Cassandra is pretty awesome, sure I am biased, but it rocks. Always on, tuneable consistency and multi-master architecture? Let’s get our web scale on and build a highly available app that never goes down!
Hold on a second. There is one key piece of the puzzle that has a massive impact on your applications availability: the client driver.
In this talk we will go through the how to best configure your clients to make the most of failure handling and tuneable consistency in Cassandra.
Natural language processing techniques transition from machine learning to de...Divya Gera
Natural Language processing, its need, business applications, NLP with machine learning, Text data preprocessing for machine learning, NLP with Deep Learning.
PR-214: FlowNet: Learning Optical Flow with Convolutional NetworksHyeongmin Lee
제 PR12 첫번째 발표 논문은 FlowNet이라는 논문입니다.
Optical Flow는 비디오의 인접한 Frame에 대하여 각 Pixel이 첫 번째 Frame에서 두 번째 Frame으로 얼마나 이동했는지의 Vector를 모든 위치에 대하여 나타낸 Map입니다. Video에 Motion을 분석하는 일은 매우 중요하기 때문에, 이러한 Optical Flow 역시 굉장히 중요한 요소 중 하나인데요, 이번 영상에서는 고전적인 Computer Vision에서 쓰였던 다양한 Optical Flow 알고리즘들과, Deep Learning Based로 Optical Flow를 구하는 Neural Network인 FlowNet에 대하여 알아보겠습니다.
감사합니다!!
영상 링크: https://youtu.be/Z_t0shK98pM
논문 링크: http://openaccess.thecvf.com/content_iccv_2015/html/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.html
Presented at All Things Open 2022
Presented by Danny McCormick
Title: Streaming Data Pipelines With Apache Beam
Abstract: Handling big data presents big problems. Along with traditional concerns like scalability and performance, the increasingly common need for live streaming data processing introduces problems like late or incomplete data from flaky data sources. Apache Beam is an open source, unified model for defining both batch and streaming data-parallel processing pipelines that addresses these challenges. Using one of the open source Beam SDKs, you can build a program that defines a pipeline to be executed by one of Beam’s supported distributed processing back-ends, which include Apache Flink, Apache Spark, and Google Cloud Dataflow.
This talk will explore some problems associated with processing large datasets at scale and how you can write Apache Beam pipelines that address those issues. It will include a demo of a basic Beam streaming pipeline.
Takeaways: an understanding of some challenges associated with large datasets, the Apache Beam model, and how to write a basic Beam streaming pipeline
Audience: anyone dealing with big datasets or interested in data processing at scale.
Cassandra is pretty awesome, sure I am biased, but it rocks. Always on, tuneable consistency and multi-master architecture? Let’s get our web scale on and build a highly available app that never goes down!
Hold on a second. There is one key piece of the puzzle that has a massive impact on your applications availability: the client driver.
In this talk we will go through the how to best configure your clients to make the most of failure handling and tuneable consistency in Cassandra.
Natural language processing techniques transition from machine learning to de...Divya Gera
Natural Language processing, its need, business applications, NLP with machine learning, Text data preprocessing for machine learning, NLP with Deep Learning.
PR-214: FlowNet: Learning Optical Flow with Convolutional NetworksHyeongmin Lee
제 PR12 첫번째 발표 논문은 FlowNet이라는 논문입니다.
Optical Flow는 비디오의 인접한 Frame에 대하여 각 Pixel이 첫 번째 Frame에서 두 번째 Frame으로 얼마나 이동했는지의 Vector를 모든 위치에 대하여 나타낸 Map입니다. Video에 Motion을 분석하는 일은 매우 중요하기 때문에, 이러한 Optical Flow 역시 굉장히 중요한 요소 중 하나인데요, 이번 영상에서는 고전적인 Computer Vision에서 쓰였던 다양한 Optical Flow 알고리즘들과, Deep Learning Based로 Optical Flow를 구하는 Neural Network인 FlowNet에 대하여 알아보겠습니다.
감사합니다!!
