Lectures of CS-721 (Network Performance Evaluation) taught for the Virtual University by Junaid Qadir.
To access other resources, visit http://sites.google.com/site/netperfeval
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Application of soft computing techniques in electrical engineeringSouvik Dutta
This document discusses the application of soft computing techniques in electrical engineering. It begins with an introduction to soft computing and its key elements including fuzzy logic, neural networks, evolutionary computation, machine learning and probabilistic reasoning. It then discusses hard computing versus soft computing, defining hard computing as requiring precise analytical models and definitions, while soft computing can handle imprecision. The document outlines several soft computing techniques - neural networks, fuzzy logic, and their applications in power system economic load dispatch and generation level determination to solve complex, non-linear optimization problems in electrical engineering. In conclusion, soft computing provides alternatives to traditional techniques for electrical engineering problems involving uncertainty.
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Soft computing is an approach to engineering that is inspired by nature. It includes techniques like fuzzy logic, probabilistic reasoning, evolutionary computation, neural networks, and machine learning. These techniques are useful for problems that are too complex or undefined for conventional analytical or hard computing techniques. Soft computing provides approximate solutions and can handle imprecise data. It has applications in areas like robotics, artificial intelligence, and machine translation.
Human in the loop: Bayesian Rules Enabling Explainable AIPramit Choudhary
The document provides an overview of a presentation on enabling explainable artificial intelligence through Bayesian rule lists. Some key points:
- The presentation will cover challenges with model opacity, defining interpretability, and how Bayesian rule lists can be used to build naturally interpretable models through rule extraction.
- Bayesian rule lists work well for tabular datasets and generate human-understandable "if-then-else" rules. They aim to optimize over pre-mined frequent patterns to construct an ordered set of conditional statements.
- There is often a tension between model performance and interpretability. Bayesian rule lists can achieve accuracy comparable to more opaque models like random forests on benchmark datasets while maintaining interpretability.
This document provides an overview of algorithmic issues in computational intelligence optimization from design to implementation. It discusses key concepts in optimization problems including analytical approaches, exact methods, approximate iterative methods, and metaheuristics. It also examines challenges in optimizing real-world problems that are highly non-linear, multi-modal, computationally expensive, and have memory/time constraints. The document concludes by discussing the need for algorithms to balance exploration and exploitation and to adapt to problem landscapes.
Soft computing is a field that uses approximate solutions and techniques like fuzzy logic, neural networks, and evolutionary computation to problems that are too complex for traditional binary logic-based computing. It aims to achieve human-like decision making by incorporating uncertainty, imprecision, and partial truth into solutions. The main goal of soft computing is to develop intelligent machines that can provide solutions to real-world problems that are difficult to model mathematically.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Application of soft computing techniques in electrical engineeringSouvik Dutta
This document discusses the application of soft computing techniques in electrical engineering. It begins with an introduction to soft computing and its key elements including fuzzy logic, neural networks, evolutionary computation, machine learning and probabilistic reasoning. It then discusses hard computing versus soft computing, defining hard computing as requiring precise analytical models and definitions, while soft computing can handle imprecision. The document outlines several soft computing techniques - neural networks, fuzzy logic, and their applications in power system economic load dispatch and generation level determination to solve complex, non-linear optimization problems in electrical engineering. In conclusion, soft computing provides alternatives to traditional techniques for electrical engineering problems involving uncertainty.
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Soft computing is an approach to engineering that is inspired by nature. It includes techniques like fuzzy logic, probabilistic reasoning, evolutionary computation, neural networks, and machine learning. These techniques are useful for problems that are too complex or undefined for conventional analytical or hard computing techniques. Soft computing provides approximate solutions and can handle imprecise data. It has applications in areas like robotics, artificial intelligence, and machine translation.
Human in the loop: Bayesian Rules Enabling Explainable AIPramit Choudhary
The document provides an overview of a presentation on enabling explainable artificial intelligence through Bayesian rule lists. Some key points:
- The presentation will cover challenges with model opacity, defining interpretability, and how Bayesian rule lists can be used to build naturally interpretable models through rule extraction.
- Bayesian rule lists work well for tabular datasets and generate human-understandable "if-then-else" rules. They aim to optimize over pre-mined frequent patterns to construct an ordered set of conditional statements.
- There is often a tension between model performance and interpretability. Bayesian rule lists can achieve accuracy comparable to more opaque models like random forests on benchmark datasets while maintaining interpretability.
This document provides an overview of algorithmic issues in computational intelligence optimization from design to implementation. It discusses key concepts in optimization problems including analytical approaches, exact methods, approximate iterative methods, and metaheuristics. It also examines challenges in optimizing real-world problems that are highly non-linear, multi-modal, computationally expensive, and have memory/time constraints. The document concludes by discussing the need for algorithms to balance exploration and exploitation and to adapt to problem landscapes.
Soft computing is a field that uses approximate solutions and techniques like fuzzy logic, neural networks, and evolutionary computation to problems that are too complex for traditional binary logic-based computing. It aims to achieve human-like decision making by incorporating uncertainty, imprecision, and partial truth into solutions. The main goal of soft computing is to develop intelligent machines that can provide solutions to real-world problems that are difficult to model mathematically.
Soft computing is an approach to building computationally intelligent systems that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and evolutionary computation. These techniques were developed to mimic human-like intelligence by accommodating imprecision and exploiting uncertainty. Soft computing is used to build intelligent systems that can learn and adapt to new environments. Neuro-fuzzy systems combine neural networks and fuzzy logic to create adaptive and knowledge-based intelligent systems.
1. The document discusses model interpretation and techniques for interpreting machine learning models, especially deep neural networks.
2. It describes what model interpretation is, its importance and benefits, and provides examples of interpretability algorithms like dimensionality reduction, manifold learning, and visualization techniques.
