DCMI IEEE LTSC Joint taskforce at DC2007
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This document discusses the work of the DCMI-IEEE LTSC Task Force to specify how to express Learning Object Metadata (LOM) instances using terms and constructs from the Dublin Core Metadata Initiative (DCMI) Abstract Model. The Task Force aims to develop a Recommended Practice that defines how LOM elements can be mapped to DCMI Abstract Model classes, properties and encoding schemes to allow LOM metadata to be combined with DCMI metadata in applications. The Task Force has produced initial analyses and examples and plans further work to create an application profile and transformation between LOM and the DCMI Abstract Model representation.
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The document discusses work done by the KMR Group at CID Knowledge Management Research Group on several projects including PADLR, Edutella, modular content archives, and portfolios. Specifically:
1) The group is working on building query and mapping services for the Edutella peer-to-peer framework for exchanging metadata.
2) Current work includes developing and testing schemas and APIs for queries, and methods for registration and mapping in hubs.
3) The group is also working on projects involving graphical query languages, evaluating Edutella for user tools, and integrating portfolios.
CID presentation för VINNOVA 2003-03-28
The document discusses interactive learning environments (ILEs) being explored by researchers at the Centre for User Oriented IT Design. The ILEs aim to address fundamental educational issues by designing open, learner-centric e-learning architectures using next-generation web technologies. Several PhD projects are investigating topics like designing ILEs for global learning, supporting interest-driven learning, and developing reusable learning content. Prototypes are being developed and evaluated in collaboration with educational partners. The vision is for a "knowledge manifold" to support question-based, interest-driven lifelong learning through distributed knowledge resources and certified teachers.
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DC-2008 Tutorial 3 - Dublin Core and other metadata schemas
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Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Artificial Intelligence for XMLDevelopment
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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.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
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Microsoft - Power Platform_G.Aspiotis.pdf
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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20240609 QFM020 Irresponsible AI Reading List May 2024
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DCMI IEEE LTSC Joint taskforce at DC2007
- 8. Goals conform to utilizes encodes TF goal (via LOM-DCAM) currently (via LOM-RDF) DCMI Abstract Model DCMI Terms LOM Terms DC Application Profile MARC Terms DC-XML RDF HTML Vocabularies Encodings LOM
- 16. DCAM property DCAM describes relationships... ...using statements: identifies identifies represents classifies Resource Value Statement Property URI Value URI Value String Syntax Encoding Scheme URI Language Tag Vocabulary Enc. Scheme URI
- 19. More complex example (LOM) <lifecycle> <contribute> <role> <source>LOMv1.0</source> <value>author</author> </role> <date> <dateTime>2002-04-05</dateTime> </date> </contribute> </lifecycle>
- 20. More complex DCAM example lom:contribute lom:role dc:date Description 1 Description 2 Description set
- 25. A High-Level view of Metadata Frameworks LOM Data Model
- 27. Comments?
- 32. Interpreting XML and RDF metadata