scholarly journals Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

2019 ◽  
Vol 33 ◽  
pp. 606-613 ◽  
Author(s):  
Muhammad Raza Khan ◽  
Joshua E. Blumenstock
Keyword(s):  
Developing Countries ◽  
Mobile Phone ◽  
Large Scale ◽  
Rapid Expansion ◽  
Global Poverty ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Humanitarian Response ◽  
Learning Tasks ◽  
Supervised Learning Algorithms

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.

scholarly journals Sentence Compression Using BERT and Graph Convolutional Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 9910
Author(s):  
Yo-Han Park ◽  
Gyong-Ho Lee ◽  
Yong-Seok Choi ◽  
Kong-Joo Lee
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Compression Model ◽  
Sentence Compression ◽  
Dependency Tree ◽  
Proposed Model ◽  
Syntactic Information ◽  
Input Sentence

Sentence compression is a natural language-processing task that produces a short paraphrase of an input sentence by deleting words from the input sentence while ensuring grammatical correctness and preserving meaningful core information. This study introduces a graph convolutional network (GCN) into a sentence compression task to encode syntactic information, such as dependency trees. As we upgrade the GCN to activate a directed edge, the compression model with the GCN layers can distinguish between parent and child nodes in a dependency tree when aggregating adjacent nodes. Furthermore, by increasing the number of GCN layers, the model can gradually collect high-order information of a dependency tree when propagating node information through the layers. We implement a sentence compression model for Korean and English, respectively. This model consists of three components: pre-trained BERT model, GCN layers, and a scoring layer. The scoring layer can determine whether a word should remain in a compressed sentence by relying on the word vector containing contextual and syntactic information encoded by BERT and GCN layers. To train and evaluate the proposed model, we used the Google sentence compression dataset for English and a Korean sentence compression corpus containing about 140,000 sentence pairs for Korean. The experimental results demonstrate that the proposed model achieves state-of-the-art performance for English. To the best of our knowledge, this sentence compression model based on the deep learning model trained with a large-scale corpus is the first attempt for Korean.

scholarly journals Hybrid Low-Order and Higher-Order Graph Convolutional Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fangyuan Lei ◽  
Xun Liu ◽  
Qingyun Dai ◽  
Bingo Wing-Kuen Ling ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Representation Learning ◽  
Higher Order ◽  
Semisupervised Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Proposed Model ◽  
Low Order

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.

scholarly journals Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes

2020 ◽  
Vol 34 (04) ◽  
pp. 5892-5899
Author(s):  
Ke Sun ◽  
Zhouchen Lin ◽  
Zhanxing Zhu
Keyword(s):  
Supervised Learning ◽  
State Of The Art ◽  
Difficult Problem ◽  
Superior Performance ◽  
Learning Approach ◽  
Training Algorithm ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Learning Tasks ◽  
Multi Stage

Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.

scholarly journals The Use of Mobile Phones for the Prevention and Control of Arboviral Diseases: A Scoping Review

2020 ◽  
Author(s):  
Maria Angelica Carrillo ◽  
Axel Kroeger ◽  
Rocio Cardenas Sanchez ◽  
Sonia Diaz Monsalve ◽  
Silvia Runge Ranzinger
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Scoping Review ◽  
Information Transfer ◽  
Large Scale ◽  
Grey Literature ◽  
Real Life ◽  
Rapid Expansion ◽  
Health Concern ◽  
Arboviral Diseases

Abstract Background: The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes. Methods: We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced. Results: The systematic literature search identified 1289 records, 32 of which met the inclusion criteria. From the reference lists of included articles 4 records were identified coming to a total of 36 studies. The content analysis identified five mobile phone categories: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). These devices were used for surveillance, prevention and management. In general, mobile phone-based studies reported good performance, acceptance by users, usability in downloads as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors and disease. It can be concluded that there are great opportunities for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases . Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

scholarly journals The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

2020 ◽  
Author(s):  
Maria Angelica Carrillo ◽  
Axel Kroeger ◽  
Rocio Cardenas Sanchez ◽  
Sonia Diaz Monsalve ◽  
Silvia Runge Ranzinger
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Scoping Review ◽  
Information Transfer ◽  
Large Scale ◽  
Grey Literature ◽  
Real Life ◽  
Rapid Expansion ◽  
Health Concern ◽  
Arboviral Diseases