영상 링크: https://youtu.be/Z_t0shK98pM
논문 링크: http://openaccess.thecvf.com/content_iccv_2015/html/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.html
Presented at All Things Open 2022
Presented by Danny McCormick
Title: Streaming Data Pipelines With Apache Beam
Abstract: Handling big data presents big problems. Along with traditional concerns like scalability and performance, the increasingly common need for live streaming data processing introduces problems like late or incomplete data from flaky data sources. Apache Beam is an open source, unified model for defining both batch and streaming data-parallel processing pipelines that addresses these challenges. Using one of the open source Beam SDKs, you can build a program that defines a pipeline to be executed by one of Beam’s supported distributed processing back-ends, which include Apache Flink, Apache Spark, and Google Cloud Dataflow.
This talk will explore some problems associated with processing large datasets at scale and how you can write Apache Beam pipelines that address those issues. It will include a demo of a basic Beam streaming pipeline.
Takeaways: an understanding of some challenges associated with large datasets, the Apache Beam model, and how to write a basic Beam streaming pipeline
Audience: anyone dealing with big datasets or interested in data processing at scale.
Log System As Backbone – How We Built the World’s Most Advanced Vector Databa...StreamNative
Milvus is an open-source vector database that leverages a novel data fabric to build and manage vector similarity search applications. As the world's most popular vector database, it has already been adopted in production by thousands of companies around the world, including Lucidworks, Shutterstock, and Cloudinary. With the launch of Milvus 2.0, the community aims to introduce a cloud-native, highly scalable and extendable vector similarity solution, and the key design concept is log as data.
Milvus relies on Pulsar as the log pub/sub system. Pulsar helps Milvus to reduce system complexity by loosely decoupling each micro service, making the system stateless by disaggregating log storage and computation, which also makes the system further extendable. We will introduce the overview design, the implementation details of Milvus and its roadmap in this topic.
Takeaways:
1) Get a general idea about what is a vector database and its real-world use cases.
2) Understand the major design principles of Milvus 2.0.
3) Learn how to build a complex system with the help of a modern log system like Pulsar.
Whoops, The Numbers Are Wrong! Scaling Data Quality @ NetflixDataWorks Summit
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into details of our data quality solution. We’ll cover what worked, what didn’t work so well, and what we plan to work on next. We’ll conclude with some tips and lessons learned for ensuring data quality on big data.
Building a Real-Time Analytics Application with Apache Pulsar and Apache PinotAltinity Ltd
Building a Real-Time Analytics Application with
Apache Pulsar and Apache Pinot
While the demands for real-time analytics are growing in leaps and bounds, the analytics software must rely on streaming platforms for ingesting high volumes of data that's traveling in lightning speed down the pipeline. We will take a look at 2 powerful open source Apache platforms: Pulsar and Pinot, that work hand-in-hand together to deliver the analytical results which bring great value to your systems.
Presenters: Mary Grygleski - Streaming Developer Advocate &
Mark Needham - Developer Relations Engineer at StarTree
Note: This webinar will be recorded and later posted on our Webinar page (https://altinity.com/webinarspage/) or Altinity official Youtube channel (https://www.youtube.com/@Altinity).
From the NYC Machine Learning meetup on Jan 17, 2013: http://www.meetup.com/NYC-Machine-Learning/events/97871782/
Video is available here: http://vimeo.com/57900625
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...DataStax
Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.
About the Speakers
Prasanna Padmanabhan Engineering Manager, Netflix
Prasanna leads the Data Systems for Personalization team at Netflix. His primary focus is on building various big data infrastructure components that help their algorithmic engineers to innovate faster and improve personalization for Netflix members. In the past, he has built distributed data systems that leverages both batch and stream processing.
