The document summarizes research on using machine learning techniques for optimizing parking policies. It discusses using parking data from various sources like sensors and payments to set pricing, guide enforcement, and help drivers find spaces. Pricing models are developed to maximize the overall value people get from the parking system. A voting rule is proposed as a simple way to adjust prices based on occupancy levels over time. Spatial and temporal sampling techniques are explored to reduce sensor costs while still obtaining high quality data, such as prioritizing observations of locations with higher predictive uncertainty.
Garuda Robotics x DataScience SG Meetup (Sep 2015)Eugene Yan Ziyou
What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer “selfie” drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data.
In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges.
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016MLconf
Alex Smola is the Manager of the Cloud Machine Learning Platform at Amazon. Prior to his role at Amazon, Smola was a Professor in the Machine Learning Department of Carnegie Mellon University and cofounder and CEO of Marianas Labs. Prior to that he worked at Google Strategic Technologies, Yahoo Research, and National ICT Australia. Prior to joining CMU, he was professor at UC Berkeley and the Australian National University. Alex obtained his PhD at TU Berlin in 1998. He has published over 200 papers and written or coauthored 5 books.
Abstract summary
Personalization and Scalable Deep Learning with MXNET: User return times and movie preferences are inherently time dependent. In this talk I will show how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, I will show how to train large scale distributed parallel models using MXNet efficiently. This includes a brief overview of key components of defining networks, of optimization, and a walkthrough of the steps required to allocate machines, and to train a model.
Garuda Robotics x DataScience SG Meetup (Sep 2015)Eugene Yan Ziyou
What exactly goes on in the commercial drone/UAV industry in Singapore and globally? Behind the hype of consumer “selfie” drones lies a vast number of interesting commercial applications, where drones become an enabler for enterprises to gain new aerial perspectives of their facilities and estates, to make intelligent decisions incorporating this additional dimension of data.
In this presentation, we will look at one such drones-at-work application to reveal some of the behind-the-scene processes and technologies employed. Specifically, we will dive into the precision agriculture domain and share some of the computer vision problems we face, and take a look at various potential solutions to these challenges.
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
Alex Smola, Director of Machine Learning, AWS/Amazon, at MLconf SF 2016MLconf
Alex Smola is the Manager of the Cloud Machine Learning Platform at Amazon. Prior to his role at Amazon, Smola was a Professor in the Machine Learning Department of Carnegie Mellon University and cofounder and CEO of Marianas Labs. Prior to that he worked at Google Strategic Technologies, Yahoo Research, and National ICT Australia. Prior to joining CMU, he was professor at UC Berkeley and the Australian National University. Alex obtained his PhD at TU Berlin in 1998. He has published over 200 papers and written or coauthored 5 books.
Abstract summary
Personalization and Scalable Deep Learning with MXNET: User return times and movie preferences are inherently time dependent. In this talk I will show how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, I will show how to train large scale distributed parallel models using MXNet efficiently. This includes a brief overview of key components of defining networks, of optimization, and a walkthrough of the steps required to allocate machines, and to train a model.
Approximate "Now" is Better Than Accurate "Later"NUS-ISS
How does Twitter track the top trending topics?
How does Amazon keep track of the top-selling items for the day?
How many cabs have been booked this month using your App?
Is the password that a new user is choosing a common/compromised password?
Modern web-scale systems process billions of transactions and generate terabytes of data every single day. In order to find answers to questions against this data, one would initiate a multi-minute query against a NoSQL datastore or kick off a batch job written in a distributed processing framework such as Spark or Flink. However, these jobs are throughput-heavy and not suited for realtime low-latency queries. However, you and your customers would like to have all this information "right now".
At the end of this talk, you'll realize that you can power these low-latency queries and with incredibly low memory footprint "IF" you are willing to accept answers that are, say, 96-99% accurate. This talk introduces some of the go-to probabilistic data structures that are used by organisations with large amounts of data - specifically Bloom filter, Count Min Sketch and HyperLogLog.
