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Ad-Hoc and Sensor networks Symposium

作者： Fredrick Mzee Awuor Chih-Yu Wang Taiwan International Graduate Program Social Networks and Human-Centered Computing Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica

ISBN: (纸本)9781479966653

Massive machine type communication (MTC) brings new challenges to next generation wide-area networks. Random Access Channel procedure (RACH) used in LTE-Advanced system for request delivery, for instance, is inefficient in handling massive number of concurrent access requests. The S-Aloha approach in RACH will lead to unsustainable delays and resource wastage due to collisions from concurrent access in massive MTC networks. To address this issue, we propose a distributed queue based MTC communication protocol that exploits multiple packet reception (MPR) capability of MTC devices to self-organize themselves into a logical queue in a distributed way such that the devices connect to BS depending on their positions in the queue. We derive the algorithm and present analysis of its performance in regard to delay and throughput. The algorithm behaves like S-Aloha when MTC traffic load is low and seamlessly and smoothly switches to a reservation based protocol during heavy traffic. From the simulation results, the proposed scheme exhibits better performance compared to conventional RACH procedure as it increases number of successful transmissions by 2.7 times and reduces both the average access delay and transmission failure probability by 20%.

关键词： Concurrent Access Queue Minesite Technical Committees cellular system S-ALOHA

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Learning Adaptation and Generalization from human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis 6

Learning Adaptation and Generalization from Human-Inspired M...

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6th IEEE international Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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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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Reactive supervision: A new method for collecting sarcasm data

arXiv

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arXiv 2020年

作者： Shmueli, Boaz Ku, Lun-Wei Ray, Soumya Social Networks and Human-Centered Computing Taiwan International Graduate Program Taiwan Institute of Information Science Academia Sinica Taiwan Institute of Service Science National Tsing Hua University Taiwan

Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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socialNLP EmotionGIF 2020 challenge overview: Predicting reaction GIF categories on social media

arXiv

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arXiv 2021年

We present an overview of the EmotionGIF 2020 Challenge, held at the 8th international Workshop on Natural Language Processing for social Media (socialNLP), in conjunction with ACL 2020. The challenge required predicting affective reactions to online texts, and includes the EmotionGIF dataset, with tweets labeled for the reaction categories. The novel dataset included 40K tweets with their reaction GIFs. Due to the special circumstances of year 2020, two rounds of the competition were conducted. A total of 84 teams registered for the task. Of these, 25 teams successfully submitted entries to the evaluation phase in the first round, while 13 teams participated successfully in the second round. Of the top participants, five teams presented a technical report and shared their code. The top score of the winning team using the Recall@K metric was 62.47%. Copyright © 2021, The Authors. All rights reserved.

关键词： social networking (online)

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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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Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN network with Self Attention Model

Audio-Driven Facial Landmark Generation in Violin Performanc...

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international Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ting-Wei Lin Chao-Lin Liu Li Su Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians’ facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://***/kevinlin91/icassp_music2face).

关键词： Visualization Codes Avatars Signal processing Acoustics Multiple signal classification Speech processing

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Explainable and Adaptable Augmentation in Knowledge Attention network for Multi-Agent Deep Reinforcement Learning Systems

Explainable and Adaptable Augmentation in Knowledge Attentio...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728187082

ISBN: (纸本)9781728187099

The scale of modem Artificial Intelligence systems has been growing and entering more research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning (DRL) methods. More specifically, multi-agent DRL methods have been widely applied to address the problems of high-dimensional computation, which interpret the conditions that real-world systems mainly encounter and the issues that require resolving. However, the current approaches of DL and DRL are often challenged for their untransparent and time-consuming modeling processes in their attempt to achieve a practical and applicable inference based on human-level perspective and acceptance. This paper presents an explainable and adaptable augmented knowledge attention network for multi-agent DRL systems, which uses game theory simulation to tackle the problem of non-stationarity at the beginning, while improving the learning exploration built upon the strategic ontology to achieve the learning convergence more efficiently for autonomous agents. We anticipate that our approach will facilitate future research studies and potential research inspections of emerging multi-agent DRL systems for increasingly complex and autonomous environments.

关键词： Knowledge engineering Adaptation models Reinforcement learning Ontologies Inspection Modems Game theory

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Investigation of a Single-Channel Frequency-Domain Speech Enhancement network to Improve End-to-End Bengali Automatic Speech Recognition Under Unseen Noisy Conditions

Investigation of a Single-Channel Frequency-Domain Speech En...

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Oriental COCOSDA international Conference on Speech Database and Assessments

作者： Md Mahbub E Noor Yen-Ju Lu Syu-Siang Wang Supratip Ghose Chia-Yu Chang Ryandhimas E. Zezario Shafique Ahmed Wei-Ho Chung Yu Tsao Hsin-Min Wang Taiwan International Graduate Program in Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taiwan National Tsing Hua University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan University of Information Technology & Sciences Bangladesh

ISBN: (纸本)9781665408714

Due to the presence of distortion, most of the single-channel frequency-domain speech enhancement (SE) approaches are still challenging for downstream automatic speech recognition (ASR) tasks, even with satisfactory improvements in enhancing speech quality and intelligibility. Recently, transformer-based models have shown better performance in speech processing tasks. Therefore, we intend to explore a transformer-based SE model, which is fine-tuned through a two-stage training scheme. Pre-training is performed using a feature-level optimization criterion through SE loss, and then a pre-trained end-to-end ASR model is used to fine-tune the SE model using an ASR-oriented optimization criterion through SE and ASR losses. We evaluate the proposed approach on low-resourced Bengali language, which has not received as much attention as resource-rich English or Mandarin languages in both SE and ASR fields. Experimental results show that it can improve the performance of SE and ASR under severe unseen noisy conditions and its performance is reasonably good compared with other state-of-the-art SE methods.

关键词： Training Frequency-domain analysis Training data Speech enhancement Transformers Robustness Noise measurement

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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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