174 research outputs found
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of
knowledge graph completion (KGC) models have been proposed to predict missing
links from known triples. Among those, several works have achieved more
advanced results via exploiting the structure information on KGs with Graph
Convolutional Networks (GCN). However, we observe that entity embeddings
aggregated from neighbors in different directions are just simply averaged to
complete single-tasks by existing GCN based models, ignoring the specific
requirements of forward and backward sub-tasks. In this paper, we propose a
Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction
information, the multi-head self-attention is applied to specifically combine
embeddings in different directions based on various entities and sub-tasks, the
geometric constraints are imposed to adjust the distribution of embeddings, and
the traditional binary cross-entropy loss is modified to reflect the triple
uncertainty. Moreover, the competitive experiments results on several benchmark
datasets verify the effectiveness of our model
Transparent Assessment of the Supervision Information in China's Food Safety: A Fuzzy-ANP Comprehensive Evaluation Method
Improving transparency of food safety supervision information can reduce the occurrence of information asymmetry, decrease food safety incidents, and promote socially joint regulation for food safety. In this study, an index system of food safety supervision information transparency (FSSIT) is constructed using the fuzzy-ANP comprehensive evaluation model. Using this system, the FSSIT in China is evaluated. A total of 1651 questionnaires containing 139525 data are collected from food and drug administration (FDA), consumer association (CA), and media at the central, provincial, prefectural, and county levels. Empirical results indicate that the FSSIT achieves a qualified level; however, the works of FDA, CA, and media still present deficiencies. The information transparency in the entirety presents deficiencies and gradually declines when that in the administrative level decreases. The economic development level indirectly determines the transparency level, and the transparency level does not conform to China's current economic development level
Contextual Dictionary Lookup for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to solve the incompleteness of
knowledge graphs (KGs) by predicting missing links from known triples, numbers
of knowledge graph embedding (KGE) models have been proposed to perform KGC by
learning embeddings. Nevertheless, most existing embedding models map each
relation into a unique vector, overlooking the specific fine-grained semantics
of them under different entities. Additionally, the few available fine-grained
semantic models rely on clustering algorithms, resulting in limited performance
and applicability due to the cumbersome two-stage training process. In this
paper, we present a novel method utilizing contextual dictionary lookup,
enabling conventional embedding models to learn fine-grained semantics of
relations in an end-to-end manner. More specifically, we represent each
relation using a dictionary that contains multiple latent semantics. The
composition of a given entity and the dictionary's central semantics serves as
the context for generating a lookup, thus determining the fine-grained
semantics of the relation adaptively. The proposed loss function optimizes both
the central and fine-grained semantics simultaneously to ensure their semantic
consistency. Besides, we introduce two metrics to assess the validity and
accuracy of the dictionary lookup operation. We extend several KGE models with
the method, resulting in substantial performance improvements on widely-used
benchmark datasets
A cloud-based remote sensing data production system
The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts āmasterāslaveā architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a āminimum data transferringā strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, the scheduling strategy of each production task mainly depends on the Ganglia monitoring results, thus the computing resources can be allocated or expanded adaptively. Finally, we evaluated the proposed programme using test experiments performed at global, regional and local areas, and the results showed that our proposed cloud-based remote sensing production system could deal with massive remote sensing data and different products generating, as well as on-demand remote sensing computing and information service
Robust Quadratic Regression and Its Application to Energy-Growth Consumption Problem
We propose a robust quadratic regression model to handle the statistics inaccuracy. Unlike the traditional robust statistic approaches that mainly focus on eliminating the effect of outliers, the proposed model employs the recently developed robust optimization methodology and tries to minimize the worst-case residual errors. First, we give a solvable equivalent semidefinite programming for the robust least square model with ball uncertainty set. Then the result is generalized to robust models under l1- and lā-norm critera with general ellipsoid uncertainty sets. In addition, we establish a robust regression model for per capital GDP and energy consumption in the energy-growth problem under the conservation hypothesis. Finally, numerical experiments are carried out to verify the effectiveness of the proposed models and demonstrate the effect of the uncertainty perturbation on the robust models
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