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
It’s no longer needed supercomputers and a team with PhDs from MIT to create predictive models based on data. We are witnessing innovations in machine learning that are making it an increasingly accessible field. This lecture aims to demystify machine learning through exposure to concepts and use of a number of technologies. In this talk, we will address the types of problems and the algorithms, always applied to real problems. Also, open source tools like Scikit-learn will be presented as well as a way to practice and try these ideas through competitions like Kaggle.
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.
Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York. She is an Editorial Board member of the journal Machine Learning.
Cortes’ research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.
Abstract Summary:
Harnessing Neural Networks:
Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times.
In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.
Capitalico / Chart Pattern Matching in Financial Trading Using RNNAlpaca
Capitalico is a web/mobile platform that utilizes deep learning to help financial traders build automated trading system by understanding their trading charts. In this talk I show many of the techniques we developed to achieve the best performance and accuracy in deep learning for sequence pattern matching.
Deep Learning with Python: Getting started and getting from ideas to insights in minutes.
PyData Seattle 2015
Alex Korbonits (@korbonits)
This presentation was given July 25, 2015 at the PyData Seattle conference hosted by PyData and NumFocus.
Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017MLconf
Irina Rish is a researcher at the AI Foundations department of the IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems (“autonomic computing”) to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 60 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds 24 patents and several IBM awards. Irina currently serves on the editorial board of the Artificial Intelligence Journal (AIJ). As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling.
Abstract Summary:
Learning About the Brain and Brain-Inspired Learning:
Quantifying mental states and identifying statistical biomarkers of mental disorders from neuroimaging data is an exciting and rapidly growing research area at the intersection of neuroscience and machine learning, with the particular focus on interpretability and reproducibility of learned models. We will discuss promises and limitations of machine-learning methods in such applications, focusing on recent applications of deep learning methods such as recurrent convnets to the analysis of “brain movies” (EEG) data. On the other hand, besides the above “AI to Brain” direction, we will also discuss the “Brain to AI”, namely, borrowing ideas from neuroscience to improve machine learning, with specific focus on adult neurogenesis and online model adaptation in representation learning.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introducing-machine-learning-and-how-to-teach-machines-to-see-a-presentation-from-tryolabs/
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the “Introduction to Machine Learning and How to Teach Machines to See” tutorial at the September 2020 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. He then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. Parodi will also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
QCon Rio - Machine Learning for EveryoneDhiana Deva
Já não são mais necessários supercomputadores e times de PhDs do MIT para a criação de modelos preditivos baseados em dados. Estamos presenciando inovações em Aprendizado de Máquina que estão tornando este campo cada vez mais acessível.
Esta palestra tem como objetivo desmistificar o aprendizado de máquina, através da exposição de conceitos e uso de uma série de tecnologias.
Serão abordados os tipos de problemas desta área(classificação, regressão, clusterização, redução de dimensionalidade, etc.), suas as etapas (normalização, treinamento, otimização, regularização, etc.) e seus algoritmos, desde regressão linear, k-means, passando por árvores de decisão e até redes neurais, sempre aplicadas a problemas reais.
Na palestra, também conheceremos ferramentas como Sckit-learn, Pandas, R, MATLAB e Amazon Machine Learning, além de uma forma para praticar e experimentar estas ideias através de competições como o Kaggle.
Applying Deep Learning to Enhance Momentum Trading Strategies in StocksLawrence Takeuchi
Contact author: larrytakeuchi@gmail.com
Abstract
We use an autoencoder composed of stacked restricted Boltzmann machines to extract
features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period
versus 10.53% for basic momentum.
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Approximate "Now" is Better Than Accurate "Later"NUS-ISS
How does Twitter track the top trending topics?
How does Amazon keep track of the top-selling items for the day?
How many cabs have been booked this month using your App?
Is the password that a new user is choosing a common/compromised password?