Roopa Tangirala Engineering Manager, Netflix
Roopa Tangirala is an experienced engineering leader with extensive background in databases, be they distributed or relational. She manages the database engineering team at Netflix responsible for operating cloud persistent and semipersistent runtime stores for Netflix, which includes Cassandra, Elasticsearch, Dynomite and MySQL databases, by ensuring data availability, durability, and scalability to meet the growing business needs.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAILviv Startup Club
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
AI & BigData Online Day 2021
Website - https://aiconf.com.ua/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)AI Frontiers
Spoken dialogue systems have nearly 30 years of history, which can be divided into three generations: symbolic-rule or template based (before late 90’s), statistical learning based, and deep learning based (since 2014). This talk will briefly survey the history of conversational systems, and analyze why and how the underlying technology moved from one generation to the next. Strengths and weaknesses of these three largely distinct types of bot technology are examined and future directions are discussed. Part of this talk is based on my recent article: How deep reinforcement learning can help chatbots, Venturebeat, Aug 2016.
2017 Tutorial - Deep Learning for Dialogue SystemsMLReview
In the past decade, goal-oriented spoken dialogue systems (SDS) have been the most promi-nent component in today’s virtual personal assistants (VPAs). Among these VPAs, Microsoft’s Cortana, Apple’s Siri, Amazon Alexa, Google Assistant, and Facebook’s M, have incorporated SDS modules in various devices, which allow users to speak naturally in order to finish tasks more efficiently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applicatins of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Thus, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges. We target an audience of students and practitioners who have some deep learning background and want to get more familiar with conversational dialog systems.
Log System As Backbone – How We Built the World’s Most Advanced Vector Databa...StreamNative
Milvus is an open-source vector database that leverages a novel data fabric to build and manage vector similarity search applications. As the world's most popular vector database, it has already been adopted in production by thousands of companies around the world, including Lucidworks, Shutterstock, and Cloudinary. With the launch of Milvus 2.0, the community aims to introduce a cloud-native, highly scalable and extendable vector similarity solution, and the key design concept is log as data.
Milvus relies on Pulsar as the log pub/sub system. Pulsar helps Milvus to reduce system complexity by loosely decoupling each micro service, making the system stateless by disaggregating log storage and computation, which also makes the system further extendable. We will introduce the overview design, the implementation details of Milvus and its roadmap in this topic.
Takeaways:
1) Get a general idea about what is a vector database and its real-world use cases.
2) Understand the major design principles of Milvus 2.0.
3) Learn how to build a complex system with the help of a modern log system like Pulsar.
Whoops, The Numbers Are Wrong! Scaling Data Quality @ NetflixDataWorks Summit
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into details of our data quality solution. We’ll cover what worked, what didn’t work so well, and what we plan to work on next. We’ll conclude with some tips and lessons learned for ensuring data quality on big data.
Building a Real-Time Analytics Application with Apache Pulsar and Apache PinotAltinity Ltd
Building a Real-Time Analytics Application with
Apache Pulsar and Apache Pinot
While the demands for real-time analytics are growing in leaps and bounds, the analytics software must rely on streaming platforms for ingesting high volumes of data that's traveling in lightning speed down the pipeline. We will take a look at 2 powerful open source Apache platforms: Pulsar and Pinot, that work hand-in-hand together to deliver the analytical results which bring great value to your systems.
Presenters: Mary Grygleski - Streaming Developer Advocate &
Mark Needham - Developer Relations Engineer at StarTree
Note: This webinar will be recorded and later posted on our Webinar page (https://altinity.com/webinarspage/) or Altinity official Youtube channel (https://www.youtube.com/@Altinity).
From the NYC Machine Learning meetup on Jan 17, 2013: http://www.meetup.com/NYC-Machine-Learning/events/97871782/
Video is available here: http://vimeo.com/57900625
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...DataStax
Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.
About the Speakers
Prasanna Padmanabhan Engineering Manager, Netflix
Prasanna leads the Data Systems for Personalization team at Netflix. His primary focus is on building various big data infrastructure components that help their algorithmic engineers to innovate faster and improve personalization for Netflix members. In the past, he has built distributed data systems that leverages both batch and stream processing.
