Flood Map Desktop (FMD) is free software that helps users create digital flood insurance rate maps (DFIRMs) using GIS. It allows users to set up geodatabases and project settings to manage flood mapping projects according to FEMA standards. FMD includes tools to create layers, assign symbology, generate maps in multiple formats, and produce final deliverables meeting FEMA requirements. Users report that FMD makes the flood mapping process four times more efficient than previous methods.
The document provides an introduction to the ArcGIS Pipeline Data Model (APDM), which is a standardized data model for storing pipeline geospatial data. It describes the core components of a geographic information system (GIS) and how the APDM implements these components using ESRI's geodatabase tools. This includes discussions of feature classes, object classes, attributes, relationships, and how the pipeline data is structured and related within the APDM schema.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
Flood Map Desktop (FMD) is free software that helps users create digital flood insurance rate maps (DFIRMs) using GIS. It allows users to set up geodatabases and project settings to manage flood mapping projects according to FEMA standards. FMD includes tools to create layers, assign symbology, generate maps in multiple formats, and produce final deliverables meeting FEMA requirements. Users report that FMD makes the flood mapping process four times more efficient than previous methods.
The document provides an introduction to the ArcGIS Pipeline Data Model (APDM), which is a standardized data model for storing pipeline geospatial data. It describes the core components of a geographic information system (GIS) and how the APDM implements these components using ESRI's geodatabase tools. This includes discussions of feature classes, object classes, attributes, relationships, and how the pipeline data is structured and related within the APDM schema.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
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1. Address, Street and Political (ASP) Jefferson County, Colorado
Logical GeoDatabase (GDB) Schema
POST-ESRI CONSULTING DRAFT
SHEET 1 OF 8
Overview
The principal objective of this project is the migration of legacy address, street and political data and
applications to a modernized GIS environment. The primary goals are: MANAGING At least three data modeling design options exist for managing coincidence between same linear and polygonal features:
Topologies: Create distinct linear and polygonal datasets managed through topologies established in an “ASP” feature dataset.
Establishing an addressing environment that is extensible and scalable to meet future needs,
Establishing a “practical” normalization of address and related data,
COINCIDENCE Linear Layering: Use a linear layering attribute from a “base” line feature class in conjunction with ancillary point feature classes to derive polygonal
data (aka “Sidwell” model). Other line-based derivatives are created from other line attributes. Coincidence of line and poly features requires no topologies.
Establishing an environment to manage many-to-many data correspondences, Atomic Line and Polygons: Create “atomic” line and poly feature classes from whence polygonal derivatives are created via base-poly dissolves and linear
Ensuring coincidence amongst same linear and polygonal features, and derivatives from line attributes. The number of topologies required for managing coincidence is minimal.
Preserving crucial tabular relationships- in particular how political polygonal attributes, like city
code, county precinct, etc, are associated with linear street and point-based address features. The goal in all designs is ensuring coincidence: we need same features to contain exact (identical) geometries.
TOPOLOGIES The first design for managing coincidence uses “topologies”. The “ASP” feature dataset and “ASP_Topology” table (below left) list participating entities. Linear and polygonal topological categories
(below) depict individual feature classes participating in the topologies. Polygonal features are further categorized by group type relative their coincidence. Note that with topologies in place,
Topology
Topology concerns spatial feature relationships, like coincidence, connectivity, and adjacency, which impact
tabular relationships. Note that the implementation of topology depends on the underlying GIS data model. coincidence is maintained by editing the geometry of “ASP_Topology” versus individual feature classes.
In the ESRI world, topological implementations have two basic forms: the traditional “coverage” model of POINT TOPOLOGY
A feature dataset containing feature classes with point, line and POLYGONAL TOPOLOGY
Arc/Info Workstation and the current “geodatabase” model associated with ArcGIS Desktop and Server. polygon topologies. A point feature class containing the set of all point features CITY GROUP CENSUS GROUP
SOURCE: Manually implemented.
representing street intersections.
COVERAGE VERSUS GEODATABASE Feature Dataset
SOURCE: “geocode” coverage and processing (to be developed) A polygonal feature class containing the subset of polygons A polygonal feature class containing the subset of polygons
representing census tracts.