Modern web-scale systems process billions of transactions and generate terabytes of data every single day. In order to find answers to questions against this data, one would initiate a multi-minute query against a NoSQL datastore or kick off a batch job written in a distributed processing framework such as Spark or Flink. However, these jobs are throughput-heavy and not suited for realtime low-latency queries. However, you and your customers would like to have all this information "right now".
At the end of this talk, you'll realize that you can power these low-latency queries and with incredibly low memory footprint "IF" you are willing to accept answers that are, say, 96-99% accurate. This talk introduces some of the go-to probabilistic data structures that are used by organisations with large amounts of data - specifically Bloom filter, Count Min Sketch and HyperLogLog.
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
It’s no longer needed supercomputers and a team with PhDs from MIT to create predictive models based on data. We are witnessing innovations in machine learning that are making it an increasingly accessible field. This lecture aims to demystify machine learning through exposure to concepts and use of a number of technologies. In this talk, we will address the types of problems and the algorithms, always applied to real problems. Also, open source tools like Scikit-learn will be presented as well as a way to practice and try these ideas through competitions like Kaggle.
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017MLconf
Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.
Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York. She is an Editorial Board member of the journal Machine Learning.
Cortes’ research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.
Abstract Summary:
Harnessing Neural Networks:
Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times.
In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.
Capitalico / Chart Pattern Matching in Financial Trading Using RNNAlpaca
Capitalico is a web/mobile platform that utilizes deep learning to help financial traders build automated trading system by understanding their trading charts. In this talk I show many of the techniques we developed to achieve the best performance and accuracy in deep learning for sequence pattern matching.
Deep Learning with Python: Getting started and getting from ideas to insights in minutes.
PyData Seattle 2015
Alex Korbonits (@korbonits)
This presentation was given July 25, 2015 at the PyData Seattle conference hosted by PyData and NumFocus.
Irina Rish, Researcher, IBM Watson, at MLconf NYC 2017MLconf
Irina Rish is a researcher at the AI Foundations department of the IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems (“autonomic computing”) to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 60 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds 24 patents and several IBM awards. Irina currently serves on the editorial board of the Artificial Intelligence Journal (AIJ). As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling.
Abstract Summary:
Learning About the Brain and Brain-Inspired Learning:
Quantifying mental states and identifying statistical biomarkers of mental disorders from neuroimaging data is an exciting and rapidly growing research area at the intersection of neuroscience and machine learning, with the particular focus on interpretability and reproducibility of learned models. We will discuss promises and limitations of machine-learning methods in such applications, focusing on recent applications of deep learning methods such as recurrent convnets to the analysis of “brain movies” (EEG) data. On the other hand, besides the above “AI to Brain” direction, we will also discuss the “Brain to AI”, namely, borrowing ideas from neuroscience to improve machine learning, with specific focus on adult neurogenesis and online model adaptation in representation learning.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introducing-machine-learning-and-how-to-teach-machines-to-see-a-presentation-from-tryolabs/
Facundo Parodi, Research and Machine Learning Engineer at Tryolabs, presents the “Introduction to Machine Learning and How to Teach Machines to See” tutorial at the September 2020 Embedded Vision Summit.
What is machine learning? How can machines distinguish a cat from a dog in an image? What’s the magic behind convolutional neural networks? These are some of the questions Parodi answers in this introductory talk on machine learning in computer vision.
Parodi introduces machine learning and explores the different types of problems it can solve. He explains the main components of practical machine learning, from data gathering and training to deployment. He then focuses on deep learning as an important machine learning technique and provides an introduction to convolutional neural networks and how they can be used to solve image classification problems. Parodi will also touches on recent advancements in deep learning and how they have revolutionized the entire field of computer vision.
QCon Rio - Machine Learning for EveryoneDhiana Deva
Já não são mais necessários supercomputadores e times de PhDs do MIT para a criação de modelos preditivos baseados em dados. Estamos presenciando inovações em Aprendizado de Máquina que estão tornando este campo cada vez mais acessível.