Abstract Background: The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes.Methods: We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced.Results: The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are benefits for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

scholarly journals The Use of Mobile Phones for the Prevention and Control of Arboviral Diseases: A Scoping Review

2020 ◽  
Author(s):  
Maria Angelica Carrillo ◽  
Axel Kroeger ◽  
Rocio Cardenas Sanchez ◽  
Sonia Diaz Monsalve ◽  
Silvia Runge Ranzinger
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Scoping Review ◽  
Information Transfer ◽  
Large Scale ◽  
Grey Literature ◽  
Real Life ◽  
Rapid Expansion ◽  
Health Concern ◽  
Arboviral Diseases

Abstract Background: The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes.Methods: We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced. Results: The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are great opportunities for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

scholarly journals The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

2021 ◽  
Author(s):  
Maria Angelica Carrillo ◽  
Axel Kroeger ◽  
Rocio Cardenas Sanchez ◽  
Sonia Diaz Monsalve ◽  
Silvia Runge Ranzinger
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Scoping Review ◽  
Information Transfer ◽  
Large Scale ◽  
Grey Literature ◽  
Real Life ◽  
Rapid Expansion ◽  
Health Concern ◽  
Arboviral Diseases

Abstract Background: The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes. Methods: We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced. Results: The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are benefits for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

scholarly journals Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2198
Author(s):  
Chaoyue Li ◽  
Lian Zou ◽  
Cien Fan ◽  
Hao Jiang ◽  
Yifeng Liu
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Feature Learning ◽  
Superior Performance ◽  
Sparse Graph ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Spatial Graph ◽  
Multi Stage ◽  
The Time Domain

Graph convolutional networks (GCNs), which model human actions as a series of spatial-temporal graphs, have recently achieved superior performance in skeleton-based action recognition. However, the existing methods mostly use the physical connections of joints to construct a spatial graph, resulting in limited topological information of the human skeleton. In addition, the action features in the time domain have not been fully explored. To better extract spatial-temporal features, we propose a multi-stage attention-enhanced sparse graph convolutional network (MS-ASGCN) for skeleton-based action recognition. To capture more abundant joint dependencies, we propose a new strategy for constructing skeleton graphs. This simulates bidirectional information flows between neighboring joints and pays greater attention to the information transmission between sparse joints. In addition, a part attention mechanism is proposed to learn the weight of each part and enhance the part-level feature learning. We introduce multiple streams of different stages and merge them in specific layers of the network to further improve the performance of the model. Our model is finally verified on two large-scale datasets, namely NTU-RGB+D and Skeleton-Kinetics. Experiments demonstrate that the proposed MS-ASGCN outperformed the previous state-of-the-art methods on both datasets.

scholarly journals The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maria Angelica Carrillo ◽  
Axel Kroeger ◽  
Rocio Cardenas Sanchez ◽  
Sonia Diaz Monsalve ◽  
Silvia Runge-Ranzinger
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Scoping Review ◽  
Information Transfer ◽  
Large Scale ◽  
Grey Literature ◽  
Real Life ◽  
Rapid Expansion ◽  
Health Concern ◽  
Arboviral Diseases

Abstract Background The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes. Methods We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced. Results The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are benefits for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

scholarly journals The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

2020 ◽  
Author(s):  
Maria Angelica Carrillo ◽  
Axel Kroeger ◽  
Rocio Cardenas Sanchez ◽  
Sonia Diaz Monsalve ◽  
Silvia Runge Ranzinger
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Scoping Review ◽  
Information Transfer ◽  
Large Scale ◽  
Grey Literature ◽  
Real Life ◽  
Rapid Expansion ◽  
Health Concern ◽  
Arboviral Diseases

Abstract Background: The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes.Methods: We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced. Results: The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are benefits for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

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