Dr. Jay Myung presents an overview of his program, Computational Cognition and Robust Decision Making, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
Modelling Framework of a Neural Object RecognitionIJERA Editor
In many industrial, medical and scientific image processing applications, various feature and pattern recognition
techniques are used to match specific features in an image with a known template. Despite the capabilities of
these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features
within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex
and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating
neural network techniques such image processing systems with much number of images can be trained until the
system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a
machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in
the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar
Modelling Framework of a Neural Object RecognitionIJERA Editor
In many industrial, medical and scientific image processing applications, various feature and pattern recognition
techniques are used to match specific features in an image with a known template. Despite the capabilities of
these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features
within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex
and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating
neural network techniques such image processing systems with much number of images can be trained until the
system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a
machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in
the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
TOP 5 Most View Article From Academia in 2019sipij
TOP 5 Most View Article From Academia in 2019
Signal & Image Processing : An International Journal (SIPIJ)
ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Hand Gesture Recognition using OpenCV and Pythonijtsrd
Hand gesture recognition system has developed excessively in the recent years, reason being its ability to cooperate with machine successfully. Gestures are considered as the most natural way for communication among human and PCs in virtual framework. We often use hand gestures to convey something as it is non verbal communication which is free of expression. In our system, we used background subtraction to extract hand region. In this application, our PCs camera records a live video, from which a preview is taken with the assistance of its functionalities or activities. Surya Narayan Sharma | Dr. A Rengarajan "Hand Gesture Recognition using OpenCV and Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38413/hand-gesture-recognition-using-opencv-and-python/surya-narayan-sharma
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Thought powered typing is the emerging technology in computer and information technology field. it uses many algorithms and techniques that allows person to type words or phrases by just thinking.it is the technology very useful for paralyzed people.thought powered typing hence is an technology related to brain computer interface that makes computing easier.
DoD Business Capability Lifecycle (BCL) Guide (Draft)GovCloud Network
BCL is tailored for the rapid delivery of enterprise business capability. It combines multiple, disjointed oversight processes into a single process. It recognizes that technology rapidly evolves and changes, and consequently, BCL mandates rapid capability delivery – within
eighteen months or less of program initiation. BCL is outcome-based, and modeled on best commercial practices. The process allows for the fact that not all solutions are purely technical. The entire DOTMLPF (Doctrine, Organization, Training, Materiel, Leadership
and education, Personnel and Facilities) spectrum of potential solutions are considered.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Hand Gesture Recognition using OpenCV and Pythonijtsrd
Hand gesture recognition system has developed excessively in the recent years, reason being its ability to cooperate with machine successfully. Gestures are considered as the most natural way for communication among human and PCs in virtual framework. We often use hand gestures to convey something as it is non verbal communication which is free of expression. In our system, we used background subtraction to extract hand region. In this application, our PCs camera records a live video, from which a preview is taken with the assistance of its functionalities or activities. Surya Narayan Sharma | Dr. A Rengarajan "Hand Gesture Recognition using OpenCV and Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38413/hand-gesture-recognition-using-opencv-and-python/surya-narayan-sharma
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
COVID-19 detection from scarce chest X-Ray image data using few-shot deep lea...Shruti Jadon
In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate.
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Thought powered typing is the emerging technology in computer and information technology field. it uses many algorithms and techniques that allows person to type words or phrases by just thinking.it is the technology very useful for paralyzed people.thought powered typing hence is an technology related to brain computer interface that makes computing easier.
DoD Business Capability Lifecycle (BCL) Guide (Draft)GovCloud Network
BCL is tailored for the rapid delivery of enterprise business capability. It combines multiple, disjointed oversight processes into a single process. It recognizes that technology rapidly evolves and changes, and consequently, BCL mandates rapid capability delivery – within
eighteen months or less of program initiation. BCL is outcome-based, and modeled on best commercial practices. The process allows for the fact that not all solutions are purely technical. The entire DOTMLPF (Doctrine, Organization, Training, Materiel, Leadership
and education, Personnel and Facilities) spectrum of potential solutions are considered.
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
Social Network Analysis of a Professional Online Social Media Collaboration Community. Tuning and optimizing based on observed social network dynamics and user behavior.
Starting with an EIA–748–C compliant Earned Value Management System, integrating an Agile Software Development Lifecycle (Agile) is straightforward when there is a Bright Line between the Performance Measurement Baseline (PMB) and the Sprints and Tasks of the Agile Software Development Process.
