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An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

IEEE Transactions on Affective Computing 2023年第3期14卷 1857-1875页

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung Univresity Institute of Electronics Hsinchu300093 Taiwan National Yang Ming Chiao Tung Univresity Department of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung Hsing University Artificial Intelligence and Data Science Program Taichung40227 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. © 2010-2012 IEEE.

关键词： Surveys

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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International Conference on electrical engineering and Informatics, ICEEI

作者： Richard Medyanto Nicholas Sanjaya Suryadiputra Liawatimena Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers.

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Seatbelt-Embroidered Metamaterials for In-Vehicle Vital Sign Monitoring

Seatbelt-Embroidered Metamaterials for In-Vehicle Vital Sign...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Zeng, Qihang Tian, Xi Nguyen, Dat T. Yang, Xin Chia, Patrick Wu, Changsheng Ho, John S. National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore SIA-NUS Digital Aviation Corporate Laboratory Singapore National University of Singapore Integrative Sciences & Engineering NUS Graduate School Singapore National University of Singapore Department of Materials Science and Engineering Singapore

ISBN: (纸本)9798350347647

Continuous in-vehicle physiological monitoring can improve safety through early detection of driver fatigue and drowsiness. However, developing a system capable of effective and robust physiological monitoring in practical driving scenarios is challenging due to the noisy vehicular environment in which the sensor is exposed to engine vibrations, unpredictable road conditions and random body motions. In this work, we introduce a seatbelt-integrated metamaterial sensor for unobtrusive and continuous in-vehicle respiration and heartbeat monitoring. The metamaterial sensor is fabricated by digital embroidery of conductive threads and can continuously monitor respiration and heartbeat in a contactless manner. Our sensor supports confined microwave surface waves that are modulated by subtle local physiological motions, which are sensitively transduced into changes in the phase of the transmitted signal. We developed an algorithm based on variational mode decomposition (VMD) for noise-robust extraction of respiration and heart rate. We experimentally demonstrate the embroidered metamaterial in real-life driving scenarios and show accurate heartbeat detection with a median inter-beat interval (IBI) detection error of 23.5 ms during driving. Our results highlight the potential of embroidered metamaterial sensors in driver physiological monitoring applications, especially for the unobtrusive detection of fatigue and drowsiness. © 2023 IEEE.

关键词： Surface waves

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Recent Advances in Quantum Computing for Drug Discovery and Development

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IEEE Nanotechnology Magazine 2023年第2期17卷 26-30页

作者： Wang, Pei-Hua Chen, Jen-Hao Yang, Yu-Yuan Lee, Chien Tseng, Yufeng Jane Chung Yuan Christian University Quantum Information Center Taoyuan City320314 Taiwan National Taiwan University College of Electrical Engineering and Computer Science Graduate Institute of Biomedical Electronics and Bioinformatics Taipei10617 Taiwan Chunghwa Telecom Co. Ltd. Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei10617 Taiwan National Taiwan University Center for Quantum Science and Engineering Taipei10617 Taiwan

Drug discovery and development is a time-consuming and cost-intensive process. computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.

关键词： Proteins

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Planning Data Poisoning Attacks on Heterogeneous Recommender Systems in a Multiplayer Setting

Planning Data Poisoning Attacks on Heterogeneous Recommender...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Yeh, Chin-Yuan Chen, Hsi-Wen Yang, De-Nian Lee, Wang-Chien Yu, Philip S. Chen, Ming-Syan National Taiwan University Graduate Institute of Communication Engineering Taiwan National Taiwan University Department of Electrical Engineering Taiwan Academia Sinica Institute of Information Science Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan The Pennsylvania State University Department of Computer Science and Engineering United States University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350322279

Data poisoning attacks against recommender systems (RecSys) often assume a single seller as the adversary. However, in reality, there are usually multiple sellers attempting to promote their items through RecSys manipulation. To obtain the best data poisoning plan, it is important for an attacker to anticipate and withstand the actions of his opponents. This work studies the problem of Multiplayer Comprehensive Attack (MCA) from the perspective of the attacker, considering the subsequent attacks by his opponents. In MCA, we target the Heterogeneous RecSys, where user-item interaction records, user social network, and item correlation graph are used for recommendations. To tackle MCA, we present the Multilevel Stackelberg Optimization over Progressive Differentiable Surrogate (MSOPDS). The Multilevel Stackelberg Optimization (MSO) method is used to form the optimum strategies by solving the Stackelberg game equilibrium between the attacker and his opponents, while the Progressive Differentiable Surrogate (PDS) addresses technical challenges in deriving gradients for candidate poisoning actions. Experiments on Heterogeneous RecSys trained with public datasets show that MSOPDS outperforms all examined prior works by up to 10.6% in average predicted ratings and up to 11.4% in HitRate@3 for an item targeted by an attacker facing one opponent. Source code provided in https://***/jimmy-academia/MSOPDS. © 2023 IEEE.

