A 1974 graduate school (Physics) paper comparing the performance of numerical methods in FORTRAN and APL for modeling systems of differential equations
This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two
powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then
proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state.
Experimental results show that proposed hybrid algorithm is effective and its performance including speed
and accuracy beats other version.
These are the first slides of the the PhD called Metaheuristics for solving the Time And Space Assembly Line Balancing Problem (TSALBP).
They were presented at the IPMU conference in 2008.
Quantum algorithm for solving linear systems of equationsXequeMateShannon
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b. We consider the case where one doesn't need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x'Mx for some matrix M. In this case, when A is sparse, N by N and has condition number kappa, classical algorithms can find x and estimate x'Mx in O(N sqrt(kappa)) time. Here, we exhibit a quantum algorithm for this task that runs in poly(log N, kappa) time, an exponential improvement over the best classical algorithm.
This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two
powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then
proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their
actions in selecting next state.
Experimental results show that proposed hybrid algorithm is effective and its performance including speed
and accuracy beats other version.
These are the first slides of the the PhD called Metaheuristics for solving the Time And Space Assembly Line Balancing Problem (TSALBP).
They were presented at the IPMU conference in 2008.
Quantum algorithm for solving linear systems of equationsXequeMateShannon
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b. We consider the case where one doesn't need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x'Mx for some matrix M. In this case, when A is sparse, N by N and has condition number kappa, classical algorithms can find x and estimate x'Mx in O(N sqrt(kappa)) time. Here, we exhibit a quantum algorithm for this task that runs in poly(log N, kappa) time, an exponential improvement over the best classical algorithm.
The IMPL console executable (IMPL.exe) can be called from any DOS command prompt window where its Intel Fortran source code can be found in Appendix A. The IMPL console is useful given that it allows you to model and solve problems configured in an IML (Industrial Modeling Language) file. Problems coded using IPL (Industrial Programming Language) in many computer programming languages can use the IMPL console source code as a prototype.
The IMPL console reads several input files and writes several output files which are described in this document. There are several console flags that can be specified as command line arguments and are described below.
An older presentation I gave on temporal logic and model checking. Note that the diamond operator (signifying eventuality) does not appear properly in the uploaded slide.
Swarm Intelligence Heuristics for Graph Coloring ProblemMario Pavone
In this research work we present two novel swarm
heuristics based respectively on the ants and bees artificial
colonies, called AS-GCP and ABC-GCP. The first is based
mainly on the combination of Greedy Partitioning Crossover
(GPX), and a local search approach that interact with the
pheromone trails system; the last, instead, has as strengths
three evolutionary operators, such as a mutation operator; an
improved version of GPX and a Temperature mechanism. The
aim of this work is to evaluate the efficiency and robustness of both developed swarm heuristics, in order to solve the classical Graph Coloring Problem (GCP). Many experiments have been performed in order to study what is the real contribution of variants and novelty designed both in AS-GCP and ABC-GCP.
A first study has been conducted with the purpose for setting
the best parameters tuning, and analyze the running time
for both algorithms. Once done that, both swarm heuristics
have been compared with 15 different algorithms using the
classical DIMACS benchmark. Inspecting all the experiments
done is possible to say that AS-GCP and ABC-GCP are very
competitive with all compared algorithms, demonstrating thus
the goodness of the variants and novelty designed. Moreover,
focusing only on the comparison among AS-GCP and ABCGCP is possible to claim that, albeit both seem to be suitable to solve the GCP, they show different features: AS-GCP presents a quickly convergence towards good solutions, reaching often the best coloring; ABC-GCP, instead, shows performances more robust, mainly in graphs with a more dense, and complex topology. Finally, ABC-GCP in the overall has showed to be more competitive with all compared algorithms than AS-GCP as average of the best colors found.
Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal RecoveryAleksandar Dogandžić
I will describe a projected Nesterov’s proximal-gradient (PNPG) approach for sparse signal reconstruction. The objective function that we wish to minimize is a sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the ℓ₁-norm penalty on the signal's linear transform coefficients or gradient map, respectively. The PNPG approach employs projected Nesterov's acceleration step with restart and an inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking. Thanks to step-size adaptation, PNPG does not require Lipschitz continuity of the gradient of the NLL. We establish O(k⁻²) and PNPG iterate convergence results that account for inexactness of the iterative proximal mapping. The tuning of PNPG is largely application-independent. Tomographic and compressed-sensing reconstruction experiments with Poisson generalized linear and Gaussian linear measurement models demonstrate the performance of the proposed approach.
