We offer cutting-edge solutions to our clients, drawing the technical expertise of our well-qualified consultants in specialist domains of process engineering. We work with them to identify the critical areas of improvement within the facility and offer best-in-class solutions which can be implement to yield significant cost, quality and operational benefits.
3D Modeling & Visualization solution provides true workflow-managed integration between the process engineering design basis and detailed engineering disciplines, extending across and beyond the project enterprise.
We offer cutting-edge solutions to our clients, drawing the technical expertise of our well-qualified consultants in specialist domains of process engineering. We work with them to identify the critical areas of improvement within the facility and offer best-in-class solutions which can be implement to yield significant cost, quality and operational benefits.
3D Modeling & Visualization solution provides true workflow-managed integration between the process engineering design basis and detailed engineering disciplines, extending across and beyond the project enterprise.
Data Summer Conf 2018, “From the math to the business value: machine learning...Provectus
Javier will illustrate the “life-cycle” of a machine learning project, particularly the building of a recommender system, from the technical design (business problem and detailed algorithmic solution) to deployment. I will also briefly mentioned other examples (e.g. marketing multi-channel attribution models using Markov-Chain models, or boosted methods and deep learning for risk modelling) trying to emphasise the journey from the code to the business with a couple of histories of success and failure.
- A high-level overview of artificial intelligence
- The importance of predictions across different domains of life
- Big (text) data
- Competition as a discovery process
- Domain-general learning
- Computer vision and natural language processing
- Elements of a machine learning system
- A hierarchy of problem classes
- Data collection
- The purpose of a model
- Logistic loss function
- Likelihood, log likelihood and maximum likelihood
- Ockham's Razor
- Intelligence as sequence prediction
- Building blocks of neural networks: neurons, weights and layers
- Logistic regression as a neural network
- Sigmoid function
- A look at backpropagation
- Gradient descent
- Convolutional neural networks
- Max-pooling
- Deep neural networks
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep learning is a subset of machine learning in which algorithms are used to model high-level abstractions in data. By using a deep learning algorithm, a computer can learn to recognize complex patterns in data, such as images or spoken language. Deep learning is used in a variety of applications, including image classification, natural language processing, and time series prediction.
Study on Data Augmentation Methods for Sonar Image Analysisharmonylab
Data augmentation plays an important role in deep learning. Recently, RandAugment and Augmix have been proposed as effective augmentation methods. In aquaculture, echo sounder image analysis is used for catching specific kind of fish to improve annual catches. Therefore, it remains significant to research for effects of different methods and find suitable augmentation method for echo sounder image analysis.
This research aims at finding effective settings for RandAugment and Augmix by comparing effects of different transformation methods. The experiment results show improvement on recall rate and f1 score of distinguishing tuna in echo sounder images with suitable transformation methods.
Information to Wisdom: Commonsense Knowledge Extraction and Compilation - Part 3Dr. Aparna Varde
This is the 3rd part of the tutorial on commonsense knowledge (CSK) at ACM WSDM 2021 by Simon Razniewski, Niket Tandon and Aparna Varde. It focuses on evaluation of the acquired knowledge, both intrinsic & extrinsic, as well as highlights, outlook with a brief perspective on COVID and open issues for further research.
Abstract: Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pitt’s birth date, or Angelina Jolie’s movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
Exploiting Distributional Semantic Models in Question AnsweringPierpaolo Basile
This paper investigates the role of Distributional
Semantic Models (DSMs) in Question Answering (QA), and
specifically in a QA system called QuestionCube. QuestionCube is
a framework for QA that combines several techniques to retrieve
passages containing the exact answers for natural language questions.
It exploits Information Retrieval models to seek candidate
answers and Natural Language Processing algorithms for the
analysis of questions and candidate answers both in English and
Italian. The data source for the answer is an unstructured text
document collection stored in search indices.
In this paper we propose to exploit DSMs in the QuestionCube
framework. In DSMs words are represented as mathematical
points in a geometric space, also known as semantic space. Words
are similar if they are close in that space. Our idea is that
DSMs approaches can help to compute relatedness between users’
questions and candidate answers by exploiting paradigmatic
relations between words. Results of an experimental evaluation
carried out on CLEF2010 QA dataset, prove the effectiveness of
the proposed approach.
