The Experts below are selected from a list of 37725 Experts worldwide ranked by ideXlab platform
Paco Calvo - One of the best experts on this subject based on the ideXlab platform.
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how many mechanisms are needed to analyze speech a connectionist simulation of Structural Rule learning in artificial language acquisition
Cognitive Science, 2011Co-Authors: Aarre Laakso, Paco CalvoAbstract:Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting Structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional Rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating that a single statistical mechanism can mimic the apparent discovery of Structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis.
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a connectionist simulation of Structural Rule learning in language acquisition
Proceedings of the Annual Meeting of the Cognitive Science Society, 2008Co-Authors: Aarre Laakso, Paco CalvoAbstract:According to a dual-mechanism hypothesis, although statistical computations based on nonadjacent transitional probabilities may suffice for speech segmentation, an additional Rule-following mechanism is required in order to extract Structural information out of the linguistic stream. We present a neural network study that shows how statistics alone can support the discovery of Structural regularities, beyond the segmentation of speech, disconfirming the dualmechanism hypothesis.
Aarre Laakso - One of the best experts on this subject based on the ideXlab platform.
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how many mechanisms are needed to analyze speech a connectionist simulation of Structural Rule learning in artificial language acquisition
Cognitive Science, 2011Co-Authors: Aarre Laakso, Paco CalvoAbstract:Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting Structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional Rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating that a single statistical mechanism can mimic the apparent discovery of Structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis.
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a connectionist simulation of Structural Rule learning in language acquisition
Proceedings of the Annual Meeting of the Cognitive Science Society, 2008Co-Authors: Aarre Laakso, Paco CalvoAbstract:According to a dual-mechanism hypothesis, although statistical computations based on nonadjacent transitional probabilities may suffice for speech segmentation, an additional Rule-following mechanism is required in order to extract Structural information out of the linguistic stream. We present a neural network study that shows how statistics alone can support the discovery of Structural regularities, beyond the segmentation of speech, disconfirming the dualmechanism hypothesis.
Alfredo Coutino - One of the best experts on this subject based on the ideXlab platform.
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Structural fiscal Rule a better discipline than a fiscal council
Social Science Research Network, 2017Co-Authors: Alfredo CoutinoAbstract:In 2011 we recommended the application of a Structural fiscal Rule to the case of Mexico, given the country's repetitive inability to implement a genuine fiscal reform. At that time, we said that due to the absence of a fiscal reform that increases tax revenues significantly, Mexico needs to adopt a Structural correction in its public finance through the implementation of a Rule. The Structural Rule would eliminate budget volatility and would give fiscal policy more countercyclical power. Since the Structural Rule promotes fiscal certainty, the country would reinforce investors' confidence and would strengthen public finances. We established that the Rule would not substitute the fiscal reform needed, but it would make the reform less urgent since it would introduce a Structural discipline in the government expenditure, which would also make the budgeting process more efficient. At the end of 2012, the newly elected government announced it intended to apply a Structural balance Rule, but no such Rule was ever applied. In 2014, a fiscal reform was implemented. However, given the absence of the Structural Rule, the gains generated by the reform vanished as a result of more government expenses. An increasing fiscal imbalance and accelerating debt were the costs paid by the country for not implementing the fiscal Rule. More recently, an independent fiscal council has been recommended by the IMF. In Part 1 of this paper, we reproduce the Structural fiscal Rule proposed in 2011, highlighting its main advantages. In Part 2, we make an assessment on the nature of a fiscal council and the main weaknesses for its application in Mexico.
Stelios Kapetanakis - One of the best experts on this subject based on the ideXlab platform.
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a Structural Rule based approach for design patterns recovery
International Conference on Software Engineering, 2017Co-Authors: Mohammed Ghazi Alobeidallah, Miltos Petridis, Stelios KapetanakisAbstract:Design patterns have a key role in the software development process. They describe both structure, behavior of classes and their relationships. Design patterns can improve software documentation, speed up the development process and enable large-scale reuse of software architectures. This paper presents a Multiple Levels Detection Approach (MLDA) to recover design pattern instances from Java source code. MLDA is able to extract design pattern instances based on a generated class level representation of an investigated system. Specifically, MLDA presents what is the so-called Structural Search Model (SSM) which incrementally builds the structure of each design pattern based on the generated source code model. Moreover, MLDA uses a Rule-based approach to match the method signatures of the candidate design instances to that of the subject system. As the experiment results illustrate, MLDA is able to extract 23 design patterns with reasonable detection accuracy.
X U Fayan - One of the best experts on this subject based on the ideXlab platform.
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common Structural Rule computation software csr sdp of china classification society
Computer-Aided Engineering, 2009Co-Authors: X U FayanAbstract:To improve the efficiency of ship design,China Classification Society(CCS) developed a common Structural Rule computation software CSR-SDP for CSR-BC/OT proposed for the design of bulk carrier and double hull oil tanker by International Association of Classification Societies(IACS) in 2006. CSR-SDP is constituted by CSR-SDP(BC) and CSR-SDP(OT). The former is for bulk carrier and the latter is for double hull oil tanker. CSR-SDP is a ship structure design and Rules-checking system that includes the functions such as design based on Rules,load computation,and analysis,evaluation and fatigue life-span evaluation on hull structure strength. It meets the Rules of CSR-BC/OT better and the requirements of ship design and drawing auditing.