Optimized Reversible Vedic Multipliers for High Speed Low Power Operationsijsrd.com
Multiplier design is always a challenging task; how many ever novel designs are proposed, the user needs demands much more optimized ones. Vedic mathematics is world renowned for its algorithms that yield quicker results, be it for mental calculations or hardware design. Power dissipation is drastically reduced by the use of Reversible logic. The reversible Urdhva Tiryakbhayam Vedic multiplier is one such multiplier which is effective both in terms of speed and power. In this paper we aim to enhance the performance of the previous design. The Total Reversible Logic Implementation Cost (TRLIC) is used as an aid to evaluate the proposed design. This multiplier can be efficiently adopted in designing Fast Fourier Transforms (FFTs) Filters and other applications of DSP like imaging, software defined radios, wireless communications.
Urban strategies to promote resilient cities The case of enhancing Historic C...inventionjournals
This research tackles disaster prevention problems in dense urban areas, concentrating on the urban fire challenge in Historic Cairo district, Egypt, through disaster risk management approach. The study area suffers from the strike of several urban fire outbreaks, that resulted in disfiguring historic monuments and destroying unregulated traditional markets. Therefore, the study investigates the significance of hazard management and how can urban strategies improve the city resilient through reducing the impact of natural and man-made threats. The main findings of the research are the determination of the vulnerability factors in Historic Cairo district, either regarding management deficiency or issues related to the existing urban form. It is found that the absence of the mitigation and preparedness phases is the main problem in the risk management cycle in the case study. Additionally, the coping initiatives adopted by local authorities to address risks are random and insufficient. The study concludes with recommendations which invoke incorporating hazard management stages (pre disaster, during disaster and post disaster) into the process of evolving development planning. Finally, solutions are offered to mitigate, prepare, respond and recover from fire disasters in the case study. The solutions include urban policies, land-use planning, urban design outlines, safety regulation and public awareness and training.
FEEDBACK LINEARIZATION AND BACKSTEPPING CONTROLLERS FOR COUPLED TANKSieijjournal
This paper investigates the usage of some sophisticated and advanced nonlinear control algorithms in order to control a nonlinear Coupled Tanks System. The first control procedure is called the Feedback linearisation control (FLC), this type of control has been found a successful in achieving a global exponential asymptotic stability, with very short time response, no significant overshooting is recorded and with a negligible norm of the error. The second control procedure is the approaches of Back stepping control (BC) which is a recursive procedure that interlaces the choice of a Lyapunov function with the design of feedback control, from simulation results it shown that this method preserves tracking, robust control and it can often solve stabilization problems with less restrictive conditions may been countered in other methods. Finally both of the proposed control schemes guarantee the asymptoticstability of the closed loop system meeting trajectory tracking objectives.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The best known deterministic polynomial-time algorithm for primality testing right now is due to
Agrawal, Kayal, and Saxena. This algorithm has a time complexity O(log15=2(n)). Although this algorithm is
polynomial, its reliance on the congruence of large polynomials results in enormous computational requirement.
In this paper, we propose a parallelization technique for this algorithm based on message-passing
parallelism together with four workload-distribution strategies. We perform a series of experiments on an
implementation of this algorithm in a high-performance computing system consisting of 15 nodes, each with
4 CPU cores. The experiments indicate that our proposed parallelization technique introduce a significant
speedup on existing implementations. Furthermore, the dynamic workload-distribution strategy performs
better than the others. Overall, the experiments show that the parallelization obtains up to 36 times speedup.
The IMPL console executable (IMPL.exe) can be called from any DOS command prompt window where its Intel Fortran source code can be found in Appendix A. The IMPL console is useful given that it allows you to model and solve problems configured in an IML (Industrial Modeling Language) file. Problems coded using IPL (Industrial Programming Language) in many computer programming languages can use the IMPL console source code as a prototype.
The IMPL console reads several input files and writes several output files which are described in this document. There are several console flags that can be specified as command line arguments and are described below.
An older presentation I gave on temporal logic and model checking. Note that the diamond operator (signifying eventuality) does not appear properly in the uploaded slide.
Swarm Intelligence Heuristics for Graph Coloring ProblemMario Pavone
In this research work we present two novel swarm
heuristics based respectively on the ants and bees artificial
colonies, called AS-GCP and ABC-GCP. The first is based
mainly on the combination of Greedy Partitioning Crossover
(GPX), and a local search approach that interact with the
pheromone trails system; the last, instead, has as strengths
three evolutionary operators, such as a mutation operator; an
improved version of GPX and a Temperature mechanism. The
aim of this work is to evaluate the efficiency and robustness of both developed swarm heuristics, in order to solve the classical Graph Coloring Problem (GCP). Many experiments have been performed in order to study what is the real contribution of variants and novelty designed both in AS-GCP and ABC-GCP.
