Presentation by Pier Luca Lanzi at the Optimization by Building and Using Probabilistic Models (OBUPM 2008) workshop at the Genetic and Evolutionary Computation Conference (GECCO-2008)
Real-Coded Extended Compact Genetic Algorithm based on Mixtures of ModelsPier Luca Lanzi
This document describes a real-coded extended compact genetic algorithm (RECGA) based on mixtures of models. RECGA uses probabilistic models like Bayesian optimization algorithms but with simpler models than typically used in BOAs. It applies an estimation of distribution algorithm approach using probabilistic models to generate new candidate solutions rather than using recombination and mutation operators as in traditional genetic algorithms. The RECGA method is presented as a way to take advantages of both extended compact genetic algorithms and Bayesian optimization algorithms while using less complex probabilistic models.
A Practical Schema Theorem for Genetic Algorithm Design and Tuningkknsastry
The document discusses the results of a study on the effects of a new drug on memory and cognitive function in older adults. The double-blind study involved 100 participants aged 65-80 who were given either the drug or a placebo daily for 6 months. Researchers found that those who received the drug performed significantly better on memory and problem-solving tests at the end of the study compared to those who received the placebo.
Presentation describes about aid that Genetic Algorithm provides to Technical Analysts who try to fit a trend lines and different indicators into huge data-sets present to predict the Share Market Trend.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
Pairwise and Problem-Specific Distance Metrics in the Linkage Tree Genetic Al...Martin Pelikan
1. The document proposes and analyzes two distance metrics for the linkage tree genetic algorithm (LTGA): a pairwise metric and a problem-specific metric.
2. Experiments on optimization problems show the pairwise metric significantly improves LTGA scalability. The problem-specific metric, informed by problem structure, yields further speedups on some problems but mixed results on others.
3. Future work aims to design more robust problem-specific metrics and methods to learn metrics from problem instances, improving LTGA performance on complex problems.
Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Ge...sadique_ghitm
The document discusses face recognition techniques using PCA, LDA, and a genetic algorithm. It begins with an overview of face recognition and challenges. It then describes 5 face databases used in the study. Experimental results showing the recognition accuracy of PCA and LDA on the databases are presented. A proposed method applying a genetic algorithm to face recognition is described. The genetic algorithm approach aims to address issues with PCA and LDA, such as requiring multiple images per person. Experimental results showing the improving recognition accuracy of the genetic algorithm over generations are also presented. The conclusions discuss how the genetic algorithm approach reduces problems with PCA and LDA, such as data storage and computation requirements.
Real-Coded Extended Compact Genetic Algorithm based on Mixtures of ModelsPier Luca Lanzi
This document describes a real-coded extended compact genetic algorithm (RECGA) based on mixtures of models. RECGA uses probabilistic models like Bayesian optimization algorithms but with simpler models than typically used in BOAs. It applies an estimation of distribution algorithm approach using probabilistic models to generate new candidate solutions rather than using recombination and mutation operators as in traditional genetic algorithms. The RECGA method is presented as a way to take advantages of both extended compact genetic algorithms and Bayesian optimization algorithms while using less complex probabilistic models.
A Practical Schema Theorem for Genetic Algorithm Design and Tuningkknsastry
The document discusses the results of a study on the effects of a new drug on memory and cognitive function in older adults. The double-blind study involved 100 participants aged 65-80 who were given either the drug or a placebo daily for 6 months. Researchers found that those who received the drug performed significantly better on memory and problem-solving tests at the end of the study compared to those who received the placebo.
Presentation describes about aid that Genetic Algorithm provides to Technical Analysts who try to fit a trend lines and different indicators into huge data-sets present to predict the Share Market Trend.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
Pairwise and Problem-Specific Distance Metrics in the Linkage Tree Genetic Al...Martin Pelikan
1. The document proposes and analyzes two distance metrics for the linkage tree genetic algorithm (LTGA): a pairwise metric and a problem-specific metric.
2. Experiments on optimization problems show the pairwise metric significantly improves LTGA scalability. The problem-specific metric, informed by problem structure, yields further speedups on some problems but mixed results on others.
