The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Irit Peled - One of the best experts on this subject based on the ideXlab platform.
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INHIBITING FACTORS IN GENERATING EXAMPLES BY MATHEMATICS TEACHERS AND STUDENT TEACHERS: THE CASE OF Binary Operation
Journal for Research in Mathematics Education, 1996Co-Authors: Orit Zaslavsky, Irit PeledAbstract:The main objectives of this study were to identify difficulties encountered by mathematics teachers and student teachers associated with the concept of Binary Operation regarding the associative and commutative properties and to reveal possible sources for them. Thirty-six in-service mathematics teachers and 67 preservice mathematics teachers participated in the study. All participants were presented with a task calling for the generation of a counterexample, namely, a Binary Operation that is commutative but not associative. Responses to the task were analyzed according to four categories: correctness, productiveness, mathematical content, and underlying difficulties. The findings point to similarities and differences between the two groups. Both groups exhibited a weak concept by failing to produce a correct example and by using a limited content search-space. These findings suggest two main inhibiting factors: one related to the overgeneralization of the properties of basic Binary Operations and the other related to pseudo-similarities attributed to these properties, which seem to be created by the recurring theme of order. Teachers were superior to student teachers on the categories of correctness and productiveness. This study is part of a larger study the aim of which was to investigate ways in which mathematics teachers and student teachers generate counterexamples in mathematics. Two main goals of the study were, first, to identify difficulties encountered in generating examples related to a number of mathematical topics that teachers and student teachers are expected to teach and, second, to reveal possible sources of difficulty for them. The possible sources of difficulty in generating such examples were presumed to include the following: incomplete knowledge, inability to process existing knowledge, misconceptions, and insufficient logical knowledge. The case of Binary Operation represents a case in which the relevant knowledge is assumed to be available to secondary mathematics teachers and student teachers; however, that knowledge requires processing in order to produce such examples. The current work is designed to probe for factors inhibiting the processing involved in generating counterexamples, factors that in turn may shed light on the limited Binary Operation concept. The search for these limiting factors was carried out by an extensive analysis of the processes and difficulties involved in generating the required examples. As indicated by Bratina (1986), the task of generating an example is considered powerful in terms of revealing strengths and weaknesses. The notion of a Binary Operation is dealt with in various stages and courses at different levels and contexts throughout the mathematics education that prospective mathematics teachers experience. However, in many cases prospective teachers do not manage to integrate their various encounters into one comprehensive, abstract
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Inhibiting Factors in Generating Examples by Mathematics Teachers and Student Teachers: The Case of Binary Operation
Journal for Research in Mathematics Education, 1996Co-Authors: Orit Zaslavsky, Irit PeledAbstract:The main objectives of this study were to identify difficulties encountered by mathematics teachers and student teachers associated with the concept of Binary Operation regarding the associative and commutative properties and to reveal possible sources for them. Thirty-six in-service mathematics teachers and 67 preservice mathematics teachers participated in the study. All participants were presented with a task calling for the generation of a counterexample, namely, a Binary Operation that is commutative but not associative. Responses to the task were analyzed according to four categories: correctness, productiveness, mathematical content, and underlying difficulties. The findings point to similarities and differences between the two groups. Both groups exhibited a weak concept by failing to produce a correct example and by using a limited content search-space. These findings suggest two main inhibiting factors: one related to the overgeneralization of the properties of basic Binary Operations and the other related to pseudo-similarities attributed to these properties, which seem to be created by the recurring theme of order. Teachers were superior to student teachers on the categories of correctness and productiveness.
Orit Zaslavsky - One of the best experts on this subject based on the ideXlab platform.
