The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform

A. Rahmoun - One of the best experts on this subject based on the ideXlab platform.

  • AICCSA - The EE-method, an evolutionary engineering Developer Tool: neural net character mapping
    The 3rd ACS IEEE International Conference onComputer Systems and Applications 2005., 2005
    Co-Authors: A. Lehireche, A. Rahmoun
    Abstract:

    Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. In this paper, we show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such method. Also, to improve the effectiveness of the evolvability principle on a complex systems, we present in this paper a more complex example: an evolved neural net pattern recognizer that maps an input character image to a standard representation i.e. image or code . This application needs a recurrent neural net of 105 neurons, so the weights table contains 11025 entries, the evolution process has to tune 11025 parameters. The search space is: 2/sup 11025*7/ where 7 is the weight binary code. This task is hyper complex. The results show that the evolvability principle is effective.

  • The EE-method, an evolutionary engineering Developer Tool: neural net character mapping
    The 3rd ACS IEEE International Conference onComputer Systems and Applications 2005., 2005
    Co-Authors: A. Lehireche, A. Rahmoun
    Abstract:

    Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. In this paper, we show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such method. Also, to improve the effectiveness of the evolvability principle on a complex systems, we present in this paper a more complex example: an evolved neural net pattern recognizer that maps an input character image to a standard representation i.e. image or code . This application needs a recurrent neural net of 105 neurons, so the weights table contains 11025 entries, the evolution process has to tune 11025 parameters. The search space is: 2/sup 11025*7/ where 7 is the weight binary code. This task is hyper complex. The results show that the evolvability principle is effective.

  • The EE-method, an evolutionary engineering Developer Tool: neural net case study
    ACS IEEE International Conference on Computer Systems and Applications 2003. Book of Abstracts., 2003
    Co-Authors: A. Lehireche, A. Rahmoun
    Abstract:

    Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. We show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such a method. To bring the EE-method into operation, we had implemented software to build/evolve neural net-based systems. As an example, we present an evolved neural net Xor.

A. Lehireche - One of the best experts on this subject based on the ideXlab platform.

  • AICCSA - The EE-method, an evolutionary engineering Developer Tool: neural net character mapping
    The 3rd ACS IEEE International Conference onComputer Systems and Applications 2005., 2005
    Co-Authors: A. Lehireche, A. Rahmoun
    Abstract:

    Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. In this paper, we show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such method. Also, to improve the effectiveness of the evolvability principle on a complex systems, we present in this paper a more complex example: an evolved neural net pattern recognizer that maps an input character image to a standard representation i.e. image or code . This application needs a recurrent neural net of 105 neurons, so the weights table contains 11025 entries, the evolution process has to tune 11025 parameters. The search space is: 2/sup 11025*7/ where 7 is the weight binary code. This task is hyper complex. The results show that the evolvability principle is effective.

  • The EE-method, an evolutionary engineering Developer Tool: neural net character mapping
    The 3rd ACS IEEE International Conference onComputer Systems and Applications 2005., 2005
    Co-Authors: A. Lehireche, A. Rahmoun
    Abstract:

    Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. In this paper, we show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such method. Also, to improve the effectiveness of the evolvability principle on a complex systems, we present in this paper a more complex example: an evolved neural net pattern recognizer that maps an input character image to a standard representation i.e. image or code . This application needs a recurrent neural net of 105 neurons, so the weights table contains 11025 entries, the evolution process has to tune 11025 parameters. The search space is: 2/sup 11025*7/ where 7 is the weight binary code. This task is hyper complex. The results show that the evolvability principle is effective.

  • The EE-method, an evolutionary engineering Developer Tool: neural net case study
    ACS IEEE International Conference on Computer Systems and Applications 2003. Book of Abstracts., 2003
    Co-Authors: A. Lehireche, A. Rahmoun
    Abstract:

    Summary form only given. Evolutionary engineering (EE) challenge is to prove that it is possible to build systems (i.e. solutions) without going through any design process. Evolutionary engineering is defined to be "the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems". Our main goal is to show that the EE-method is a good setting. We show step by step, using the EE-method, how to build a neural net based system. The EE-method can be viewed as just a GP appliance. The need of a well-specified approach determines the necessity for such a method. To bring the EE-method into operation, we had implemented software to build/evolve neural net-based systems. As an example, we present an evolved neural net Xor.