Roopa Tangirala Engineering Manager, Netflix
Roopa Tangirala is an experienced engineering leader with extensive background in databases, be they distributed or relational. She manages the database engineering team at Netflix responsible for operating cloud persistent and semipersistent runtime stores for Netflix, which includes Cassandra, Elasticsearch, Dynomite and MySQL databases, by ensuring data availability, durability, and scalability to meet the growing business needs.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAILviv Startup Club
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
AI & BigData Online Day 2021
Website - https://aiconf.com.ua/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)AI Frontiers
Spoken dialogue systems have nearly 30 years of history, which can be divided into three generations: symbolic-rule or template based (before late 90’s), statistical learning based, and deep learning based (since 2014). This talk will briefly survey the history of conversational systems, and analyze why and how the underlying technology moved from one generation to the next. Strengths and weaknesses of these three largely distinct types of bot technology are examined and future directions are discussed. Part of this talk is based on my recent article: How deep reinforcement learning can help chatbots, Venturebeat, Aug 2016.
2017 Tutorial - Deep Learning for Dialogue SystemsMLReview
In the past decade, goal-oriented spoken dialogue systems (SDS) have been the most promi-nent component in today’s virtual personal assistants (VPAs). Among these VPAs, Microsoft’s Cortana, Apple’s Siri, Amazon Alexa, Google Assistant, and Facebook’s M, have incorporated SDS modules in various devices, which allow users to speak naturally in order to finish tasks more efficiently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applicatins of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Thus, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems, and summarizing the challenges. We target an audience of students and practitioners who have some deep learning background and want to get more familiar with conversational dialog systems.
An overview of some key concepts of chatbots, with some do's and don'ts.
We will happily present the high-resolution version of this presentation, extended with additional detailed slides, and a clear explanation at your offices. Contact us for that.
SXSW 2015 Shredding Wireframes: Intro to Rapid PrototypingKyle Outlaw
This 2015 workshop at SXSW covered:
- Current state of UX, limitations of common deliverables (e.g. wireframes)
- POV on prototyping and why it's important in user experience design
- Available tools (Invision, Justinmind, etc)
- Case study: using the Tech Summit app as an example
- What about the spec?
Key Takeaways
- Wireframes are near obsolete
- Why prototype
- Available methods
- Learn about available tools, pros and cons
- Documenting detailed functionality (annotating the prototype)
Games for Analysis of Technologies in Human-Intensive Systems - Dr. Tim Dasey...SeriousGamesAssoc
Recognizing what technologies will be useful prior to prototyping is error prone, with resulting higher-than-acceptable developmental rejection rates. MIT Lincoln Laboratory (MIT LL) has been using serious games to aid in technology assessment programs. This approach combines economic game theory with rapid-play, rapidly-developed digital simulations to collect quantitative data, improve qualitative feedback, and crowdsource the ingenuity of human experts.
AAMAS-2006 TANDEM Design Method (poster format)Steve Goschnick
Literally an A2-sized poster as an Overview of the TANDEM Design Method for mashups of web services and similar user-created content. Complemented a short paper presented at the AAMAS Conference in 2006, held at the Future University-Hakodate, Japan.
Identifying MMORPG Bots: A Traffic Analysis ApproachAcademia Sinica
MMORPGs have become extremely popular among network gamers. Despite their success, one of MMORPG’s greatest challenges is the increasing use of game bots, i.e., autoplaying game clients. The use of game bots is considered unsportsmanlike and is therefore forbidden. To keep games in order, game police, played by actual human players, often patrol game zones and question suspicious players. This practice, however, is labor-intensive and ineffective. To address this problem, we analyze the traffic generated by human players vs. game bots and propose solutions to automatically identify game bots.
Taking Ragnarok Online, one of the most popular MMOGs, as our subject, we study the traffic generated by mainstream game bots and human players. We find that their traffic is distinguishable by: 1) the regularity in the release time of client commands, 2) the trend and magnitude of traffic burstiness in multiple time scales, and 3) the sensitivity to network conditions. We propose four strategies and two integrated schemes to identify bots. For our data sets, the conservative scheme completely avoids making false accusations against bona fide players, while the progressive scheme tracks game bots down more aggressively. Finally, we show that the proposed methods are generalizable to other games and robust against counter-measures from bot developers.
