Preprocessing
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The document outlines the key steps in preprocessing text data: 1. Tokenization breaks text into individual words by removing punctuation and numbers and splitting on spaces. 2. Stop words, like "the" and "an", are removed to focus on meaningful words. 3. Stemming reduces words to their root form using an algorithm like Porter stemming. This groups related words together but does not always find the true root. 4. A vocabulary is created by taking the union of all words from all documents after stemming to prepare for further analysis.
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Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes.
To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.High Availability On-Premises FME Flow.pdf
High Availability On-Premises FME Flow.pdfSafe Software
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FME Flow is a highly robust tool for transforming data both automatically and by user-initiated workflows. At the Finnish telecommunications company Elisa, FME Flow serves processes and internal stakeholders that require 24/7 availability from underlying systems, while imposing limitations on the use of cloud based systems. In response to these business requirements, Elisa has implemented a high-availability on-premises setup of FME Flow, where all components of the system have been duplicated or clustered. The goal of the presentation is to provide insights into the architecture behind the high-availability functionality. The presentation will show in basic technical terms how the different parts of the system work together. Basic level understanding of IT technologies is required to understand the technical portion of the presentation, namely understanding the purpose of the following components: load balancer, FME Flow host nodes, FME Flow worker nodes, network file storage drives, databases, and external authentication services. The presentation will also outline our lessons learned from the high-availability project, both benefits and challenges to consider.Reducing Conflicts and Increasing Safety Along the Cycling Networks of East-F...
Reducing Conflicts and Increasing Safety Along the Cycling Networks of East-F...Safe Software
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In partnership with the Belgian Province of East-Flanders this project aimed to reduce conflicts and increase safety along a cycling route between the cities of Oudenaarde and Ghent. To achieve this goal, the current cycling network data needed some extra key information, including: Speed limits for segments, Access restrictions for different users (pedestrians, cyclists, motor vehicles, etc.), Priority rules at intersections. Using a 360¡ã camera and GPS mounted on a measuring bicycle, we collected images of traffic signs and ground markings along the cycling lanes building up mobile mapping data. Image recognition technologies identified the road signs, creating a dataset with their locations and codes. The data processing entailed three FME workspaces. These included identifying valid intersections with other networks (e.g., roads, railways), creating a topological network between segments and intersections and linking road signs to segments and intersections based on proximity and orientation. Additional features, such as speed zones, inheritance of speed and access to neighbouring segments were also implemented to further enhance the data. The final results were visualized in ArcGIS, enabling analysis for the end users. The project provided them with key insights, including statistics on accessible road segments, speed limits, and intersection priorities. These will make the cycling paths more safe and uniform, by reducing conflicts between users.FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptx
FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptxFIDO Alliance
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FIDO Seminar: Evolving Landscape of Post-Quantum Cryptography¡°Why It¡¯s Critical to Have an Integrated Development Methodology for Edge AI,...
¡°Why It¡¯s Critical to Have an Integrated Development Methodology for Edge AI,...Edge AI and Vision Alliance
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/why-its-critical-to-have-an-integrated-development-methodology-for-edge-ai-a-presentation-from-lattice-semiconductor/
Sreepada Hegade, Director of ML Systems and Software at Lattice Semiconductor, presents the ¡°Why It¡¯s Critical to Have an Integrated Development Methodology for Edge AI¡± tutorial at the May 2025 Embedded Vision Summit.
The deployment of neural networks near sensors brings well-known advantages such as lower latency, privacy and reduced overall system cost¡ªbut also brings significant challenges that complicate development. These challenges can be addressed effectively by choosing the right solution and design methodology. The low-power FPGAs from Lattice are well poised to enable efficient edge implementation of models, while Lattice¡¯s proven development methodology helps to mitigate the challenges and risks associated with edge model deployment.
In this presentation, Hegade explains the importance of an integrated framework that tightly consolidates different aspects of edge AI development, including training, quantization of networks for edge deployment, integration with sensors and inferencing. He also illustrates how Lattice¡¯s simplified tool flow helps to achieve the best trade-off between power, performance and efficiency using low-power FPGAs for edge deployment of various AI workloads.¡°Addressing Evolving AI Model Challenges Through Memory and Storage,¡± a Prese...
¡°Addressing Evolving AI Model Challenges Through Memory and Storage,¡± a Prese...Edge AI and Vision Alliance
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/addressing-evolving-ai-model-challenges-through-memory-and-storage-a-presentation-from-micron/
Wil Florentino, Senior Segment Marketing Manager at Micron, presents the ¡°Addressing Evolving AI Model Challenges Through Memory and Storage¡± tutorial at the May 2025 Embedded Vision Summit.
In the fast-changing world of artificial intelligence, the industry is deploying more AI compute at the edge. But the growing diversity and data footprint of transformers and models such as large language models and large multimodal models puts a spotlight on memory performance and data storage capacity as key bottlenecks. Enabling the full potential of AI in industries such as manufacturing, automotive, robotics and transportation will require us to find efficient ways to deploy this new generation of complex models.
