The paper focuses on how to construct student models in the online virtual educational *** firstly analyze social interaction between students in the community,and present some algorithms to model students'(person...
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The paper focuses on how to construct student models in the online virtual educational *** firstly analyze social interaction between students in the community,and present some algorithms to model students'(personal and shared) needs, preferences or knowledge structures in interaction ***,the paper proposes an integrated student model combined with student modeling in information services and task processes,and emphasizes that it builds up a foundation to personalize information services and customize online educational programs.
A joint speech signal enhancement based on singular value decomposition filter after spectral subtraction (SSVD) is proposed in this paper. The residual noise after spectral subtraction, which results for audible musi...
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ISBN:
(纸本)7801501144
A joint speech signal enhancement based on singular value decomposition filter after spectral subtraction (SSVD) is proposed in this paper. The residual noise after spectral subtraction, which results for audible musical noise, is reduced further by SVD filter. The matrix size in spectral domain can be reduced half, and larger step-length adopted by SVD filter in spectral domain leads to lower cost, which make sure that the system can work in real-time. A novel speech/pause detector based on entropy(ESPD) is proposed too. The new detector improves the performance of the whole noise suppression system significantly.
A new method for speech signal reconstruction is proposed by performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in high-...
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ISBN:
(纸本)7801501144
A new method for speech signal reconstruction is proposed by performing a nonlinear Kernel Principal Component Analysis (KPCA). By the use of kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, and reconstruct vectors mapping from input space by those dominant principal components. As the reconstructed vectors is expressed in high dimensional feature space and they could not exist pre-image in input space. For finding pre-image, we use iteration method to approximate the pre-image. The experimental results using KPCA in data reconstruction and denoising in speech signal show that it had many potential advantages comparing with PCA.
Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capa...
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Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.
Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SS...
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Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is availab.e at https://***/ADMIS-TONGJI/RGCN-PSA/.
This volume presents the accepted papers for the 4th International Conference onGridandCooperativecomputing(GCC2005),heldinBeijing,China,during November 30 – December 3, *** conferenceseries of GCC aims to provide an...
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ISBN:
(数字)9783540322771
ISBN:
(纸本)9783540305101
This volume presents the accepted papers for the 4th International Conference onGridandCooperativecomputing(GCC2005),heldinBeijing,China,during November 30 – December 3, *** conferenceseries of GCC aims to provide an international forum for the presentation and discussion of research trends on the theory, method, and design of Grid and cooperative computing as well as their scienti?c, engineering and commercial applications. It has become a major annual event in this area. The First International Conference on Grid and Cooperative computing (GCC2002)***2003received550submissions,from which 176 regular papers and 173 short papers were accepted. The acceptance rate of regular papers was 32%, and the total acceptance rate was 64%. GCC 2004 received 427 main-conference submissions and 154 workshop submissions. The main conference accepted 96 regular papers and 62 short papers. The - ceptance rate of the regular papers was 23%. The total acceptance rate of the main conference was 37%. For this conference, we received 576 submissions. Each was reviewed by two independent members of the International Program Committee. After carefully evaluating their originality and quality, we accepted 57 regular papers and 84 short papers. The acceptance rate of regular papers was 10%. The total acc- tance rate was 25%.
PRIMA is a series of workshops on agent computing and multi-agent systems, integrating the activities in Asia and Pacific Rim countries. Agent computing and multi-agent systems are computational systems in which sever...
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ISBN:
(数字)9783540368601
ISBN:
(纸本)9783540367079
PRIMA is a series of workshops on agent computing and multi-agent systems, integrating the activities in Asia and Pacific Rim countries. Agent computing and multi-agent systems are computational systems in which several autonomous or se- autonomous agents interact with each other or work together to perform some set of tasks or satisfy some set of goals. These systems may involve computational agents that are homogeneous or heterogeneous, they may involve activities on the part of agents having common or distinct goals, and they may involve participation on the part of humans and intelligent agents. The aim of PRIMA 2006 was to bring together Asian and Pacific Rim researchers and developers from academia and industry to report on the latest technical advances or domain applications and to discuss and explore scientific and practical problems as raised by the participants. PRIMA 2006 received 203 submitted papers. Each paper was reviewed by two internationally renowned Program Committee members. After careful reviews, 39 regular papers and 57 short papers were selected for this volume. We would like to thank all the authors who submitted papers to the workshop. We are very grateful to all Program Committee members and reviewers for their splendid work in reviewing the papers. Finally, we thank the editorial staff of Springer for publishing this volume in the Lecture Notes in Artificial Intelligence series.
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