3. The document aims to help make machine learning models more transparent and understandable to humans in order to build trust and improve model evaluation, debugging and feature engineering.
This document discusses neuro fuzzy systems and soft computing. It provides the following key points:
1. Neuro-fuzzy systems combine fuzzy logic and neural networks, allowing the system to learn from data and maintain interpretable fuzzy rules. It can be viewed as a 3-layer neural network with fuzzy rules in the hidden layer.
2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
3. Soft computing constituents include neural networks, fuzzy sets, approximate reasoning, and derivative-free optimization methods like genetic algorithms and simulated annealing. These work together to enable learning
Artificial intelligence is a field of study that uses computational techniques to simulate human intelligence processes like learning, reasoning, and problem solving. It includes approaches like expert systems, neural networks, genetic algorithms, fuzzy logic systems, and swarm intelligence methods. The goal is to develop tools that can perform tasks requiring human-level intelligence.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
Deep learning for detecting anomalies and software vulnerabilitiesDeakin University
This document provides an overview of deep learning and its applications in anomaly detection and software vulnerability detection. It discusses key deep learning architectures like feedforward networks, recurrent neural networks, and convolutional neural networks. It also covers unsupervised learning techniques such as word embedding, autoencoders, RBMs, and GANs. For anomaly detection, it describes approaches for multichannel anomaly detection, detecting unusual mixed-data co-occurrences, and modeling object lifetimes. It concludes by discussing applications in detecting malicious URLs, unusual source code, and software vulnerabilities.
This document provides an introduction to machine learning and neural networks. It discusses key concepts like supervised vs unsupervised learning, classification vs regression problems, and performance evaluation metrics. It also covers foundational machine learning techniques like k-nearest neighbors for classification and regression. Descriptive statistics concepts like mean, variance, correlation and covariance are introduced. Finally, it discusses visualizing data through scatter plots and histograms.
Talk of Ali Mousavi "Event-Modelling An Engineering Solution for Control and Analysis of Complex Systems" at 116th regular meeting of INCOSE Russian chapter, 14-Sep-2016
A Structured Approach for Conducting a Series of Controlled Experiments in So...Richard Müller
In the field of software visualization controlled experiments are an important instrument to investigate the specific reasons, why some software visualizations excel the expectations on providing insights and ease task solving while others fail doing so. Despite this, controlled experiments in software visualization are rare. A reason for this is the fact that performing such evaluations in general, and particularly performing them in a way that minimizes the threats to validity, is hard to accomplish. In this paper, we present a structured approach on how to conduct a series of controlled experiments in order to give empirical evidence for advantages and disadvantages of software visualizations in general and of 2D vs. 3D software visualizations in particular.
In IVAPP'14: Proceedings of the 5th International Conference on Visualization Theory and Applications, 2014.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
This document provides an overview of deep learning 1.0 and discusses potential directions for deep learning 2.0. It summarizes limitations of deep learning 1.0 such as lack of reasoning abilities and discusses how incorporating memory and reasoning capabilities could help address these limitations. The document outlines several approaches being explored for neural memory and reasoning, including memory networks, neural Turing machines, and self-attentive associative memories. It argues that memory and reasoning will be important for developing more human-like artificial general intelligence.
The aim of "SP Theory of Intelligence" is Simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme
This document presents a methodology for program comprehension consisting of four main stages:
1) Loading source code into a symbolic form using GrammarWare.
2) Creating an atomistic model (ModelWare) to represent the smallest possible structures for modeling source code.
3) Simulating the code symbolically using SimulationWare to perform static analysis comparable to dynamic analysis.
4) Collecting knowledge using KnowledgeWare by constructing representations for tasks like code inspection and error detection.
The methodology is implemented in a tool called JavaMaster which handles Java code according to the main stages. The formalism combines reverse engineering for maintenance with forward engineering for new code design.
A Proposition on Memes and Meta-Memes in Computing for Higher ...butest
This document proposes a framework for memetic computing with higher-order learning capabilities. It discusses how current computational intelligence techniques have limitations and how problem complexity is outpacing algorithm development. The framework is inspired by how the brain learns and generalizes solutions across different problems through a hierarchical architecture of processing units (memes and meta-memes) and memory. This brain-inspired approach is proposed as a new class of memetic computing that can autonomously learn patterns across multiple temporal and spatial scales to better solve complex problems.
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
IRJET-In sequence Polemical Pertinence via Soft Enumerating RepertoireIRJET Journal
This document discusses the use of soft computing techniques in image forensics. It begins by defining soft computing as a multi-disciplinary field involving fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning. It then discusses several soft computing techniques - fuzzy logic, neural networks, and genetic algorithms - and how they can help address challenges in image forensics by dealing with imprecision and uncertainty. The document concludes that soft computing tools show promise for analyzing the large amounts of data involved in supply chain management problems and aiding managers' decision making in complex environments. It identifies several areas where further research could help improve solutions or develop new approaches by integrating additional algorithms.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Teachers noticed their students were struggling with writing action research abstracts. An example abstract is provided to demonstrate how to concisely summarize the key elements of motivation, methods, and implications in 3 sentences or less to help students learn the proper abstract format. The example focuses on an educator's work exploring how to improve student writing skills through implementing peer feedback techniques in the classroom.
Proving Lower Bounds to answer the P versus NP Questionguest383ed6
1. The document discusses research into proving lower bounds on the complexity of problems in the NP class in order to help answer the P versus NP question.
2. It describes current techniques like diagonalization and combinatorial circuits that are used to prove lower bounds and the limitations of these methods.