A first study has been conducted with the purpose for setting
the best parameters tuning, and analyze the running time
for both algorithms. Once done that, both swarm heuristics
have been compared with 15 different algorithms using the
classical DIMACS benchmark. Inspecting all the experiments
done is possible to say that AS-GCP and ABC-GCP are very
competitive with all compared algorithms, demonstrating thus
the goodness of the variants and novelty designed. Moreover,
focusing only on the comparison among AS-GCP and ABCGCP is possible to claim that, albeit both seem to be suitable to solve the GCP, they show different features: AS-GCP presents a quickly convergence towards good solutions, reaching often the best coloring; ABC-GCP, instead, shows performances more robust, mainly in graphs with a more dense, and complex topology. Finally, ABC-GCP in the overall has showed to be more competitive with all compared algorithms than AS-GCP as average of the best colors found.
Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal RecoveryAleksandar Dogandžić
I will describe a projected Nesterov’s proximal-gradient (PNPG) approach for sparse signal reconstruction. The objective function that we wish to minimize is a sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the ℓ₁-norm penalty on the signal's linear transform coefficients or gradient map, respectively. The PNPG approach employs projected Nesterov's acceleration step with restart and an inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking. Thanks to step-size adaptation, PNPG does not require Lipschitz continuity of the gradient of the NLL. We establish O(k⁻²) and PNPG iterate convergence results that account for inexactness of the iterative proximal mapping. The tuning of PNPG is largely application-independent. Tomographic and compressed-sensing reconstruction experiments with Poisson generalized linear and Gaussian linear measurement models demonstrate the performance of the proposed approach.
Optimized Reversible Vedic Multipliers for High Speed Low Power Operationsijsrd.com
Multiplier design is always a challenging task; how many ever novel designs are proposed, the user needs demands much more optimized ones. Vedic mathematics is world renowned for its algorithms that yield quicker results, be it for mental calculations or hardware design. Power dissipation is drastically reduced by the use of Reversible logic. The reversible Urdhva Tiryakbhayam Vedic multiplier is one such multiplier which is effective both in terms of speed and power. In this paper we aim to enhance the performance of the previous design. The Total Reversible Logic Implementation Cost (TRLIC) is used as an aid to evaluate the proposed design. This multiplier can be efficiently adopted in designing Fast Fourier Transforms (FFTs) Filters and other applications of DSP like imaging, software defined radios, wireless communications.
Urban strategies to promote resilient cities The case of enhancing Historic C...inventionjournals
This research tackles disaster prevention problems in dense urban areas, concentrating on the urban fire challenge in Historic Cairo district, Egypt, through disaster risk management approach. The study area suffers from the strike of several urban fire outbreaks, that resulted in disfiguring historic monuments and destroying unregulated traditional markets. Therefore, the study investigates the significance of hazard management and how can urban strategies improve the city resilient through reducing the impact of natural and man-made threats. The main findings of the research are the determination of the vulnerability factors in Historic Cairo district, either regarding management deficiency or issues related to the existing urban form. It is found that the absence of the mitigation and preparedness phases is the main problem in the risk management cycle in the case study. Additionally, the coping initiatives adopted by local authorities to address risks are random and insufficient. The study concludes with recommendations which invoke incorporating hazard management stages (pre disaster, during disaster and post disaster) into the process of evolving development planning. Finally, solutions are offered to mitigate, prepare, respond and recover from fire disasters in the case study. The solutions include urban policies, land-use planning, urban design outlines, safety regulation and public awareness and training.
FEEDBACK LINEARIZATION AND BACKSTEPPING CONTROLLERS FOR COUPLED TANKSieijjournal
This paper investigates the usage of some sophisticated and advanced nonlinear control algorithms in order to control a nonlinear Coupled Tanks System. The first control procedure is called the Feedback linearisation control (FLC), this type of control has been found a successful in achieving a global exponential asymptotic stability, with very short time response, no significant overshooting is recorded and with a negligible norm of the error. The second control procedure is the approaches of Back stepping control (BC) which is a recursive procedure that interlaces the choice of a Lyapunov function with the design of feedback control, from simulation results it shown that this method preserves tracking, robust control and it can often solve stabilization problems with less restrictive conditions may been countered in other methods. Finally both of the proposed control schemes guarantee the asymptoticstability of the closed loop system meeting trajectory tracking objectives.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The best known deterministic polynomial-time algorithm for primality testing right now is due to
Agrawal, Kayal, and Saxena. This algorithm has a time complexity O(log15=2(n)). Although this algorithm is
polynomial, its reliance on the congruence of large polynomials results in enormous computational requirement.