Aaron Roth, Associate Professor, University of Pennsylvania, at MLconf NYC 2017MLconf
Aaron Roth is an Associate Professor of Computer and Information Sciences at the University of Pennsylvania, affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. Previously, he received his PhD from Carnegie Mellon University and spent a year as a postdoctoral researcher at Microsoft Research New England. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and a Yahoo! ACE award. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory and mechanism design, learning theory, and the intersections of these topics. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.”
Abstract Summary:
Differential Privacy and Machine Learning:
In this talk, we will give a friendly introduction to Differential Privacy, a rigorous methodology for analyzing data subject to provable privacy guarantees, that has recently been widely deployed in several settings. The talk will specifically focus on the relationship between differential privacy and machine learning, which is surprisingly rich. This includes both the ability to do machine learning subject to differential privacy, and tools arising from differential privacy that can be used to make learning more reliable and robust (even when privacy is not a concern).
Replication in Data Science - A Dance Between Data Science & Machine Learning...June Andrews
We use Iterative Supervised Clustering as a simple building block for exploring Pinterest's Content. But simplicity can unlock great power and with this building block we show the shocking result of how hard it is to replicated data science conclusions. This begs us to challenge the future for When is Data Science a House of Cards?
This talk describes a study that showed that integrating foveation into modern convolutional neural network improves their robustness to adversarial attacks and common image corruptions. These slides are of a talk given by Muhammad Ahmed Shah at Riken AIP, Tokyo, Japan as part of the TrustML Young Scientist Seminar.
Functional specialization in human cognition: a large-scale neuroimaging init...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault [1]. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Deep Learning: Changing the Playing Field of Artificial Intelligence - MaRS G...MaRS Discovery District
Deep learning is changing the field of artificial intelligence and revolutionizing our online experience, with applications including speech and image recognition. Information and communications technology giants such as Google, Facebook, IBM and Baidu, among others, are rapidly deploying deep learning into new products and services.
Behind all of the present-day excitement about deep learning are years of high risk and hard work by a small group of eminent computer scientists and theorists connected through the Canadian Institute for Advanced Research (CIFAR).
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Data Summer Conf 2018, “From the math to the business value: machine learning...Provectus
Javier will illustrate the “life-cycle” of a machine learning project, particularly the building of a recommender system, from the technical design (business problem and detailed algorithmic solution) to deployment. I will also briefly mentioned other examples (e.g. marketing multi-channel attribution models using Markov-Chain models, or boosted methods and deep learning for risk modelling) trying to emphasise the journey from the code to the business with a couple of histories of success and failure.
- A high-level overview of artificial intelligence
- The importance of predictions across different domains of life
- Big (text) data
- Competition as a discovery process
- Domain-general learning
- Computer vision and natural language processing
- Elements of a machine learning system
- A hierarchy of problem classes
- Data collection
- The purpose of a model
- Logistic loss function
- Likelihood, log likelihood and maximum likelihood
- Ockham's Razor
- Intelligence as sequence prediction
- Building blocks of neural networks: neurons, weights and layers
- Logistic regression as a neural network
- Sigmoid function
- A look at backpropagation
- Gradient descent
- Convolutional neural networks
- Max-pooling
- Deep neural networks
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep learning is a subset of machine learning in which algorithms are used to model high-level abstractions in data. By using a deep learning algorithm, a computer can learn to recognize complex patterns in data, such as images or spoken language. Deep learning is used in a variety of applications, including image classification, natural language processing, and time series prediction.
Study on Data Augmentation Methods for Sonar Image Analysisharmonylab
Data augmentation plays an important role in deep learning. Recently, RandAugment and Augmix have been proposed as effective augmentation methods. In aquaculture, echo sounder image analysis is used for catching specific kind of fish to improve annual catches. Therefore, it remains significant to research for effects of different methods and find suitable augmentation method for echo sounder image analysis.
This research aims at finding effective settings for RandAugment and Augmix by comparing effects of different transformation methods. The experiment results show improvement on recall rate and f1 score of distinguishing tuna in echo sounder images with suitable transformation methods.
