DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Dcim distributed cache invalidation method for maintaining cache consistency in wireless mobile networks
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This paper introduces the Distributed Cache Invalidation Mechanism (DCIM), a client-based caching scheme designed for maintaining cache consistency in mobile ad hoc networks (MANETs). The DCIM utilizes adaptive time to live (TTL), piggybacking, and prefetching techniques to enhance data consistency while minimizing communication costs, outperforming existing pull-based and push-based methods. Performance analyses demonstrate that DCIM provides superior availability, delay, and traffic efficiency when compared to traditional cache consistency mechanisms.
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WebdriverIO & JavaScript: The Perfect Duo for Web Automationdigitaljignect In today’s dynamic digital landscape, ensuring the quality and dependability of web applications is essential. While Selenium has been a longstanding solution for automating browser tasks, the integration of WebdriverIO (WDIO) with Selenium and JavaScript marks a significant advancement in automation testing. WDIO enhances the testing process by offering a robust interface that improves test creation, execution, and management. This amalgamation capitalizes on the strengths of both tools, leveraging Selenium’s broad browser support and WDIO’s modern, efficient approach to test automation. As automation testing becomes increasingly vital for faster development cycles and superior software releases, WDIO emerges as a versatile framework, particularly potent when paired with JavaScript, making it a preferred choice for contemporary testing teams.
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CapCut Pro Crack is a powerful tool that has taken the digital world by storm, offering users a fully unlocked experience that unleashes their creativity. With its user-friendly interface and advanced features, it’s no wonder why aspiring videographers are turning to this software for their projects.
Curietech AI in action - Accelerate MuleSoft development
Curietech AI in action - Accelerate MuleSoft developmentshyamraj55 CurieTech AI in Action – Accelerate MuleSoft Development
Overview:
This presentation demonstrates how CurieTech AI’s purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort
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Key Highlights:
Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration
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DataWeave automation: Generate mappings from tables or samples—95%+ complete within minutes
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Integration flow generation: Auto-create Mule flows based on specifications—speeds up boilerplate development
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Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling
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Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code
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Why Now?
Achieve 10× productivity gains, slashing development time from hours to minutes
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Maintain high accuracy with code quality matching or exceeding manual efforts
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Ideal for developers, architects, and teams wanting to scale MuleSoft projects with AI efficiency
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CurieTech AI transforms MuleSoft development into an AI-accelerated workflow—letting you focus on innovation, not repetition.
10 Key Challenges for AI within the EU Data Protection Framework.pdf
10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash 10 Key Challenges for AI within the EU Data Protection Framework
A Constitutional Quagmire - Ethical Minefields of AI, Cyber, and Privacy.pdf
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GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now
Connecting Data and Intelligence: The Role of FME in Machine Learning
Connecting Data and Intelligence: The Role of FME in Machine LearningSafe Software In this presentation, we want to explore powerful data integration and preparation for Machine Learning. FME is known for its ability to manipulate and transform geospatial data, connecting diverse data sources into efficient and automated workflows. By integrating FME with Machine Learning techniques, it is possible to transform raw data into valuable insights faster and more accurately, enabling intelligent analysis and data-driven decision making.
Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...
Agentic AI for Developers and Data Scientists Build an AI Agent in 10 Lines o...All Things Open Presented at All Things Open RTP Meetup
Presented by William Hill - Developer Advocate, NVIDIA
Title: Agentic AI for Developers and Data Scientists
Build an AI Agent in 10 Lines of Code and the Concepts Behind the Code
Abstract: In this talk we will demonstrate building a working data science AI agent in 10 lines of basic Python code in a Colab notebook. Our AI Agent will perform LLM prompt-driven visual analysis using open-source libraries. In this session we will show how to develop an AI Agent using GPUs through NVIDIA’s developer program and Google Colab notebooks. After coding our AI Agent, we will break down the 10 lines of code. We will show the key components and open source library integrations that enable the agent's functionality, focusing on practical implementation details and then the theoretical concepts. The presentation concludes with a survey of current LLM technologies and the latest trends in developing AI applications for enthusiasts and enterprises
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante We live in an ever evolving landscape for cyber threats creating security risk for your production systems. Mitigating these risks requires participation throughout all stages from development through production delivery - and by every role including architects, developers QA and DevOps engineers, product owners and leadership. No one is excused! This session will cover examples of common mistakes or missed opportunities that can lead to vulnerabilities in production - and ways to do better throughout the development lifecycle.
9-1-1 Addressing: End-to-End Automation Using FME
9-1-1 Addressing: End-to-End Automation Using FMESafe Software This session will cover a common use case for local and state/provincial governments who create and/or maintain their 9-1-1 addressing data, particularly address points and road centerlines. In this session, you'll learn how FME has helped Shelby County 9-1-1 (TN) automate the 9-1-1 addressing process; including automatically assigning attributes from disparate sources, on-the-fly QAQC of said data, and reporting. The FME logic that this presentation will cover includes: Table joins using attributes and geometry, Looping in custom transformers, Working with lists and Change detection.
