Building a Recommendation Engine with Java EE
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The document discusses building a recommendation engine using Java EE and various NoSQL databases such as Neo4j. It covers aspects like data types, querying with TinkerPop, and provides examples of using the framework for creating and managing data relationships. Additionally, it includes practical implementations related to users' friends and preferences while emphasizing the migration to microservices.
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Kubernetes Security Act Now Before It’s Too LateMichael Furman In today's cloud-native landscape, Kubernetes has become the de facto standard for orchestrating containerized applications, but its inherent complexity introduces unique security challenges. Are you one YAML away from disaster?
This presentation, "Kubernetes Security: Act Now Before It’s Too Late," is your essential guide to understanding and mitigating the critical security risks within your Kubernetes environments. This presentation dives deep into the OWASP Kubernetes Top Ten, providing actionable insights to harden your clusters.
We will cover:
The fundamental architecture of Kubernetes and why its security is paramount.
In-depth strategies for protecting your Kubernetes Control Plane, including kube-apiserver and etcd.
Crucial best practices for securing your workloads and nodes, covering topics like privileged containers, root filesystem security, and the essential role of Pod Security Admission.
Don't wait for a breach. Learn how to identify, prevent, and respond to Kubernetes security threats effectively.
It's time to act now before it's too late!
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FIDO Seminar: Targeting Trust: The Future of Identity in the Workforce.pptx
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Your startup on AWS - How to architect and maintain a Lean and Mean account
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High Availability On-Premises FME Flow.pdf
High Availability On-Premises FME Flow.pdfSafe Software 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.
Oracle Cloud Infrastructure AI Foundations
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Crypto Super 500 - 14th Report - June2025.pdf
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“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 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—but 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.
War_And_Cyber_3_Years_Of_Struggle_And_Lessons_For_Global_Security.pdf
War_And_Cyber_3_Years_Of_Struggle_And_Lessons_For_Global_Security.pdfbiswajitbanerjee38 Russia is one of the most aggressive nations when it comes to state coordinated cyberattacks — and Ukraine has been at the center of their crosshairs for 3 years. This report, provided the State Service of Special Communications and Information Protection of Ukraine contains an incredible amount of cybersecurity insights, showcasing the coordinated aggressive cyberwarfare campaigns of Russia against Ukraine.
It brings to the forefront that understanding your adversary, especially an aggressive nation state, is important for cyber defense. Knowing their motivations, capabilities, and tactics becomes an advantage when allocating resources for maximum impact.
Intelligence shows Russia is on a cyber rampage, leveraging FSB, SVR, and GRU resources to professionally target Ukraine’s critical infrastructures, military, and international diplomacy support efforts.
The number of total incidents against Ukraine, originating from Russia, has steadily increased from 1350 in 2021 to 4315 in 2024, but the number of actual critical incidents has been managed down from a high of 1048 in 2022 to a mere 59 in 2024 — showcasing how the rapid detection and response to cyberattacks has been impacted by Ukraine’s improved cyber resilience.
Even against a much larger adversary, Ukraine is showcasing outstanding cybersecurity, enabled by strong strategies and sound tactics. There are lessons to learn for any enterprise that could potentially be targeted by aggressive nation states.
Definitely worth the read!
“Addressing Evolving AI Model Challenges Through Memory and Storage,” a Prese...
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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.
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https://lu.ma/9d5fntgj
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“From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...
“From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...Edge AI and Vision Alliance For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/from-enterprise-to-makers-driving-vision-ai-innovation-at-the-extreme-edge-a-presentation-from-sony-semiconductor-solutions/
Amir Servi, Edge Deep Learning Product Manager at Sony Semiconductor Solutions, presents the “From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge” tutorial at the May 2025 Embedded Vision Summit.
Sony’s unique integrated sensor-processor technology is enabling ultra-efficient intelligence directly at the image source, transforming vision AI for enterprises and developers alike. In this presentation, Servi showcases how the AITRIOS platform simplifies vision AI for enterprises with tools for large-scale deployments and model management.
Servi also highlights his company’s collaboration with Ultralytics and Raspberry Pi, which brings YOLO models to the developer community, empowering grassroots innovation. Whether you’re scaling vision AI for industry or experimenting with cutting-edge tools, this presentation will demonstrate how Sony is accelerating high-performance, energy-efficient vision AI for all.
PyData - Graph Theory for Multi-Agent Integration
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TrustArc Webinar - 2025 Global Privacy Survey
TrustArc Webinar - 2025 Global Privacy SurveyTrustArc How does your privacy program compare to your peers? What challenges are privacy teams tackling and prioritizing in 2025?
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This webinar features an expert panel discussion and data-driven insights to help you navigate the shifting privacy landscape. Whether you are a privacy officer, legal professional, compliance specialist, or security expert, this session will provide actionable takeaways to strengthen your privacy strategy.
This webinar will review:
- The emerging trends in data protection, compliance, and risk
- The top challenges for privacy leaders, practitioners, and organizations in 2025
- The impact of evolving regulations and the crossroads with new technology, like AI
Predictions for the future of privacy in 2025 and beyond
“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
“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
“From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...
