Presently, power quality and power system protection are the two biggest challenges faced by power systems. In this paper, both problems have been addressed by means of designing a fuzzy logic based fault detector and...
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Graph Neural Networks (GNNs) have recently been shown to be quite effective in modeling graph-structured data. Recent methods such as RGCN and SACN, have achieved the most advanced results in knowledge graph completio...
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ISBN:
(纸本)9781665411578
Graph Neural Networks (GNNs) have recently been shown to be quite effective in modeling graph-structured data. Recent methods such as RGCN and SACN, have achieved the most advanced results in knowledge graph completion. However, previous efforts are mostly restricted to aggregating information given by neighboring nodes only, ignoring the information given by neighboring edges. this paper proposes a novel Entities and Relations Aware Graph Convolutional Network (ERA-GCN), with an encoder-decoder framework which jointly embeds both entities and relations in a multi-relation graph. In the encoder end, ERA-GCN uses a weighted graph convolutional network to capture both graph structure and neighborhood information. In the decoder end, we utilize Conv-TransE to retain the translational property between entity and relation embedding, leading to better link prediction performance. We evaluate our proposed method on standard FB15k-237 and WNISRR datasets, and achieve about 11% relative improvement compared to current state-of-the-art ConvE in terms of HITS@l, HITS@3 and HITS@10.
Biomedical event trigger extraction, as one of the sub-task of biomedical event extraction, plays an important role in biomedical research. A biomedical event trigger is a word or phrase that marks the emergence of a ...
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ISBN:
(数字)9781510650510
ISBN:
(纸本)9781510650503
Biomedical event trigger extraction, as one of the sub-task of biomedical event extraction, plays an important role in biomedical research. A biomedical event trigger is a word or phrase that marks the emergence of a certain biomedical event. the recent works are usually based on the rule and the machine learning methods. However, the rule-based methods heavily rely on the concrete rules enumerated by the field expert and usually need a large amount of expert knowledge. the machine learning-based approaches usually utilize many handcraft features such as n-gram, lexicon, pose-tag and shortest dependency path. As a result, these methods based on machine learning can suffer from handcraft engineering with expensive time costs and the problem of generalization in the field transition. Withthe popularization of deep learning techniques, some effective frameworks in Natural Language Processing (NLP), such as adversarial training, self-attention mechanism, graph convolutional network, have been proposed to enhance the model performance for the NLP, especially the information extraction. As a task in the information extraction field, the frameworks mentioned above have been applied in the biomedical trigger identification subtask. this paper attempts to employ an external version of the recurrent neural network (RNN), i.e., bidirectional Gated Recurrent Unit (Bi-GRU) network, to extract the biomedical event trigger existing in the biological literature. Specifically, we first transform each token and entity label in the sentence to a word sequence with token index and an entity label sequence with entity label index. Subsequently, the above two sequences will be fed into the embedding layer to obtain the concatenated tensor between them. Moreover, we put the tensor into the Bi-GRU to generate the contextual encoding, which will be fed in a linear layer with an activation function to predict the probability distribution of the trigger. the final experiment on the MLEE data
Antenna-in-Package(AiP) technology has an advantage that a system can maintain antenna performance while being small in size and highly integrated. In this paper, based on LTCC technology, a rectangular corner cut mic...
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作者:
Venkitakrishnan, Rani P.TCS Lab
Spoken Tutorial Project Department of Chemical Engineering Indian Institute of Technology Bombay Powai Mumbai400076 India
the ability to read with ease and to understand the meaning of sentences is essential for students. India has 22 official languages which are spoken in the various states. Children of migrant laborers face the task of...
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Fashion is an area that people experience every day. Fashion can be seen as homogenizing, since encouraging everyone to dress in a certain way that is influenced, e.g. by celebrities and social media. However, nowaday...
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ISBN:
(纸本)9789897583957
Fashion is an area that people experience every day. Fashion can be seen as homogenizing, since encouraging everyone to dress in a certain way that is influenced, e.g. by celebrities and social media. However, nowadays, fashion is also a search for individuality and personal expression. Hence, this work is about the development of an intelligent web application to help people by providing them with clothing suggestions based on their previous garment selections at the registration stage and after determining each user's individual style thanks to machine learning techniques such as Naive Bayesian Networks. the resulting intelligent system has been thoroughly tested on real-world datasets as well as successfully released to end-users.
the component method has been widely used in prediction of structural behaviour of beam-to-column joints. In this paper, the moment-rotation (M-φ) behaviour of stainless steel double extended end-plate connections wa...
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A kinematic and workspace analysis of Spherical 3-RRR Coaxial Parallel Robot are developed in this paper. Position and orientation was determined through the inverse and forward kinematics using geometric and numerica...
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Modern information systems connecting software, physical systems, and people, are usually characterized by high dynamism. these dynamics introduce uncertainties, which in turn may harm the quality of service and lead ...
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ISBN:
(纸本)9783030575069;9783030575052
Modern information systems connecting software, physical systems, and people, are usually characterized by high dynamism. these dynamics introduce uncertainties, which in turn may harm the quality of service and lead to incomplete, inaccurate, and unreliable results. In this context, self-adaptation is considered as an effective approach for managing run-time uncertainty. However, classical approaches for quality engineering are not suitable to deal with run-time adaptation, as they are mainly used to derive the steady-state solutions of a system at design-time. In this paper, we envision a Continuous Model-basedengineering Process that makes use of architectural analysis in conjunction with experimentation to have a wider understanding of the system under development. these two activities are performed incrementally, and jointly used in a feedback loop to provide insights about the quality of the system-to-be.
the goal of the present innovation is to give engineering students an innovative educational experience through their natural engagement with mobile devices. It aims to motivate the integration of technology in the te...
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