CaaS: Description of the SIV Use Case
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The document provides information about what constitutes cybercrime and the types of criminal activities that fall under this category. It discusses how cybercrime involves illegal activities that are committed online using computers and networks. Some examples of cybercrime variants mentioned include hacking, cyber squatting, phishing, cyber stalking, and vishing - which aim to illegally access systems, steal personal and financial information, or harass individuals over the internet. The document also notes how cybercriminals can include children, dissatisfied employees, professional hackers, and crackers.
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van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
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Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software Fuzzing
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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.
Presentation of the OECD Artificial Intelligence Review of Germany
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- 1. Slides for dissemination activities Hasan Koc
- 2. Use Case – Business Process Making processes simple!
- 3. Change of Meter Installation of Meter Capability Change of Meter Removal of Meter Determine reading reason Installation of Archive Meter messages Period Meter Reading Business Process (Reference Process + Variants) Loop over all Registers Patterns Installation of Meter Meter Period meter reading Removal of Meter Determine reading reason Loop over all Registers Archive messages Determine metering point master data A B C A B Determine and check register master data Validate meter reading Validate meter reading C A Determine and check register master data Determine metering point master data Write meter reading Write meter reading Write meter reading Period meter reading Loop over all Registers Determine and check register master data Determine metering point master data Period meter reading Period Meter Reading Archive messages Loop over all Registers Period Meter Reading Removal of Meter Determine reading reason Period Meter Reading Change Write meter of Meter reading Installation of Determine and check register master data Determine metering point master data Change of Meter Removal of Meter Determine reading reason Period meter reading B Write meter reading C Write meter reading Validate meter reading common meta-model A B C Validate meter reading Write meter reading same modeling tool Variation Point depending on Runtime Indicator Context Model Market role = energy supplier = water supplier = …. Legislation = Germany / EU = …. Concepts (part of EM / Goal Model) Market role User Error External App Gas supplier Grid operator 3 Write meter readin
- 4. Instantiation of the Meta-model 4
Editor's Notes
- Register / Zählwerk: A physical or logical counter measuring energy products.Zählpunkt / Metering Point: A point where energy products are measured.