In this paper, we propose a parallelization technique for this algorithm based on message-passing
parallelism together with four workload-distribution strategies. We perform a series of experiments on an
implementation of this algorithm in a high-performance computing system consisting of 15 nodes, each with
4 CPU cores. The experiments indicate that our proposed parallelization technique introduce a significant
speedup on existing implementations. Furthermore, the dynamic workload-distribution strategy performs
better than the others. Overall, the experiments show that the parallelization obtains up to 36 times speedup.
Classification of Iris Data using Kernel Radial Basis Probabilistic Neural N...Scientific Review SR
Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been
successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is
extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The
kernel function is a generalization of the distance metric that measures the distance between two data points as the
data points are mapped into a high dimensional space. During the comparing of the four constructed classification
models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as
proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding
performance in this regard
Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Ne...Scientific Review
Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. During the comparing of the four constructed classification models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding performance in this regard.
Design of airfoil using backpropagation training with mixed approachEditor Jacotech
Levenberg-Marquardt back-propagation training method has some limitations associated with over fitting and local optimum problems. Here, we proposed a new algorithm to increase the convergence speed of Backpropagation learning to design the airfoil. The aerodynamic force coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A feedforward neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. In the proposed algorithm, for output layer, we used the cost function having linear & nonlinear error terms then for the hidden layer, we used steepest descent cost function. Results indicate that this mixed approach greatly enhances the training of artificial neural network and may accurately predict airfoil profile.
Keynote of HOP-Rec @ RecSys 2018
Presenter: Jheng-Hong Yang
These slides aim to be a complementary material for the short paper: HOP-Rec @ RecSys18. It explains the intuition and some abstract idea behind the descriptions and mathematical symbols by illustrating some plots and figures.
Computational intelligence based simulated annealing guided key generation in...ijitjournal
In this paper, a Computational Intelligence based Simulated Annealing (SA) guided approach is use to
construct the key stream. SA is a randomization technique for solving optimization problems. It is a
procedure for finding good quality solutions to a large diversity of combinatorial optimization problems.
This technique can assist to stay away from the problem of getting stuck in local optima and to escort
towards the globally optimum solution. It is inspired by the annealing procedure in metallurgy. At high
temperatures, the molecules of liquid move freely with respect to one another. If the liquid is cooled slowly,
thermal mobility is lost. Parametric tests are done and results are compared with some existing classical
techniques, which shows comparable results for the proposed system.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Planning projects usually starts with tasks and milestones. The planner gathers this information from the participants – customers, engineers, subject matter experts. This information is usually arranged in the form of activities and milestones. PMBOK defines “project time management” in this manner. The activities are then sequenced according to the projects needs and mandatory dependencies.
Increasing the Probability of Project SuccessGlen Alleman
Risk Management is essential for development and production programs. Information about key cost, performance and schedule attributes are often uncertain or unknown until late in the program.
Risk issues that can be identified early in the program, which may potentially impact the program, termed Known Unknowns, can be alleviated with good risk management. -- Effective Risk Management 2nd Edition, Page 1, Edmund Conrow, American Institute of Aeronautics and Astronautics, 2003
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From Principles to Strategies How to apply Principles, Practices, and Processes of Systems Engineering to solve complex technical, operational,
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Building a Credible Performance Measurement BaselineGlen Alleman
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Only when capabilities are defined can we start with requirements elicitation
Starting with the development of a Rough Order of Magnitude (ROM) estimate of work and duration, creating the Product Roadmap and Release Plan, the Product and Sprint Backlogs, executing and statusing the Sprint, and informing the Earned Value Management Systems, using Physical Percent Complete of progress to plan.
Program Management Office Lean Software Development and Six SigmaGlen Alleman
Successfully combining a PMO, Agile, and Lean / 6 starts with understanding what benefit each paradigm brings to the table. Architecting a solution for the enterprise requires assembling a “Systems” with processes, people, and principles – all sharing the goal of business improvement.
This resource document describes the Program Governance Road map for product development, deployment, and sustainment of products and services in compliance with CMS guidance, ITIL IT management, CMMI best practices, and other guidance to assure high quality software is deployed for sustained operational success in mission critical domains.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Here’s what you’ll gain:
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Defect reporting
SAP heatmap example with demo
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
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A time study in numerical methods programming
1. A TIME STUDY IN NUMERICAL METHODS PROGRAM.MING
by
Glen B. Alleman
and
John L. Richardson
Department of Physics
University of California at Irvine
Irvine, California 92664
prepared for
APL VI
Anaheim, California
May 14-17, 1974
HiTRODUCTION
With the digitial computer firmly established as a
research tool used by the scientist and engineer alike, a
careful examination of some of the techniques used to solve
the problems faced by the scientific user is warranted,
This paper describes a test undertaken to determine the
effectiveness of two different programming languages in
providing solutions to numerical analysis problems found
in scientific investagation. Some of the questions asked
were; 1) Can APL compete with a batch processed FORTRAN
job in solving common numerical analysis problems?