3. The researcher aims to conduct an experiment applying diagonalization and circuit techniques simultaneously to problems like the Traveling Salesman Problem to develop a more efficient new technique for determining lower bounds.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
This document discusses the U-Net architecture for biomedical image segmentation. It begins with an outline of the document's topics, including the dissertation topic on medical image analysis using deep learning. It then reviews neural network concepts like convolution and pooling. The main content explains the U-Net architecture, which uses a contracting path to capture context and an expansive path to enable precise localization. It concatenates features from the contracting and expansive paths to generate semantic segmentation maps for biomedical images. The document concludes by mentioning potential applications of U-Net in the author's dissertation for tasks like analyzing breast density from CT images.
Soft computing is an approach to building computationally intelligent systems that is tolerant to imprecision and uncertainty. It includes techniques like fuzzy logic, neural networks, and evolutionary computation. These techniques were developed to mimic human-like intelligence by accommodating imprecision and exploiting uncertainty. Soft computing is used to build intelligent systems that can learn and adapt to new environments. Neuro-fuzzy systems combine neural networks and fuzzy logic to create adaptive and knowledge-based intelligent systems.
1. The document discusses model interpretation and techniques for interpreting machine learning models, especially deep neural networks.
2. It describes what model interpretation is, its importance and benefits, and provides examples of interpretability algorithms like dimensionality reduction, manifold learning, and visualization techniques.
3. The document aims to help make machine learning models more transparent and understandable to humans in order to build trust and improve model evaluation, debugging and feature engineering.
This document discusses neuro fuzzy systems and soft computing. It provides the following key points:
1. Neuro-fuzzy systems combine fuzzy logic and neural networks, allowing the system to learn from data and maintain interpretable fuzzy rules. It can be viewed as a 3-layer neural network with fuzzy rules in the hidden layer.
2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
3. Soft computing constituents include neural networks, fuzzy sets, approximate reasoning, and derivative-free optimization methods like genetic algorithms and simulated annealing. These work together to enable learning
Artificial intelligence is a field of study that uses computational techniques to simulate human intelligence processes like learning, reasoning, and problem solving. It includes approaches like expert systems, neural networks, genetic algorithms, fuzzy logic systems, and swarm intelligence methods. The goal is to develop tools that can perform tasks requiring human-level intelligence.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
Deep learning for detecting anomalies and software vulnerabilitiesDeakin University
This document provides an overview of deep learning and its applications in anomaly detection and software vulnerability detection. It discusses key deep learning architectures like feedforward networks, recurrent neural networks, and convolutional neural networks. It also covers unsupervised learning techniques such as word embedding, autoencoders, RBMs, and GANs. For anomaly detection, it describes approaches for multichannel anomaly detection, detecting unusual mixed-data co-occurrences, and modeling object lifetimes. It concludes by discussing applications in detecting malicious URLs, unusual source code, and software vulnerabilities.
This document provides an introduction to machine learning and neural networks. It discusses key concepts like supervised vs unsupervised learning, classification vs regression problems, and performance evaluation metrics. It also covers foundational machine learning techniques like k-nearest neighbors for classification and regression. Descriptive statistics concepts like mean, variance, correlation and covariance are introduced. Finally, it discusses visualizing data through scatter plots and histograms.
Talk of Ali Mousavi "Event-Modelling An Engineering Solution for Control and Analysis of Complex Systems" at 116th regular meeting of INCOSE Russian chapter, 14-Sep-2016
A Structured Approach for Conducting a Series of Controlled Experiments in So...Richard Müller
In the field of software visualization controlled experiments are an important instrument to investigate the specific reasons, why some software visualizations excel the expectations on providing insights and ease task solving while others fail doing so. Despite this, controlled experiments in software visualization are rare. A reason for this is the fact that performing such evaluations in general, and particularly performing them in a way that minimizes the threats to validity, is hard to accomplish. In this paper, we present a structured approach on how to conduct a series of controlled experiments in order to give empirical evidence for advantages and disadvantages of software visualizations in general and of 2D vs. 3D software visualizations in particular.
In IVAPP'14: Proceedings of the 5th International Conference on Visualization Theory and Applications, 2014.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
This document provides an overview of deep learning 1.0 and discusses potential directions for deep learning 2.0. It summarizes limitations of deep learning 1.0 such as lack of reasoning abilities and discusses how incorporating memory and reasoning capabilities could help address these limitations. The document outlines several approaches being explored for neural memory and reasoning, including memory networks, neural Turing machines, and self-attentive associative memories. It argues that memory and reasoning will be important for developing more human-like artificial general intelligence.
The aim of "SP Theory of Intelligence" is Simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme
This document presents a methodology for program comprehension consisting of four main stages:
1) Loading source code into a symbolic form using GrammarWare.
2) Creating an atomistic model (ModelWare) to represent the smallest possible structures for modeling source code.
3) Simulating the code symbolically using SimulationWare to perform static analysis comparable to dynamic analysis.
4) Collecting knowledge using KnowledgeWare by constructing representations for tasks like code inspection and error detection.
The methodology is implemented in a tool called JavaMaster which handles Java code according to the main stages. The formalism combines reverse engineering for maintenance with forward engineering for new code design.
A Proposition on Memes and Meta-Memes in Computing for Higher ...butest
This document proposes a framework for memetic computing with higher-order learning capabilities. It discusses how current computational intelligence techniques have limitations and how problem complexity is outpacing algorithm development. The framework is inspired by how the brain learns and generalizes solutions across different problems through a hierarchical architecture of processing units (memes and meta-memes) and memory. This brain-inspired approach is proposed as a new class of memetic computing that can autonomously learn patterns across multiple temporal and spatial scales to better solve complex problems.