3. Future work aims to design more robust problem-specific metrics and methods to learn metrics from problem instances, improving LTGA performance on complex problems.
Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Ge...sadique_ghitm
The document discusses face recognition techniques using PCA, LDA, and a genetic algorithm. It begins with an overview of face recognition and challenges. It then describes 5 face databases used in the study. Experimental results showing the recognition accuracy of PCA and LDA on the databases are presented. A proposed method applying a genetic algorithm to face recognition is described. The genetic algorithm approach aims to address issues with PCA and LDA, such as requiring multiple images per person. Experimental results showing the improving recognition accuracy of the genetic algorithm over generations are also presented. The conclusions discuss how the genetic algorithm approach reduces problems with PCA and LDA, such as data storage and computation requirements.
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
Visualizing, Modeling and Forecasting of Functional Time Serieshanshang
The document discusses visualization and forecasting of functional time series data. It introduces visualization methods like rainbow plots, functional bagplots, and functional highest density region boxplots which can detect outliers. It also covers modeling and forecasting functional time series, as well as seasonal univariate time series using a functional approach. Several outlier detection techniques for functional data are compared, including those based on functional depth, integrated squared error, and robust Mahalanobis distance.
A Modular Genetic Algorithm Specialized for Linear ConstraintsStefano Costanzo
This algorithm combines modules and operators of standard GAs with specilized routines aimed at archieving enhanched performance on istances with specific types of constraints, in particular linear.
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
The document discusses using a genetic algorithm to optimize air traffic congestion through peak and off-peak pricing. It models the problem as a bi-level optimization with a central planner setting prices and airlines minimizing costs. The genetic algorithm was able to find pricing solutions that reduced total flight delays while maintaining revenue neutrality for air navigation service providers. Future work includes further analyzing cost distributions across airlines and applying decentralized peak load pricing with individual air navigation service providers setting prices.
Image segmentation using a genetic algorithm and hierarchical local searchMartin Pelikan
This document proposes using a genetic algorithm and hierarchical local search to perform image segmentation. It maps image segmentation to an optimization problem using the Potts spin glass model. A steady-state genetic algorithm is used to find segmentations, applying crossover between parents and hierarchical local search to improve solutions. Experiments on house and dog images demonstrate the algorithm can efficiently segment images, with hierarchical local search necessary to avoid getting stuck in poor local optima.
This document discusses operations management and forecasting. It explains that operations management deals with designing and managing processes, products, services and supply chains to deliver goods and services customers want. Forecasting helps managers reduce uncertainty by predicting future demand to match supply. The document then discusses various forecasting methods including qualitative judgmental methods and quantitative mathematical modeling methods. It covers short, medium and long-range forecasting as well as different time series and causal modeling techniques.
Improving the accuracy of k-means algorithm using genetic algorithmKasun Ranga Wijeweera
This document proposes using a genetic algorithm to improve the accuracy of k-means clustering by selecting better initial centroids. It describes generating an initial population of random centroids, evaluating their fitness using a k-means objective function, and evolving the population over generations using selection, crossover and mutation to converge on high-fitness initial centroids. Testing showed this genetic k-means approach produced more accurate and globally optimized clustering results than random initial centroids.
The document provides an overview of genetic algorithms. It describes how genetic algorithms are inspired by natural selection and survival of the fittest. A genetic algorithm uses a population of solutions that evolves toward better solutions, where the fittest solutions are more likely to reproduce and pass on their traits to the next generation. The algorithm iterates through selection, crossover and mutation operators until convergence is reached.
Yes you can play Monopoly with a Genetic Algorithm - Niels ZeilemakerGoDataDriven
Niels Zeilemaker is a Data Hacker at GoDataDriven, where he works for a wide range of companies where he spends his time doing feature engineering and building models. Before joining GDD, he finished his PhD thesis at the Technical University of Delft. In this talk he will explain how to 'solve' Monopoly with a genetic algorithm. He will first go into the official rules of Monopoly, as nobody seems to actually play according to the rules. Then into how to use a genetic algorithm to optimize a general buying rule, and finally how to optimize the number of houses to buy.
