This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
Modeling XCS in class imbalances: Population sizing and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially.
The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Virscidian Poster Asms2010 Final Version LetterMark Bayliss
ASMS 2010 Poster - Mark Bayliss, Virscidian Inc - Towards automated evaluation of result accuracy for LC/MS/UV/ELSD/CLND substance screening – supporting Library Management and Medicinal Chemistry
Modeling XCS in class imbalances: Population sizing and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially.
The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Virscidian Poster Asms2010 Final Version LetterMark Bayliss
ASMS 2010 Poster - Mark Bayliss, Virscidian Inc - Towards automated evaluation of result accuracy for LC/MS/UV/ELSD/CLND substance screening – supporting Library Management and Medicinal Chemistry
Komogortsev Qualitative And Quantitative Scoring And Evaluation Of The Eye Mo...Kalle
This paper presents a set of qualitative and quantitative scores designed to assess performance of any eye movement classification algorithm. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (IHMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the "best" classification algorithm is provided for several applications. General recommendations for the selection of the input parameters for each algorithm are
provided.
Genetic Programming based Image SegmentationTarundeep Dhot
Genetic Programming based Image Segmentation with Applications to Biomedical Object Detection. Published paper of our research work. Published at Genetic and Evolutionary Computation Conference (GECCO) 2009.
Komogortsev Qualitative And Quantitative Scoring And Evaluation Of The Eye Mo...Kalle
This paper presents a set of qualitative and quantitative scores designed to assess performance of any eye movement classification algorithm. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (IHMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the "best" classification algorithm is provided for several applications. General recommendations for the selection of the input parameters for each algorithm are
provided.
Genetic Programming based Image SegmentationTarundeep Dhot
Genetic Programming based Image Segmentation with Applications to Biomedical Object Detection. Published paper of our research work. Published at Genetic and Evolutionary Computation Conference (GECCO) 2009.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Framework for Automated Association Mining Over Multiple Databasesertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-framework-for-automated-association-mining-over-multiple-databases/
Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance.
Applying soft computing techniques to corporate mobile security systemsPaloma De Las Cuevas
Corporate workers increasingly use their own devices for work purposes, in a trend that has come to be called the "Bring Your Own Device" (BYOD) philosophy and companies are starting to include it in their policies. For this reason, corporate security systems need to be redefined and adapted, by the corporate Information Technology (IT) department, to these emerging behaviours. This work proposes applying soft-computing techniques, in order to help the Chief Security Officer (CSO) of a company (in charge of the IT department) to improve the
security policies.
The actions performed be company workers under a BYOD situation will be treated as events: an action or set of actions yielding to a response. Some of those events might cause a non compliance with some corporate policies, and then it would be necessary to define a set of security rules (action, consequence). Furthermore, the processing of the extracted knowledge will allow the rules to be adapted.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
In this presentation I will show a set of important topics about Software Engineering Empirical Studies that can be useful for increasing quality on your thesis and monographs in general. You can read this presentation and to think about how to do a good experimentation by apply its objectives, validation methods, questions, answers expected, define metrics and measuring it.I will exhibit how the researchers selected the data for avoid case studies in a biased way using a GQM methodology to sort the study in a simpler view as well.
Network Metrics and Measurements in the Era of the Digital EconomiesPavel Loskot
Rapidly evolving socio-technical systems require radically new approaches to measure and monitor their performance. The system complexity and desire for autonomy requires to move from simple metrics to whole metrics frameworks. The metrics and measurements of complex systems is emerging as a new discipline.
Genetic Algorithms and Genetic Programming for Multiscale Modelingkknsastry
Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and
addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably
and accurately.
Towards billion bit optimization via parallel estimation of distribution algo...kknsastry
This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm to solve very large scale problems with millions to billions of variables. The paper presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of compact genetic algorithm (cGA). The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling up to a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The compact GA, on the other hand, is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billion-variable problems across the landscape of search problems.