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
IRJET-In sequence Polemical Pertinence via Soft Enumerating RepertoireIRJET Journal
This document discusses the use of soft computing techniques in image forensics. It begins by defining soft computing as a multi-disciplinary field involving fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning. It then discusses several soft computing techniques - fuzzy logic, neural networks, and genetic algorithms - and how they can help address challenges in image forensics by dealing with imprecision and uncertainty. The document concludes that soft computing tools show promise for analyzing the large amounts of data involved in supply chain management problems and aiding managers' decision making in complex environments. It identifies several areas where further research could help improve solutions or develop new approaches by integrating additional algorithms.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Teachers noticed their students were struggling with writing action research abstracts. An example abstract is provided to demonstrate how to concisely summarize the key elements of motivation, methods, and implications in 3 sentences or less to help students learn the proper abstract format. The example focuses on an educator's work exploring how to improve student writing skills through implementing peer feedback techniques in the classroom.
Proving Lower Bounds to answer the P versus NP Questionguest383ed6
1. The document discusses research into proving lower bounds on the complexity of problems in the NP class in order to help answer the P versus NP question.
2. It describes current techniques like diagonalization and combinatorial circuits that are used to prove lower bounds and the limitations of these methods.
3. The researcher aims to conduct an experiment applying diagonalization and circuit techniques simultaneously to problems like the Traveling Salesman Problem to develop a more efficient new technique for determining lower bounds.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
This document discusses the U-Net architecture for biomedical image segmentation. It begins with an outline of the document's topics, including the dissertation topic on medical image analysis using deep learning. It then reviews neural network concepts like convolution and pooling. The main content explains the U-Net architecture, which uses a contracting path to capture context and an expansive path to enable precise localization. It concatenates features from the contracting and expansive paths to generate semantic segmentation maps for biomedical images. The document concludes by mentioning potential applications of U-Net in the author's dissertation for tasks like analyzing breast density from CT images.
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
This document contains summaries of several papers related to artificial intelligence and neural networks:
1. The first paper discusses using recurrent neural networks to plan robot motions in variable environments.
2. The second paper describes using neural network models to classify brain signals and mind states from EEG data.
3. The third paper proposes using coarticulation composite models in acoustic-phonetic decoding to improve recognition rates beyond phonemes, diphones, and triphones.
This document discusses techniques for creating an artificial eye, including using sound to see, retinal and cortical implants, and image processing with artificial neural networks. It presents methods for acquiring images with a PC camera in Matlab, implementing an artificial neural network for object recognition, and using correlation and ANNs for object recognition and path planning. The document concludes that this research could improve assistance for the blind through better performance and reliability.
[PR12] understanding deep learning requires rethinking generalizationJaeJun Yoo
The document discusses a paper that argues traditional theories of generalization may not fully explain why large neural networks generalize well in practice. It summarizes the paper's key points:
1) The paper shows neural networks can easily fit random labels, calling into question traditional measures of complexity.
2) Regularization helps but is not the fundamental reason for generalization. Neural networks have sufficient capacity to memorize data.
3) Implicit biases in algorithms like SGD may better explain generalization by driving solutions toward minimum norm.
4) The paper suggests rethinking generalization as the effective capacity of neural networks may differ from theoretical measures. Understanding finite sample expressivity is important.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
The document summarizes three studies conducted as part of a PhD investigating the process of process modeling and its relation to modeling quality.
The first study visualized how people create process models through an analysis of modeling actions. The second study explored relationships between modeling behaviors and quality, finding that structured modeling related to better understandability. The third study developed the Structured Process Modeling Theory to explain observations of modeling styles and their cognitive impacts.
ICSE’14 Workshop Keynote Address: Emerging Trends in Software Metrics (WeTSOM’14).
Data about software projects is not stored in metrc1, metric2,…,
but is shared between them in some shared, underlying,shape.
Not every project has thesame underlying simple shape; many projects have different,
albeit simple, shapes.
We can exploit that shape, to great effect: for better local predictions; for transferring
lessons learned; for privacy-preserving data mining/
The document outlines the thesis defense of Violeta Damjanovic for her PhD in ambient intelligence and adaptive online experiments. The thesis addresses integrating probabilistic knowledge from pervasive semantic web environments into ontological models to enable adaptive and intelligent experimental environments. The proposed solution involves mechanisms for transforming probabilistic asynchronous process knowledge into ontologies and collecting ambient process knowledge to develop an adaptive semantic ambient system called AmIART. The thesis is expected to contribute to integrating the pervasive semantic web into online experimenting systems and adaptive systems considering uncertain knowledge.
https://telecombcn-dl.github.io/2017-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.
The document discusses prototype-based machine learning and its applications in bio-medical domains. It provides an overview of unsupervised and supervised prototype-based learning techniques, including competitive learning, Kohonen's self-organizing map (SOM), and learning vector quantization (LVQ). Examples of applying these methods to cluster proteomics data and identify biomarkers for rheumatoid arthritis are also mentioned.
The report covers the diverse field of neural networks compiled from various sources into a compact yet detailed form. It also has the formal report writing pattern incorporated in it.
The importance of sustainable and efficient computational practices in artificial intelligence (AI) and deep learning has become increasingly critical. This webinar focuses on the intersection of sustainability and AI, highlighting the significance of energy-efficient deep learning, innovative randomization techniques in neural networks, the potential of reservoir computing, and the cutting-edge realm of neuromorphic computing. This webinar aims to connect theoretical knowledge with practical applications and provide insights into how these innovative approaches can lead to more robust, efficient, and environmentally conscious AI systems.