We have envisioned that computers will understand natural language and predict what we need help in order to complete tasks via conversational interactions. This talk focuses on context-aware understanding in different levels: 1) word-level contexts in sentences 2) sentence-level contexts in dialogues. Word-level contexts contribute both semantic and syntactic relations, which benefit sense representation learning and knowledge-guided language understanding. Also, sentence-level contexts may significantly affect dialogue-level performance. This talk investigates how misunderstanding of a single-turn utterance degrades the success rate of an end-to-end reinforcement learning based dialogue system. Then we will highlight challenges and recent trends driven by deep learning and intelligent assistants.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Monitoring Java Application Security with JDK Tools and JFR Events
End-to-End Task-Completion Neural Dialogue Systems
1. End-to-End Task-Completion
Neural Dialogue Systems
Xiujun Li Yun-Nung (Vivian) Chen Lihong Li Jianfeng Gao Asli Celikyilmaz
The 8th International Joint Conference on Natural Language Processing
https://github.com/MiuLab/TC-Bot
1
2. Dialogue Interactions
“I want to talk” Chit-chat
“I have a question” Information consumption
“I need to get this done” Task completion
Task-oriented
Non task-oriented
2
3. Dialogue Interactions
“I want to talk” Chit-chat
“I have a question” Information consumption
“I need to get this done” Task completion
• What is the paper review schedule?
• Which room is the dialogue tutorial in?
• When is the IJCNLP 2017 conference?
• What does NLP stand for?
3
4. Dialogue Interactions
“I want to talk” Chit-chat
“I have a question” Information consumption
“I need to get this done” Task completion
• Book me the flight from Seattle to Taipei
• Reserve a table at Din Tai Fung for 5 people, 7PM tonight
• Purchase two movie tickets about Thor 3 at 9PM tonight
4
Goal: navigating users to complete tasks (transactions, API calls, etc.)
Challenges: including multiple information consumption
5. Task-Completion Dialogues
• Crowdsourced data
– Domain: Movie
– Dialogue act: intent & slot-value pairs
• Problem setting
– User wants to achieve a specific task, i.e. buying a movie ticket
– Agent tries to understand user’s goal via dialogue before booking the ticket
Turn Speaker Intent & Slots Utterance
1 User greeting(greeting=hi) Hi
1 User
request(moviename; city=Houma;
state=Louisiana; date=this week; genre=foreign)
Are there any good movies showing around
Houma Louisiana this week?
2 Agent request(date)
What date would you like me to look for a
reservation
3 User inform(date=9th) How about the 9th
4 Agent inform(result={}; genre=foreign)
Unfortunately here are no foreign movies
playing at this time
5
6. Rule-Based Agent
• Gather all information via a
series of requests (form-filling)
• Issues
1) No notion about what requests
can be skipped
User goal – Two tickets for “Deadpool” tomorrow
9PM at AMC Pacific Place 11 theater, Seattle.
User: Do you have any ticket for movie ‘Deadpool’ at 9:00 pm?
Agent: Which theater do you want?
User: AMC pacific place 11 theater, please!
Agent: How many tickets do you want?
User: Two!
Agent: What date?
User: Tomorrow!
Agent: Which city do you want?
User: Seattle!
Agent: Do you have any specification for zip code?
User: No, I do not care.
Agent: Do you have any distance constraint?
User: No, I do not care.
Agent: How about video format? 2d? Imax 3d?
User: Any kind is okay.
Agent: Do you have any constraint to price?
User: No!
Agent: Okay, I will issue 2 tickets for you, tomorrow 9:00 pm at
AMC pacific place 11 theater, Seattle, movie ‘Deadpool’.
User: Okay, thanks.
Agent: Thanks!