2) Is it useful to trade execution speed for code density
or vice-versa? 3) Is APL an easier language, from the
view-point of the novice user, in which to code his problem?
4) Can APL be cost effective in an environment where large
"number-crunching" problems are an everyday event,
These questions were asked with the hope of clearing
up some of the false ideas held about both FORTRAN and AP[,
amoung scientific programmers. The FORTRAN community
holding that a fast object module is well worth the coding
and compilation expense, while the APL advocate stating that
compact on-line solutions provide faster resolution of the
users problems. The test results may be interpretered in
many ways and it is hoped that the results will lead to more
exploration of this field of computing; i.e. the cost
effective solution to a specific numerical problem.
6
2. problem
loo}: ;;it
areas wcrr..:::
personel knowledge of
wor1d proqrvm:ming application~
Numerical
2) Solutions to Individual
3) and tr:~a Matrix
4) Systems of Linear Equations
5) Solutions to Ordinary Different.ial
6) Solution to Partial Differential
Prom each of these subjects one
~hoo(rnn. '.rhis was don<;i from tlrn aspect
first
ct is hoped thilt a through survey of programming
/ill be continued at a later
The actual coding of algorithm
.nherent advantages of the source language .i.n
1ope of producing the fastest, most
,ossible. with any proqra.m wr.l
.here are many ways of CJErnerttting code and
in<il running program may structurally be far removed from
he original algorithm. We tried to avoid this of
oding and kept to the so-called "straight line" method,
7
.l55
3. TEST METHOD
The selected algorithms were coded in APL and FORTRAN.
The FORTRAN programs were compiled under G-Level IBM FOR'I'RAN
and ran as batch jobs, while the APL programs ran under
Scientific Time Sharing's version of the IBM program product.
A benchmark function was used to record the APL I-Beam 21 time
as a measure of the CPU execution time. It is not quite clear
as to what I-Beam 21 actually measures in terms of monitor
overhead, but it is the only means available to the user to
record his execution time. For the FORTRAl'i programs, the
execution time in the GO step was recorded from the batch
accounting sheet attached to the listing. These times were
compared in an effort to determine some type of cost analysis
between the two languages. The results are far from conclusive
but do point out some basic trends in the use of APL under
scientific programming conditions. Although the selected
algorithms may be rejected as meaningful benchmarks by some,
there are lessons to be noted in each case.
The DATA section includes timings of FORTRAN and APL
along w'iht the dimensions of the data arrays used in running
the algorithm. This information is presented graphically in
an attempt to project the results to larger systems of test
data.
DESCRIPTION OF ALGORITHMS
The following algorithms were choosen to be used
in the comparsion test:
1) Romberg Integration
2) Bairstow's Root Finding Method
3) Jacobi's Eigenvalue Method
4) Gauss-Jordan Solution to Linear Systems
5) Runga-Kutta Solution to Differential Equations
6) Laplace's Solution to Partial Differential Equations
These algorithms were choosen from the original objectives
but do not represent a complete set of numerical analysis
procedures to be used in solving the subject area objectives.
Listed on the following pages is an outline of the
individual algorithms, along with the listings of the
<lPL and FORTRAN programs implementing the algorithms.
5. BAIRSTOW'S METHOD FOR FINDING COMPLEX ROOT IN A POLYNOMIAL
PURPOSE:
CONVENTION:
Compute the real and complex roots of the
real polynomial
p(x) = c 1 + c 2x + . . . + cn+lxn
using Bairstow's iterative method of
quadratic factorization.
The polynomial coefficients and the initial
starting roots are passed as arguments to
both programs.
details.)
(See individual programs for
SUBROUTINES: FORTRAN, None.
METHOD:
APL, Q - solves roots of quadratic
equation.
S - performs synthetic division.
Every real polynomial of degree greater than
one can be factored in the form
p(x) = q(x) r(x)
where
is quadratic. If q(x) is reducable, that is
if q(x) is a product of two real linear factors
p(x) has a pair of real roots; and if q(x) is
irreduciable, p(x) has a complex conjugate
pair of roots. If r(x) has degree exceeding
one, it too may be factored as above, and so on.
REFERENCE:
SOURCE:
Scientific Subroutine Eackage, International
Business Machines, H20-02025-3
FORTRAN, John L. Richardson, U. C. Irvine
APL, John L. Richardson, u. C. Irvine
6. CONV:E:NTION:
ARGUMENTS:
lnd
scalar
Ct i)
and eigenvectora
) where i
which satisfy the
(i) (i) (i)
where a(i) arc column vectors.
The real symmetric matrix
0
convergence
to1eranco are given as arguments to both
FORT.RAN and
!3UBHOU'rINES: None.
