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SG-DSN: A Semantic Graph-based Dual-Stream Network for facial expression recognition
SG-DSN: یک شبکه جریان دوگانه مبتنی بر نمودار معنایی برای تشخیص حالت چهره-2021 Facial expression recognition (FER) is a crucial task for human emotion analysis and has attracted wide interest in the field of computer vision and affective computing. General convolutional-based FER meth- ods rely on the powerful pattern abstraction of deep models, but they lack the ability to use semantic information behind significant facial areas in physiological anatomy and cognitive neurology. In this work, we propose a novel approach for expression feature learning called Semantic Graph-based Dual- Stream Network (SG-DSN), which designs a graph representation to model key appearance and geometric facial changes as well as their semantic relationships. A dual-stream network (DSN) with stacked graph convolutional attention blocks (GCABs) is introduced to automatically learn discriminative features from the organized graph representation and finally predict expressions. Experiments on three lab-controlled datasets and two in-the-wild datasets demonstrate that the proposed SG-DSN achieves competitive performance compared with several latest methods.© 2021 Published by Elsevier B.V. Keywords: Facial expression recognition | Affective computing | Graph representation | Graph convolutional attention block | Semantic relationship |
مقاله انگلیسی |
2 |
Discovering and merging related analytic datasets
کشف و ادغام مجموعه داده های تحلیلی مرتبط-2020 The production of analytic datasets is a significant big data trend and has gone well beyond the scope of traditional
IT-governed dataset development. Analytic datasets are now created by data scientists and data analysts using big
data frameworks and agile data preparation tools. However, despite the profusion of available datasets, it remains quite
difficult for a data analyst to start from a dataset at hand and customize it with additional attributes coming from other
existing datasets. This article describes a model and algorithms that exploit automatically extracted and user-defined
semantic relationships for extending analytic datasets with new atomic or aggregated attribute values. Our framework
is implemented as a REST service in SAP HANA and includes a careful theoretical analysis and practical solutions for
several complex data quality issues Key words: schema augmentation | schema complement | data quality | SAP HANA |
مقاله انگلیسی |
3 |
Multi-sense embeddings through a word sense disambiguation process
توکاری چند حسه از طریق فرایند تفسیر کلمه حس-2019 Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA) , that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems. Keywords: Multi-sense | embeddings Natural language processing | Word similarity | Synset |
مقاله انگلیسی |
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Mining massive hierarchical data using a scalable probabilistic graphical model
استخراج داده های سلسله مراتبی عظیم با استفاده از یک احتمال احتمالی مقیاس پذیرمدل گرافیکی-2018 Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning
and data mining. The crucial limitation of those models, however, is their scalability. The
Bayesian Network, which is one of the most common PGMs used in machine learning and
data mining, demonstrates this limitation when the training data consists of random vari
ables, in which each of them has a large set of possible values. In the big data era, one
could expect new extensions to the existing PGMs to handle the massive amount of data
produced these days by computers, sensors and other electronic devices. With hierarchi
cal data - data that is arranged in a treelike structure with several levels - one may see
hundreds of thousands or millions of values distributed over even just a small number of
levels. When modeling this kind of hierarchical data across large data sets, unrestricted
Bayesian Networks may become infeasible for representing the probability distributions.
In this paper, we introduce an extension to Bayesian Networks that can handle massive
sets of hierarchical data in a reasonable amount of time and space. The proposed model
achieves high precision and high recall when used as a multi-label classifier for the anno
tation of mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com, the model was able to predict latent semantic relationships among
search keywords with high accuracy.
Keywords: Probabilistic model ، Mass spectrometry annotation ، Big data ، Large scale machine learning ، Smantic discovery |
مقاله انگلیسی |
5 |
Mining massive hierarchical data using a scalable probabilistic graphical model
استخراج داده های سلسله مراتبی عظیم با استفاده از یک مدل گرافیکی احتمالی مقیاس پذیر-2018 Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning
and data mining. The crucial limitation of those models, however, is their scalability. The
Bayesian Network, which is one of the most common PGMs used in machine learning and
data mining, demonstrates this limitation when the training data consists of random vari
ables, in which each of them has a large set of possible values. In the big data era, one
could expect new extensions to the existing PGMs to handle the massive amount of data
produced these days by computers, sensors and other electronic devices. With hierarchi
cal data - data that is arranged in a treelike structure with several levels - one may see
hundreds of thousands or millions of values distributed over even just a small number of
levels. When modeling this kind of hierarchical data across large data sets, unrestricted
Bayesian Networks may become infeasible for representing the probability distributions.
In this paper, we introduce an extension to Bayesian Networks that can handle massive
sets of hierarchical data in a reasonable amount of time and space. The proposed model
achieves high precision and high recall when used as a multi-label classifier for the anno
tation of mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com, the model was able to predict latent semantic relationships among
search keywords with high accuracy.
Keywords: Probabilistic model ، Mass spectrometry annotation ، Big data ، Large scale machine learning ، Smantic discovery |
مقاله انگلیسی |
6 |
Mining various semantic relationships from unstructured user-generated web data
استخراج روابط معنایی مختلف از داده های بدون ساختار وب ایجاد شده توسط کاربر-2015 Article history:Received 6 July 2013 Received in revised form 30 September 2014Accepted 14 November 2014Available online 24 November 2014Keywords:Semantic relationships Search logsSocial annotations Large-scale web dataWith the emergence of Web 2.0, the amount of user-generated web data has sharply increased. Thus, many studies have proposed techniques to extract wisdom from these user-generated datasets. Some of these works have focused on extracting semantic relationships through the use of search logs or social annotations, but only hierarchical relationships have been considered. The goal of this paper is to detect various semantic relationships (hierarchical and non-hierarchical) between concepts using search logs and social annotations. The experimental results demonstrate that our proposed approach constructs adequate relationships.© 2014 Elsevier B.V. All rights reserved.
Keywords: Semantic relationships | Search logs | Social annotations | Large-scale web data |
مقاله انگلیسی |