关键词： Recommender systems

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Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation

arXiv

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arXiv 2023年

作者： Yandan, Zheng Anran, Hao Tuan, Luu Anh School of Computer Science and Engineering Singapore Interdisciplinary Graduate Program HealthTech Nanyang Technological University Singapore

Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition and Relation Extraction) separately and ignore the cross-correlation of entity and relation instances as well as the existence of similar instances across unlabeled data. To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data. Specifically, we construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We then employ a propagation learning scheme to leverage the affinities between labelled and unlabeled samples. Experiments on benchmark datasets show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks. We show that the joint semi-supervised learning of the two tasks benefits from their codependency and validates the importance of utilizing the shared information between unlabeled data. © 2023, CC BY.

关键词： Extraction

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Achieving Effective Artifact Subspace Reconstruction in EEG Using Real-Time Video-Based Artifact Identification

Achieving Effective Artifact Subspace Reconstruction in EEG ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Sunghyun Kang Kyungho Won Heegyu Kim Jihoon Baek Minkyu Ahn Sung Chan Jun School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology (GIST) Gwangju Republic of Korea School of Computer Science and Electrical Engineering Handong Global University Pohang Republic of Korea School of Electrical Engineering and Computer Science / AI Graduate School Gwangju Institute of Science and Technology (GIST) Gwangju Republic of Korea

Identifying and minimizing physiological artifacts in EEG is challenging because these artifacts may corrupt the underlying brain activity severely. In this work, we proposed a hybrid approach to detect/reduce EEG artifacts by combining the MediaPipe face mesh model and artifact subspace reconstruction (ASR). Four types of artifacts, eye blinking, horizontal/vertical eye movements, and jaw movement during EEG measurement were generated to test our approach. We observed that real-time video-based artifact identification achieved over 95% accuracy in detecting eye blinking, horizontal eye movement, and jaw movement. Moreover, the targeted noise reduction was effective in analyzing the signal-to-noise ratio (SNR) for each specific artifact. This work may contribute to improving the reliability and accuracy of EEG data analysis in the real-world and online scenarios by providing a practical and effective approach to identifying and reducing physiological artifacts in real-time.

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Real-time Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video in Compressed Domain

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APSIPA Transactions on Signal and Information Processing 2023年第4期12卷

作者： Ardianto, Sandy Hang, Hsueh-Ming Cheng, Wen-Huang Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Our goal is to develop real-time vehicle detection and tracking schemes for fisheye traffic monitoring video using the temporal information in the compressed domain without decoding the entire video. Two algorithms are proposed. The first algorithm starts with a conventional single-frame detector, but we introduce a multi-frame information fusion stage to improve the final detection and tracking accuracy, which is implemented using multi-modal bi-directional LSTM (MM bi-LSTM) network. The second algorithm first constructs multi-frame motion trail image, and then a single-image multi-head detector is designed to produce bounding boxes of an individual frame. The first scheme can be viewed as a detect-to-track design, and the second scheme is track-to-detect. We tested our proposals on the ICIP2020 VIP Cup dataset in H.265 video format. The aforementioned algorithms are applied to the motion fields and residual images in the H.265 compressed data set. It turns out that their detection and tracking performances are on par with their pixel-domain counterparts, and they can achieve the state-of-the-art accuracy of conventional video object detectors and trackers. If the decoding process for video compression is not counted, their computational complexities are much lower than the conventional pixel-domain video object detectors and trackers. © 2023 S. Ardianto, H.-M. Hang and W.-H. Cheng.

关键词： Image compression

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Characterization of 940 nm PCSEL Under Nanosecond Pulsed Operation with Peak Power of Up to 3.12 W 16

Characterization of 940 nm PCSEL Under Nanosecond Pulsed Ope...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Lim, Jun-Yuan Liu, Te-Hua Hong, Yu-Heng Kuo, Hao-Chung Wu, Chao-Hsin Graduate School of Advanced Technology National Taiwan University Taipei106319 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei106319 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei106319 Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei106319 Taiwan

This paper examines 940 nm PCSELs for pulsed operation, demonstrating watt-level output at nanosecond pulse widths. The findings underscore the potential of PCSELs for high-performance pulse operations. oels codes: (1... 详细信息

ISBN: (纸本)9798350372076

关键词： Semiconductor lasers

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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