Esta palestra tem como objetivo desmistificar o aprendizado de máquina, através da exposição de conceitos e uso de uma série de tecnologias.
Serão abordados os tipos de problemas desta área(classificação, regressão, clusterização, redução de dimensionalidade, etc.), suas as etapas (normalização, treinamento, otimização, regularização, etc.) e seus algoritmos, desde regressão linear, k-means, passando por árvores de decisão e até redes neurais, sempre aplicadas a problemas reais.
Na palestra, também conheceremos ferramentas como Sckit-learn, Pandas, R, MATLAB e Amazon Machine Learning, além de uma forma para praticar e experimentar estas ideias através de competições como o Kaggle.
Applying Deep Learning to Enhance Momentum Trading Strategies in StocksLawrence Takeuchi
Contact author: larrytakeuchi@gmail.com
Abstract
We use an autoencoder composed of stacked restricted Boltzmann machines to extract
features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period
versus 10.53% for basic momentum.
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Dynamic Pricing in Ride-Hailing PlatformsHamed Shams
NOTE: To maintain the confidentiality, sensitive company information are masked/removed from this document.
This case study examines surge pricing in ride-hailing platforms, with a focus on UBER's hexagonal indexing system (H3). The study explores the challenges of implementing this system in Snapp, particularly H3's inability to adapt to extreme fluctuations. More precisely, having a prefixed hexagon resolution level for each city is not always optimal, as it would result in considerable no-decision cases due to the lack of signals.
To address this issue, the author proposes a machine learning based approach and discusses the impact of launching this solution into production.
Machine Learning statistical model using Transportation datajagan477830
As the world is growing rapidly the people and the vehicles we use to move from one place to another, so the transportation is playing a vital role in making human lives easiest to travel from one place to another, everyday more and more vehicles are being produced and being bought by the people around the world, be it Electric, Hydrogen, petrol, diesel or solar powered.
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Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing.
Similar to [215]streetwise machine learning for painless parking (20)
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
4. 1. Parking Data
What?
mobile cameras, cell phonespolicies, mapsin-street sensors
traffic flow special events, …satellites (pollution) surveys
payments, violations
5. 1. Parking Data
Why?
Use cases
1. set parking policy: prices, demarcation, …
2. guide enforcement officers to offenders
3. guide drivers to the best vacancies
Value
1. less time wasted finding a space
2. less pollution, better health
3. access to local businesses
4. fair-and-transparent
Challenge
maximise the utility (value minus cost) of the data
6. 1. Parking Data
Who?
Many cities have deployed smart parking systems since 2010
San Francisco, London, Moscow, …
Our contributions
Since 2012, we have deployed 6 new technologies in 3 major cities
Los Angeles. Basic pricing method, time-of-week subdivision, real-time pricing as
rate, optimal learning while selling, surveys, effectiveness evaluation, non-payment
evaluation (and not deployed: real-time parking guidance, real-time pricing as integral)
Washington DC. Spatiotemporal sampling: sensor allocation and reconstruction,
spatial queueing for demarcation decisions
Berkeley. Fusion of temporal sampling with payment data
Awards for this work included ITS Innovation, IPI Innovation and MIT Top-50
8. Zoeter et al , New Algorithms for Parking Demand Management. Proc. 20th ACM KDD, 2014
Glasnapp et al, Understanding Dynamic Pricing for Parking in Los Angeles. Intl. Conf. HCI in Business, 2014
2. Pricing
Context. Demand-Based Pricing
9. 2. Pricing
Problem
Learn on-street parking prices to make the city happier
⇒ maximize the rate at which people get value from the system (not revenue)
Challenges
1. Model or forecast for value when driver behavior varies in 5 dimensions
frequency, location, arrival, duration, legality-fraction
2. Ensure simplicity so drivers remember and city official can explain
in the face of huge variations in demand in space and time
3. How big should price increments be?
too large ⇒ prices might oscillate from month-to-month
too small ⇒ system may have no useful effect
10. 2. Pricing
Model for Value
Goal. Choose appropriate reward function
If more people are parked, then
• more people get value
• but the distance to a space increases
For a geometric distribution of vacancies
distance to a space =
𝑓
1−𝑓
where the occupancy fraction is 𝑓
But this is singular as 𝑓 → 1!
