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arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： social networking (online)

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Demo: CAR: The cleanest air routing algorithm for path navigation with minimal PM2.5 exposure on the move 16

Demo: CAR: The cleanest air routing algorithm for path navig...

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16th ACM International Conference on Mobile Systems, Applications, and Services,MobiSys 2018

作者： Mahajan, Sachit Tang, Yu-Siou Wu, Dong-Yi Tsai, Tzu-Chieh Chen, Ling-Jyh Social Networks and Human Centered Computing Program TIGP Taiwan Institute of Information Science Academia Sinica Taiwan National Chengchi University Taiwan

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Self-Supervised Denoising Autoencoder with Linear Regression Decoder for Speech Enhancement

Self-Supervised Denoising Autoencoder with Linear Regression...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ryandhimas E. Zezario Tassadaq Hussain Xugang Lu Hsin-Min Wang Yu Tsao Research Center for Information Technology Innovation Academia Sinica Taiwan Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taiwan National Institute of Information and Communications Technology Japan Institute of Information Science Academia Sinica Taiwan

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Nonlinear spectral mapping-based models based on supervised learning have successfully applied for speech enhancement. However, as supervised learning approaches, a large amount of labelled data (noisy-clean speech pairs) should be provided to train those models. In addition, their performances for unseen noisy conditions are not guaranteed, which is a common weak point of supervised learning approaches. In this study, we proposed an unsupervised learning approach for speech enhancement, i.e., denoising autoencoder with linear regression decoder (DAELD) model for speech enhancement. The DAELD is trained with noisy speech as both input and target output in a self-supervised learning manner. In addition, with properly setting a shrinkage threshold for internal hidden representations, noise could be removed during the reconstruction from the hidden representations via the linear regression decoder. Speech enhancement experiments were carried out to test the proposed model. Results confirmed that the proposed DAELD could achieve comparable and sometimes even better enhancement performance as compared to the conventional supervised speech enhancement approaches, in both seen and unseen noise environments. Moreover, we observe that higher performances tend to achieve by DAELD when the training data cover more diverse noise types and signal-tonoise-ratio (SNR) levels.

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Content-Based Echo Chamber Detection on social Media Platforms

Content-Based Echo Chamber Detection on Social Media Platfor...

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International Conference on Advances in social Network Analysis and Mining, ASONAM

作者： Fernando H. Calderón Li-Kai Cheng Ming-Jen Lin Yen-Hao Huang Yi-Shin Chen Social Networks and Human Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan National Taiwan University Taipei City Taiwan

ISBN: (数字)9781450368681

ISBN: (纸本)9781450368681

“Echo chamber” is a metaphorical description of a situation in which beliefs are amplified inside a closed network, and social media platforms provide an environment that is well-suited to this phenomenon. Depending on the scale of the echo chamber, a user's judgment of different opinions may be restricted. The current study focuses on detecting echoing interaction between a post and its related comments to then quantify the predominating degree of echo chamber behavior on Facebook pages. To enable such detection, two content-based features are designed; the first aids stance representation of comments on a particular discussion topic, and the second focuses on the type and intensity of emotion elicited by a subject. This work also introduces data-driven semi-supervised approaches to extract such features from social media data.

关键词： Feature extraction social network services Fans Urban areas Connectors Linguistics Computational modeling

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Comparative Study of Masking and Mapping Based on Hierarchical Extreme Learning Machine for Speech Enhancement

Comparative Study of Masking and Mapping Based on Hierarchic...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Ryandhimas E. Zezario Join W. C. Sigalingging Tassadaq Hussain Jia-Ching Wang Yu Tsao Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Central University Taoyuan Taiwan Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan

In this study, we compare the speech enhancement performance based on hierarchical extreme learning machine (HELM) with two distinct strategies: masking and mapping. Experimental results of the perceptual evaluation of speech quality (PESQ) show that for both of limited and sufficient amounts of training data, mapping-based HELM tends to be more effective to improve the performance of speech enhancement.

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A Vector Mosquitoes Classification System Based on Edge computing and Deep Learning

A Vector Mosquitoes Classification System Based on Edge Comp...

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Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Huang, Li-Pang Hong, Ming-Hong Luo, Cyuan-Heng Mahajan, Sachit Chen, Ling-Jyh Acad Sinica Inst Informat Sci Taipei Taiwan Natl Taiwan Normal Univ Taiwan Int Grad Program Social Networks & Human Centered Comp Program Taipei Taiwan Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781728112299

In recent years, we have witnessed a sudden increase in mosquito-borne diseases and related casualties. This makes it important to have an efficient mosquito classification system. In this paper, we implement a mosquito classification system which is capable of identifying Aedes and Culex (types of the mosquito) automatically. To facilitate the implementation of such Internet of Things (IoT) based system, we first create a trap device with a stable area for filming mosquitoes. Then, we analyze video frames in order to reduce the video size for transmission. We also build a model to identify different types of mosquitoes using deep learning. Later, we fine-tune the edge computing on the trap device to optimize the system efficiency. Finally, we integrate the device and the model into a mosquito classification system and test the system in wild fields in Taiwan. The tests show significant results when the experiments are conducted in the rural area. We are able to achieve an accuracy of 98% for validation data and 90.5% for testing data.