Information to Wisdom: Commonsense Knowledge Extraction and Compilation - Part 3Dr. Aparna Varde
This is the 3rd part of the tutorial on commonsense knowledge (CSK) at ACM WSDM 2021 by Simon Razniewski, Niket Tandon and Aparna Varde. It focuses on evaluation of the acquired knowledge, both intrinsic & extrinsic, as well as highlights, outlook with a brief perspective on COVID and open issues for further research.
Abstract: Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pitt’s birth date, or Angelina Jolie’s movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
Exploiting Distributional Semantic Models in Question AnsweringPierpaolo Basile
This paper investigates the role of Distributional
Semantic Models (DSMs) in Question Answering (QA), and
specifically in a QA system called QuestionCube. QuestionCube is
a framework for QA that combines several techniques to retrieve
passages containing the exact answers for natural language questions.
It exploits Information Retrieval models to seek candidate
answers and Natural Language Processing algorithms for the
analysis of questions and candidate answers both in English and
Italian. The data source for the answer is an unstructured text
document collection stored in search indices.
In this paper we propose to exploit DSMs in the QuestionCube
framework. In DSMs words are represented as mathematical
points in a geometric space, also known as semantic space. Words
are similar if they are close in that space. Our idea is that
DSMs approaches can help to compute relatedness between users’
questions and candidate answers by exploiting paradigmatic
relations between words. Results of an experimental evaluation
carried out on CLEF2010 QA dataset, prove the effectiveness of
the proposed approach.
Aaron Roth, Associate Professor, University of Pennsylvania, at MLconf NYC 2017MLconf
Aaron Roth is an Associate Professor of Computer and Information Sciences at the University of Pennsylvania, affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. Previously, he received his PhD from Carnegie Mellon University and spent a year as a postdoctoral researcher at Microsoft Research New England. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and a Yahoo! ACE award. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory and mechanism design, learning theory, and the intersections of these topics. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.”
Abstract Summary:
Differential Privacy and Machine Learning:
In this talk, we will give a friendly introduction to Differential Privacy, a rigorous methodology for analyzing data subject to provable privacy guarantees, that has recently been widely deployed in several settings. The talk will specifically focus on the relationship between differential privacy and machine learning, which is surprisingly rich. This includes both the ability to do machine learning subject to differential privacy, and tools arising from differential privacy that can be used to make learning more reliable and robust (even when privacy is not a concern).
Replication in Data Science - A Dance Between Data Science & Machine Learning...June Andrews
We use Iterative Supervised Clustering as a simple building block for exploring Pinterest's Content. But simplicity can unlock great power and with this building block we show the shocking result of how hard it is to replicated data science conclusions. This begs us to challenge the future for When is Data Science a House of Cards?
This talk describes a study that showed that integrating foveation into modern convolutional neural network improves their robustness to adversarial attacks and common image corruptions. These slides are of a talk given by Muhammad Ahmed Shah at Riken AIP, Tokyo, Japan as part of the TrustML Young Scientist Seminar.
Functional specialization in human cognition: a large-scale neuroimaging init...Ana Luísa Pinho
Linking brain systems and mental functions requires accurate descriptions of behavioral tasks and fine demarcations of brain regions. Functional Magnetic Resonance Imaging (fMRI) has contributed to the investigation of brain regions involved in a variety of cognitive processes. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a permanent cohort performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The first release of the IBC dataset consists of data acquired from thirteen participants during performance of a dozen of tasks. Raw data from this release are publicly available in the OpenNeuro repository and derived statistical maps can be found in NeuroVault [1]. These maps reveal a successful cognitive encoding of many psychological domains in large areas of the human brain. Indeed, main findings of the original studies were replicated at higher resolution. Our results thus provide a comprehensive revision of the neural correlates underlying behavior, highlighting nonetheless the spatial variability of functional signatures between participants. In addition, this dataset supports investigations using alternative approaches to group-level analysis of task-specific studies. For instance, such rich task-wise dataset can be applied to mega-analytic encoding models towards the development of a brain-atlasing framework, by systematically mapping functional signatures associated with the cognitive components of the tasks.
Deep Learning: Changing the Playing Field of Artificial Intelligence - MaRS G...MaRS Discovery District
Deep learning is changing the field of artificial intelligence and revolutionizing our online experience, with applications including speech and image recognition. Information and communications technology giants such as Google, Facebook, IBM and Baidu, among others, are rapidly deploying deep learning into new products and services.
