Introduction to Adaptive Resonance Theory (ART) neural networks including:
Introduction (Stability-Plasticity Dilemma)
ART Network
ART Types
Basic ART network Architecture
ART Algorithm and Learning
ART Computational Example
ART Application
Conclusion
Main References
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
Introduction to Adaptive Resonance Theory (ART) neural networks including:
Introduction (Stability-Plasticity Dilemma)
ART Network
ART Types
Basic ART network Architecture
ART Algorithm and Learning
ART Computational Example
ART Application
Conclusion
Main References
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
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Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
Hardware Implementation of Spiking Neural Network (SNN)supratikmondal6
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This project work was carried out under the supervision of Dr. Gaurav Trivedi (IIT Guwahati, Electrical Engineering) and under the mentorship of Mr. Ashvinikumar Pruthviraj Dongre (IIT Guwahati, PhD Scholar). In this project we have tried to implement the SNN for image classification in FPGA by
developing an efficient and realistic architecture and also by incorporating a technique of weight change according to
Step-Wise STDP learning curve.
This presentation gives an insight to effects of microwave radiation from cell phone towers around us. The impact of such towers on animals, plants & humans is discussed and some simple solutions are also presented.
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This presentation gives brief description of Wi-Fi Technolgy, standards, applications,topologies, how Wi-Fi network works, security,advantages and innovations.
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These are the steps to be followed to use the Optimetrics feature in HFSS. This feature lets a user to optimize his/her design and its parameters by employing several techniques.
A Multi-Band PIFA with Slotted Ground Plane Naveen Kumar
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A multiband PIFA is proposed which operates on DCS, PCS, 3G, 4G, Bluetooth, WLAN & GPS bands. This antenna is designed and simulated in HFSS. The results shows good gain and radiation pattern at all resonant frequencies.
Study of Planar Inverted - F Antenna (PIFA) for mobile devices Naveen Kumar
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A brief study of planar inverted-F antenna is given. Basic structure of PIFA is discussed and effect of various parameters is explained. Techniques to improve bandwidth coverage by the antenna are also discussed.
A compact planar inverted-F antenna with slotted ground planeNaveen Kumar
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Design of a small and thin PIFA antenna for handheld devices covering several cellular communication bands such as UMTS, Bluetooth, WiMAX, 4G LTE, WLAN.
Ground plane of the antenna is used as a radiator along with the main patch.
Routing in Integrated circuits is an important task which requires extreme care while placing the modules and circuits and connecting them with each other.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
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The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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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.
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Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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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.
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- 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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
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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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
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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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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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:
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• 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.
Mission to Decommission: Importance of Decommissioning Products to Increase E...
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Adaptive Resonance Theory
1.
2. ď‚ž Unsupervised ANN
ď‚ž Stability-Plasticity Dilemma
ď‚ž Adaptive Resonance Theory basics
ď‚ž ART Architecture
ď‚ž Algorithm
ď‚ž Types of ART NN
ď‚ž Applications
ď‚ž References
3. ď‚ž Usually 2-layer ANN
ď‚ž Only input data are given
ď‚ž ANN must self-organise
output
 Two main models: Kohonen’s
SOM and Grossberg’s ART
ď‚ž Clustering applications
Output layer
Feature layer
4.
5. Every learning system faces the plasticity-stability dilemma.
ď‚ž The plasticity-stability dilemma poses few questions :
6. ART stands for "Adaptive Resonance Theory", invented by Stephen
Grossberg in 1976.
ď‚žART represents a family of neural networks.
ď‚žThe basic ART System is an unsupervised learning model.
ď‚žThe term "resonance" refers to resonant state of a neural network in
which a category prototype vector matches close enough to the current
input vector. ART matching leads to this resonant state, which permits
learning. The network learns only in its resonant state.
7. The key innovation of ART is the use of
“expectations.”
› As each input is presented to the network, it is compared
with the prototype vector that is most closely matches
(the expectation).
› If the match between the prototype and the input vector is
NOT adequate, a new prototype is selected. In this way,
previous learned memories (prototypes) are not eroded by
new learning.
9. ď‚ž The L1-L2 connections are instars, which performs a clustering
(or categorization) operation. When an input pattern is presented,
it is multiplied (after normalization) by the L1-L2 weight matrix.
