UC JT-LSL Translator
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This is the presentation used by the University of Cincinnati team during the 2008 Siemens PLM Connection Conference in Orlando, FL.
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In this paper we propose a new watermarking schema i.e. the combination of color space and wavelet transform. Watermarking is a technique that authenticates a digital picture by hiding the secret information into the image. Now, a lot of algorithms and methods have been developed for greyscale images but the particularities of color spaces have to be studied. On the other hand, the wavelet transform allows different possibilities of integrating a mark because of the uses of different parameters: the scale of decomposition, size, shape and localisation of the mark, and the used color space, etc. The RGB (Red, Green, and Blue) values of each pixel of the host color image as well as the color key image are converted to HSV (Hue, Saturation, and Value) values. Then the orthogonal D4 Wavelet transform is applied in each plate of host image and key image. Now insert the key component into appropriate blocks of host image’s different plates. The experimental results show the effectiveness of our algorithm.
Geometric modeling111431635 geometric-modeling-glad (1)
This document discusses different types of geometric modeling methods including wireframe, surface, and solid modeling. Wireframe modeling uses points and lines to define objects but does not represent actual surfaces or volumes. Surface modeling defines the outer surfaces of an object. Solid modeling precisely defines the enclosed volume of an object using its faces, edges, and vertices. Constructive solid geometry and boundary representation are two common solid modeling techniques. CSG uses Boolean operations to combine primitive shapes, while boundary representation stores topological information about faces, edges, and vertices. Feature-based modeling allows shapes to be created through operations like extruding, revolving, sweeping, and filling.
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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.
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AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
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.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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What will you get from this session?
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Test Automation with generative AI and Open AI.
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GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
National Security Agency - NSA mobile device best practices
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
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AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
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UiPath Test Automation using UiPath Test Suite series, part 6
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GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
National Security Agency - NSA mobile device best practices
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Communications Mining Series - Zero to Hero - Session 1
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20240605 QFM017 Machine Intelligence Reading List May 2024
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GraphRAG for Life Science to increase LLM accuracy
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UC JT-LSL Translator
- 1. Peeking Into Second Life – A JT-Linden Scripting Language Translator Hemant Ramaswami Ratnadeep Paul Shreenivasan Manievannan Sam Anand ( [email_address] ) University of Cincinnati
- 30. Thank You