Empirical Analysis of ideal recombination on random decomposable problemskknsastry
This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifically, additively decomposable problems of order k whose subsolution fitnesses are sampled from the standard uniform distribution U[0,1] are analyzed. The scalability of the selectorecombinative GA is investigated for 10,000 rADP instances. The validity of facetwise models in bounding the population size, run duration, and the number of function evaluations required to successfully solve the problems is also verified. Finally, rADP instances that are easiest and most difficult are also investigated.
Population sizing for entropy-based model buliding In genetic algorithms kknsastry
This paper presents a population-sizing model for the entropy-based model building in genetic algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of the selection pressure on population sizing is also incorporated. The proposed model indicates that the population size
required for building an accurate model scales as Θ(m log m), where m is the number of substructures and proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(<em>l</em> log<em>l</em>), where <em>l</em> is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(<em>l</em> log<em>l</em>). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(<em>l</em>).
Automated alphabet reduction with evolutionary algorithms for protein structu...kknsastry
This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a protein is represented. The potential benefits of this procedure are faster and easier learning process as well as the generation of more compact and human-readable classifiers. The dimensionality reduction procedure we propose consists on the reduction of the 20-letter amino acid (AA) alphabet, which is normally used to specify a protein sequence, into a lower cardinality alphabet. This reduction comes about by a clustering of AA types accordingly to their physical and chemical similarity. Our automated reduction procedure is guided by a fitness function based on the Mutual Information between the AA-based input attributes of the dataset and the protein structure feature that being predicted. <br />
To search for the optimal reduction, the Extended Compact Genetic Algorithm (ECGA) was used, and afterwards the results of this process were fed into (and validated by) BioHEL, a genetics-based machine learning technique. BioHEL used the reduced alphabet to induce rules for protein structure prediction features. BioHEL results are compared to two standard machine learning systems. Our results show that it is possible to reduce the size of the alphabet used for prediction from twenty to just three letters resulting in more compact, i.e. interpretable, rules. Also, a protein-wise accuracy performance measure suggests that the loss of accuracy accrued by this substantial alphabet reduction is not statistically significant when compared to the full alphabet.
Analyzing probabilistic models in hierarchical BOA on traps and spin glasseskknsastry
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.
Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Modeling XCS in class imbalances: Population size and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially. The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
In this study we present a detailed analysis of the extended compact genetic algorithm (ECGA). Based on the analysis, empirical relations for population sizing and convergence time have been derived and are compared with the existing relations. We then apply ECGA to a non-azeotropic binary working fluid power cycle optimization problem. The optimal power cycle obtained improved the cycle efficiency by 2.5% over that existing cycles, thus illustrating the capabilities of ECGA in solving real-world problems.
Silicon Cluster Optimization Using Extended Compact Genetic Algorithmkknsastry
This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation.
A Practical Schema Theorem for Genetic Algorithm Design and Tuningkknsastry
This paper develops the theory that can enable the design of genetic algorithms and choose the parameters such that the proportion of the best building blocks grow. A practical schema theorem has been used for this purpose and its ramification for the choice of selection operator and parameterization of the algorithm is explored. In particular stochastic universal selection, tournament selection, and truncation selection schemes are employed to verify the results. Results agree with the schema theorem and indicate that it must be obeyed in order to ascertain sustained growth of good building blocks. The analysis suggests that schema theorem alone is insufficient to guarantee the success of a selectorecombinative genetic algorithm.
This study addresses the issue of building-block supply in the initial population. Facetwise models for supply of a single building block as well as for supply of all schemata in a partition have been developed. An estimate for the population size required to ensure the presence of all raw building blocks has been derived using these facetwise models. The facetwise models and the population-sizing estimate are verified with computational results.
This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique.
Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algori...kknsastry
A recent study Sastry and Xiao (2001) proposed a highly reliable cluster optimization algorithm which employed extended compact genetic algorithm (ECGA) along with Nelder-Mead simplex search. This study utilizes an efficiency enhancement technique for the ECGA based cluster optimizer to reduce the population size and the number of function evaluation requirements, yet retaining the high reliability of predicting the lowest energy structure. Seeding of initial population with lowest energy structures of smaller cluster has been employed as the efficiency enhancement technique. Empirical results indicate that the population size and total number of function evaluations scale up with the cluster size are reduced from O(n4.2) and O(n8.2) to O(n0.83) and O(n2.45) respectively.
Modeling Tournament Selection with Replacement Using Apparent Added Noisekknsastry
This paper analyzes the effects of tournament selection with replacement on the convergence time and population sizing for selectorecombinative genetic algorithms. This paper empirically demonstrates that the run duration remains the same and is not affected whether the tournament selection is performed with or without replacement. However, the population size required is more if tournament selection is performed with replacement rather than without replacement to attain the same level of accuracy. An approximate population-sizing model is derived based on apparent added noise for the case of tournament selection with replacement. The proposed model is verified with experimental results.
Evaluation Relaxation as an Efficiency-Enhancement Technique: Handling Varian...kknsastry
This study develops a decision-making strategy for deciding between fitness functions with differing bias values. Simple, yet practical facetwise models are derived to aid the decision-making process. The decision making strategy is designed to provide maximum speed-up and thereby enhance the efficiency of GA search processes. Results indicate that bias can be handled temporally and that significant speed-up values can be obtained.
Analysis of Mixing in Genetic Algorithms: A Surveykknsastry
Ensuring building-block (BB) mixing is critical to the success of genetic and evolutionary algorithms. There has been a growing interest in analyzing and understanding BB mixing and it is necessary to organize and categorize representative literature. This paper presents an exhaustive survey of studies on one or more aspects of mixing. In doing so, a classification of the literature based on the role of recombination operators assumed by those studies is developed. Such a classification not only highlights the significant results and unifies existing work, but also provides a foundation for future research in understanding mixing in genetic algorithms.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
Substructrual surrogates for learning decomposable classification problems: implementation and first results
1. Substructural Surrogates for Learning
Decomposable Classification Problems:
Implementation and First Results
Albert Orriols-Puig1,2 Kumara Sastry2
David E. Goldberg2 Ester Bernadó-Mansilla1
1Research Group in Intelligent Systems
Enginyeria i Arquitectura La Salle, Ramon Llull University
2Illinois
Genetic Algorithms Laboratory
Department of Industrial and Enterprise Systems Engineering
University of Illinois at Urbana Champaign
2. Motivation
Nearly decomposable functions in engineering systems
• (Simon,69; Gibson,79; Goldberg,02)
Design decomposition principle in:
– Genetic Algorithms (GAs)
• (Goldberg,02; Pelikan,05; Pelikan, Sastry & Cantú-Paz, 06)
– Genetic Programming (GPs)
• (Sastry & Goldberg, 03)
– Learning Classifier Systems (LCS)
• (Butz, Pelikan, Llorà & Goldberg, 06)
• (Llorà, Sastry, Goldberg & de la Ossa, 06)
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 2
IWLCS’07
3. Aim
Our approach: build surrogates for classification.
