cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25721/py5m-6s46
title: Polka du pré Petit Jean
DOI: 10.25721/0xfm-ba37
title: Polka vosgienne
DOI: 10.25721/2bpd-2n97
title: "Le jianosti", danse des raquettes
DOI: 10.25721/4gc7-r924
title: La soyotte vosgienne
DOI: 10.25721/d1bv-z774
title: La gambine
DOI: 10.25721/17j8-6145
title: Jean-Baptiste faudrait te marier
DOI: 10.25721/1mya-my62
title: Quand j'étais chez mon père garçon à marier
DOI: 10.25440/smu.12062811
title: Data from: Her Voice Lingers on and her Memory is Strategic: Effects of Gender and Emotional Prosody on Directed Forgetting

description: This record contains the underlying research data for the publication "Her Voice Lingers on and her Memory is Strategic: Effects of Gender and Emotional Prosody on Directed Forgetting" and the full-text is available from: https://ink.library.smu.edu.sg/soss_research/1157The literature on directed forgetting has employed exclusively visual words. Thus, the potentially interesting aspects of a spoken utterance, which include not only vocal cues (e.g., <em>prosody</em>) but also the <em>speaker</em> and the <em>listener</em>, have been neglected. This study demonstrates that prosody alone does not influence directed-forgetting effects, while the sex of the speaker and the listener significantly modulate directed-forgetting effects for spoken utterances. Specifically, forgetting costs were attenuated for female-spoken items compared to male-spoken items, and forgetting benefits were eliminated among female listeners but not among male listeners. These results suggest that information conveyed in a female voice draws attention to its distinct perceptual attributes, thus interfering with retention of the semantic meaning, while female listeners' superior capacity for processing the surface features of spoken utterances may predispose them to spontaneously employ adaptive strategies to retain content information despite distraction by perceptual features. Our findings underscore the importance of sex differences when processing spoken messages in directed forgetting.
DOI: 10.25440/smu.12062811.v1
title: Data from: Her Voice Lingers on and her Memory is Strategic: Effects of Gender and Emotional Prosody on Directed Forgetting

description: This record contains the underlying research data for the publication "Her Voice Lingers on and her Memory is Strategic: Effects of Gender and Emotional Prosody on Directed Forgetting" and the full-text is available from: https://ink.library.smu.edu.sg/soss_research/1157The literature on directed forgetting has employed exclusively visual words. Thus, the potentially interesting aspects of a spoken utterance, which include not only vocal cues (e.g., <em>prosody</em>) but also the <em>speaker</em> and the <em>listener</em>, have been neglected. This study demonstrates that prosody alone does not influence directed-forgetting effects, while the sex of the speaker and the listener significantly modulate directed-forgetting effects for spoken utterances. Specifically, forgetting costs were attenuated for female-spoken items compared to male-spoken items, and forgetting benefits were eliminated among female listeners but not among male listeners. These results suggest that information conveyed in a female voice draws attention to its distinct perceptual attributes, thus interfering with retention of the semantic meaning, while female listeners' superior capacity for processing the surface features of spoken utterances may predispose them to spontaneously employ adaptive strategies to retain content information despite distraction by perceptual features. Our findings underscore the importance of sex differences when processing spoken messages in directed forgetting.
DOI: 10.25440/smu.12062814
title: Data from: Extended Comprehensive Study of Association Measures for Fault Localization

description: This record contains the underlying research data for the publication "Extended Comprehensive Study of Association Measures for Fault Localization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/1818Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to <em>association measures</em> that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the relationship strength between the execution of program elements and program failures. We investigate the effectiveness of 40 association measures from the literature on locating bugs. Our empirical evaluations involve single-bug and multiple-bug programs. We find there is no best single measure for all cases. Klosgen and Ochiai outperform other measures for localizing single-bug programs. Although localizing multiple-bug programs, Added Value could localize the bugs with on average smallest percentage of inspected code, whereas a number of other measures have similar performance. The accuracies of the measures in localizing multi-bug programs are lower than single-bug programs, which provokes future research.
DOI: 10.25440/smu.12062814.v1
title: Data from: Extended Comprehensive Study of Association Measures for Fault Localization

description: This record contains the underlying research data for the publication "Extended Comprehensive Study of Association Measures for Fault Localization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/1818Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to <em>association measures</em> that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the relationship strength between the execution of program elements and program failures. We investigate the effectiveness of 40 association measures from the literature on locating bugs. Our empirical evaluations involve single-bug and multiple-bug programs. We find there is no best single measure for all cases. Klosgen and Ochiai outperform other measures for localizing single-bug programs. Although localizing multiple-bug programs, Added Value could localize the bugs with on average smallest percentage of inspected code, whereas a number of other measures have similar performance. The accuracies of the measures in localizing multi-bug programs are lower than single-bug programs, which provokes future research.
DOI: 10.25440/smu.12062820
title: Data from: China's Innovation Landscape

description: This record contains the underlying research data for the publication "China's Innovation Landscape" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/3569The People's Republic of China has experienced three decades of sustained, strong annual economic growth as it transitions from a centrally planned economy to a free market. Currently the world's second largest economy, China recognizes scientific and technological innovation as an increasingly important strategy to fuel the next phase of its productivity growth. However, the drivers and trajectories of China's scientific and technological growth remain under-investigated. To understand elements of China's innovative activities, particularly in science and technology, an analysis of comprehensive patent data provided by the State Intellectual Property Office (SIPO) of China is presented here.
DOI: 10.25440/smu.12062826
title: Data from: Manifold Learning for Jointly Modeling Topic and Visualization