Application of Genetic Algorithm in Software TestingGhanshyam Yadav
This document summarizes a seminar presentation on using genetic algorithms for software testing. It discusses how genetic algorithms can be applied to generate test cases automatically by representing test paths as chromosomes that undergo processes of selection, crossover and mutation. The goal is to optimize test paths by focusing on the most critical ones first to improve testing efficiency. The presentation provides details on how the genetic algorithm would work by assigning weights to paths in a program's control flow graph and using the weights to select paths for reproduction and mutation over multiple iterations.
Genetic algorithms are a type of artificial intelligence search technique inspired by natural selection. They work by randomly generating an initial population of solutions, evaluating their fitness, then breeding new solutions through selection, crossover and mutation over many generations until an optimal solution is found. Some key steps include randomly initializing a population, determining fitness, selecting parents, performing crossover on parents to create new solutions, mutating new solutions, determining fitness of new population, and repeating until a stopping criteria is met such as a good enough solution being found. Genetic algorithms have been applied to many optimization and search problems across various domains.
This document outlines examples of big time series data and techniques for visualizing and forecasting them. It discusses four examples of hierarchical and grouped time series data: Australian tourism demand, Australian labor market participation, PBS pharmaceutical sales, and spectacle sales. It then covers various visualization methods like kite diagrams, STL decomposition, seasonal stacked bar charts, and correlation graphs to explore patterns in large time series datasets.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
Visualization and forecasting of big time series dataRob Hyndman
The document discusses various techniques for visualizing and forecasting large time series data. It provides examples of large time series data sets, including Australian tourism demand data with over 300 bottom-level time series, UK spectacle sales data with over 1 million bottom-level series, and Australian labor market participation data with over 1,000 occupations. It then describes several visualization methods like kite diagrams, STL decompositions, seasonal stacked bar charts, correlation graphs, and feature analysis to analyze patterns and relationships in large time series data sets.
This document provides an overview of genetic algorithms. It begins with an introduction to genetic algorithms, noting they were developed in the 1970s, inspired by Darwinian evolution. It then describes key features of genetic algorithms, including that they maintain a population of solutions, use reproduction, mutation and crossover to create new populations, and favor fitter solutions. The document discusses various methods for population selection, including roulette wheel selection, rank selection and tournament selection. It also covers the anatomy of a genetic algorithm and provides a simple example to maximize x2 to demonstrate the genetic algorithm process.
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Optimizing the Performance-Related Configurations of Object-Relational Mappin...corpaulbezemer
The document discusses optimizing ORM framework performance configurations using a genetic algorithm. It finds that the default configurations are often significantly slower than optimal configurations. A genetic algorithm can guide the optimization of configurations across objectives like CPU usage, memory usage, and execution time. Experiments show the genetic algorithm finds configurations close to optimal in 5-20 minutes, much faster than trial-and-error. Developers should care about ORM performance configuration due to potential performance degradations from defaults.
Solving the traveling salesman problem by genetic algorithmAlex Bidanets
The document discusses the traveling salesman problem and genetic algorithms. The traveling salesman problem involves finding the shortest route to visit each city on a list only once and return to the origin city. Genetic algorithms provide a method to solve optimization problems like the traveling salesman problem. Genetic algorithms work by initializing a population of solutions and using operators like crossover and mutation to generate new populations, selecting the fittest solutions to reproduce until a condition is met. The genetic algorithm approach allows the traveling salesman problem to be solved effectively without prior knowledge of the problem.
Genetic algorithms are an optimization technique that uses processes inspired by biological evolution such as inheritance, mutation, selection, and crossover. This document provides examples of how genetic algorithms work and concludes with summarizing the key aspects of genetic algorithms.
The document provides an introduction to the R programming language. It discusses that R is an open-source programming language for statistical analysis and graphics. It can run on Windows, Unix and MacOS. The document then covers downloading and installing R and R Studio, the R workspace, basics of R syntax like naming conventions and assignments, working with data in R including importing, exporting and creating calculated fields, using R packages and functions, and resources for R help and tutorials.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
task scheduling in cloud datacentre using genetic algorithmSwathi Rampur
Task scheduling and resource provisioning is the core and challenging issues in cloud environment. Processes running in the cloud environment will race for available resources in order to complete their tasks with the minimum execution time; it is clear that we need an efficient scheduling technique for mapping between processes running and available resources. In this research paper, we are presented a non-traditional optimization technique, which mimics the process of evolution and based on the mechanics of natural selection and natural genetics called Genetic algorithm (GA), which minimizes the execution time and in turn reduces computation cost. We had done comparison with Round Robin algorithm and used CloudSim toolkit for our tests, results shows that Meta heuristic GA gives better performance than other scheduling algorithm.