ď‚ž A competition is performed at Layer 2 to determine which row of
the weight matrix is closest to the input vector. That row is then
moved toward the input vector.
ď‚ž After learning is complete, each row of the L1-L2 weight matrix is
a prototype pattern, which represents a cluster (or a category) of
input vectors.
10. ď‚ž Learning of ART networks also occurs in a set of feedback
connections from Layer 2 to Layer 1. These connections are
outstars which perform pattern recall.
ď‚ž When a node in Layer 2 is activated, this reproduces a
prototype pattern (the expectation) at layer 1.
ď‚ž Layer 1 then performs a comparison between the
expectation and the input pattern.
ď‚ž When the expectation and the input pattern are NOT closely
matched, the orienting subsystem causes a reset in Layer 2.
11. ď‚ž The reset disables the current winning neuron, and the
current expectation is removed.
ď‚ž A new competition is then performed in Layer 2, while the
previous winning neuron is disable.
ď‚ž The new winning neuron in Layer 2 projects a new
expectation to Layer 1, through the L2-L1 connections.
ď‚ž This process continues until the L2-L1 expectation provides a
close enough match to the input pattern.
12. ď‚ž Bottom-up weights bij
ď‚ž Top-down weights tij
› Store class template
ď‚ž Input nodes
› Vigilance test
› Input normalisation
ď‚ž Output nodes
› Forward matching
ď‚ž Long-term memory
› ANN weights
ď‚ž Short-term memory
› ANN activation pattern top down
bottom up (normalised)
13.
14. ď‚ž The basic ART system is unsupervised learning
model. It typically consists of
› a comparison field and a recognition field composed of
neurons,
› a vigilance parameter, and
› a reset module
15. ď‚ž Comparison field
› The comparison field takes an input vector (a one-dimensional array of
values) and transfers it to its best match in the recognition field. Its best match
is the single neuron whose set of weights (weight vector) most closely
matches the input vector.
ď‚ž Recognition field
› Each recognition field neuron, outputs a negative signal proportional to that
neuron's quality of match to the input vector to each of the other recognition
field neurons and inhibits their output accordingly. In this way the recognition
field exhibits lateral inhibition, allowing each neuron in it to represent a
category to which input vectors are classified.
16. ď‚ž Vigilance parameter
› After the input vector is classified, a reset module compares the
strength of the recognition match to a vigilance parameter. The
vigilance parameter has considerable influence on the system.
ď‚ž Reset Module
› The reset module compares the strength of the recognition match to
the vigilance parameter.
› If the vigilance threshold is met, then training commences.
17. Adapt winner
node
Initialise uncommitted
node
new pattern
categorisation
known unknown
recognition
comparison
ď‚ž Incoming pattern matched with
stored cluster templates
ď‚ž If close enough to stored template
joins best matching cluster,
weights adapted
ď‚ž If not, a new cluster is initialised
with pattern as template
18. ď‚ž ART-1
› Binary input vectors
› Unsupervised NN that can be complemented with external
changes to the vigilance parameter
ď‚ž ART-2
› Real-valued input vectors
19. ď‚ž ART-3
› Parallel search of compressed or distributed pattern
recognition codes in a NN hierarchy.
› Search process leads to the discovery of appropriate
representations of a non stationary input environment.
› Chemical properties of the synapse emulated in the search
process
20. 1 2 3
1 2 3 4Input
layer
Output layer
with inhibitory
connections
),( 3,44,3 tb
The ART-1 Network
21. • Mobile robot control
• Facial recognition
• Land cover classification
• Target recognition
• Medical diagnosis
• Signature verification
22.  S. Rajasekaran, G.A.V. Pai, “Neural Networks, Fuzzy Logic and
Genetic Algorithms”, Prentice Hall of India, Adaptive Resonance
Theory, Chapter 5.
 Jacek M. Zurada, “Introduction to Artificial Neural Systems”, West
Publishing Company, Matching & Self organizing maps, Chapter 7.
ď‚ž Adaptive Resonance Theory, Soft computing lecture notes,
“http://www.myreaders.info/html/soft_computing.html”