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Article
Robust and reliable estimation via recursive nonlinear dynamic data reconciliation based on cubature Kalman filter
Since measurements of process variables are subject to measurements errors as well as process variability, data reconciliation is the procedure of optimally adjusting measured date so that the adjusted values ...
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Article
Multi-objective service composition model based on cost-effective optimization
The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of servic...
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Dynamic hypersphere SVDD without describing boundary for one-class classification
Support vector data description (SVDD), an efficient one-class classification method, captures the spherically shaped boundary around the same class data and achieves classification for setting the boundary re...
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Article
Implicit discourse relation detection using concatenated word embeddings and a gated relevance network
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Chinese Word Segmentation via BiLSTM+Semi-CRF with Relay Node
Semi-Markov conditional random fields (Semi-CRFs) have been successfully utilized in many segmentation problems, including Chinese word segmentation (CWS). The advantage of Semi-CRF lies in its inherent abilit...
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Article
Syntax-guided text generation via graph neural network
Text generation is a fundamental and important task in natural language processing. Most of the existing models generate text in a sequential manner and have difficulty modeling complex dependency structures. ...
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Text information aggregation with centrality attention
A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves an aggregation process of combining the representations of all the words, such as p...
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Article
Dual-axial self-attention network for text classification
Text classification is an important task in natural language processing and numerous studies aim to improve the accuracy and efficiency of text classification models. In this study, we propose an effective and...
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Article
Open AccessParadigm Shift in Natural Language Processing
In the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a b...
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Article
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing (NLP) and yield state-of-the-art results on many downstream tasks. Recent studies on adapting BERT ...
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\(\cal{Y}\) -Tuning: an efficient tuning paradigm for large-scale pre-trained models via label representation learning
With current success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters. Previous wo...
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ChatGPT: potential, prospects, and limitations
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Open AccessMulti-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning
In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement...
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Article
CPT: a pre-trained unbalanced transformer for both Chinese language understanding and generation
In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese pre-trained unbalanced transformer (CPT). Different from previous Chinese PTMs, CPT is designed to utilize...
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Article
MOSS: An Open Conversational Large Language Model
Conversational large language models (LLMs) such as ChatGPT and GPT-4 have recently exhibited remarkable capabilities across various domains, capturing widespread attention from the public. To facilitate this ...