The resources listed here are the starting point for anyone interested in applying the principles developed in this briefing for integrating Agile with Earned Value Management projects
Dr. Tristen Nguyen, presents an overview of his program, Science of Information, Computation and Fusion, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
A benchmark dataset to evaluate sensor displacement in activity recognitionOresti Banos
This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future.
This presentation illustrates part of the work described in the following article:
* Banos, O., Toth, M. A., Damas, M., Pomares, H., Rojas, I., Amft, O.: A benchmark dataset to evaluate sensor displacement in activity recognition. In: Proceedings of the 14th International Conference on Ubiquitous Computing (Ubicomp 2012), Pittsburgh, USA, September 5-8, (2012)
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
In this we have studied survey of how NASA build their first Machine Learning enabled Rover to send it on Mars. Hope you Like it! If any improvements or copyright content removal needed feel free to communicate.
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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.
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.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Myung - Computational Cognition and Robust Decision Making - Spring Review 2013
1. 1DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution15 February 2013
Integrity Service Excellence
Jay Myung, PhD
Program Officer
AFOSR/RTC
Air Force Research Laboratory
Computational Cognition and
Robust Decision Making
Date: 6 March 2013
2. 2DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution
2013 AFOSR SPRING REVIEW
NAME: Jay Myung Years with AFOSR: 1.8
BRIEF DESCRIPTION OF PORTFOLIO
Support experimental and computational modeling work in:
1. Understanding cognitive processes underlying human performance in
complex problem solving tasks;
2. Achieving robust and seamless symbiosis between humans and systems
in decision making;
3. Creating machine intelligent systems that exhibit human-level
performance in uncertain and dynamic environments.
LIST SUB-AREAS IN PORTFOLIO
1. Mathematical and Computational Cognition
2. Robust Decision Making in Human-System Interface
3. Computational and Machine Intelligence
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Program Roadmap
Computer science: Develop computational algorithms
(i.e., software) implementable in artificial systems.
Neuroscience: Offer insights into how the brain implements
natural intelligence on its neural hardware.
Cognitive science: Identify the mind’s invariants from
behavioral experiments.
Mind as computational
learning algorithm (software)
running on the brain
(hardware)
General purpose algorithms that the brain uses
to achieve adaptive intelligent computation.
Natural or artificial intelligence as computational learning
algorithms requiring multi-disciplinary approaches
Neural
Cognitive
Computational N
M
D
I
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Program Trends
• Neurocomputational Cognition
• Bio-inspired Computing Machines
• Robust Decision Making and Classification
• Memory, Categorization, and Reasoning
• Belief and Preference in Decision Making under Uncertainty
• Human-System Interface
• Computational Intelligence
• Meta-modeling
• Optimal Learning and Planning
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1. Mathematical and Computational Cognition
Goal:
Advance the computational modeling of human cognition in attention,
memory, categorization, reasoning, and decision making.
Challenges and Strategy:
• Seek algorithms for adaptive intelligence inspired by neuroscience
• Multidisciplinary efforts cutting across mathematics, cognitive science,
neuroscience, computer science, and electrical engineering.
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Neurocognitive Information Processing
A. Lazar (Columbia, EECS)
Neuronal Information Processing
(Hodgkin & Huxley, 1963, Nobel Prize)
“Cognition is a kind of Neural Computation.”
Aurel Lazar
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Neurocognitive Information Processing
A. Lazar (Columbia, EECS)
Scientific Challenge: The Holy Grail of Neuroscience
- How does the brain work?
- Can we “identify” the underlying neural circuit computations from
neural and behavioral data?
- Reverse engineering problem (i.e., system identification)
?
??
?
(Behavioral data) (To-be-identified) (Neural data)
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Neurocognitive Information Processing
A. Lazar (Columbia, EECS)
For future AF: Implementation of
computational algorithms extracted from
reverse engineering of insect flight control
systems for designing nano air vehicles.
Objective: Develop a formal methodology for
identifying sensory neural circuits of the fruit fly
brain.
Technical approach: Dynamic signal processing
systems; convex optimization; parallel computing;
frame theory.
DoD benefits: Next-generation brain-inspired
information processing machines.
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Mathematical Theory of Memristor Minds
L. Chua (Berkeley, EECS)
Objective: Uncover fundamental biophysical mechanisms
of single neuronal information processing.