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INHIBITING FACTORS IN GENERATING EXAMPLES BY MATHEMATICS TEACHERS AND STUDENT TEACHERS: THE CASE OF Binary Operation
Journal for Research in Mathematics Education, 1996Co-Authors: Orit Zaslavsky, Irit PeledAbstract:The main objectives of this study were to identify difficulties encountered by mathematics teachers and student teachers associated with the concept of Binary Operation regarding the associative and commutative properties and to reveal possible sources for them. Thirty-six in-service mathematics teachers and 67 preservice mathematics teachers participated in the study. All participants were presented with a task calling for the generation of a counterexample, namely, a Binary Operation that is commutative but not associative. Responses to the task were analyzed according to four categories: correctness, productiveness, mathematical content, and underlying difficulties. The findings point to similarities and differences between the two groups. Both groups exhibited a weak concept by failing to produce a correct example and by using a limited content search-space. These findings suggest two main inhibiting factors: one related to the overgeneralization of the properties of basic Binary Operations and the other related to pseudo-similarities attributed to these properties, which seem to be created by the recurring theme of order. Teachers were superior to student teachers on the categories of correctness and productiveness. This study is part of a larger study the aim of which was to investigate ways in which mathematics teachers and student teachers generate counterexamples in mathematics. Two main goals of the study were, first, to identify difficulties encountered in generating examples related to a number of mathematical topics that teachers and student teachers are expected to teach and, second, to reveal possible sources of difficulty for them. The possible sources of difficulty in generating such examples were presumed to include the following: incomplete knowledge, inability to process existing knowledge, misconceptions, and insufficient logical knowledge. The case of Binary Operation represents a case in which the relevant knowledge is assumed to be available to secondary mathematics teachers and student teachers; however, that knowledge requires processing in order to produce such examples. The current work is designed to probe for factors inhibiting the processing involved in generating counterexamples, factors that in turn may shed light on the limited Binary Operation concept. The search for these limiting factors was carried out by an extensive analysis of the processes and difficulties involved in generating the required examples. As indicated by Bratina (1986), the task of generating an example is considered powerful in terms of revealing strengths and weaknesses. The notion of a Binary Operation is dealt with in various stages and courses at different levels and contexts throughout the mathematics education that prospective mathematics teachers experience. However, in many cases prospective teachers do not manage to integrate their various encounters into one comprehensive, abstract
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Inhibiting Factors in Generating Examples by Mathematics Teachers and Student Teachers: The Case of Binary Operation
Journal for Research in Mathematics Education, 1996Co-Authors: Orit Zaslavsky, Irit PeledAbstract:The main objectives of this study were to identify difficulties encountered by mathematics teachers and student teachers associated with the concept of Binary Operation regarding the associative and commutative properties and to reveal possible sources for them. Thirty-six in-service mathematics teachers and 67 preservice mathematics teachers participated in the study. All participants were presented with a task calling for the generation of a counterexample, namely, a Binary Operation that is commutative but not associative. Responses to the task were analyzed according to four categories: correctness, productiveness, mathematical content, and underlying difficulties. The findings point to similarities and differences between the two groups. Both groups exhibited a weak concept by failing to produce a correct example and by using a limited content search-space. These findings suggest two main inhibiting factors: one related to the overgeneralization of the properties of basic Binary Operations and the other related to pseudo-similarities attributed to these properties, which seem to be created by the recurring theme of order. Teachers were superior to student teachers on the categories of correctness and productiveness.
Shyhkang Jeng - One of the best experts on this subject based on the ideXlab platform.
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speaker verification using kernel based Binary classifiers with Binary Operation derived features
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Hungshin Lee, Yu Tso, Yunfan Chang, Hsinmin Wang, Shyhkang JengAbstract:In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a Binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the “within-speaker” group or the “betweenspeaker” group. To solve the problem, we employ various Binary Operations to retain the basic relationship between any pair of ivectors to form a single vector for training the discriminative models. This study also investigates the correlation of achievable performances with the number of training pairs and the various combinations of basic Binary Operations, using the SVM and DNN Binary classifiers. The experiments are conducted on the male portion of the core task in the NIST 2005 Speaker Recognition Evaluation (SRE), and the results are competitive or even better, in terms of normalized decision cost function (minDCF) and equal error rate (EER), while compared to other non-probabilistic based models, such as the conventional speaker SVMs and the LDA-based cosine distance scoring.