– Probabilistic models Induce the form of a surrogate
– Regression Estimate the coefficients of the surrogate
– Use the surrogate to classify new examples
Surrogates used for efficiency enhancement of:
– GAs:
• (Sastry, Pelikan & Goldberg, 2004)
• (Pelikan & Sastry, 2004)
• (Sastry, Lima & Goldberg, 2006)
– LCSs:
• (Llorà, Sastry, Yu & Goldberg, 2007)
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 3
IWLCS’07
4. Outline
1. Methodology
2. Implementation
3. Test Problems
4. Results
5. Discussion
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 4
IWLCS’07
5. 1. Methodology
2. Implementation
3. Test Problems
Overview of the Methodology 3. Results
4. Discussion
5. Conclusions
Structural Surrogate Classification
Model Layer Model Layer Model Layer
•Infer structural form •Use the surrogate
• Extract dependencies
and the coefficients function to predict the
between attributes
based on the class of new
• Expressed as:
structural model examples
• Linkage Groups
• Matrices (Sastry, Lima & Goldberg, 2006)
• Bayesian Networks
• Example: x-or
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 5
IWLCS’07
6. 1. Methodology
2. Implementation
3. Test Problems
Surrogate Model Layer 3. Results
4. Discussion
5. Conclusions
Input: Database of labeled examples with nominal attributes
Substructure identified by the structural model layer
– Example: [0 2] [1]
Surrogate form induced from the structural model:
f(x0, x1, x2) = cI + c0x0 + c1x1 + c2x2 + c0,2x0x2
Coefficients of surrogates: linear regression methods
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 6
IWLCS’07
7. 1. Methodology
2. Implementation
3. Test Problems
Surrogate Model Layer 3. Results
4. Discussion
5. Conclusions
Map input examples to schema-index matrix
– Consider all the schemas:
{0*0, 0*1, 1*0, 1*1, *0*, *1*}
[0 2] [1]
– In general, the number of schemas to be considered is:
Number of linkage
groups
Cardinality of the variable jth
of the ith linkage group
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 7
IWLCS’07
8. 1. Methodology
2. Implementation
3. Test Problems
Surrogate Model Layer 3. Results
4. Discussion
5. Conclusions
Map each example to a binary vector of size msch
– For each building block, the entry corresponding to the schemata contained
in the example is one. All the other are zero.
[0 2] [1]
0*0 0*1 1*0 1*1 *0* *1*
010 1 0 0 0 0 1
100 0 0 1 0 1 0
111 0 0 0 1 0 1
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 8
IWLCS’07
9. 1. Methodology
2. Implementation
3. Test Problems
Surrogate Model Layer 3. Results
4. Discussion
5. Conclusions
This results in the following matrix
n input
examples
Each class is mapped to an integer
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 9
IWLCS’07
10. 1. Methodology
2. Implementation
3. Test Problems
Surrogate Model Layer 3. Results
4. Discussion
5. Conclusions
Solve the following linear system to estimate the
coefficients of the surrogate model
Input instances mapped Class or label of each
to the schemas matrix input instance
Coefficients of the
surrogate model
To solve it, we use multi-dimensional least squares fitting
approach:
– Minimize:
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 10
IWLCS’07
11. 1. Methodology
2. Implementation
3. Test Problems
Overview of the Methodology 3. Results
4. Discussion
5. Conclusions
Structural Surrogate Classification
Model Layer Model Layer Model Layer
•Infer structural form •Use the surrogate
• Extract dependencies
and the coefficients function to predict the
between attributes
based on the class of new
• Expressed as:
structural model examples
• Linkage Groups
• Matrices (Sastry, Lima & Goldberg, 2006)
• Bayesian Networks
• Example: x-or
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 11
IWLCS’07
12. 1. Methodology
2. Implementation
3. Test Problems
Classification Model Layer 3. Results
4. Discussion
5. Conclusions
Use the surrogate to predict the class of a new input instance
Map the example
New input to a vector e of length msch Output = round (x·e)
example according to the schemas
Coefficients of the
surrogate model
0*0 0*1 1*0 1*1 *0* *1*
Output = x·(001010)’
001 0 0 1 0 1 0
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 12
IWLCS’07
13. 1. Methodology
2. Implementation
3. Test Problems
Overview of the Methodology 3. Results
4. Discussion
5. Conclusions
Structural Surrogate Classification
Model Layer Model Layer Model Layer
•Infer structural form •Use the surrogate
• Extract dependencies
and the coefficients function to predict the
between attributes
based on the class of new
• Expressed as:
structural model examples
• Linkage Groups
• Matrices (Sastry, Lima & Goldberg, 2006)
• Bayesian Networks
• Example: x-or
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 13
IWLCS’07
14. Outline
1. Methodology
2. Implementation
3. Test Problems
4. Results
5. Discussion
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 14
IWLCS’07
15. 1. Methodology
How do we obtain the structural 2. Implementation
3. Test Problems
3. Results
model? 4. Discussion
5. Conclusions
Greedy extraction of the structural model for classification
(gESMC): Greedy search à la eCGA (Harik, 98)
1. Start considering all variables independent. sModel = [1] [2] … [n]
2. Divide the dataset in ten-fold cross-validation sets: train and test
3. Build the surrogate with the train set and evaluate with the test set
4. While there is improvement
1. Create all combinations of two subset merges
Eg. { [1,2] .. [n]; [1,n] .. [2]; [1]..[2,n]}
2. Build the surrogate for all candidates. Evaluate the quality with the test set
3. Select the candidate with minimum error
4. If the quality of the candidate is better than the parent, accept the model
(update smodel) and go to 4.