Behind all of the present-day excitement about deep learning are years of high risk and hard work by a small group of eminent computer scientists and theorists connected through the Canadian Institute for Advanced Research (CIFAR).
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
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.
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.
3. AVast Challenge/Opportunity
“This suggests that there are,at the very
least, aquarter of amillion distinct
English words,excluding inflections,and
words from technical and regional
vocabulary[JonnatohantGocod]overed by the OED ...”
2
[Oxford English Dictionary]
4. AVast Challenge/Opportunity
[Jonathan Good]
“This suggests that there are,at the very
least, a quarter of a million distinct
English words,excluding inflections,and
words from technical and regional
vocabulary not covered by the OED ...”
2
[Oxford English Dictionary]
Novel methods and applications to link
digital image content with human language.
22. Summary
15
• Link any characteristic to any keyword.
• Fast and highly scalable:
millions of images and thousands of keywords.
• Base for subsequent imaging applications with
semantic awareness.
40. Semantic Component
0 50 200 250
6
5
4
3
2
1
0
100 150
pixel value
v
al
u
e
z
red
green
blue
significance values for rose
22
41. Semantic Component
0 50 200 250
6
5
4
3
2
1
0
100 150
pixel value
v
al
u
e
z
red
green
blue
0 200
0
50
100
150
200
250
100
input value
outputvalue
red
green
blue
identit
f 0 =
⇢
1/ (1 + Sz)
1 + S|z|
if z Ç 0
if z < 0
S global scale parameter
significance values for rose Tone mapping function f
22
60. 9000+ Color Names
47
• XKCD color survey,psychophysical experiment.
• 950 English color names + color values.
61. 9000+ Color Names
48
• XKCD color survey,psychophysical experiment.
• 950 English color names + color values.
• Translate to 9 other languages:
Chinese,French,German,Italian,Japanese,Korean,
Portuguese,Russian,and Spanish.
62. 9000+ Color Names
49
• XKCD color survey,psychophysical experiment.
• 950 English color names + color values.
• Translate to 9 other languages:
Chinese,French,German,Italian,Japanese,Korean,
Portuguese,Russian,and Spanish.
• Example:柔和的粉红色,soft pink, rose tendre,sanftes
pink, rosa tenue,ソフトピンク,부드러운녹색,rosa
suave,нежно розовый,rosa suave.
70. Soft Pink
柔和的粉红色,cn
soft pink,en
rose tendre,fr
sanftes pink,de
rosatenue,it
ソフトピンク,jp
부드러운녹색,ko
rosasuave,pt
rosasuave,es
Language and country restrict.
54
74. Conclusions & FutureWork
sunset layout 5
0
−5
−10
−15
Easily scalable statistical framework.
input grass
Semantic image enhancement for
tone-mapping,color and depth-of-field.
periwinkle blue
Automatic color naming and an
interactive online color thesaurus.
61
75. •Du-Sik Park,Youngshin Kwak,Hyunwook Ok and Chang-Yeong Kim,Preferred skin color
reproduction on the display,JEI,2006.
•Gianluigi Ciocca,Claudio Cusano,Francesca Gasparini and Raimondo Schettini,ContentAware
Image Enhancement,Artificial Intelligence and Human-Oriented Computing,2007.
•Liad Kaufman.Dani Lischinski and MichaelWerman,Content-AwareAutomatic Photo
Enhancement,Computer Graphics Forum,2012.
•BaoyuanWang,YizhouYu,Tien-TsinWong,Chun Chen andYing-Qing Xu, Data-Driven Image
ColorTheme Enhancement,ACM SIGGRAPH,2010.
•NailaMurray,Sandra Skaff and Luca Marchesotti,Towards Automatic ConceptTransfer,
SIGGRAPH/Eurographics Symposium on Non-PhotorealisticAnimation and Rendering,2011.
•FrankWilcoxon,Individual Comparisons by Ranking Methods,Biometrics Bulletin,1945.
•Shaojie Zhuo andTerence Sim,Defocus map estimation from a single image,Pattern
Recognition,2011.
•Sung Ju Hwang,Ashish Kapoor and Sing Bing Kang,Context-BasedAutomatic Local Image
Enhancement,ECCV,2012.
Reference