Unsupervised learning with Spark
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This document discusses unsupervised learning techniques, including distances, clustering algorithms, and examples from Styria practice. It introduces common distance measures like Euclidean, Manhattan, and Mahalanobis distances. For clustering, it describes K-means clustering and provides a Spark example. It also discusses using convolutional neural network outputs as unsupervised learning features and shows examples of semi-manual photo clustering, T-SNE concept visualization, and automatic learned hierarchies from Styria projects.
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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.
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Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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).
Northern Engraving | Nameplate Manufacturing Process - 2024
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!
The Microsoft 365 Migration Tutorial For Beginner.pptx
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You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
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Unsupervised learning with Spark
- 1. Marko Velic PhD Data Science Department Styria Medijski Servisi d.o.o. marko.velic@styria.hr UNSUPERVISED LEARNING (WITH SPARK)
- 2. CONTENTS Distances • Eucledian • Manhattan • Mahalanobis • Cosine Similarity Clustering • K-Means • Example (Spark) Examples from Styria practice (not Spark – for now) 10.03.2016 2
- 4. UNSUPERVISED LEARNING Opservations are not assigned to classes Computer program is not ‘supervised’ throughout the learning process Usually the task is to find ‘meaningful’ groups within data Decision is made based on distances i.e. similarities among data points 10.03.2016 4
- 5. DISTANCES 10.03.2016 5 • To decide upon the groups we have to introduce similarity measure or contrary – a distance measure • Pitagora’s theorem – Euclidean distance • dist((2, -1), (-2, 2))= √((2 - (-2))² + ((-1) - 2)²) = √((2 + 2)² + (-1 - 2)²) = √((4)² + (-3)²) = √(16 + 9) = √25 = 5
- 6. DISTANCES & APPROACHES 10.03.2016 6 Source: http://en.wikipedia.org/wiki/Man hattan_distance Manhattan/Cityblock/Taxicab • dist((x, y), (a, b)) = |x - a| + |y - b| Normalization! Mahalanobis – considers variance • “multidimensional z-score” Cosine similarity Autoencoders – ‘unsupervised’ neural nets Non-unsupervised but based on distances • ReliefF measure, KNN classifier ... etc...
- 7. K-MEANS 7 Simplified: 1. Randomly place centroids 2. Find the closest 3. Put centroid in the middle 4. GOTO 2 Image source: http://www.javabeat.net/2011/05/k-means- clustering-algorithms-in-mahout/
- 8. DEMO (SPARK!) K-means clustering of photos (ie. their vector representations) Convolutional neural network as a supervised model and its outputs as features for unsupervised models Vector representations after the pooling layers, after every convolutional layer (Caffe) Clustering in Spark 8
- 10. SEMI-MANUAL CLUSTERING OF PHOTOS 10Gruping photos based in visual features, Enes Deumić, Styria Data Science Team
- 11. SEMI-MANUAL CLUSTERING OF PHOTOS 11Gruping photos based in visual features, Enes Deumić, Styria Data Science Team
- 12. NATURAL LANGUAGE PROCESSING 10.03.2016 12 T-sne concept visualization; vecernji.hr, Styria Data Science Team
- 13. AUTOMATIC (LEARNED) HIERARCHIES 13 Hierarchical clustering, Florijan Stamenković, Styria Data Science Team
- 15. CONCLUSION Distances • Eucledian • Manhattan • Mahalanobis • Cosine Similarity Clustering • K-Means We can nicely combine supervised and unsupervised features SparkNet: Training Deep Networks in Spark http://arxiv.org/pdf/1511.06051v4.pdf https://news.developer.nvidia.com/caffe-on-spark-for- deep-learning-from-yahoo/ 10.03.2016 15
- 16. THANK YOU!