Webinar Speaker: Prof. Claudio Gallicchio, Assistant Professor, University of Pisa
Claudio Gallicchio is an Assistant Professor at the Department of Computer Science of the University of Pisa, Italy. His research involves merging concepts from Deep Learning, Dynamical Systems, and Randomized Neural Systems, and he has co-authored over 100 scientific publications on the subject. He is the founder of the IEEE CIS Task Force on Reservoir Computing, and the co-founder and chair of the IEEE Task Force on Randomization-based Neural Networks and Learning Systems. He is an associate editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
The Ghazalian Project for the AI Era: A Multiplex Critical AI ApproachJunaid Qadir
Presentation by Junaid Qadir (Qatar University) at
Symposium on Ghazali on Education: Contemporary Practical Applications from an Enduring Legacy - Day 2
Organized by Hamad Bin Khalifa University (HBKU)
Fundamentals of Artificial Intelligence — QU AIO Leadership in AIJunaid Qadir
1. The document discusses Junaid Qadir's background and research interests which include ethics of AI, safety of AI, and mitigating antisocial online behavior.
2. It provides an overview of the fundamentals of artificial intelligence, including definitions of AI, the history and development of AI, and examples of modern AI applications.
3. The document then focuses on machine learning, describing supervised and unsupervised learning, deep learning, and reinforcement learning. It also discusses important concerns regarding bias, interpretability, privacy, and reliability in machine learning models.
On Writing Well — 6 Tips on Bringing Clarity to WritingJunaid Qadir
This document provides tips for clear writing and summarizing a longer document. It discusses 6 tips for bringing clarity to writing: 1) using a top-down approach with big picture first, 2) ensuring the writing flows connectedly, 3) having coherence and unity, 4) focusing on the reader with empathy, 5) maintaining consistency in design, and 6) developing style and flair. It then goes on to discuss each of these tips in more detail and provides additional guidance on clear writing and summarization.
Ihsan for Muslim Professionals Short CourseJunaid Qadir
Ihsan for Muslim Professionals Short Course
by JUNAID QADIR, Information Technology University.
Ramadan 1441, May 2020.
Watch the entire short course at
https://www.youtube.com/playlist?list=PL4AueLFeEG0DAqrRgnZKQR163mvpOOkz-
A Thinking Person's Guide to Using Big Data for Development: Myths, Opportuni...Junaid Qadir
A Thinking Person's Guide to Using Big Data for Development: Myths, Opportunities, and Pitfalls
Accompanying Paper Available at:
Caveat Emptor: The Risks of Using Big Data for Human Development
IEEE Technology and Society Magazine 38(3):82-90
DOI: 10.1109/MTS.2019.2930273
September 2019
https://www.researchgate.net/publication/335745617_Caveat_Emptor_The_Risks_of_Using_Big_Data_for_Human_Development
On Analyzing Self-Driving Networks: A Systems Thinking Approach Junaid Qadir
This document provides an overview of systems thinking approaches for analyzing self-driving networks. It discusses the problems with conventional non-systems thinking, such as mental models and reductionism. It then defines key concepts in systems thinking like feedback loops, leverage points, and archetypes. The document applies these concepts to challenges in internet architecture like spam, privacy, and quality of service. It also discusses ethical and policy challenges for self-driving networks, like who will make ethical decisions. The document concludes that systems thinking is needed to understand complex interactions in self-driving networks and their effects on stakeholders.
IWCMC Invited Talk for the E-5G PresentationJunaid Qadir
The unprecedented rapid adoption of mobile technology has motivated great interest in using mobile technology for health (mHealth). Bolstering the mHealth promise are three important trends: Firstly, big data—through which there has been unprecedented commoditization and opening up of data through the instrumentation of modern phones and environments (e.g., using the native sensors in mobile phones or using embedded devices in the so-called Internet of Things). This opens up the opportunity of collecting individual level ``small data’’ that can be used to provide personalized healthcare. Secondly, artificial intelligence (AI) and machine learning (ML) advances have democratized diagnostic capabilities to some extent and further significant improvement is expected. With advances in computational capabilities of mobile phones, and with resource augmentation from clouds, it will be possible to support data and computation intensive mHealth applications. Finally, high-performance communication (e.g., high throughput and low latency) capabilities can change the landscape of healthcare in terms of operational efficiency and accuracy and enable a range of telehealth services. In this talk, we will present the research agenda for bringing the 5G-Enabled Health Revolution.
1) Director Junaid Qadir oversees the IHSAN Lab at ITU-Punjab focused on ICT for human development, systems and networking.
2) Qadir has over 50 journal publications including 35 in ISI-indexed journals with an h-index of 13 and cumulative impact factor of 60.
3) Qadir's research interests include applied machine learning, cognitive networking, big data for development, and computational social science with a focus on impact and utility.
The document discusses effective learning techniques based on scientific research. It begins by contrasting a fixed versus growth mindset for learning. It then outlines common failures in learning like not engaging oneself, managing time poorly, or seeing failure and social aspects of learning. The bulk of the document discusses evidence-backed learning techniques like retrieval practice (testing), spacing out practice over time, and interleaving different topics. It emphasizes that while these techniques require more effort initially, they lead to better long-term retention and flexibility. The concluding remarks summarize that small changes can greatly improve learning, illusions of fluency should be avoided, retrieval practice and spaced interleaving should be incorporated, and some difficulty is desirable for durable learning.
Taming limits with approximate networkingJunaid Qadir
Internet is the linchpin of modern society, which the various threads of modern life weave around. But being a part of the bigger energy- guzzling industrial economy, it is vulnerable to disruption. It is widely believed that our society is exhausting its vital resources to meet our energy requirements, and the cheap fossil fuel fiesta will soon abate as we cross the tipping point of global oil production. We will then enter the long arc of scarcity, constraints, and limits— a post-peak “long emergency” that may subsist for a long time. To avoid the collapse of the networking ecosystem in this long emer- gency, it is imperative that we start thinking about how network- ing should adapt to these adverse “undeveloping” societal condi- tions. We propose using the idea of “approximate networking”— which will provide good-enough networking services by employ- ing contextually-appropriate tradeoffs—to survive, or even thrive, in the conditions of scarcity and limits.