5. Otherwise, stop. sModel is optimal.
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 15
IWLCS’07
16. Outline
1. Methodology
2. Implementation
3. Test Problems
4. Results
5. Discussion
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 16
IWLCS’07
17. 1. Methodology
2. Implementation
3. Test Problems
Test Problems 3. Results
4. Discussion
5. Conclusions
Two-level hierarchical problems (Butz, Pelikan, Llorà & Goldberg, 2006)
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 17
IWLCS’07
18. 1. Methodology
2. Implementation
3. Test Problems
Test Problems 3. Results
4. Discussion
5. Conclusions
Problems in the lower level
Condition
length (l)
Position of the left-most
– Position Problem: 000110 :3 one-valued bit
Condition
length (l)
– Parity Problem: Number of 1 mod 2
01001010 :1
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 18
IWLCS’07
19. 1. Methodology
2. Implementation
3. Test Problems
Test Problems 3. Results
4. Discussion
5. Conclusions
Problems in the higher level
Condition
length (l)
Integer value of the input
– Decoder Problem: 000110 :5
Condition
length (l)
– Count-ones Problem: Number of ones of the input
000110 :2
Combinations of both levels:
– Parity + decoder (hParDec) - Parity + count-ones (hParCount)
– Position + decoder (hPosDec) - Position + count-ones (hPosCount)
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 19
IWLCS’07
20. Outline
1. Methodology
2. Implementation
3. Test Problems
4. Results
5. Discussion
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 20
IWLCS’07
21. 1. Methodology
2. Implementation
3. Test Problems
Results with 2-bit lower level blocks 3. Results
4. Discussion
5. Conclusions
First experiment:
– hParDec, hPosDec, hParCount, hPosCount
– Binary inputs of length 17.
– The first 6 bits are relevant. The others are irrelevant
– 3 blocks of two-bits low order problems
– Eg: hPosDec
Interacting variables:
10 01 00 00101010101 [x0 x1] [x2 x3] [x4 x5]
irrelevant bits
210
Output = 21
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 21
IWLCS’07
22. 1. Methodology
2. Implementation
3. Test Problems
Results with 2-bit lower level blocks 3. Results
4. Discussion
5. Conclusions
Performance of gESMC compared to SMO and C4.5:
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 22
IWLCS’07
23. 1. Methodology
2. Implementation
3. Test Problems
Results with 2-bit lower level blocks 3. Results
4. Discussion
5. Conclusions
Form and coefficients of the surrogate models
– Interacting variables [x0 x1] [x2 x3] [x4 x5]
– Linkages between variables are detected
– Easy visualization of the salient variable interactions
– All models only consist of the six relevant variables
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 23
IWLCS’07
24. 1. Methodology
2. Implementation
3. Test Problems
Results with 2-bit lower level blocks 3. Results
4. Discussion
5. Conclusions
C4.5 created trees:
– HPosDec and HPosCount
• Trees of 53 nodes and 27 leaves on average
• Only the six relevant variables in the decision nodes
– HParDec
• Trees of 270 nodes and 136 leaves on average
• Irrelevant attributes in the decision nodes
– HParCount
• Trees of 283 nodes and 142 leaves on average
• Irrelevant attributes in the decision nodes
SMO built support vector machines:
– 351, 6, 6, and 28 machines with 17 weights each one were
created for HPosDec, HPosCount, HParDec, and HParCount
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 24
IWLCS’07
25. 1. Methodology
2. Implementation
3. Test Problems
Results with 3-bit lower level blocks 3. Results
4. Discussion
5. Conclusions
Second experiment:
– hParDec, hPosDec, hParCount, hPosCount
– Binary inputs of length 17.