occupancy fraction
mean spaces to first vacancy
- geometric distribution
11. 2. Pricing
Distance to a Space
The singularity is unrealistic as occupancy
fractions vary spatially
spatial autocorrelation of
occupancy fraction (LA 2012 data)
mean spaces to first vacancy
- geometric distribution
- real data
occupancy fraction
12. 2. Pricing
Simple Valuation Model
occupancy fraction
Gradient Ascent
Move up the gradient of the total valuation
w.r.t. price 𝒑, so
new-price – old-price
∼
𝝏
𝝏𝒑
𝓡
valuation−rate 𝒑, 𝒕, 𝒙 𝒅𝒙 𝒅𝒕
⇒ simple for machine-learning scientists,
but NOT for citizens and officials...
valuation rate (per space, per unit time)
= constant per person parked
- k × distance travelled per arrival
13. 2. Pricing
Simple Valuation Model
occupancy fraction
⇒ simple for machine-learning scientists,
but NOT for citizens and officials…
valuation rate (per space, per unit time)
= constant per person parked
- k × distance travelled per arrival
Towards a simpler rule
Maximizing the black curve is nearly the
same as maximizing the red curve, whose
gradients are -1, 0 or 1
approximation
Gradient Ascent
Move up the gradient of the total valuation
w.r.t. price 𝒑, so
new-price – old-price
∼
𝝏
𝝏𝒑
𝓡
valuation−rate 𝒑, 𝒕, 𝒙 𝒅𝒙 𝒅𝒕
14. 2. Pricing
Voting Rule
Voting Rule
If 𝑯 − 𝑳 > 0.3 then increase the price
If 𝑳 − 𝑯 > 0.3 then decrease the price
Prices are on a ladder $0.5, $1, $1.50, $2, $3, …, $7 per hour
Definition
The high vote, 𝑯, is the fraction of time that the system is over 90% occupied
The low vote, 𝑳, is the fraction of time that the system is under 70% occupied
15. 2. Pricing (A Tale of Two Cities)
Comparison
Average Occupancy Rule (used in San Francisco)
If average occupancy fraction > 𝟎. 𝟖 then increase the price by $0.25
If average occupancy fraction < 𝟎. 𝟔 then decrease the price by $0.25
average occupancy fraction 0.67 0.67
average occupancy rule price same price same
voting rule price down price up
mean distance to vacancy 2 spaces 11 spaces
occupancy
fraction
Scenario A Scenario B
16. 2. Pricing - How did prices ($/HR) change?
Area is proportional to the sum over spaces of the
number of hours at the given price
Before (1st June 2012) After (1st January 2013)
$5
$4
$3
$2
$150
$1
$050
$4
$3
$2
$1
50% of prices decreased
yet revenue increased by 12%
17. 2. Pricing
Does it work?
Price increase from $4/hour to $5/hour
No vacancy = full (red)
More availability (yellow and green)
Occupancy time series for 701 South Olive Street, one row = one weekday (Mon-Fri not Sat-Sun)
Underused (blue)
… but is this
• the impact of the price change
• a change in sensor signal-processing
• a lucky coincidence
• something else?
19. Handicapped placards
• 80% of parking in highly-congested areas now goes for free to handicapped placard users
• Maybe, many such drivers use placards illegally
• Law-changing takes time, but is ongoing
Minimum price
• The minimum acceptable price ($0.5 per hour) was already reached in under-used areas
• But occupancy there has continued to increase substantially
• It’s hard to distinguish between economic improvement and long-term price-change latency
Political acceptance
Unlike SFPark, LA ExpressPark receives positive press coverage and continues to expand today
Clinchant et al, Using Analytics to Understand On-Street Parking. Proc. 22nd World Congress on ITS, 2015
2. Pricing
Does it work? Results for 2013-16
21. 3. Sampling
Motivation
Solutions
1. Spatial sampling:
don’t observe all stalls
2. Temporal sampling:
don’t observe all the time
Deployed in Washington DC and
Berkeley
Problem
LA’s sensors are too
expensive. Can we
combine sensing
methods to ensure high-
quality data while saving
90% of the costs?