ASP representing city code. Note: These are not legal municipal
Feature Class - Point
The “geocode” coverage is made up of street and non-street (political) linear features which when closing on themselves form polygons with political attributes. Intersection
boundaries but simply define incorporated status for addresses. SOURCE: “geocode” coverage polys and processing (to be developed)
Member Name Member Type SOURCE: “geocode” coverage polys and processing (to be developed)
In this model, topology is built and stored. Each line segment has direction, and when topology is established, knows its left and right polygons. Each polygon FC Topology Rank 2
Feature Class - Polygon
CensusTract
Feature Class - Polygon
carries political attributes- county precinct, city code (municipal status), etc. This allows for the generation of derivative political datasets for each political ASP_Topology Topology Origin FC Topology Rule Destination FC
CityCode
BaseLine Line
attribute, using dissolve, and ensures coincidence between the base lines, polygons and all polygonal derivatives. BasePoly Polygon Intersection Must Be Covered By Endpoint Of Street
FC Topology Rank 5
CensusBlock Polygon FC Topology Rank 5 Origin FC Topology Rule Destination FC
CensusBlockGroup Polygon
CensusTract Polygon Origin FC Topology Rule Destination FC CensusTract Must Not Overlap
From an addressing perspective, an address“matches” (is associated with) one side of one and only-one street segment. Since that side of the street segment CityCode Polygon
CityCode Must Not Overlap
CensusTract Must Not Have Gaps
CityPrecinct Polygon CensusTract Must Be Covered By Feature Class Of CensusBlock
also carries a link to a political polygon, two joins (address to street, street to political) give us an address-political association containing no ambiguity. CityWard Polygon LINEAR TOPOLOGY
CityCode
CityCode
Must Not Have Gaps
Must Be Covered By Feature Class Of CityPrecinct
CommissionerDistrict Polygon
CountyPrecinct Polygon
FedHouse Polygon
A polygonal feature class containing the subset of polygons
The topological relationships of coincidence, connectivity and adjacency essentially “come for free” in the coverage data model, making it very useful for the A linear feature class containing the subset of all linear features A polygonal feature class containing the subset of polygons
Intersection Point representing census block groups.
NonStreetLine Line representing named streets, roads or highways. representing city wards.
demands of addressing. Deficiencies exist, however- it may be difficult to normalize, poor at managing many-to-many correspondences, and in some cases, Patrol Polygon SOURCE: “geocode” coverage and processing (to be developed)
SOURCE: “geocode” coverage polys and processing (to be developed)
RTD Polygon SOURCE: “geocode” coverage polys and processing (to be developed)
difficult to extend or scale. SouthJeffCo Polygon Feature Class - Polygon
StateHouse Polygon Feature Class - Line Feature Class - Polygon CensusBlockGroup
StateSenate Polygon Street CityWard
Street Line
Only one of 26 defined topology rules come “for free” in the geodatabase model- the other 25 must be explicitly defined. However, if the geodatabase is well TIFA Polygon FC Topology Rank 5
UnnamedRoad Line FC Topology Rank 1 FC Topology Rank 5 Origin FC Topology Rule Destination FC
designed and the necessary topologies are well defined, the geodatabase model may greatly excel in the areas where the coverage model is weak. Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC
CensusBlockGroup Must Not Overlap
The ASP topologies for managing coincidence and other point, line Street Must Be Covered By Feature Class Of BaseLine CityWard Must Not Overlap CensusBlockGroup Must Not Have Gaps
CensusBlockGroup Must Be Covered By Feature Class Of CensusBlock
Feature class (FC) topologies must exist within a feature dataset (FD) in the geodatabase. and polygon feature relationships. Street
Street
Must Not Overlap With
Must Not Overlap With
UnnamedRoad
NonStreetLine
CityWard
CityWard
Must Not Have Gaps
Must Be Covered By Feature Class Of CityPrecinct
SOURCE: Manually implemented.
A polygonal feature class containing the subset of polygons
DEPICTING COINCIDENCE Topology
ASP_Topology
Feature Class Rank
A linear feature class containing the subset of all linear features
representing unnamed roads, lanes or drives.
SOURCE: “roadrpiv” coverage (or “geocode”, if incorporated)
A polygonal feature class containing the subset of polygons
representing city precincts.
SOURCE: “geocode” coverage polys and processing (to be developed)
representing census blocks. (Note: several polys may be required in
order to represent one census block).