Wasteful! (very likely
the user does not care)
6
7. Rule-Based Agent
• Gather all information via a
series of requests (form-filling)
• Issues
1) No notion about what requests
can be skipped
2) No principle way to deal with
errors/uncertainty in language
understanding
User goal – Two tickets for “Hail Caesar” tomorrow
7:15PM at AMC Lowes Oak tree 6, Seattle.
User: Do you any ticket for movie ‘Hail Caesar’ at theater
AMC Lowes Oak Tree 6?
Agent: What start time do you prefer?
User: 7:15 pm Tomorrow
Agent: Which city you are looking for?
User: Seattle
Agent: How many tickets do you want?
User: Two
Agent: Okay, I have issued two tickets for movie ‘Hail Caeser’
tomorrow 7:50 pm at theater AMC Lowes Oak Tree 6, Seattle.
User: Oh, no.
Agent: Thanks!
Task failed!
(cannot correct the errors from proceeding component)
7
8. Rule-Based Agent
• Gather all information via a
series of requests (form-filling)
• Issues
1) No notion about what requests
can be skipped
2) No principle way to deal with
errors/uncertainty in language
understanding
3) Do not know how to
recommend options if the
user’s goal is not achievable
8
9. Task-Oriented Dialogue System Framework
Language Understanding
(LU)
Natural Language
Generation (NLG)
Semantic Frame
request_ticket
(moviename=Star Wars;
numberofpeople=5)
System Action/ Policy
request(theater)
Text Response
“Which theater do you prefer?”
Text Input
“Book 5 tickets for movie Star Wars”
Knowledge
Database
Dialogue State Tracking
(DST)
Dialogue Policy
Learning
Dialogue Management (DM)
9
State Representation
10. Task-Oriented Dialogue System Framework
Language Understanding
(LU)
Natural Language
Generation (NLG)
Dialogue Act
Natural Language
Natural Language
Knowledge
Database
Dialogue State Tracking
(DST)
Dialogue Policy
Learning
Dialogue Management (DM)
Dialogue Act
10
State Representation
User
System
11. User Simulation in Frame-Level Semantics
User
Dialogue Act
Knowledge
Database
Dialogue State Tracking
(DST)
Dialogue Policy
Learning
Dialogue Management (DM)
Error Model Controller
• Recognition error
• LU error
User Model
User Simulation
System
Dialogue Act
User
Dialogue Act
11
State Representation
12. User Simulation in Natural Language Level
Natural Language
Generation (NLG)
User Model
User Simulation
System
Dialogue Act
Language
Understanding
(LU)
NL
12
Knowledge
Database
Dialogue State Tracking
(DST)
Dialogue Policy
Learning
Dialogue Management (DM)
State RepresentationUser
Dialogue Act
13. Task-Completion Neural Dialogue Systems
• Language Understanding
– Joint semantic frame parsing by BLSTM (Hakkani-Tur et al., 2016)
• Dialogue State Tracking
– Available results returned by the formed symbolic query
– Latest user dialogue action
• Dialogue Policy Learning
– Reinforcement learning policy (Mnih et al., 2015)
• Natural Language Generation
– Template-based
– Model-based: semantically-conditioned LSTM generation (SC-LSTM)
(Wen et al., 2015)
13
Supervised
Supervised
Supervised
Reinforcement
+ Reinforcement (fine-tuning)
+ Reinforcement (fine-tuning)
+ Reinforcement (fine-tuning)
14. wi
<slot>
wi+1
O
EOS
<intent>
wi
<slot>
wi+1
O
EOS
<intent>
End-to-End Neural Dialogue Systems
• LU, DST (neural dialogue system), and NLG (user simulation)
are trained in supervised way
• End-to-end training for dialogue policy learning
Knowledge
Database
Neural
Dialogue
System
User Model
User Simulation
Dialogue
Policy
Natural
Language
w
0
w1 w2
NLG
EOS
User Goal
wi
<slot>
wi+1
O
EOS
<intent>
LU
𝑠𝑡
DST
𝑠1 𝑠2 𝑠 𝑛
𝑎1 𝑎2 𝑎 𝑘
……
…
Dialogue Policy Learning
14
15. Reinforcement Learning Agent
• Dialogue policy learning
– Deep Q-network: estimate Q-value given the state and action pair
• Reward
– Success: agent answers all the requested slots based on the user’s
constraints, and book the movie tickets within the max turns
– Failure: 1) agent finds no matching movie based on the user’s constraints
or 2) exceed the max turns
15
16. Experiments
• Rule-based agent
– Actions
• ask question (request)
• answer question (inform)
• give multiple_choice to user
• say “confirm_answer” to user
• say “closing”, “thanks”
– Agent asks the slot in a priority
order, but within a sliding
window (say, size = 2 or 3),
there is randomness.