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ICASSP - Speaker verification using kernel-based Binary classifiers with Binary Operation derived features
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Hungshin Lee, Yu Tso, Yunfan Chang, Hsinmin Wang, Shyhkang JengAbstract:In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a Binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the “within-speaker” group or the “betweenspeaker” group. To solve the problem, we employ various Binary Operations to retain the basic relationship between any pair of ivectors to form a single vector for training the discriminative models. This study also investigates the correlation of achievable performances with the number of training pairs and the various combinations of basic Binary Operations, using the SVM and DNN Binary classifiers. The experiments are conducted on the male portion of the core task in the NIST 2005 Speaker Recognition Evaluation (SRE), and the results are competitive or even better, in terms of normalized decision cost function (minDCF) and equal error rate (EER), while compared to other non-probabilistic based models, such as the conventional speaker SVMs and the LDA-based cosine distance scoring.
Amlan J Pal - One of the best experts on this subject based on the ideXlab platform.
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key to design functional organic molecules for Binary Operation with large conductance switching
Chemical Physics Letters, 2003Co-Authors: Anirban Bandyopadhyay, Amlan J PalAbstract:We have selected a new class of material which shows presence of large conductance switching. The acceptor groups present in the molecules result in low OFF-state current in the devices and hence large ON/OFF ratio of 100,000. The ON-state in the molecules has been due to conjugation restoration via electroreduction. The ON/OFF ratio decreased to 4 in devices based on molecules without any acceptor groups. The devices have undergone 'write-read-erase-read' sequence for hours, showing their applicability in random-access-memory elements.
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large conductance switching and Binary Operation in organic devices role of functional groups
Journal of Physical Chemistry B, 2003Co-Authors: Anirban Bandhopadhyay, Amlan J PalAbstract:We have realized tuning of electronic memory-switching property via functional group modification in solid-state devices. Apart from their large ON/OFF ratio and long memory retention time, solid-state devices sustain repetitive switching between the two ON/OFF states at high frequencies. We have chosen several molecular systems with same backbone structure and tuned ON/OFF ratio from 4 to 300 000 simply by increasing the number of deactivating groups. A key to the large ON/OFF ratio in these devices has been the presence of acceptor groups in the molecules and consequently low OFF state current. We analyzed the appearance of ON state in terms of conjugation restoration of the molecules. A redox active group has been found to be necessary in the molecules for continuous flip-flop between “1” and “0” states for random access memory (RAM) applications.
Hungshin Lee - One of the best experts on this subject based on the ideXlab platform.
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speaker verification using kernel based Binary classifiers with Binary Operation derived features
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Hungshin Lee, Yu Tso, Yunfan Chang, Hsinmin Wang, Shyhkang JengAbstract:In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a Binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the “within-speaker” group or the “betweenspeaker” group. To solve the problem, we employ various Binary Operations to retain the basic relationship between any pair of ivectors to form a single vector for training the discriminative models. This study also investigates the correlation of achievable performances with the number of training pairs and the various combinations of basic Binary Operations, using the SVM and DNN Binary classifiers. The experiments are conducted on the male portion of the core task in the NIST 2005 Speaker Recognition Evaluation (SRE), and the results are competitive or even better, in terms of normalized decision cost function (minDCF) and equal error rate (EER), while compared to other non-probabilistic based models, such as the conventional speaker SVMs and the LDA-based cosine distance scoring.
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ICASSP - Speaker verification using kernel-based Binary classifiers with Binary Operation derived features
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Hungshin Lee, Yu Tso, Yunfan Chang, Hsinmin Wang, Shyhkang JengAbstract:In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a Binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the “within-speaker” group or the “betweenspeaker” group. To solve the problem, we employ various Binary Operations to retain the basic relationship between any pair of ivectors to form a single vector for training the discriminative models. This study also investigates the correlation of achievable performances with the number of training pairs and the various combinations of basic Binary Operations, using the SVM and DNN Binary classifiers. The experiments are conducted on the male portion of the core task in the NIST 2005 Speaker Recognition Evaluation (SRE), and the results are competitive or even better, in terms of normalized decision cost function (minDCF) and equal error rate (EER), while compared to other non-probabilistic based models, such as the conventional speaker SVMs and the LDA-based cosine distance scoring.