Dance, Lean Smart Parking. The Parking Professional, vol. 30, no. 6, 2014
22. 3. Sampling
Problem P1
Informally
Given
• a normally-distributed discrete-time time-series
• noisy measurements that come at a cost
Question
When should you measure so as to minimise the cost
of prediction errors plus measurement costs?
black = true time-series (unobserved)
red = forecast standard-deviation
blue = costly measurements
Formally
Time-series
𝑿 𝒕+𝟏 = 𝑨𝑿 𝒕 + 𝑵(𝟎, 𝑸)
Actions
𝑎 𝑡 = 1 for good measurement, cost 𝑐
𝑎 𝑡 = 0 for poor measurement, no cost
Measurements
𝒀 𝒕 = 𝑩𝑿 𝒕 + 𝑵 𝟎, 𝑹(𝒂 𝒕)
History
𝐻𝑡 = (𝑎1, 𝑎2, … , 𝑎 𝑡−1, 𝒀 𝟏, 𝒀 𝟐, … , 𝒀𝒕−𝟏)
Policy
𝑎 𝑡 = 𝜋(𝐻𝑡)
Forecasts
𝑿 𝒕 = 𝐄 𝑿 𝒕 𝐻𝑡] given by the Kalman filter
Objective
min
𝜋
𝐄 σ 𝑡=1
∞
𝛾 𝑡 𝑿 𝒕 − 𝑿 𝒕
2
+ 𝑐 𝑎 𝑡
time
23. 3. Sampling
Problem P1: Examples
Parking
time-series occupancy of a block face
measurements from mobile cameras and payment data
Military
time-series position of a submarine
measurements by sonar
Telecommunications
time-series position of a handset
measurements with 5G antenna
24. 3. Sampling
Problem P1: Related Work
P1 addresses the basic machine learning trade-off between
• the cost of data acquisition
• the cost of errors due to a lack of data
in a particularly simple way
If we solve P1, then we also solve
• “the LQG control problem with costly measurements”(Meier et al, 1967)
The continuous-time version of this problem was solved only recently (Le Ny et al, 2011)
Niño-Mora and Villar (2009) conjectured that an optimal policy for P1 is a threshold policy
• i.e. measure if and only if the posterior variance exceeds a threshold.
Meier et al, Optimal control of measurement subsystems. IEEE TAC, 1967
Le Ny et al, Scheduling continuous-time Kalman filters. IEEE TAC, 2011
Niño-Mora and Villar, Multi-target tracking via restless bandit marginal productivity indices. IEEE CDC, 2009
25. 3. Sampling
Attention Mechanism Problem
Street 1
Street 2
Street 3
observation times
Given
• 𝑛 time-series to track, as in P1
• with 𝑚 sensors, where 𝑚 < 𝑛
Question
Which time-series should you measure at each time, so
as to minimise the total prediction error?
Discussion
• This problem has state space ℝ 𝑛
and 𝑛
𝑚
actions!
