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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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Text and Speech Adjustable Bimodal Emotion Recognition in Conversational Interface

Text and Speech Adjustable Bimodal Emotion Recognition in Co...

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IEEE international Symposium on Consumer Electronics (ISCE)

作者： Komang Rinartha Lun-Wei Ku Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Speech emotion recognition (SER) is a Machine Learning (ML) topic that is now receiving a lot of research attention. This can be attributed to its growing capacity, improvements in algorithms, and utilization in practical situations. Furthermore, speech contains not just auditory information but also lexical information. Both speech-based emotion recognition and text-based emotion recognition, called bimodal emotion recognition, can be highly beneficial for human interaction. Take an example, maintaining the communication between elder parents who are left behind after their children leave for education, employment, and family dynamics who can be greatly affected, both physically and emotionally, by feelings of loneliness and social isolation. Bimodal emotion recognition employs both text-based and audio-based methods to identify emotions. Each algorithm was trained separately and then combined using a late fusion technique. RoBERTa text-based emotion recognition can be combined with CNN audio-based emotion recognition by using late fusion to create a bimodal emotion recognition that can obtain more than 50% of accuracy by combining text and audio in a similar proportion or relying on text more than audio. By adjusting the proportion of text or audio emotion recognition, we can get better result of recognition and it can be customized by the user's preference. We elaborate on creating bimodal emotion recognition, conversational interface, a framework for emotion monitoring system, and give the example of a combined algorithm to perform emotion recognition.

关键词： Emotion recognition Accuracy Machine learning algorithms Text recognition Computational modeling Heuristic algorithms Speech recognition Machine learning Real-time systems Monitoring

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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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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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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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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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Influence of land-sea breeze on PM _2.5 prediction in central and southern taiwan using composite neural network

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Scientific Reports 2023年第1期13卷 1-9页

作者： Kibirige, George William Huang, Chiao Cheng Liu, Chao Lin Chen, Meng Chang Institute of Information Science Academia Sinica New Taipei Taiwan Social Networks and Human Centered Computing Program Taiwan International Graduate Program New Taipei Taiwan National Chengchi University New Taipei Taiwan

PM2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning me...

PM_2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central taiwan in the months when land-sea breeze dominates the accumulation of PM_2.5. © 2023, The Author(s).

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DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection

DeepGuard: Deep Generative User-behavior Analytics for Ranso...

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IEEE international Conference on Intelligence and Security Informatics, ISI

作者： Gaddisa Olani Ganfure Chun-Feng Wu Yuan-Hao Chang Wei-Kuan Shih Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan National Tsing Hua University Hsinchu City Taiwan

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

关键词： Training Training data Organizations Tools Ransomware Security Informatics

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