ME'l'HOD: The procedure .is in parts. First
orthogonal similarity trimsformation
c p
takes place, which A
tridiagonal form. '.rhe second
calculation of some or all of
of c, whi1e the third step is the calculation
of the corresponding eigenvectors of
reference for a more detailHd
11
for
7. GAUSS-JORDAN SOLUTION TO SYSTEMS OF LINEAR EQUATIONS
PURPOSE:
CONVENTION:
SUBROUTINE:
METHOD:
REFERENCE:
SOURCE:
Find the solution to the system of linear
equations given in the form of an augmented
matrix A such that
A = [B I u I]
The coefficients of matrix B, the vector u
and the identity matrix I are given as
arguments to both the programs.
None.
Let the starting array be the n by (n+m)
augmented matrix A, consisting of an n by n
coefficient matrix with m appended columns.
Let k = 1,2, ... ,n be the pivot counter, so
that akk is the pivot element for the kth pa~s
of the reduction. It is understood that the
values of the elements of A will be modified
tluring computation_ by the follow algorithm
ak;
akj + -'-"'L for j = n+m,n+m-1, •.• ,k
akk
aij + aij - aikakj for j = n+m,n+m-1, ••• ,k
and i = 1,2, ••• ,n (i~k) and k = 1,2, •.• ,n
Brice Carnahan, Applied Numerical Methods,
John Wiley and Sons, 1969
FORTRAN, Glen B. Alleman, u.c. Irvine
APL, VEG IB MAT (GENERIC FUNC7'JON)
RUNGA-KUTTA SOLUTION TO ORDINARY DIFFERENTIAL EQUATIONS
PURPOSE:
CONVENTION:
USE:
Integrate a given differential equation
of the form
~ = f(x,y)
using the Runga-Kutta technique.
The ordinary differential equation
~ = f(x,y)
with the initial condition
is solved numerically using the fourth-
order Runga-Kutta integration process.
This is a single step method in which the
value of y at x xn is used to compute
The equation to be integrated must be provided
by the user along with the initial conditions
and the step increment.
SUBROUTINES: FORTRAN, FUN - user defined function containing
the function to be integrated.
APL, FUN - same as above.
METHOD: Given the formula
where for a given step size h
12
8. REFERENCE:
SOURCE:
ko hf(xn ' yn)
kl hf (xn + h/2 Yn + k
0
/2)
k
2 hf(xn + h/2 Yn + kl/2)
k3 hf (xn + h
' Yn + k2)
Erwin Kreyszig, Advanced Engineer~
Mathematics, John Wiley and Sons, 1972
Henrici, Discrete Variable Methods in
Ordinary Differential ~quations, John Wiley
and Sons, 1962
FORTRAN, Glen B. Alleman, u.c. Irvine
APL, Glen B. Alleman, u.c. Irvine
LAPLACE'S EQUATION STEADY S'I'ATE HEAT FLOW PROBLEM
PURPOSE:
CONVENTION:
USE:
Solve the second order partial differential
equation
0
This is a boundary value problem envolving
a closed surface R of finite dimension. The
solution is found in terms of a steady state
flux from a fixed boundary source.
The boundary values must be defined for a
given rectangular array along with the
tolerance used to determine the condition of
steady state.
SUBROUTINES: None.
METHOD: Given
0
V2
u 0 in the region R
and
u(x,y) = g(x,y) on the surface s
with Mx and My being integers such that
and
giving the finite differnece equation
(Ax) 2 (Ay) 2
9. REFERENCE:
SOURCE:
or producing Laplace's differnece equation
4
with i = 1,2, •.• ,Mx-l and j
v ..
l1J
Brice Carnahan, A£plied Numerical Methods,
John Wiley and Sons, 1969
FORTRAN, Glen B. Alleman, U.C. Irvine
APL, John L. Richardson, u.c. Irvine
DATA ANALYSIS
The following section provides a brief discussion of
the data produced during the comparsion test. No attempt
has been made to throughly explain the results of the test
due to the extreme complex nature of the individual
language's internal operation. The results can be viewed
then from a more simplistic point of reference; that is both
F'ORTRAN and APL can be considered virtual machines running
on a host machine whos internal operation is not known to
the user. What we were attempting to measure then, was how
much effort each language must expend to perform a given
algorithm.
ROMBERG INTEGRATION OF FOURIER COEFFICIENTS
This problem uses the Romberg integration technique
to compute the Fourier coefficients of a user defined function.