关键词： Mosquito Classification Internet of Things Deep Learning Convolutional Neural Network Image Processing

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An empirical study of PM2.5 forecasting using neural network

An empirical study of PM2.5 forecasting using neural network

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2017 IEEE SmartWorld Ubiquitous Intelligence and computing, Advanced and Trusted Computed, Scalable computing and Communications, Cloud and Big Data computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017

作者： Mahajan, Sachit Chen, Ling-Jyh Tsai, Tzu-Chieh Social Networks and Human Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Science Academia Sinica Taiwan Department of Computer Science National Chengchi University Taiwan

ISBN: (纸本)9781538604342

In the recent years, a lot of efforts have been made to regulate air pollutant levels in most of the developed and developing countries. Fine particulate matter (PM2.5) is considered to be one of the major reasons behind deteriorating public health and a lot of efforts are being made to keep a check on PM2.5 levels. Accurately forecasting PM2.5 level is a challenging task and has been highly dependent on model based approaches. In this paper, we explore new possibilities to hourly forecast PM2.5. Choosing the right forecasting model becomes a very important aspect when it comes to improvement in prediction accuracy. We used Neural Network Autoregression (NNAR) method for the prediction task. The paper also provides a comparative analysis of prediction performance for additive version of Holt-Winters method, autoregressive integrated moving average (ARIMA) model and NNAR model. The experimentation and evaluation is done using real world measurement data from Airbox Project, which shows that our proposed method accurately does the prediction with significantly low error. © 2017 IEEE.

关键词： Neural networks

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SwapItUp: A Face Swap Application for Privacy Protection 31

SwapItUp: A Face Swap Application for Privacy Protection

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31st IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Mahajan, Sachit Chen, Ling-Jyh Tsai, Tzu-Chieh Taiwan Int Grad Program Social Networks & Human Centered Comp Program Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan Natl Chengchi Univ Dept Comp Sci Taipei Taiwan

ISBN: (纸本)9781509060290

There is a growing concern over the issues related to online privacy due to large availability of high quality images. To tackle the privacy concerns a face swapping application is proposed. There is a library of face images, which is created by downloading images from different sources on Internet. For any given image, first of all facial landmarks are detected. The second image is rotated and scaled so that it can properly fit over the input image. To make sure that the new image looks natural, color balance adjustment is done. After that blending of features from the second image onto the input image is done. It is also shown how this system can be used to creating appealing and funny photographs for entertainment purposes. We conclude with a study that shows the high quality of images produced by this system as compared to existing face swap applications and also limitations of this system.

关键词： face swap feature detection privacy protection

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SwapItUp: A Face Swap Application for Privacy Protection

SwapItUp: A Face Swap Application for Privacy Protection

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Sachit Mahajan Ling-Jyh Chen Tzu-Chieh Tsai Department of Computer Science National Chengchi University Social Networks and Human Centered Computing Program Taiwan International Graduate Program Academia Sinica Institute of Information Science

ISBN: (纸本)9781509060306

There is a growing concern over the issues related to online privacy due to large availability of high quality images. To tackle the privacy concerns a face swapping application is proposed. There is a library of face images, which is created by downloading images from different sources on Internet. For any given image, lirst of all facial landmarks are detected. The second image is rotated and scaled so that it can properly lit over the input image. To make sure that the new image looks natural, color balance adjustment is done. After that blending of features from the second image onto the input image is done. It is also shown how this system can be used to creating appealing and funny photographs for entertainment purposes. We conclude with a study that shows the high quality of images produced by this system as compared to existing face swap applications and also limitations of this system.

关键词： Face Image color analysis Three-dimensional displays Solid modeling Skin Privacy Image resolution

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Facebook reaction-based emotion classifier as cue for sarcasm detection

arXiv

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arXiv 2018年

作者： Kuo, Po Chen Calderon Alvarado, Fernando H. Chen, Yi-Shin Department of Computer Science National Tsing Hua University GuangFu Rd. Sec.2 No. 101 Hsinchu30013 Taiwan Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica 128 Academia Road Sec. 2. Nangang Taipei115 Taiwan Institute of Information Systems and Applications National Tsing Hua University GuangFu Rd. Sec.2 No. 101 Hsinchu30013 Taiwan

Online social media users react to content in them based on context. Emotions or mood play a significant part of these reactions, which has filled these platforms with opinionated content. Different approaches and applications to make better use of this data are continuously being developed. However, due to the nature of the data, the variety of platforms, and dynamic online user behavior, there are still many issues to be dealt with. It remains a challenge to properly obtain a reliable emotional status from a user prior to posting a comment. This work introduces a methodology that explores semi-supervised multilingual emotion detection based on the overlap of Facebook reactions and textual data. With the resulting emotion detection system we evaluate the possibility of using emotions and user behavior features for the task of sarcasm detection. More than 1 million English and Chinese comments from over 62,000 public Facebook pages posts have been collected and processed, conducted experiments show acceptable performance metrics. Copyright © 2018, The Authors. All rights reserved.

关键词： Sentiment analysis

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