See the video at: https://www.youtube.com/watch?v=4hKvgIi-HZY
Presentation on "The pedagogy of online education: historical overview and future directions" at the VU 3rd e-Learning and Distance Education Conference (ELDEC) conference.
This document provides 7 steps for doing your best research. It first discusses focusing on doing great research by aiming high, working hard, and learning from research leaders. The second step covers work and time management, emphasizing the importance of prioritizing important non-urgent tasks over urgent unimportant ones. Specializing while also maintaining generalist knowledge is the sixth step. Maintaining balance between work and personal life is also emphasized.
This document outlines a presentation on big data for development (BD4D). It discusses the rise of big data and how BD4D techniques like data analytics can be applied. Potential BD4D applications include healthcare, emergency response, and agriculture. Data sources include mobile phones, crowdsourcing, and social media. The presentation also covers BD4D research in Pakistan using mobile data and challenges like data bias, privacy and causation. Open research areas are suggested to further mitigate challenges and advance predictive and multimodal BD4D analytics.
This document discusses effective teaching methods and common mistakes made by learners. It outlines seven common learner mistakes: having a fixed mindset, failing to engage in learning, failing to manage time, failing not realizing that failing is key to learning, failing to realize learning is social, being a "learning monogamist", and not learning how to learn. It then discusses what good teaching entails, including adopting humility, excellence, and kindness. Good teaching avoids creating an "illusion of learning" and encourages continual self-improvement in both knowledge and character.
Common Student mistakes: What We Can Learn From Socrates, the Cognitive Scien...Junaid Qadir
The document discusses 7 common mistakes students make and provides solutions for each. The mistakes are: 1) having a fixed mindset, 2) failing to engage yourself in learning, 3) failing to manage time, 4) failing to realize that failing is key to learning, 5) failing to realize that learning is social, 6) being a learning monogamist, and 7) not learning how to learn. For each mistake, the document provides 3 solutions such as developing a growth mindset, asking questions to engage more actively, and seeking feedback from others. The overall message is that students can improve their learning by avoiding these common pitfalls and applying the suggested strategies.
1. The lecture discusses ethics and netiquette when using information and communication technologies.
2. It outlines basic principles for social conduct online such as respecting others' privacy, intellectual property, and time.
3. The lecture also discusses avoiding harmful, unethical, or illegal behaviors like spreading misinformation or abusing positions of power or trust.
On The Necessity Of Loving The Prophet (Sallalahu Alaihi Wassalam)Junaid Qadir
This presentation presents a chapter of the book 'Ash-Shifa' by Qadi Iyad and talks about the great position occupied by the Messenger of Allah (Sallalahu Alaihi Wassalam).
Reference: Aisha Bewley: "Muhammad - Messenger of Allah, Ash-Shifa of Qadi Iyad"
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
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تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
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3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
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Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
2. Today’s agenda:
1 What is Network Performance Evaluation
(NPE)?
2 About this course: contents and resources
3. 1
What is NPE?
ˉ Importance of Computer Networks
ˉ Importance of NPE
ˉ What does performance mean?
4. NPE (design, analysis and
evaluation)
1 For a given packet arrival pattern and desired
performance (e.g., low packet drops), what
should be the size of router buffers? (design
question)
2 What is the quantitative improvement in
performance of the router after the latest
software (or hardware) upgrade? (analysis/
evaluation question)
5. Goals of NPE
1 Comparison of design alternatives
2 System dimensioning
3 Relationship (causal/ correlational) modeling
4 Performance description in appropriate metrics.
10. The Science and Art of NPE
Part A
12 lectures
12 lectures
Lecture 2-13
Statistical
The Science of background
The Art of NPE
NPE (grammar of
science)
11. The Science and Art of NPE
Part A
12 lectures
The Science of NPE
"The fundamental principle of science, the
definition almost, is this: the sole test of the
validity of any idea is experiment." -
Richard Feynman.
'In questions of science the authority of a
thousand is not worth the humble
reasoning of a single individual.' Galileo
Galilei.
12. The Science and Art of NPE
Part A
12 lectures
Statistical background
It is better to be approximately right than be exactly wrong – John
Tukey
13. The Science and Art of NPE
Part A
12 lectures
The Art of NPE
“All models are wrong; some are
useful." – George Box.
"Excellence in statistical graphics
consists of complex ideas
communicated with clarity, precision,
and efficiency." – Edward Tufte.
15. Empirical/ Experimental NPE
Empirical/
Part B
experimental NPE
13 lectures
Lecture 14-26
Workload Design of Internet
Modeling Experiments Measurement
16. Empirical/ Experimental NPE
Part B
13 lectures
Workload Modeling
"The essence of modeling, as opposed to just
observing and recording, is one of abstraction.
This means two things: generalization and
simplification." - Dror Feitelson.
17. Empirical/ Experimental NPE
Part B
13 lectures
Design of Experiments
Does the „magic
tablet‟ work as
advertised or is it
just a gimmick?
Hypothesis testing is at the very core of the scientific method.
18. Empirical/ Experimental NPE
Part B
13 lectures
Internet Measurement
"Measure what is measurable, and make
measurable what is not so." - Galileo
Galilei
19. Module 3/3
Simulation/ Modeling based
NPE
Part C
18 lectures
Lecture 2-13 Lecture 14-26 Lecture 27-44
20. Simulation/ Modeling based NPE
Part C
18 lectures
Lecture 27-44
Probability and
Stochastic Analytical Simulation
Processes Modeling Modeling
Preliminaries
21. Simulation/ Modeling based NPE
Part C
18 lectures
Stochastic Background
PROBABILITY
RANDOM VARIABLE’S VALUE
22. Simulation/ Modeling based NPE
Part C
18 lectures
Analytical Modeling
SERVER
PACKET
QUEUE
ROUTER
"The essence of modeling is one of abstraction.