Although both the FORTRAN and APL programs loop many times
there is a large difference in the execution times, with
the FORTRAN program consuming six to seven times the cpu
time of the APL program. This difference may be attributed
to the intial set up time required for the FORTRAN program
to compute the indices to the Romberg tableaus. The manip-
ulation of the Romberg tableaus in APL is done through
vector operations while it is done through individual
components in the FORTRAN version. It should be noted then
that operations with multi-dimensional arrays are considerably
slower on FORTRAN.
14
10. BAIRSTOW 1
S ROOT FINDING METHOD
This algorithm iterates to find the real and complex
roots of a user defined polynomial. Once again the large
difference in execution time is noted. Both the FORTRAN
and APL programs are coded in a similar manner with each
performing approximatly the same number of iterations.
Since APL has to set up and interpret each section of code
and the overhead for this operation is expensive in terms
of execution time.
JACOBI'S EIGENVALUE METHOD
Jacobi's method again is an iterating algorithm and
the APL execution times reflect this fact. Although there
are an equal number of arithmetic operations performed, it
is the looping operation that comsumes the largest amount of
computing time.
GAUSS-JORDAN
This was a loaded algorithm as APL can solve systems.
of equations using a machine language internal operation.
The reas·on for this comparsion was to determine if a well
coded FORTRAN algorithm could come close to the generic
operation domino (ffi). It is obivious this primative function
is a powerful tool in solving linear systems.
RUNGE-KUTTA SOLUTION TO DIFFERENTIAL EQUATIONS
This algorithm was loaded in favor of FORTRAN by
coding it in an identical manner in APL. (See program
listings). As can be seen from the data, coding an APL
program in the style of FORTRAN has disastrous results.
A look at the graph will show this type of coding should
never be used except in the most simplist applications.
LAPLACE'S EQUATION
This algorithm is tailored to APL's ability to handle
multi-dimensional arrays directly. The only limitation
seems to be the workspace required to store two copies
of the temperature grid when doing the matrix operations,
a problem not faced by the FORTRAN user operating in an
80K partition.
11. 16
TEST DATA FOR: ROMBERG IN'l'EGARTION OF FOURIER COEFFICIENTS TEST DATA FOR: GAUSS-JORDAN REDUCTION
STORAGE STORAGE
FORTRAN FUNCTION TIME/s SOURCE/LOAD MOD. FORTRAN SIZE OF MATRIX TIME/s SOURCE/LOAD MOD.
SIN x 2min 6.44s 7698 / 34040 BYTES 0.183s 8954 / 39854 BYTES
cos x 2min 10.2ls 6 0.433s
2 SIN 2X 2min 9.85s 8 0.600s
2 cos 2X 2min 4.83s 10 0.916s
2 cos x +3 SIN 2X 2min 56.73s 12 l.433s
14 l.733s
STORAGE
ill FUNCTION TIME/s PROGRAM/DATA 16 2.526s
SIN x Omin 18.Sls 4000 BYTES
STORAGE
cos x Omin 19.00s APL SIZE OF MATRIX TIME/s PROGRAM/DATA
2 SIN 2X Omin 19.83s 4 0.016s (ORDER) *2 + ORDER
2 cos 2X Omin 22.lls 6 0.016s
2 cos x + 3 SIN 2x Omin 25.lOs 8 0.034s
10 0.050s
12 0.067s
14 O.lOOs
16 0.125s
15. 0
N
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Nva~aoa aod saNoJas NI awr~
0
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28
TEST DATA FOR: LAPLACE'S EQUATION
FORTRAN SIZE OF GRID TIME/s
4 x 4 0.33s
6 x 0.36s
8 x 8 0.45s
10 x 10 0.7ls
12 x 12 l.03s
14 x 14 1. 65s
16 x 16 2.6ls
18 x 18 4.04s
20 x 20 5.88s
22 x 22 8.57s
24 x 24 12.25s
APL SIZE OF GRID '.l'IME/s
4 x 4 0.44s
6 x 6 0.70s
B x 8 l.lls
10 x 10 l. 6ls
12 x 12 2.27s
14 x 14 3.00s
16 x 16 4.02s
18 x 18 S.12s
20 x 20 6.0Ss
22 x 22 7.23s
24 x 24 8.52s
20
STORAGE
SOURCE/LOAD MOD.
8626 / 28528 BYTES
STORAGE
PROGRAM/DATA
(GRID SIZE)*2
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CONCLUSION
While this study is far from complete, it does point
to some interseting facts concerning the use of APL in a
numerical analysis application. Breed and Lathwell [l) have
reported execution times for APL which are 5 to 10 times
slower than compiled FORTRAN code, while Foster [2) has
reported execution times between 4 to 15 times faster for
FORTRJN compiled code opposed to interpreted APL code.
These execution times are comparable to the times found
during the test conducted in this paper. Under our test
conditions the range of execution time went from 4 to 1 in
favor of to 50 to 1 in favor of compiled FORTRAN code.