Visualizing, Modeling and Forecasting of Functional Time Serieshanshang
The document discusses visualization and forecasting of functional time series data. It introduces visualization methods like rainbow plots, functional bagplots, and functional highest density region boxplots which can detect outliers. It also covers modeling and forecasting functional time series, as well as seasonal univariate time series using a functional approach. Several outlier detection techniques for functional data are compared, including those based on functional depth, integrated squared error, and robust Mahalanobis distance.
A Modular Genetic Algorithm Specialized for Linear ConstraintsStefano Costanzo
This algorithm combines modules and operators of standard GAs with specilized routines aimed at archieving enhanched performance on istances with specific types of constraints, in particular linear.
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
The document discusses using a genetic algorithm to optimize air traffic congestion through peak and off-peak pricing. It models the problem as a bi-level optimization with a central planner setting prices and airlines minimizing costs. The genetic algorithm was able to find pricing solutions that reduced total flight delays while maintaining revenue neutrality for air navigation service providers. Future work includes further analyzing cost distributions across airlines and applying decentralized peak load pricing with individual air navigation service providers setting prices.
Image segmentation using a genetic algorithm and hierarchical local searchMartin Pelikan
This document proposes using a genetic algorithm and hierarchical local search to perform image segmentation. It maps image segmentation to an optimization problem using the Potts spin glass model. A steady-state genetic algorithm is used to find segmentations, applying crossover between parents and hierarchical local search to improve solutions. Experiments on house and dog images demonstrate the algorithm can efficiently segment images, with hierarchical local search necessary to avoid getting stuck in poor local optima.
This document discusses operations management and forecasting. It explains that operations management deals with designing and managing processes, products, services and supply chains to deliver goods and services customers want. Forecasting helps managers reduce uncertainty by predicting future demand to match supply. The document then discusses various forecasting methods including qualitative judgmental methods and quantitative mathematical modeling methods. It covers short, medium and long-range forecasting as well as different time series and causal modeling techniques.
Improving the accuracy of k-means algorithm using genetic algorithmKasun Ranga Wijeweera
This document proposes using a genetic algorithm to improve the accuracy of k-means clustering by selecting better initial centroids. It describes generating an initial population of random centroids, evaluating their fitness using a k-means objective function, and evolving the population over generations using selection, crossover and mutation to converge on high-fitness initial centroids. Testing showed this genetic k-means approach produced more accurate and globally optimized clustering results than random initial centroids.
The document provides an overview of genetic algorithms. It describes how genetic algorithms are inspired by natural selection and survival of the fittest. A genetic algorithm uses a population of solutions that evolves toward better solutions, where the fittest solutions are more likely to reproduce and pass on their traits to the next generation. The algorithm iterates through selection, crossover and mutation operators until convergence is reached.
Yes you can play Monopoly with a Genetic Algorithm - Niels ZeilemakerGoDataDriven
Niels Zeilemaker is a Data Hacker at GoDataDriven, where he works for a wide range of companies where he spends his time doing feature engineering and building models. Before joining GDD, he finished his PhD thesis at the Technical University of Delft. In this talk he will explain how to 'solve' Monopoly with a genetic algorithm. He will first go into the official rules of Monopoly, as nobody seems to actually play according to the rules. Then into how to use a genetic algorithm to optimize a general buying rule, and finally how to optimize the number of houses to buy.
Application of Genetic Algorithm in Software TestingGhanshyam Yadav
This document summarizes a seminar presentation on using genetic algorithms for software testing. It discusses how genetic algorithms can be applied to generate test cases automatically by representing test paths as chromosomes that undergo processes of selection, crossover and mutation. The goal is to optimize test paths by focusing on the most critical ones first to improve testing efficiency. The presentation provides details on how the genetic algorithm would work by assigning weights to paths in a program's control flow graph and using the weights to select paths for reproduction and mutation over multiple iterations.