This means two things: generalization and
simplification."
23. Simulation/ Modeling based NPE
Part C
18 lectures
Simulation Modeling
Computer based simulation modeling can be performed when
experimenting with real system is not practical or efficient and when
simpler analytical methods do not exist
24. Course conclusion
Summary of this course
(Lecture 45)
Lecture 2-13 Lecture 14-26 Lecture 27-44
29. Reference for today’s lecture
Chapter 1 [Raj Jain]
Find more lecture resources at the course‟s companion site:
http://sites.google.com/site/netperfeval
30. Credits/ Acknowledgments
Books
Raj Jain, “The Art of Computer Systems Performance Analysis”
Figures and Images:
Car Crash: http://www.pclaunches.com/other_stuff/audi_uses_supercomputer_for_crash_simulations.php
Galileo‟s Portrait: http://en.wikipedia.org/wiki/File:Justus_Sustermans_-_Portrait_of_Galileo_Galilei,_1636.jpg
George Box‟s Picture: http://gallery.socionix.com/d/49618-2/1990_George_Box.jpg
Edward Tufte‟s Picture: http://www.freebase.com/view/en/envisioning_information
VU LMS: http://vulms.vu.edu.pk
Clipart supplied with Microsoft Office
These resources have been used in these lecture slides for educational purpose under the fair use doctrine.
The ownership of these resources, if copyrighted, is retained by their respective copyright owners.
Welcome to the first lecture of this new course offered by Virtual University titled: 'Network Performance and Evaluation’ being offered by Virtual University. I’m the instructor for this course and my name is JunaidQadir.Firstly, a few things about myself.I’mJunaidQadir, currently an Assistant Professor at SEECS, NUST. I received my PhD in Computer Engineering in May 2008 from the University of New South Wales (UNSW), Sydney, Australia where I worked on the research topic of "Improving broadcast performance in multi-radio multi-channel multi-rate wireless mesh networks"'.Currently, I am the director of the Cognet lab which operates under the PTCL, Cisco, NUST IP technology center of excellence situated at SEECS, NUST.Before my PhD studies, I have worked in the networking industry for around 4 years where I gained hands-on experience working on the largest data networks in the country. Amongst other places, I have worked at PTCL’s Pakistan Internet Exchange (PIE) and at the national data networks of National Telecommunication Corporation (NTC). I completed my bachelor’s degree in electrical engineering from University of Engineering and Technology (UET), Lahore, Pakistan in 2000.
Today, I want to talk about two things. Firstly, I want to introduce you to the notion of NPE and show you how fundamental it is to the efficient operation of computer and communication networks.Secondly, I want to talk about this particular course: about myself, my pedagogical approach to teaching this course, the outline of this course, and the relevant resources such as the course textbook, the course guidebook, and the course websites.
So we begin with what is NPE:Importance of NPE and its basic tasks:With computer networks playing such a central role in modern life, it is imperative that: 1) we design high-performance reliable networks and 2) we have the ability to analyze and evaluation the performance of computer networks.NPE, like probability, can often contradict intuition and therefore it would be foolhardy to only rely on common-sense and gut feeling for evaluating network performance. As an example of NPE results contradicting common-sense consider the Braess' Paradox1 which states that adding extra capacity to a network when the moving entities selfishly choose their route, can in some cases reduce overall performance. This contradicts the conventional wisdom which implies that the solution to all network problems is to increase capacity but this paradox is indicating that it may not always be productive to do so. Since we cannot rely on intuition and gut feeling only for NPE, this motivates us to develop formal tools to facilitate analysis and evaluation. NPE is also workload and metric specific, changing which can lead to dramatically different results. 1 http://en.wikipedia.org/wiki/Braess%27s_paradox
There are two broad categories of NPE questions: design questions, and analysis or evaluation questions.
NPE is about quantifying the service delivered by a computer or communication system. The goal of a performance evaluation may either be:1) a comparison of design alternatives, i.e. quantify the improvement brought by a design optionComparison of designs requires a well-defined load model: e.g., we might be interested in: comparing the power consumption of several server farm configurations; however, the exact value of its intensity does not have to be identified. 2) system dimensioning, i.e. determine the size of all system components for a given planned utilization. With a performance goal in mind (e.g., a service level agreement, SLA, of 99.999% uptime), what infrastructure is needed? System dimensioning requires a detailed estimation of the load intensity. Like any prediction exercise, this is very hazardous and liable to going very wrong.3) determining causal relationship model between design parameter and performance metric of choice4) describing the performance of a system in terms on performance metrics.
In the first module of this course, we are going to learn about the philosophy of NPE and discover that it is both a work of art and a work of science.
Scientific method is a very important method for gaining knowledge in which we focus on building knowledge through repeatable and controlled (empirical) observations. It is a useful method for NPE since it minimizes the effect of biases and forms conclusion only on the basis of experimental evidence.
Statistics is an integral tool that we use to deal with uncertainty. It helps us to quantify uncertainty and to make informed decisions in the face of uncertain circumstances. It is the grammar of science and is used by all the physical and social sciences since all measurements are beset by errors and it’s necessary for any meaningful conclusions from such data that we are able to quantify the amount of error (not in the sense of a blunder but in the sense of natural unavoidable uncertainty) To read more about the example of the man with two watches, refer to ‘Statistical Modeling: A Fresh Approach’ by Daniel Kaplan.
