetup for developers (www.devduck.pl)
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Looking for a company to build your React app? - Check us out at www.brainhub.eu
Kilka praktycznych rad o budowaniu startupu i znaczeniu technologii.
#1 Dobór startup
#2 Dobór technologii
#3 Nie potrzebujesz CTO
#4 Techniczny Wspólnik
As presented at DevDuck #3 - JavaScript meetup for developers (www.devduck.pl)
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Get know more about GraphQL
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Looking for a company to build you an electron desktop app? www.brainhub.eu
As presented at DevDuck #3 - JavaScript meetup for developers (www.devduck.pl)
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All you need to know about using React with Redux
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Looking for a company to build you an electron desktop app? www.brainhub.eu
As presented at DevDuck #2 - JavaScript meetup for developers (www.devduck.pl)
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Looking for a company to build you an react based apps? www.brainhub.eu
JavaScript and Desktop Apps - Introduction to ElectronBrainhub
As presented at DevDuck #2 - JavaScript meetup for developers (www.devduck.pl)
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Looking for a company to build you an electron desktop app? www.brainhub.eu
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
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#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
7. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
Piotr Sroczkowski Ant colony optimization
8. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
Piotr Sroczkowski Ant colony optimization
9. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
Piotr Sroczkowski Ant colony optimization
10. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
Piotr Sroczkowski Ant colony optimization
11. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
Piotr Sroczkowski Ant colony optimization
12. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
... or running such a program will be very expensive (ex. in a
cloud like AWS, Digital Ocean, Microsoft Azure...)
Piotr Sroczkowski Ant colony optimization
13. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
... or running such a program will be very expensive (ex. in a
cloud like AWS, Digital Ocean, Microsoft Azure...)
... or a user will become frustrated because even 5 seconds to
run a program could be a bad UX
Piotr Sroczkowski Ant colony optimization
14. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
What and why?
Greek heurisco
we look for not the best solution but a satisfiable one
like in real life ex. finding the best product to buy
very often it’s impossible to create an algorithm which will
find the optimum solution
... or such a program (implementation of the algorithm) will
run very long
... or a program which generates the program above will run
very long
... or running such a program will be very expensive (ex. in a
cloud like AWS, Digital Ocean, Microsoft Azure...)
... or a user will become frustrated because even 5 seconds to
run a program could be a bad UX
...
Piotr Sroczkowski Ant colony optimization
16. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - medicine / bioinformatics
clinical decision support system
Piotr Sroczkowski Ant colony optimization
17. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - medicine / bioinformatics
clinical decision support system
MSA (multiple sequence alignment) - genetics
Piotr Sroczkowski Ant colony optimization
20. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
Piotr Sroczkowski Ant colony optimization
21. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
Piotr Sroczkowski Ant colony optimization
22. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
Piotr Sroczkowski Ant colony optimization
23. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
planning database queries
Piotr Sroczkowski Ant colony optimization
24. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
planning database queries
queueing
Piotr Sroczkowski Ant colony optimization
25. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - software engineering
mutation testing
virus detection
file allocation for a distributed system
parallelization
planning database queries
queueing
virtual DOM in React
Piotr Sroczkowski Ant colony optimization
27. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - police / security
construction of facial composites from eyewitnesses
Piotr Sroczkowski Ant colony optimization
28. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - police / security
construction of facial composites from eyewitnesses
design of anti-terrorism systems
Piotr Sroczkowski Ant colony optimization
31. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
Piotr Sroczkowski Ant colony optimization
32. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
aircraft wing design
Piotr Sroczkowski Ant colony optimization
33. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
aircraft wing design
pop music production
Piotr Sroczkowski Ant colony optimization
34. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - others
finding the cheapest flight
time scheduling
aircraft wing design
pop music production
container loading
Piotr Sroczkowski Ant colony optimization
36. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Application - next others
there are so many applications so this presentation cannot
contain them all
Piotr Sroczkowski Ant colony optimization
39. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
Piotr Sroczkowski Ant colony optimization
40. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
they interact locally with the environment
Piotr Sroczkowski Ant colony optimization
41. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
they interact locally with the environment
the inspiration comes above all from the nature
Piotr Sroczkowski Ant colony optimization
42. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Characteristics
small agents / boids
they interact locally with one another
they interact locally with the environment
the inspiration comes above all from the nature
therefore (like in other heuristics) there is much randomness
Piotr Sroczkowski Ant colony optimization
46. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
Piotr Sroczkowski Ant colony optimization
47. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
Piotr Sroczkowski Ant colony optimization
48. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
Piotr Sroczkowski Ant colony optimization
49. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
Piotr Sroczkowski Ant colony optimization
50. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
multiple swarm optimization
Piotr Sroczkowski Ant colony optimization
51. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
multiple swarm optimization
altruism algorithm
Piotr Sroczkowski Ant colony optimization
52. Family of algorithms
Origin
Working
Heuristic algorithms
Swarm intelligence
Examples
ant colony optimization
bee colony optimization
firefly algorithm
bat algorithm
self-propelled particles
charged system exploration
multiple swarm optimization
altruism algorithm
artificial immunological systems
Piotr Sroczkowski Ant colony optimization
58. Family of algorithms
Origin
Working
When and where?
1992
Marco Dorigo
PhD thesis
Universit´e Libre de Bruxelles
to find the optimal path in a graph
Piotr Sroczkowski Ant colony optimization
62. Family of algorithms
Origin
Working
Principles
Ants wander randomly
They lay down pheromone trails
They follow pheromones (the pheromones increase probability
of going to a particular side)
Piotr Sroczkowski Ant colony optimization
63. Family of algorithms
Origin
Working
Principles
Ants wander randomly
They lay down pheromone trails
They follow pheromones (the pheromones increase probability
of going to a particular side)
The pheromones evaporate
Piotr Sroczkowski Ant colony optimization