Technical approach: Develop memristor models of
neuronal synapses and ion channels based on nonlinear
dynamics theory.
DoD benefits: If successful, could radically change the
notion of brain-inspired computation. Can potentially
produce much more powerful neuromorphic chips than
current state of the art.
L. Chua
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2. Robust Decision Making
in Human-System Interface
Goal:
Advance the research on mixed human-machine systems to aid inference,
communication, prediction, planning, scheduling, and decision making.
Challenges and Strategy:
• Seek computational principles for optimal symbiosis of mixed human-
machine systems in data-to-decision problems.
• Machine learning methods for robust reasoning and planning.
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Cognitive Processes of Spatial Visualization
G. Gunzelmann (AFRL, STAR team)
Objective: Explore and characterize the
representation and mechanisms of spatial cognition
in human-system interfaces.
Technical approach: Empirical studies of human
performance on lab and naturalistic tasks.
DoD benefits: Improved understanding of human
spatial information processing abilities, thereby
informing decision making regarding training and
workload assessment.
Air Force operations: Highly
complex and fundamentally spatial
Framework for spatial cognitionHuman-system interface in UAVs
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Robust Planning of Autonomous Systems
B. Williams (MIT, CSAIL)
Objective: Develop “calculus of risk” that
enables autonomous systems to operate within
specified risk bounds.
Technical approach: Planning algorithms that
reason about risk and generate course of action
to take while satisfying constraints on failure.
DoD benefits: Highly trustworthy autonomous
systems with increased probability of mission
success and reduced probability of catastrophic
failure, such as UAV loss.
B. Williams
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3. Computational and Machine Intelligence
Goal:
Advance the research on machine intelligence architectures that derive from
cognitive and biological models of human intelligence.
Challenges and Strategy:
• Seek fundamental computational principles for creating autonomous
systems that learn and function at the level of flexibility comparable to that of
humans.
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Bio-inspired Computation
J. Wiles (U. Queensland, ITEE)
iRAT: Neurorobotic testbedGrid-free: Simultaneous
localization and mapping
Objective: Develop bio-inspired algorithms that
are clock-free, grid-free, scale-free, and symbol-
free.
Technical approach: Develop and test neural
systems inspired by hippocampal architectures.
DoD benefits: Fundamental discoveries into
computation in natural systems could lead to the
development of robust and scalable machines.
J. Wiles
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Robust Intelligence in Complex Problem Solving
L. Kaelbling (MIT, EECS)
Objective: Develop algorithms that allow
autonomous agents to perform long-duration
tasks in complex and uncertain environments.
Technical approach: Formal A.I. methods for
integrating logical and probabilistic reasoning.
DoD benefits: Robust and effective battle space
planning, coordination, and surveillance in long-
horizon, large-space, and uncertain domains.
UAV mission
Laboratory testbed
L. Kaelbling
16. 16DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution
Interactions with Other Organizations
ONR (Paul Bello)
• Perception, Metacognition, and Cognitive Control Program
ONR (Tom McKenna)
• Computational Neuroscience Program
ARO (Janet Spoonamore)
• Decision and Neurosciences Program
NSF (Betty Tuller & Lawrence Gottlob)
• Perception, Action, and Cognition Program
DARPA (Gill Pratt)
• Systems of Neuromorphic Adaptive Plastic Scalable
Electronics (SyNAPSE) Program
IARPA (Brad Minnery)
• Integrated Cognitive-Neuroscience Architectures for
Understanding Sensemaking (ICArUS) Program
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Transition
NICTA (Australia) team:
- Project on large-scale lifelong-learning optimization
- Recent visits by NICTA team to Air Mobility Command and US
Transportation Command
- Access of real-world data: Huge benefits to model development
- Transition opportunities of basic research to help manage complex military
logistics processes
T. Walsh
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Recent Highlights
Korean Brain Science Initiative:
- AOARD initiative (PO: LtCol Brian Sells)
- Visit by AFOSR representatives to five
Korean universities June 2012
- Four projects at SNU and KAIST co-funded
with AOARD
DARPA SyNAPSE Program:
- Design, fabrication, and demonstration of
neuromorphic chips in real-world problems
- Ultra-low power consumption for ultra-high
processing capacity
- Visit to IBM and HRL teams Oct 2012
- Intersection with Air Force Research Lab
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Questions?
Thank you for your attention
Jay Myung, Program Officer, AFOSR/RTC
Jay.myung@afosr.af.mil
(703) 696-8478