Examining the cases where APL is faster than FORTRAN
it is noted that APL takes advantage of its array operations
to overcome the need to index multi-dimensional arrays
directly as FORTRAN has to do. In the case of the solution
to Laplace's equation AP[, uses matrix rotations to solve
the extrapolation formula versus the individual index
operations needed in FORTRAN to perform the same algorithm.
Although the initial setup time in APL is longer (see curve)
it is clear that by extending the curve of execution times
leads one to conclude that for large systems of steady-state
grids !IPD would be significantly faster than FORTRAN. In the
second case of a coded APD program being faster than FORTRAN
compiled code, vector operations were used in place of
individual indexing. This was the Romberg integration of
21
17. Fourier coefficients. In the APL program R/!011, the Romberg
tableau was reduced using vector operations on the rows of
the matrix, where the FORTRAN program was forced to perform
an element by element index to reduce the same dimension
matrix. For a given N x N matrix, AFL does N vector operations
where FORTRAN does N2
operations. The obvious conclusion
being, an algorithm which is orientated toward array operations,
either vector or multi-dimensional, runs faster when coded
in APD, due to its ability to handle such structures directly.
In the third case where APL was faster, the Gauss-Jordan
reduction algorithm, an APL primative function was run against
a hand-coded FORTRAN program. As expected the APL domino
was much faster than FORTRAN, owing this speed to the rnachine-
coded nature this generic function. In all cases where APL
was faster than FORTRAN compiled code there are potential
limitations on the size of the data arrays APD can handle.
In an IBM 36K workspace the largest grid possible in Laplace's
equation is a 24 x 24. Although this size may be useful from
the demonstrative standpoint it imposes real limitations on
the solution to large steady-state problems found in erigineering
and physics. It is clear then, for APL to remain cost
effective, the 36K workspace limitaion must be lifted.
Looking at the cases where FORTRAN was faster than APD
it will be noted looping is found in every case. From the
start looping an APL program in the same manner one would
loop FORTRAN is disastrous. Taking the worst case situation
of Jacobi's eigenvalue method, APL was 59 times slower than
FOR'rRAN in solving for the eigenvalues of a 13 x 13 real
symmetric matrix. This method iterates to find the solution
and it seems that the setup time in APL is too costly when
solving systems larger than approximately 4 x 4. Looking at
a straight-line looping program, Runge-Kutta, it is noted
APD's execution time is a linear function of the number of
points evaluated, increasing by powers of ten. One must
conclude that for algorithms that require iterations to
provide solutions APl provides a poor method for the user.
In the case of Runge-Kutta, a solution to this type of
problem may be found in a differential equation generic
function similar to the domino function used to solve linear
equations. With such a machine-language primative the most
common problem facing the scientist, the solution of a system
of linear differential equations, would be solved with the
ease APL provides the user of domino.
Not wanting to repeat the statements of Foster, Breed
and Lath.well we would like to make the following points in
the hope of improving the use of APL in scientific numerical
analysis applications.
1) The 36K workspace limitation must be increased for
22
APD to able to use its array function on large systems.
2) clearly there are problems which are beyond the
capabilities of APD as it now exists. A change of
18. implementation is called for to provide faster
execution of programs requiring looping structures.
3) Although APL provides a fast, easy to code, means
of solving scientific problems, its ease of use and
code density are traded for execution time in
"number-crunching" problems found in physics and
engineering. For example the solid state physicist
solving 150 x 150 eigenvalue problems on an every-
day basis.
Although these tests point out that APL, in its present
form, is not competitive with .a c.ompilec;l _FORTRAN program,
there are indications that it could be. With the addition
of a differential equation function, an increase in work-
space size (maybe even virtual workspaces), and a speed up
in execution time for looping structures, the language will
be able to provide cost effective solutions to the types of
problems to whieh its notation is so well suited.
APL LISTING FOR LAPLACE'S EQUATION
'lLAP((J]'l
'V Z+F LAP A;C
[1] C+(Z+A)x-F
[2] ~2•E<f/I ,A-Z+C+0.25xFx(1~A)+(1eA)+-1eA+Z
v
20. APL LISTING FOR FOURIER COEFFICIENTS CONTINUED
V£'.A[O]V
V Z+lA
[1] Z+((Q X)x(loMxX))+ol
v
Vl/:l[OJV
v Z+l/:1
[1] Z+((g X)x(2oMxX))to1
v
11 {i IS THE FUNCTION USED TO GENERATE THE FUNCTIONAL POINTS
11 USED IN THE FOURIER ANALYSIS
APL LISTING FOR SOLUTIONS TO LINEAR SYSTEMS OF EQUATIONS
RESULT,,.VEC'l'OF~HATRIX