ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning
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This document proposes ViSiL, a method for fine-grained video similarity learning that respects both the spatial structure of video frames and the temporal structure of videos. ViSiL learns a video similarity function using a 4-layer CNN that captures temporal structures in a frame-to-frame similarity matrix. Experimental results show ViSiL can accurately retrieve near-duplicate, same incident, same action, and same event videos from databases.
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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.
UiPath Test Automation using UiPath Test Suite series, part 6
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
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
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Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
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Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning
- 1. ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning Giorgos Kordopatis-Zilos Symeon Papadopoulos Ioannis Patras Ioannis Kompatsiaris
- 2. Problem statement Given two arbitrary videos, calculate their similarity based on their visual content. Query Video Complementary Scene Video Duplicate Scene Video Incident Scene Video Application scenario • Video Retrieval
- 3. Video-level methods Z. Gao et al. “ER3: A unified framework for event retrieval, recognition and recounting”. CVPR, 2017. G. Kordopatis-Zilos et al. “Near-duplicate video retrieval with deep metric learning”. ICCVW, 2017. Video similarity calculation disregards spatio-temporal information of videos
- 4. Frame-level methods Y. Jiang and J. Wang. “Partial copy detection in videos: A benchmark and an evaluation of popular methods”. Tran. on Big Data, 2016. L. Baraldi et al. “LAMV: Learning to align and match videos with kernelized temporal layers”. CVPR, 2018. Frame-to-frame similarity calculation disregards the spatial structure of frames
- 5. Motivation Fine-grained similarity calculation • Learn a video similarity function that respects: • Spatial structure of video frames (intra-frame relations) • Temporal structure of videos (inter-frame relations)
- 7. Frame-to-frame similarity Baseline frame-to-frame similarity matrix ViSiL frame-to-frame similarity matrix
- 8. Video-to-video similarity Video Similarity Learning network • 4-layer CNN • Captures the temporal structures on similarity matrix with the convolutional filters Chamfer Similarity
- 10. Experimental results Near-Duplicate Video Retrieval (CC_WEB_VIDEO) Fine-grained Incident Video Retrieval (FIVR-200K) Action Video Retrieval (ActivityNet) Event-based Video Retrieval (EVVE)
- 11. Visual examples query video database video frame-to-frame similarity matrix ViSiL output video-to-video similarity 0.8 0.5 0.7 near-duplicate videos same event videos same action videos
- 12. Thank you! Poster ID: No. 39 Code & models: https://github.com/MKLab-ITI/visil With the support of: Get in touch: Giorgos Kordopatis-Zilos: georgekordopatis@iti.gr / @g_kordo No. EP/R026424/1No. 825297