Genetic algorithms are a type of artificial intelligence search technique inspired by natural selection. They work by randomly generating an initial population of solutions, evaluating their fitness, then breeding new solutions through selection, crossover and mutation over many generations until an optimal solution is found. Some key steps include randomly initializing a population, determining fitness, selecting parents, performing crossover on parents to create new solutions, mutating new solutions, determining fitness of new population, and repeating until a stopping criteria is met such as a good enough solution being found. Genetic algorithms have been applied to many optimization and search problems across various domains.
This document outlines examples of big time series data and techniques for visualizing and forecasting them. It discusses four examples of hierarchical and grouped time series data: Australian tourism demand, Australian labor market participation, PBS pharmaceutical sales, and spectacle sales. It then covers various visualization methods like kite diagrams, STL decomposition, seasonal stacked bar charts, and correlation graphs to explore patterns in large time series datasets.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
Visualization and forecasting of big time series dataRob Hyndman
The document discusses various techniques for visualizing and forecasting large time series data. It provides examples of large time series data sets, including Australian tourism demand data with over 300 bottom-level time series, UK spectacle sales data with over 1 million bottom-level series, and Australian labor market participation data with over 1,000 occupations. It then describes several visualization methods like kite diagrams, STL decompositions, seasonal stacked bar charts, correlation graphs, and feature analysis to analyze patterns and relationships in large time series data sets.
This document provides an overview of genetic algorithms. It begins with an introduction to genetic algorithms, noting they were developed in the 1970s, inspired by Darwinian evolution. It then describes key features of genetic algorithms, including that they maintain a population of solutions, use reproduction, mutation and crossover to create new populations, and favor fitter solutions. The document discusses various methods for population selection, including roulette wheel selection, rank selection and tournament selection. It also covers the anatomy of a genetic algorithm and provides a simple example to maximize x2 to demonstrate the genetic algorithm process.
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Optimizing the Performance-Related Configurations of Object-Relational Mappin...corpaulbezemer
The document discusses optimizing ORM framework performance configurations using a genetic algorithm. It finds that the default configurations are often significantly slower than optimal configurations. A genetic algorithm can guide the optimization of configurations across objectives like CPU usage, memory usage, and execution time. Experiments show the genetic algorithm finds configurations close to optimal in 5-20 minutes, much faster than trial-and-error. Developers should care about ORM performance configuration due to potential performance degradations from defaults.
Solving the traveling salesman problem by genetic algorithmAlex Bidanets
The document discusses the traveling salesman problem and genetic algorithms. The traveling salesman problem involves finding the shortest route to visit each city on a list only once and return to the origin city. Genetic algorithms provide a method to solve optimization problems like the traveling salesman problem. Genetic algorithms work by initializing a population of solutions and using operators like crossover and mutation to generate new populations, selecting the fittest solutions to reproduce until a condition is met. The genetic algorithm approach allows the traveling salesman problem to be solved effectively without prior knowledge of the problem.
Genetic algorithms are an optimization technique that uses processes inspired by biological evolution such as inheritance, mutation, selection, and crossover. This document provides examples of how genetic algorithms work and concludes with summarizing the key aspects of genetic algorithms.
The document provides an introduction to the R programming language. It discusses that R is an open-source programming language for statistical analysis and graphics. It can run on Windows, Unix and MacOS. The document then covers downloading and installing R and R Studio, the R workspace, basics of R syntax like naming conventions and assignments, working with data in R including importing, exporting and creating calculated fields, using R packages and functions, and resources for R help and tutorials.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
1. Real-Coded Extended Compact Genetic
Algorithm based on Mixtures of Models
Pier Luca Lanzi, Luigi Nichetti, Kumara Sastry,
Davide Voltini, and David E. Goldberg
Pier Luca Lanzi
2. Estimation of Distribution Algorithms
Probabilistic New
Population Selection Model Population
Extended Compact Genetic Algorithm
OBUPM-2008, July 12, 2008, Atlanta, GA