SOURCE: “geocode” coverage polys and processing (to be developed)
The following is a simplistic rendering of how coincidence between same line and polygonal features may be managed in the geodatabase. BaseLine 1 Feature Class - Line Feature Class - Polygon Feature Class - Polygon
Street 1 UnnamedRoad CityPrecinct CensusBlock
UnnamedRoad 1
LINEAR FEATURES POLYGONAL FEATURES Intersection
NonStreetLine
2
3 FC Topology Rank 1 FC Topology Rank 5 FC Topology Rank 5
BasePoly 4 Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC
All other polygonal FCs 5
A structure that allows individual linear feature classes within the ASP feature dataset A structure that allows individual polygonal feature classes within the ASP feature See rationale (below) for more information in initial rankings. UnnamedRoad Must Be Covered By Feature Class Of BaseLine CityPrecinct Must Not Overlap CensusBlock Must Not Overlap
to be built upon one base linear feature class with coincident geometry. dataset to be built upon one base polygonal feature class built on one base linear UnnamedRoad Must Not Overlap With Street CityPrecinct Must Not Have Gaps CensusBlock Must Not Have Gaps
feature class where coincidence is maintained for all. Origin FC Topology Rule Destination FC UnnamedRoad Must Not Overlap With NonStreetLine CityPrecinct Must Be Covered By Feature Class Of BasePoly CensusBlock Must Be Covered By Feature Class Of BasePoly
BaseLine Must Not Intersect
BaseLine Must Not Overlap
StreetMaintained CityCode BaseLine Must Not Self-Overlap A linear feature class containing the subset of all linear features
subset of all street linear features subset of all polygonal features BaseLine Must Not Overlap or Touch Interior Of representing non-street (political) lines. PRECINCT GROUP OTHER POLITICAL POLYGONAL DATASETS
maintained by Jefferson County representing distinct city codes Street Must Be Covered By Feature Class Of BaseLine
SOURCE: “geocode” coverage arcs and processing (to be developed)
linear feature class (CITYCODE).
Street Must Not Overlap With UnnamedRoad A polygonal feature class containing the subset of polygons A polygonal feature class containing the subset of polygons
Street Must Not Overlap With NonStreetLine
polygonal feature class Intersection Must Be Covered By Endpoint Of Street Feature Class - Line representing federal house districts. representing the TIFA (Traffic Improvement Fee Area) district.
UnnamedRoad Must Be Covered By Feature Class Of BaseLine NonStreetLine SOURCE: “geocode” coverage polys and processing (to be developed) SOURCE: “geocode” coverage polys and processing (to be developed)
GF 00 14 69
UnnamedRoad Must Not Overlap With Street
YO 149 D = 21
8
T
UnnamedRoad Must Not Overlap With NonStreetLine
DS
Feature Class - Polygon Feature Class - Polygon
NonStreetLine Must Be Covered By Feature Class Of BaseLine FC Topology Rank 3
IEL
FedHouse TIFA
101 AINI
1
CCID = xxxxxxxx CCID = xxxxxxxx NonStreetLine Must Not Overlap With Street Origin FC Topology Rule Destination FC
RM
CITYCODE = 07 CITYCODE = 01 NonStreetLine Must Not Overlap With UnnamedRoad
UN
ST
BasePoly Must Not Overlap NonStreetLine Must Be Covered By Feature Class Of BaseLine FC Topology Rank 5 FC Topology Rank 5
BasePoly Must Not Have Gaps NonStreetLine Must Not Overlap With Street
BasePoly Boundary Must Be Covered By BaseLine Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC
NonStreetLine Must Not Overlap With UnnamedRoad
CountyPrecinct Must Not Overlap
FedHouse Must Not Overlap TIFA Must Not Overlap
CountyPrecinct Must Not Have Gaps
FedHouse Must Not Have Gaps TIFA Must Not Have Gaps
CountyPrecinct Must Be Covered By Feature Class Of BasePoly
FedHouse Must Be Covered By Feature Class Of CountyPrecinct TIFA Must Be Covered By Feature Class Of BasePoly
CommissionerDistrict Must Not Overlap
CommissionerDistrict Must Not Have Gaps
CommissionerDistrict Must Be Covered By Feature Class Of CountyPrecinct
StateHouse Must Not Overlap A polygonal feature class containing the subset of polygons A polygonal feature class containing the subset of polygons
Street CountyPrecinct StateHouse Must Not Have Gaps
representing state senate districts. representing the South JeffCo Improvement District.
subset of all linear features representing StateHouse Must Be Covered By Feature Class Of CountyPrecinct
subset of all polygonal features StateSenate Must Not Overlap SOURCE: “geocode” coverage polys and processing (to be developed) SOURCE: “geocode” coverage polys and processing (to be developed)
named streets (roads) in Jefferson representing distinct county precincts StateSenate Must Not Have Gaps
County. CPID = 5183 (COUNPREC). StateSenate Must Be Covered By Feature Class Of CountyPrecinct Feature Class - Polygon Feature Class - Polygon
linear feature class COUNPREC = 2162530005 polygonal feature class FedHouse Must Not Overlap StateSenate SouthJeffCo
FedHouse Must Not Have Gaps
8
YO 149 2169
FedHouse Must Be Covered By Feature Class Of CountyPrecinct
T
GF 00 14
DS
CityPrecinct Must Not Overlap FC Topology Rank 5 FC Topology Rank 5
IEL
101 RID =
CityPrecinct Must Not Have Gaps
1
CPID = xxxxxxxx CityPrecinct Must Be Covered By Feature Class Of BasePoly
Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC
ST
UN
COUNPREC = 2162530004 CityWard Must Not Overlap
StateSenate Must Not Overlap SouthJeffCo Must Not Overlap
CityWard Must Not Have Gaps
StateSenate Must Not Have Gaps SouthJeffCo Must Not Have Gaps
CityWard Must Be Covered By Feature Class Of CityPrecinct
StateSenate Must Be Covered By Feature Class Of CountyPrecinct SouthJeffCo Must Be Covered By Feature Class Of BasePoly
CityCode Must Not Overlap
CPID = 5187
CityCode Must Not Have Gaps
COUNPREC = 2162530006
CityCode Must Be Covered By Feature Class Of CityPrecinct
CensusBlock Must Not Overlap A polygonal feature class containing the subset of polygons A polygonal feature class containing the subset of polygons
CensusBlock Must Not Have Gaps
CensusBlock Must Be Covered By Feature Class Of BasePoly
representing state house districts. representing RTD (Ride The Denver) districts.