The model is the most basic element of the scientific method. Everything done in science is done with models. A model is any simplification, substitute or stand-in for what you are actually studying or trying to predict. Models are used because they are convenient substitutes that facilitates experimentation, analysis or observation. We've all heard about hypotheses and theories, especially in physics and chemistry. Theories usually comprise some idea that scientists have about how nature works, but that they aren't totally sure. Hypotheses and theories are merely particular kinds of models that we will refer to below as abstract models. The scientific method is a procedure for the construction and verification of models. We note here that science progresses by continuously trading current models for better models whenever they may become available and there's no right answer at which we would stop our search for better models.It must be remembered always that all models are only approximations of reality. They are used since they are useful for learning about the system and how it will behave if the system parameters are changed. The role of a NPE model can be compared to a city map. A city map has two key properties. It is an abstract representation of the real situation in that the distances between any two points on the map are not in geographical proportion, and it is simple because it is uncluttered by unimportant real-world physical details. The natural urge is to create a NPE "map” adorned with an abundance of physical detail because that would seem to constitute a more faithful representation of the computer system being analyzed. In spite of this urge, you should strive instead to make your NPE models as simple and abstract as a city map. Adding complexity does not guarantee accuracy. (Adapted from Neil Gunther’s book: ‘Analyzing Computer System Performance with Perl::PDQ’.)Unfortunately, there is no simple recipe for constructing a city map (or a NPE model). Einstein reputedly said that things should be as simple as possible, but no simpler. That should certainly be the goal for applying NPE, but like drawing any good map there are aspects that remain more in the realm of art than science.
In the second module of this course, we are going to learn about the use of empirical methods (experiment based) to perform NPE.
To perform NPE of any system, it is important that we know what input load would be applied to such a system. All performance evaluation is contingent on what workload is applied to the system, and it is possible (and is usually the case) that performance conclusions be reversed if we make different set of assumptions about what the workload would be like. We will see in the subpart 1 of the module on empirical methods (Part B of this course) that workload modeling is essentially an abstraction (or simplification) process in which we generalize the actual workload by its parameterized model so that we can perform evaluation on the system by tweaking the workload model and observing how the performance varies in response. "The essence of modeling, as opposed to just observing and recording, is one of abstraction. This means two things: generalization and simplification." - DrorFeitelson.The following is an excerpt from DrorFeitelson’s book1 on Workload Modeling:An important property of good models is simplicity. A good model doesn’t just define new useful quantities—it also leaves out many useless ones. The act of modeling distills the cumulative experience gained from performing experimental measurements, and sets then in a format that can be used as the basis for further progress [14]. In fact, this is also the basis for natural science, where experimental observations are summarized in simple laws that are actually a model of hownature operates.It should be stressed that finding new models is not easy. The largest obstacle is actually noticing that a new model is needed. It is very tempting to interpret experimental results in the light of prevailing preconceptions. This runs the risk of fitting the measurements to the theory, rather than the theory to the measurements.NPE analysts use models for various tasks: describing a variable (lets say the input to the system under consideration which is not known apriori exactly but known statistically) or to describe a process or system under study (a model may be more amenable to analysis or experimental manipulation). Both approximation facilitates (and in some cases makes possible) efficient analysis and evaluation. 1http://www.cs.huji.ac.il/~feit/wlmod/
Design of experiments uses hypothesis testing to ascertain the validity of a claim (or a hypothesis) which explains some observable phenomena. We seek to differentiate real effects from coincidental or chance effects which may also produce the same result (due to chance only).
An important part of the empirical method is measuring and learning how to measure.
In the thirdmodule of this course, we are going to learn about the use of modeling and simulation to perform NPE.
We will focus on the necessary stochastic and probability background that we shall be using for the remainder of this course.
"One of life's more disagreeable activities, namely, waiting in line, is the delightful subject of this book. One might reasonably ask, "What does it profit study such unpleasant phenomena?" from preface to Queueing Systems, Vol. 1, Leonard Kleinrock.In analytical NPE, we model the system under consideration using mathematical symbols that are mathematically related e.g, the system may be represented as a set of equations or inequalities. a sequence on sourcing statistics. Analytical models are typically mathematical models for which closed-form solutions exist and are one of the quickest methods of NPE. However, they are applicable to only a subclass of NPE systems. In this course, we will learn the utility of mathematical modeling. Mathematical models govern our economy and help forecast our weather. They predict who will win the election and decide whether your bank loan application should be granted. However, the man on the street knows little about what mathematical models are and how they work. In this course, we will use NPE to explain how mathematical models are designed, built, and validated.The most important framework that we shall study in this context would be queueing theory. It is amazing how much we can model using the plain concept of a queue of customers waiting for service from a server. We can model a router or a switch as a queue in which packets line up in a queue to obtain switching service: using queueing theory, we can obtain average case statistics like how much time does a packet wait on average in the system, at any given time, how many packets are queued in the router buffers waiting for service?
In many cases, real life systems are too complex and may not be even approximated closely by analytical models. In such cases, we usually recourse to simulation methods in which we build computational models (or computer program models) of the real system that we would like to model. Thereafter, we can analyze the system by executing the computation model (or program) and make conclusions on the basis of such executions. Simulation modeling is a very general technique and is used much in NPE studies especially for evaluation of algorithms, and systems of computer networks.
In this course, our aim is to present performance evaluation in an accessible way which can be understood and implemented by all computer engineers and scientists. The foundations of performance evaluation are in statistics and queuing theory, therefore, this course traditionally involves a lot of mathematics. However, I will aim to present ideas intuitively and in the context of concrete applications. I will also post links to resources that you can use to further develop your intuition. We will see in this course that we can often use computer simulations to develop intuition about intractable problems that can be solved by analytical models. Similarly, I would encourage you to explore active demo material including applets which will be linked to in the companion site of this course. You would learn that you can gain a lot of intuition by practicing through these demos and applets.