Gaming and Libraries LTR Handout
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The document discusses a teen Halloween party in 2008 at a public library that included videogames. It summarizes research finding that gaming can provide similar civic learning opportunities to classroom experiences by simulating civic actions and ethical decision making. The document also notes that children view video game play as a social rather than isolating activity by practicing verbal communication through multiplayer games.
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![Teen Halloween Party (2008) with videogames at the John C. Fremont Public Library. The library may well be the last safe, noncommercial space left in a community that is open to all, with no barrier to entry for anyone, in which diverse groups of people interact together in ways they don’t anywhere else. A 2008 Pew Internet and American Life Project report Teens, Video Games, and Civics found that many experiences in game play are similar to classroom-based civic learning opportunities. Those playing games often simulate civic action, help or guide other players, participate in guilds or other groups associated with the game, learn about social issues, and grapple with ethical issues. “When we talked to the children, they viewed video game play as largely a social activity, not an isolating one. It did more than provide a topic of conversation; it provided a structure through multiplayer games in which they practiced and improved their verbal communication skills.” —Lawrence Kutner and Cheryl Olson, Grand Theft Childhood: The Surprising Truth about Violent Video Games and What Parents Can Do (New York: Simon &Schuster, 2008), Kindle Locations 3676, 3717, 3855. The course laid out across the upper floor of the Downers Grove Public Library. Further reading: ,[object Object]](https://image.slidesharecdn.com/gamingltrhandout-091119133624-phpapp02/85/Gaming-and-Libraries-LTR-Handout-1-320.jpg)
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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.
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