Bkump Ed Media 20080627
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This document summarizes a workplace learning context model that formalizes learning goals. It describes: 1) A model with packages for the work process, domain knowledge, and competencies relevant for tasks. 2) An implementation of the model in a project that links tasks, learning goals, and resources. 3) Real-world applications of the approach in different domains and a prototype tool.
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During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
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Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
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Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
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3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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Bkump Ed Media 20080627
- 1. A Competence-based Approach for Formalizing Learning Goals in Work-integrated Learning Tobias Ley, Barbara Kump, Armin Ulbrich, Peter Scheir und Stefanie N. Lindstaedt Barbara Kump
- 3. A Workplace Learning Context Model 20 October 2009 / [adapted form Lindstaedt & Farmer, 2004] Worker Learner Expert
- 6. 20 October 2009 / Process Models
- 9. Modeling Learning Goals for Work-integrated Learning – the APOSDLE Approach 20 October 2009 /
- 10. Scenario 20 October 2009 / Paul, Requirements Engineer Recently changed his department Build a first cut Context Model Carry out an initial stakeholder analysis User Profile Tasks: Build a first cut Context Model Cary out an initial stakeholder analysis Knowledge & Skills: Knowledge of different types of system stakeholders Understand the Syntax and Semantics of Context Models Ability to create a Context Model Knowledge of diffent types of stakeholder analyses Allocate functions between actors according to boundaries Competency Gap Analysis Learning Opportunity Knowledge & Skills: Knowledge about actors, tasks, goals and resources Knowledge about different types of dependencies between actors
- 11. Interlinking packages of the Workplace Learning Context Model 20 October 2009 / Task-Competency Assignment Annotation of Resources Task History Knowledge & Skills
- 12. Implementation of the Workplace Learning Context Approach in the APOSDLE project 20 October 2009 / [APOSDLE meta-model, Extract] Task Demand
- 14. Real World Applications of the APOSDLE Workplace Learning Context Approach 20 October 2009 /
- 15. Four Application Partner specific APOSDLEs 20 October 2009 / Network of SMEs eConsulting for Innovation-management Large Corporation SW Simulation Public Org. & SME REACH Consulting & Event-Management RESCUE
- 16. Integrated modelling methodology 20 October 2009 / Domain Knowledge Package Process Package Competency Package
- 17. Informal Models – EADS Example 20 October 2009 / Informal Simulation Domain Model Informal Simulation Tasks Model Define Objectives Design Develop Execute
- 18. 20 October 2009 / EADS Domain Concept (Example)
- 19. 20 October 2009 / EADS Task (Example) Informal Task Demand (in terms of domain concepts)
- 20. Modelling the Task Demand: TACT tool 20 October 2009 / [TACT for modelling Requirements Engineering]
- 21. and a Prototype… 20 October 2009 /
- 22. 20 October 2009 / 1 2
- 23. 20 October 2009 / Tasks (Process Package) Learning Goals (Competency Package)
- 24. 20 October 2009 / Resource Viewer Domain Concepts (Domain Knowledge Package)
- 25. Conclusions & Outlook 20 October 2009 /
- 30. Appendix: Competence-based Knowledge Space Theory 20 October 2009 /
- 34. 20 October 2009 / Prerequisite Relation (Competencies) Subset Relation (Tasks) Competence State Task-Competency Structure Performance State (theoretically assumed)