In this presentation, Florentino explores how memory and storage are responding to this need and solving complex issues in the AI market. He examines the storage capacity and memory bandwidth requirements of edge AI use cases ranging from tiny devices with severe cost and power constraints to edge servers, and he explains how new memory technologies such as LPDDR5, LPCAMM2 and multi-port SSDs are helping system developers to meet these challenges.MuleSoft for AgentForce : Topic Center and API Catalog
MuleSoft for AgentForce : Topic Center and API Catalogshyamraj55
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This presentation dives into how MuleSoft empowers AgentForce with organized API discovery and streamlined integration using Topic Center and the API Catalog. Learn how these tools help structure APIs around business needs, improve reusability, and simplify collaboration across teams. Ideal for developers, architects, and business stakeholders looking to build a connected and scalable API ecosystem within AgentForce.Crypto Super 500 - 14th Report - June2025.pdf
Crypto Super 500 - 14th Report - June2025.pdfStephen Perrenod
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This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.The State of Web3 Industry- Industry Report
The State of Web3 Industry- Industry ReportLiveplex
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Web3 is poised for mainstream integration by 2030, with decentralized applications potentially reaching billions of users through improved scalability, user-friendly wallets, and regulatory clarity. Many forecasts project trillions of dollars in tokenized assets by 2030 , integration of AI, IoT, and Web3 (e.g. autonomous agents and decentralized physical infrastructure), and the possible emergence of global interoperability standards. Key challenges going forward include ensuring security at scale, preserving decentralization principles under regulatory oversight, and demonstrating tangible consumer value to sustain adoption beyond speculative cycles.Mastering AI Workflows with FME - Peak of Data & AI 2025
Mastering AI Workflows with FME - Peak of Data & AI 2025Safe Software
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Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FME¡¯s powerful tools for training, optimization, and seamless integrationDown the Rabbit Hole ¨C Solving 5 Training Roadblocks
Down the Rabbit Hole ¨C Solving 5 Training RoadblocksRustici Software
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Feeling stuck in the Matrix of your training technologies? You¡¯re not alone. Managing your training catalog, wrangling LMSs and delivering content across different tools and audiences can feel like dodging digital bullets. At some point, you hit a fork in the road: Keep patching things up as issues pop up¡ or follow the rabbit hole to the root of the problems.
Good news, we¡¯ve already been down that rabbit hole. Peter Overton and Cameron Gray of Rustici Software are here to share what we found. In this webinar, we¡¯ll break down 5 training roadblocks in delivery and management and show you how they¡¯re easier to fix than you might think.Viral>Wondershare Filmora 14.5.18.12900 Crack Free Download
Viral>Wondershare Filmora 14.5.18.12900 Crack Free DownloadPuppy jhon
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Wondershare Filmora Crack is a user-friendly video editing software designed for both beginners and experienced users.
June Patch Tuesday
June Patch TuesdayIvanti
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Ivanti¡¯s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we¡¯ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities. ¡°Why It¡¯s Critical to Have an Integrated Development Methodology for Edge AI,...
¡°Why It¡¯s Critical to Have an Integrated Development Methodology for Edge AI,...Edge AI and Vision Alliance
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¡°Addressing Evolving AI Model Challenges Through Memory and Storage,¡± a Prese...
¡°Addressing Evolving AI Model Challenges Through Memory and Storage,¡± a Prese...Edge AI and Vision Alliance
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Preprocessing
- 1. Steps involved in Preprocessing : 1.Tokenization : ¡ñ Tokenization : The process of breaking a stream of text into words ¡ñ Removal of Punctuation marks and numbers ¡ñ Replacing ¡®n¡¯ by Spaces ¡ñ Splitting the string by space as a delimiter ¡ñ Tokens
- 2. Graphical view of steps in Tokenization : Removal of Replacing n Using Stream of text. spaces as punctuation by Spaces marks delimiter Tokens (words)
- 3. 2. Removal of stop words : ¡ñ Passing the list of Tokens. ¡ñ Removing the unnecessary words like the, an, so, after, all, etc (stop words). ¡ñ Output : A list of meaningful words.
- 4. 3.Stemming : ¡ñ Stemming : The process for reducing inflected words to their stem, base or root form. For example : Stemming algorithm reduces ¡°fishing", "fished", "fish", and "fisher" to the root word "fish¡°. ¡ñ Stemmer used : Porter Stemmer Algorithm. ¡ñ Removing ¡®¨Cee¡¯,¡¯ ¨Ced¡¯, ¡®-ing¡¯, ¡®-ence¡¯, ¡®-er¡¯, etc. & adding ¡®y¡¯, ¡®I¡¯ as required. ¡ñ Doesn¡¯t give accurate roots . Example : stem(flying) =fli stem(fly)=fli ¡ñ Same roots for all inflected forms ¨C serves our purpose
- 5. 4. Vocabulary creation :- ¡ñ Vocabulary : Generally, vocabulary is the set of words. ¡ñ Vocabulary = Union of words from all files. ¡ñ For each document : Converting list obtained after stemming into Set & taking union. ¡ñ Processed further for Tf-idf evaluation.