CensusBlockGroup Must Not Overlap SOURCE: “geocode” coverage polys and processing (to be developed) SOURCE: “geocode” coverage polys and processing (to be developed)
CensusBlockGroup Must Not Have Gaps
CensusBlockGroup Must Be Covered By Feature Class Of CensusBlock Feature Class - Polygon Feature Class - Polygon
UnnamedRoad BasePoly (BasePolitical) Census Tract Must Not Overlap StateHouse RTD
subset of all linear features that are all polygonal features- a subset of all Census Tract Must Not Have Gaps
unnamed lanes, drives or roads. linear features Census Tract Must Be Covered By Feature Class Of CensusBlock
BPID = 5183 Patrol Must Not Overlap FC Topology Rank 5 FC Topology Rank 5
linear feature class BPID = 313366 polygonal feature class
CITYCODE = 01 Patrol Must Not Have Gaps
CITYCODE = 07
Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC
COUNPREC = 2162530005 Patrol Must Be Covered By Feature Class Of BasePoly
COUNPREC = 2162530004 RTD Must Not Overlap StateHouse Must Not Overlap RTD Must Not Overlap
RTD Must Not Have Gaps StateHouse Must Not Have Gaps RTD Must Not Have Gaps
BPID = 40674
RTD Must Be Covered By Feature Class Of BasePoly StateHouse Must Be Covered By Feature Class Of CountyPrecinct RTD Must Be Covered By Feature Class Of BasePoly
CITYCODE = 07
SouthJeffCo Must Not Overlap
COUNPREC = 2162530004
SouthJeffCo Must Not Have Gaps
BPID = 40677 SouthJeffCo Must Be Covered By Feature Class Of BasePoly
CITYCODE = 07 TIFA Must Not Overlap A polygonal feature class containing the subset of polygons A polygonal feature class containing the subset of polygons
COUNPREC = 2162530004 TIFA Must Not Have Gaps
BPID = 5187 representing commissioner districts. representing Sheriff patrol districts.
CITYCODE = 01
TIFA Must Be Covered By Feature Class Of BasePoly A linear feature class containing all street, unnamed road, and non-
SOURCE: “geocode” coverage polys and processing (to be developed) SOURCE: “geocode” coverage polys and processing (to be developed)
COUNPREC = 2162530006 street (political) linework.
SOURCE: “geocode” coverage arcs and processing (to be developed) Feature Class - Polygon Feature Class - Polygon
Topology Rankings CommissionerDistrict Patrol
Feature Class - Line
BaseLine
FC Topology Rank 5 FC Topology Rank 5
NonStreetLine BaseLine Ranks (above) are an important topological Origin FC Topology Rule Destination FC Origin FC Topology Rule Destination FC
subset of all linear features representing
non-street (political) lines.
all linear features consideration and determine what geometry moves FC Topology Rank
Origin FC
1
Topology Rule Destination FC
CommissionerDistrict Must Not Overlap Patrol Must Not Overlap
linear feature class CommissionerDistrictt Must Not Have Gaps Patrol Must Not Have Gaps
linear feature class where when editing. Here, linear features are the CommissionerDistrictt Must Be Covered By Feature Class Of CountyPrecinct Patrol Must Be Covered By Feature Class Of BasePoly
92
BaseLine Must Not Self Intersect
92
519
basis for polygonal features, hence linear feature BaseLine Must Not Self Overlap
519
=1
BaseLine Must Connect at Endpoints
=1
classes outweigh (outrank) polygonal. A polygonal feature class containing the subset of polygons
169
LID
BaseLine Must Not Intersect Or Touch Interior
D
BLI
NS
=2
representing county precincts.
D
BLI
7
7
SOURCE: “geocode” coverage polys and processing (to be developed)
134
134
PROS AND CONS
=5
=5
Feature Class - Polygon
LID
D
BLI
CountyPrecinct
NS
Employing numerous topologies to manage coincidence amongst many linear and
polygonal features represents a “textbook” approach to geodatabase design. FC Topology Rank 5
Or