– The first 6 bits are relevant. The others are irrelevant
– 2 blocks of three-bits low order problems
– Eg: hPosDec
Interacting variables:
000 100 00101010101 [x0 x1 x2] [x3 x4 x5]
irrelevant bits
0 3
Output = 3
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 25
IWLCS’07
26. 1. Methodology
2. Implementation
3. Test Problems
Results with 3-bit lower level blocks 3. Results
4. Discussion
5. Conclusions
Second experiment:
– 2 blocks of three-bits low order problems
Interesting observations:
•As expected, the method stops the search when no
M1: [0][1][2][3][4][5] improvement is found
M2: [0 1][2][3][4][5] •In HParDec, half of the times the system gets a
model that represents the salient interacting
M3: [0 1 2][3][4][5] variables due to the stochasticity of the 10-fold CV
estimation. Eg. [0 1 2 8] [3 4 9 5 6]
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 26
IWLCS’07
27. Outline
1. Methodology
2. Implementation
3. Test Problems
4. Results
5. Discussion
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 27
IWLCS’07
28. 1. Methodology
2. Implementation
3. Test Problems
Discussion 3. Results
4. Discussion
5. Conclusions
Lack of guidance from low-order substructures
– Increase the order of substructural merges
– Select randomly one of the new structural models
• Follow schemes such as simulated annealing (Korst & Aarts, 1997)
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 28
IWLCS’07
29. 1. Methodology
2. Implementation
3. Test Problems
Discussion 3. Results
4. Discussion
5. Conclusions
Creating substructural models with overlapping
structures
– Problems with overlapping linkages
– Eg: multiplexer problems
– gESMC on the 6-bit mux results in: [0 1 2 3 4 5 ]
– What we would like to obtain is: [0 1 2] [0 1 3] [0 1 4] [0 1 5]
– Enhance the methodology permitting to represent overlapping
linkages, using methods such as the design structure matrix
genetic algorithm (DMSGA) (Yu, 2006)
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 29
IWLCS’07
30. Outline
1. Methodology
2. Implementation
3. Test Problems
4. Results
5. Discussion
6. Conclusions
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 30
IWLCS’07
31. 1. Methodology
2. Implementation
3. Test Problems
Conclusions 3. Results
4. Discussion
5. Conclusions
Methodology for learning from decomposable problems
– Structural model
– Surrogate model
– Classification model
First implementation approach
– greedy search for the best structural model
Main advantage of gESMC over other learners
– It provides the structural model jointly with the classification
model.
Some limitations of gESMC:
– It depends on the guidance on the low-order structure
– Several approaches outlined as further work.
Illinois Genetic Algorithms Laboratory and Group of Research in Intelligent Systems Slide 31
IWLCS’07
32. Substructural Surrogates for Learning
Decomposable Classification Problems:
Implementation and First Results
Albert Orriols-Puig1,2 Kumara Sastry2
David E. Goldberg2 Ester Bernadó-Mansilla1
1Research Group in Intelligent Systems
Enginyeria i Arquitectura La Salle, Ramon Llull University
2Illinois
Genetic Algorithms Laboratory
Department of Industrial and Enterprise Systems Engineering
University of Illinois at Urbana Champaign