• Nevertheless, Whittle (1988) proposed a
computationally-efficient policy for this problem for
large 𝑚, 𝑛
• But to compute that policy, we must first solve P1
Example
• 4 cameras observing 800 streets in
Washington DC
• This was our original motivation for
this work
Whittle, Restless bandits: activity allocation in a changing world. J. App. Prob., 1988
26. 3. Sampling
Attention Mechanism Problem
Claim. Assuming P1 is solved, Whittle’s policy does much better than other heuristics
Example. 10 time-series, 1 sensor, weights on predictive variance 𝑤1 = 40, 𝑤2:10 = 1
time-series
time, 𝑡
colour = weighted
prediction error
Myopic policy
Observes the time-series
with the largest weighted
predictive variance
(often used in radar tracking)
Round-robin policy
Observes time-series 1, 2,
…, 10, 1, 2, …, 10, …
Whittle’s policy
27. 3. Sampling
Solution to P1
Dance and Silander, When are Kalman-Filter Restless Bandits Indexable? NIPS, 2015
Dance and Silander, Optimal Policies for Observing Time Series, in review, JMLR, 2017 (see arXiv)
Theorem (Dance and Silander, 2017)
1. A threshold policy is optimal for Problem P1
2. This result holds for many cost functions
minimum predictive variance,
minimum predictive entropy,
maximum predictive precision, …
3. There is a simple polynomial-time algorithm for approximating the threshold
28. 3. Sampling
Key to the proof
Examine the behaviour of the system under a
threshold policy
• The state 𝑥𝑡 is given by the predictive variance
• Its dynamics are given by the Kalman filter
variance updates, which are nonlinear
𝜙0 - no measurement (below threshold)
𝜙1 - measurement made (above threshold)
• So, for threshold 𝑧 we have
𝑥𝑡+1 = 𝑓 𝑥𝑡; 𝑧 ≔ ቊ
𝜙0(𝑥𝑡), 𝑥𝑡 < 𝑧
𝜙1(𝑥𝑡), 𝑥𝑡 ≥ 𝑧
action 0 action 1
threshold
29. 3. Sampling
Insights: Maps-with-Gaps
The behaviour of iterated function systems
𝑥𝑡+1 = 𝑓(𝑥𝑡)
has been extensively studied when 𝑓 is smooth
But our 𝑓(⋅; 𝑧) is discontinuous
Such maps-with-gaps are also important as models of:
• switching in electrical circuits
• neural spiking behaviour
• gene regulatory networks, …
30. 3. Sampling
Insights: Words
How does the action sequence generated by our
map change
• as we vary the threshold 𝑧
• from initial state 𝑥1 = 𝑧?
The action sequence is an infinite word on the
alphabet {0,1}
Question
What types of word does our map generate?
time,𝑡
threshold, 𝑧
black = action 0
white = action 1
31. 3. Sampling
Answer: Mechanical Words
Definition
A mechanical word is an infinite binary string whose 𝑛th letter is
𝑤 𝑛 = 𝑎(𝑛 + 1) − 𝑎𝑛 for some 𝑎 in [0,1].
Examples
𝑎 = 1/2 ⇒ the word 01 01 01 01…
𝑎 = 3/7 ⇒ the word 00100101 00100101 00100101…
Mechanical words correspond to the slopes of digital straight lines
Relation to the literature
• Kozyakin (2003) found general conditions under which nonlinear maps-with-gaps generate
mechanical words
• However, the relationship between the choice of threshold and the word generated was only
discovered for linear maps-with-gaps by Rajpathak et al (2012)
• Our work extends this threshold-to-word relationship to nonlinear maps
0 0
0 0
0
1
1
1
Kozyakin, Sturmian sequences generated by order-preserving circle maps. Inst. Information Trans., RAS, 2003
Rajpathak et al, Analysis of stable periodic orbits in the one-dimensional linear discontinuous map. Chaos, 2012
32. 3. Sampling
Open Questions
• What happens if there are more than 2 types of measurements?
• What can be said in the multivariate Gaussian case?
• What about non-Gaussian time-series?
34. Outlook
1. Widespread adoption of demand-management technologies makes sense
2. Counting cars with computer vision seems most cost-effective
⇒ room for improvement in accuracy of on-street car counts
3. Forecasting non-demarcated parking remains challenging
4. Parking policy and guidance for autonomous vehicles
⇒ enable more effective mechanisms:
routing policies, reservations, options, lotteries, …
⇒ while making life simpler for citizens