• RL agent
– Model: Deep Q-Network
– Rewards
• Success: 2 × max_turn
• Fail: - max_turn
• -1 for per turn penalty
– Actions: 45 actions
• i.e. request(starttime), inform(moviename),
confirm(question), etc.
– State transition tuples ( 𝑠𝑡, 𝑎 𝑡, 𝑟𝑡, 𝑠𝑡+1)
– Experience replay (Schaul et al., 2015)
• Priority, dynamic pool
• Starts with a pool of rule-based tuples
16
17. • Frame-level semantics Natural language
The RL agent is able to learn how to interact with users to complete tasks more
efficiently and effectively, and outperforms the rule-based agent.
End-to-End Reinforcement Learning17
18. Language Understanding Impact
• Different error rates in LU
18
Language understanding results are crucial in terms of dialogue-level performance
19. Intent Error Analysis
• Intent error type
– I0: random
– I1: within group
– I2: between group
• Intent error rate
– I3: 0.00
– I4: 0.10
– I5: 0.20
Intent errors slightly influence the RL system performance
Group 1: greeting(), thanks(), etc
Group 2: inform(xx)
Group 3: request(xx)
Between-group intent errors degrade the system performance more
request_moviename(actor=Robert Downey Jr)
request_year
19
20. Slot Error Analysis
• Slot error type
– I0: random
– I1: slot deletion
– I2: value substitution
– I3: slot substitution
• Slot error rate
– S4: 0.00
– S5: 0.10
– S6: 0.20
Slot errors significantly degrade the RL system performance
Value substitution has the largest impact on the system performance
request_moviename
(actor=Robert Downey Jr)
director Robert Downey Sr20
21. Error Comparison
• Intent error rate • Slot error rate
The RL agent has better robustness to intent errors for dialogue-level performance
Slot filling is more important than intent detection in language understanding
21
22. Human Evaluation
• Users presented with a predefined user goal
• User rating: 1 (worst) to 5 (best) based on both naturalness
and coherence of the dialogue
22
23. User goal – Two tickets for “Deadpool” tomorrow 9PM at AMC Pacific Place 11 theater, Seattle.
RULE BASED SYSTEM
User: Do you have any ticket for movie ‘Deadpool’ at 9:00 pm?
Agent: Which theater do you want?
User: AMC pacific place 11 theater, please!
Agent: How many tickets do you want?
User: Two!
Agent: What date?
User: Tomorrow!
Agent: Which city do you want?
User: Seattle!
Agent: Do you have any specification for zip code?
User: No, I do not care.
Agent: Do you have any distance constraint?
User: No, I do not care.
Agent: How about video format? 2d? Imax 3d?
User: Any kind is okay.
Agent: Do you have any constraint to price?
User: No!
Agent: Okay, I will issue 2 tickets for you, tomorrow 9:00 pm at AMC
pacific place 11 theater, Seattle, movie ‘Deadpool’.
User: Okay, thanks.
Agent: Thanks!
REINFORCEMENT LEARNING SYSTEM
User: I want two tickets for movie ‘Deadpool’.
Agent: What is the starttime you prefer?
User: 9:00 PM
Agent: Which day?
User: Tomorrow
Agent: Which city?
User: Seattle
Agent: Which theater do you prefer?
User: AMC Pacific Place 11 theater.