“From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...Edge AI and Vision Alliance
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Building a Recommendation Engine with Java EE
- 1. Building a Recommendation Engine with Java EE EE4J Otávio Santana @otaviojava [email protected] [email protected] Hilmer Chona @hchona [email protected] MedellinJUG.org
- 2. Evolution
- 3. Goods ● Land ● Gold ● Oil ● Information
- 4. Machines
- 5. The information is not enough
- 6. Big Data
- 8. Next?
- 11. JCP
- 12. Ajax
- 13. Search
- 17. Collecting
- 18. Filtering
- 20. Stark Rogers Romanoff Banners New York São Paulo Moscow San Francisco Medellin Travel Business Fun
- 21. Reasons why travel 1. Fun 2. Conferences 3. Music 4. Geek Conf 5. Family 6. History 7. Language 8. Honeymoon 9. Work 10. Meetings 11. University 12. Shopping 13. Relax
- 22. More Information name Tony Stark nationality American hero Iron man power rich when 2011 why Save the world who flying type Fun name San Francisco country USA State California Founded 1776 Travels
- 23. NoSQL ● Database ● Doesn't use structure ● Not Transaction ● BASE ● Types
- 24. Graph ● Neo4j ● InfoGrid ● Sones ● HyperGraphDB Apollo Ares Kratoswas killed by was killed by Is brother killed killed
- 25. SQL Key-value Column Document Graph Table Bucket Column Family Collection Row Key/value pair Column Document Vertex Column Key/value pair Key/value pair Vertex and Edge property Relationship Link Edge SQL vs NoSQL
- 27. Graph Grace Hopper label Person id ada name Ada Lovelace occupation scientist Ada Lovelace label Person id grace name Grace Hopper occupation scientist Knows Vertex Vertex Edge
- 28. Graph Database
- 30. TinkerPop Grace Hopper label Person id grace name Grace Hopper occupation scientist Vertex grace = graph.addVertex( T.label, "person", "id", "grace", "name", "Grace Hopper", "occupation", "scientist");
- 33. Marketing Campaign ● Engineer ● Salary 3000 ● Age between 20 and 25 years
- 34. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT * FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25
- 36. Their Friends
- 37. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT * FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25//...joins Id know ... ...
- 38. TinkerPopGrace Hopper Ada Lovelace Knows g.V().has("occupation","Engineer") .has("salary", gt(3000)) .has("age", between(20, 25)) .out("knows");
- 39. Falling in love
- 40. SQLId name Occupation Salary age gender ... ... ... ... ... ... SELECT * FROM Person WHERE occupation = "Engineer" AND salary > 3000 and age BETWEEN 20 AND 25//...joins Id know ... ... Id love ... ...
- 41. TinkerPopGrace Hopper Ada Lovelace Knows g.V().has("occupation","Engineer") .has("salary", gt(3000)) .has("age", between(20, 25)) .outE("knows") .has("feel", "love") .bothV();
- 42. JNoSQL ● Mapping API ● Communication API ● No lock-in ● Divide and conquer DAO Mapping Communication Document Key Column Graph DIANA ARTEMIS JNoSQL Data Tier
- 43. Artemis● CDI Based ● Diana Based ● Annotation Based ● Events to insert, delete, update ● Supports to Bean Validation ● Configurable and Extensible ● Query Method
- 44. Graph Database
- 45. Graph Poliana label Book age 2007 name The Shack The Shack label Person name Poliana age 25 reads where Brazil
- 46. Graph Person poliana = graphTemplate.insert(Person.builder() .withName("Poliana").withAge(25).build()); Book shack = graphTemplate.insert(Book.builder().withAge(2007) .withName("The Shack").build()); EdgeEntity<Person, Book> reads = graphTemplate.edge(poliana, "reads", shack); reads.add("where", "Brazil");
- 47. Marketing Campaign ● Engineer ● Salary 3000 ● Age between 20 and 25 years
- 48. Developers List<Person> developers = graph.getTraversalVertex() .has("salary", gte(3_000D)) .has("age", between(20, 25)) .has("occupation", "Developer") .<Person>stream().collect(toList());
- 49. Their Friends
- 50. People who developer knows List<Person> result = graph.getTraversalVertex() .has("salary", gte(3_000D)) .has("age", between(20, 25)) .has("occupation", "Developer") .out("knows")
- 51. Falling in love
- 52. People who developer loves List<Person> love = graph.getTraversalVertex() .has("salary", gte(3_000D)) .has("age", between(20, 25)) .has("occupation", "Developer") .outE("knows") .has("feel", "love") .bothV() .<Person>stream() .distinct() .collect(toList());
- 53. Book Recommendation Software Romance NoSQL Java Micro Services Effective Java The Shack Migrating to Microservice Databases NoSQL Distilled
- 54. Software Categories List<String> result = graph.getTraversalVertex() .hasLabel("Category") .has("name", "Software") .in("is") .hasLabel("Category").<Category>stream() .map(Category::getName) .collect(toList());
- 55. Query Method interface PersonRepository extends Repository<Person, Long> { List<Person> findByAddress(String address); Stream<Person> findByName(String name); Stream<Person> findByNameAndKnowsOutV(String name); Stream<Person> findByNameAndBothV(String name, String label); Optional<Person> findByNickname(String nickname); void deleteByNickName(String nickname); }
- 56. Demo JNoSQL Configuration CDI 2.0 with Java SE Neo4J + Docker