Hyderabad MuleSoft In-Person Meetup (June 21, 2025) Slides
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Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...
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The Future of Product Management in AI ERA.pdf
The Future of Product Management in AI ERA.pdfAlyona Owens Hi, I’m Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around.
Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as I’ve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs… We’ll talk about it shortly.
But before that, I want to take you back to 1997. One of my favorite movies is “Fifth Element”. It wowed me with futuristic predictions when I was a kid and I’m impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves:
Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink).
Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully).
On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldn’t be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future.
So, we as humans, when we don’t know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didn’t steal jobs—they transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.
From Manual to Auto Searching- FME in the Driver's Seat
From Manual to Auto Searching- FME in the Driver's SeatSafe Software Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Dcim distributed cache invalidation method for maintaining cache consistency in wireless mobile networks
- 1. DCIM: Distributed Cache Invalidation Method for Maintaining Cache Consistency In Wireless Mobile Networks ABSTRACT: This paper proposes distributed cache invalidation mechanism (DCIM), a client-based cache consistency scheme that is implemented on top of a previously proposed architecture for caching data items in mobile ad hoc networks (MANETs), namely COACS, where special nodes cache the queries and the addresses of the nodes that store the responses to these queries. We have also previously proposed a server-based consistency scheme, named SSUM, whereas in this paper, we introduce DCIM that is totally client-based. DCIM is a pull-based algorithm that implements adaptive time to live (TTL), piggybacking, and prefetching, and provides near strong consistency capabilities. Cached data items are assigned adaptive TTL values that correspond to their update rates at the data source, where items with expired TTL values are grouped in validation requests to the data source to refresh them, whereas unexpired ones but with high request rates are prefetched from the server. In this paper, DCIM is analyzed to assess the delay and bandwidth gains (or costs) when compared to polling every time and push-based schemes. DCIM was also implemented using ns2, and compared against client-based and server-based schemes to assess its performance experimentally. The consistency ratio, delay, GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:[email protected]
- 2. and overhead traffic are reported versus several variables, where DCIM showed to be superior when compared to the other systems. EXISTING SYSTEM: The cache consistency mechanisms in the literature can be grouped into three main categories: push based, pull based, and hybrid approaches. Push-based mechanisms are mostly server- based, where the server informs the caches about updates, whereas Pull-based approaches are client-based, where the client asks the server to update or validate its cached data. Finally, in hybrid mechanisms the server pushes the updates or the clients pull them DISADVANTAGES OF EXISTING SYSTEM: The major issue that faces client cache management concerns the maintenance of data consistency between the cache client and the data source. All cache consistency algorithms seek to increase the probability of serving from the cache data items that are identical to those on the server. However, achieving strong consistency, where cached items are identical to those on the server, requires costly communications with the server to validate (renew) cached items, considering the resource limited mobile devices and the wireless environments they operate in.
- 3. PROPOSED SYSTEM: In this paper, we propose a pull-based algorithm that implements adaptive TTL, piggybacking, and prefetching, and provides near strong consistency guarantees. Cached data items are assigned adaptive TTL values that correspond to their update rates at the data source. Expired items as well as nonexpired ones but meet certain criteria are grouped in validation requests to the data source, which in turn sends the cache devices the actual items that have changed, or invalidates them, based on their request rates. This approach, which we call distributed cache invalidation mechanism (DCIM), works on top of the COACS cooperative caching architecture. ADVANTAGES OF PROPOSED SYSTEM: TTL algorithms are popular due to their simplicity, sufficiently good performance, and flexibility to assign TTL values to individual data items. Also, they are attractive in mobile environments because of limited device energy and network bandwidth and frequent device disconnections. TTL algorithms are also completely client based and require minimal server functionality. From this perspective, TTL-based algorithms are more practical to deploy and are more scalable. This is the first complete client side approach employing adaptive TTL and achieving superior availability, delay, and traffic performance. SYSTEM ARCHITECTURE:
- 4. Overview of DCIM basic design.
- 5. Interactions between nodes in a DCIM system. ALGORITHMS USED: Algorithm 1- Decision flow at the server. Algorithm 2- Inner loop and outer loop (shaded part) functions.
- 6. Decision flow at the server.
- 7. Inner loop and outer loop (shaded part) functions.
- 8. SYSTEM CONFIGURATION:- HARDWARE CONFIGURATION:- Processor - Pentium –IV Speed - 1.1 Ghz RAM - 256 MB(min) Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA SOFTWARE REQUIREMENTS:- Operating System : LINUX Tool : Network Simulator-2 Front End : OTCL (Object Oriented Tool Command Language) REFERENCE: Kassem Fawaz, Student Member, IEEE, and Hassan Artail, Senior Member, IEEE-“DCIM: Distributed Cache Invalidation Method for Maintaining Cache Consistency in Wireless Mobile Networks”- IEEE TRANSACTIONS ON MOBILE COMPUTING VOL. 12, NO. 4, APRIL 2013.
- 10. CLOUING DOMAIN: WIRELESS NETWORK PROJECTS