Agent: Okay, I will issue 2 tickets for you, tomorrow 9:00 pm
at AMC pacific place 11 theater, Seattle, movie ‘Deadpool’.
User: Okay, thanks.
Agent: Thanks!
Dialogue Examples
Skip the requests the user may not care about to improve efficiency
Issue 1: no notion about what
requests can be skipped
23
24. User goal – Two tickets for “Hail Caesar” tomorrow 7:15PM at AMC Lowes Oak tree 6, Seattle.
Dialogue Examples
Automatically learn when and what to confirm for better robustness
REINFORCEMENT LEARNING SYSTEM
Agent: What start time do you prefer?
User: 7:15 pm Tomorrow
Agent: Which city you are looking for?
User: Seattle
Agent: Okay, I will issue you tickets for movie ‘Hail Caeser’
tomorrow 7:50 pm at theater AMC Lowes Oak Tree 6, Seattle.
User: Oh, no.
Agent: It is 7:50 pm, right?
User: Two tickets for 7:15 pm, please.
Agent: Okay, I will issue you two tickets for movie ‘Hail Caeser’
tomorrow 7:15 pm at theater AMC Lowes Oak Tree 6, Seattle.
User: Thanks!
Agent: Thanks!
RULE BASED SYSTEM
User: Do you any ticket for movie ‘Hail Caesar’ at theater
AMC Lowes Oak Tree 6?
Agent: What start time do you prefer?
User: 7:15 pm Tomorrow
Agent: Which city you are looking for?
User: Seattle
Agent: How many tickets do you want?
User: Two
Agent: Okay, I have issued two tickets for movie ‘Hail
Caeser’ tomorrow 7:50 pm at theater AMC Lowes Oak
Tree 6, Seattle.
User: Oh, no.
Agent: Thanks!
Issue 2: no principle way to deal
with LU errors/uncertainty
24
25. Conclusions
• Our neural dialogue system demonstrates
– Robustness
• selecting actions based on uncertainty and confusion by RL
• systematic analysis about language understanding errors on dialogue
system performance
slot-level errors affect system performance more than intent-level errors
slot value replacement degrades the performance most
– Flexibility
• first neural dialogue system that allows user-initiated behaviors during
conversations
– Reproducibility
• demonstrate how to evaluate RL dialogue agents using crowdsourced
datasets and simulated users in an end-to-end fashion
• guaranteeing reproducibility and consistent comparisons of competing
methods in an identical setting
25
General goal, of course involves treating natural language as a knowledge representation language. So MR involves deriving structured information from free text, and then doing something with it.
But the work that’s been done in this space represents a tangle of different agendas. Let’s unpack them a bit.
Where does it make sense for MS to focus its efforts in this space?
For 1, doesn’t drive mainstream AI research. Hobby strand, though maybe good for PR purposes. Not a good driver of research/product.
For 2 & 3 the user already has some idea of what the information need is.
For 4, the user is overwhelmed by the complexity of the problem/solution space, needs help navigating.
General goal, of course involves treating natural language as a knowledge representation language. So MR involves deriving structured information from free text, and then doing something with it.
But the work that’s been done in this space represents a tangle of different agendas. Let’s unpack them a bit.
Where does it make sense for MS to focus its efforts in this space?
For 1, doesn’t drive mainstream AI research. Hobby strand, though maybe good for PR purposes. Not a good driver of research/product.
For 2 & 3 the user already has some idea of what the information need is.
For 4, the user is overwhelmed by the complexity of the problem/solution space, needs help navigating.
General goal, of course involves treating natural language as a knowledge representation language. So MR involves deriving structured information from free text, and then doing something with it.
But the work that’s been done in this space represents a tangle of different agendas. Let’s unpack them a bit.
Where does it make sense for MS to focus its efforts in this space?
For 1, doesn’t drive mainstream AI research. Hobby strand, though maybe good for PR purposes. Not a good driver of research/product.
For 2 & 3 the user already has some idea of what the information need is.
For 4, the user is overwhelmed by the complexity of the problem/solution space, needs help navigating.