description: This record contains the underlying research data for the publication "Manifold Learning for Jointly Modeling Topic and Visualization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2248Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that Semafore significantly outperforms the state-of-the-art baselines on objective evaluation metrics.
DOI: 10.25440/smu.12062826.v1
title: Data from: Manifold Learning for Jointly Modeling Topic and Visualization

description: This record contains the underlying research data for the publication "Manifold Learning for Jointly Modeling Topic and Visualization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2248Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that Semafore significantly outperforms the state-of-the-art baselines on objective evaluation metrics.
DOI: 10.25440/smu.12062829
title: Data from: Beta Atomic Contacts: Identifying Critical Specific Contacts in Protein Binding Interfaces

description: This record contains the underlying research data for the publication "Beta Atomic Contacts: Identifying Critical Specific Contacts in Protein Binding Interfaces" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2296Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the β-skeletons in computational geometry, requires that there is no other atom in the contact spheres defined by two contact atoms; this sphere is similar to the van der Waals spheres of atoms. The statistical analysis on a large dataset shows that β contacts are only a small fraction of conventional distance-based contacts. To empirically quantify the importance of β contacts, we design βACV, an SVM classifier with β contacts as input, to classify homodimers from crystal packing. We found that our βACV is able to achieve the state-of-the-art classification performance superior to SVM classifiers with distance-based contacts as input. Our βACV also outperforms several existing methods when being evaluated on several datasets in previous works. The promising empirical performance suggests that β contacts can truly identify critical specific contacts in protein binding interfaces. β contacts thus provide a new model for more precise description of atomic organization in protein quaternary structures than distance-based contacts.
DOI: 10.25440/smu.12062829.v1
title: Data from: Beta Atomic Contacts: Identifying Critical Specific Contacts in Protein Binding Interfaces

description: This record contains the underlying research data for the publication "Beta Atomic Contacts: Identifying Critical Specific Contacts in Protein Binding Interfaces" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2296Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the β-skeletons in computational geometry, requires that there is no other atom in the contact spheres defined by two contact atoms; this sphere is similar to the van der Waals spheres of atoms. The statistical analysis on a large dataset shows that β contacts are only a small fraction of conventional distance-based contacts. To empirically quantify the importance of β contacts, we design βACV, an SVM classifier with β contacts as input, to classify homodimers from crystal packing. We found that our βACV is able to achieve the state-of-the-art classification performance superior to SVM classifiers with distance-based contacts as input. Our βACV also outperforms several existing methods when being evaluated on several datasets in previous works. The promising empirical performance suggests that β contacts can truly identify critical specific contacts in protein binding interfaces. β contacts thus provide a new model for more precise description of atomic organization in protein quaternary structures than distance-based contacts.
DOI: 10.25440/smu.12062943
title: Data from: Identifying latent structures in panel data

description: This record contains the underlying research data for the publication "Identifying latent structures in panel data" and the full-text is available from: https://ink.library.smu.edu.sg/soe_research/1911This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are consideredpenalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.
DOI: 10.25440/smu.12062943.v1
title: Data from: Identifying latent structures in panel data

description: This record contains the underlying research data for the publication "Identifying latent structures in panel data" and the full-text is available from: https://ink.library.smu.edu.sg/soe_research/1911This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are consideredpenalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.
DOI: 10.25440/smu.12062847
title: Data from: Structural and Functional Analysis of Multi-interface Domains

description: This record contains the underlying research data for the publication "Structural and Functional Analysis of Multi-interface Domains" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2297A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.
DOI: 10.25440/smu.12062847.v1
title: Data from: Structural and Functional Analysis of Multi-interface Domains

description: This record contains the underlying research data for the publication "Structural and Functional Analysis of Multi-interface Domains" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2297A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.
DOI: 10.25440/smu.12062850
title: Data from: A Convolution Kernel Approach to Identifying Comparisons in Text

description: This record contains the underlying research data for the publication "A Convolution Kernel Approach to Identifying Comparisons in Text" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2891Comparisons in text, such as in online reviews, serve as useful decision aids. In this paper, we focus on the task of identifying whether a comparison exists between a specific pair of entity mentions in a sentence. This formulation is transformative, as previous work only seeks to determine whether a sentence is comparative, which is presumptuous in the event the sentence mentions multiple entities and is comparing only some, not all, of them. Our approach leverages not only lexical features such as salient words, but also structural features expressing the relationships among words and entity mentions. To model these features seamlessly, we rely on a dependency tree representation, and investigate the applicability of a series of tree kernels. This leads to the development of a new context-sensitive tree kernel: Skip-node Kernel (SNK). We further describe both its exact and approximate computations. Through experiments on real-life datasets, we evaluate the effectiveness of our kernel-based approach for comparison identification, as well as the utility of SNK and its approximations.
DOI: 10.25440/smu.12062850.v1
title: Data from: A Convolution Kernel Approach to Identifying Comparisons in Text

description: This record contains the underlying research data for the publication "A Convolution Kernel Approach to Identifying Comparisons in Text" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/2891Comparisons in text, such as in online reviews, serve as useful decision aids. In this paper, we focus on the task of identifying whether a comparison exists between a specific pair of entity mentions in a sentence. This formulation is transformative, as previous work only seeks to determine whether a sentence is comparative, which is presumptuous in the event the sentence mentions multiple entities and is comparing only some, not all, of them. Our approach leverages not only lexical features such as salient words, but also structural features expressing the relationships among words and entity mentions. To model these features seamlessly, we rely on a dependency tree representation, and investigate the applicability of a series of tree kernels. This leads to the development of a new context-sensitive tree kernel: Skip-node Kernel (SNK). We further describe both its exact and approximate computations. Through experiments on real-life datasets, we evaluate the effectiveness of our kernel-based approach for comparison identification, as well as the utility of SNK and its approximations.