Xiang Anthony Chen - One of the best experts on this subject based on the ideXlab platform.

  • geno a Developer Tool for authoring multimodal interaction on existing web applications
    arXiv: Human-Computer Interaction, 2020
    Co-Authors: Ritam Jyoti Sarmah, Yunpeng Ding, Di Wang, Toby Jiajun Li, Xiang Anthony Chen
    Abstract:

    Supporting voice commands in applications presents significant benefits to users. However, adding such support to existing GUI-based web apps is effort-consuming with a high learning barrier, as shown in our formative study, due to the lack of unified support for creating multimodal interfaces. We present Geno---a Developer Tool for adding the voice input modality to existing web apps without requiring significant NLP expertise. Geno provides a high-level workflow for Developers to specify functionalities to be supported by voice (intents), create language models for detecting intents and the relevant information (parameters) from user utterances, and fulfill the intents by either programmatically invoking the corresponding functions or replaying GUI actions on the web app. Geno further supports multimodal references to GUI context in voice commands (e.g. "move this [event] to next week" while pointing at an event with the cursor). In a study, Developers with little NLP expertise were able to add multimodal voice command support for two existing web apps using Geno.

Alexander Egyed - One of the best experts on this subject based on the ideXlab platform.

  • Efficient detection of inconsistencies in a multi-Developer engineering environment
    2016 31st IEEE ACM International Conference on Automated Software Engineering (ASE), 2016
    Co-Authors: Andreas Demuth, Markus Riedl-ehrenleitner, Alexander Egyed
    Abstract:

    Software Developers work concurrently on different kinds of development artifacts such as requirements, architecture, design, or source code. To keep these development artifacts consistent, Developers have a wide range of consistency checking approaches available. However, most existing consistency checkers work best in context of single Tools and they are not well suited when development artifacts are distributed among different Tools and are being modified concurrently by many Developers. This paper presents a novel, cloud-based approach to consistency checking in a multi-Developer/-Tool engineering environment. It allows instant consistency checking even if Developers and their Tools are distributed and even if they do not have access to all artifacts. It does this by systematically reusing consistency checking knowledge to keep the memory/CPU cost of consistency checking to a small constant overhead per Developer. The feasibility and scalability of our approach is demonstrated through an empirical validation with 22 partly industrial system models. A prototype implementation implementation is available through the DesignSpace Engineering Cloud.

Zsolt I. Lázár - One of the best experts on this subject based on the ideXlab platform.

  • PARA - COMODI: architecture for a component-based scientific computing system
    Applied Parallel Computing. State of the Art in Scientific Computing, 2006
    Co-Authors: Zsolt I. Lázár, Lehel István Kovács, Zoltán Máthé
    Abstract:

    The COmputational MODule Integrator (COMODI) [1] is an initiative aiming at a component-based framework, component Developer Tool and component repository for scientific computing. We identify the main ingredients of a solution that would be sufficiently appealing to scientists and engineers to consider alternatives to their deeply rooted programming traditions. The overall structure of the complete solution is sketched with special emphasis on the Component Developer Tool forming the basis of COMODI. Prototypes for a framework and an automatic interface description generator are presented.

  • COMODI: Architecture for a Component-Based Scientific Computing System
    arXiv: Computational Engineering Finance and Science, 2005
    Co-Authors: Zsolt I. Lázár, Lehel István Kovács, Bazil Pârv
    Abstract:

    The COmputational MODule Integrator (COMODI) is an initiative aiming at a component based framework, component Developer Tool and component repository for scientific computing. We identify the main ingredients to a solution that would be sufficiently appealing to scientists and engineers to consider alternatives to their deeply rooted programming traditions. The overall structure of the complete solution is sketched with special emphasis on the Component Developer Tool standing at the basis of COMODI.