Yi-Wen Liao

University of Stuttgart

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i-10 index

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Co-Authors

Bin Yang

University of Stuttgart

Ja-Hwung Su

National University of Kaohsiung

Raphaël Latty

University of Stuttgart

Yi‐Shun Wang

National Changhua University of Education

Zhiyuan Su

Beijing University of Posts and Telecommunications

Patrick Schlachter

University of Stuttgart

Chiung-Wei Huang

Chia Nan University of Pharmacy and Science

Bin Yang

Nanjing University of Information Science and Technology

Bin Yang

East China Normal University

Ching‐Hsuan Yeh

Feng Chia University

Cooperative Institutions

University of Stuttgart

National Cheng Kung University

National University of Kaohsiung

Kaohsiung Chang Gung Memorial Hospital

Chang Gung University

Cheng Shiu University

National Yang Ming Chiao Tung University

National Taiwan University

National Sun Yat-sen University

Chia Nan University of Pharmacy and Science

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Research Field

Providing Stroke Sequence of Chinese Characters Facilitates Handwriting Learning in Children with Developmental Coordination Disorder.

Cognitive Science (2019)

Rong‐Ju Cherng Yi-Wen Liao Jenn-Yeu Chen

Handwriting

Sequence (biology)

Stroke

Source

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Exploring the antecedents of collaborative learning performance over social networking sites in a ubiquitous learning context

Computers in Human Behavior (2014)

Yi-Wen Liao Yueh‐Min Huang Hsin-Chin Chen Shu-Hsien Huang

Social Learning

10.1016/j.chb.2014.10.028

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Citations (83)

Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success

Government Information Quarterly (2007)

Yi‐Shun Wang Yi-Wen Liao

Information Quality

Success factors

10.1016/j.giq.2007.06.002

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Citations (782)

Investigating the Individual Difference Antecedents of Perceived Enjoyment in the Acceptance of Blogging

Deleted Journal (2010)

Yi‐Shun Wang Hsin‐Hui Lin Yi-Wen Liao

Source

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Citations (24)

Research on automated review method for harmful information of smart device

IET conference proceedings. (2023)

Wei Li Shiann-Jang Wang Jim‐Min Lin Jun Yao Xinyin Chen

As an important information exchange platform, smart device applications are generating a huge amount of multimedia data anytime and anywhere, the harmful information in the data will endanger the physical and mental health of users. In response to this issue, the paper proposes an automated review method for harmful information from smart device applications, which can review and rate the content interacted with smart device applications, thereby grading the applications, identifying the applications that affect the physical and mental health of users, and extracting all detected harmful content as evidence, and then saving it in the abnormality report. For multimodal content, this paper also proposes a new multimodal fusion algorithm for the fusion of multimodal content, which facilitates the rating and review of applications content. The results have shown that by rating and reviewing multimodal fusion content, the harmful information from applications can be well distinguished, thereby protecting the physical and mental health of users.

Information fusion

Grading (engineering)

10.1049/icp.2023.2944

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A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning

arXiv (Cornell University) (2022)

Yi-Wen Liao Bin Yang Raphaël Latty Jochen Rivoir

In post-silicon validation, tuning is to find the values for the tuning knobs, potentially as a function of process parameters and/or known operating conditions. In this sense, an more efficient tuning requires identifying the most critical tuning knobs and process parameters in terms of a given figure-of-merit for a Device Under Test (DUT). This is often manually conducted by experienced experts. However, with increasingly complex chips, manual inspection on a large amount of raw variables has become more challenging. In this work, we leverage neural networks to efficiently select the most relevant variables and present a corresponding deep-learning-aided pipeline for efficient tuning.

Leverage (statistics)

Robustness

Fine-tuning

10.48550/arxiv.2207.00336

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The Influence of Viewing Behavior and Interpersonal Relationship on Teenagers’ Violent Tendency and Perception of Happiness

Yi-Wen Liao Chiung-Wei Huang Zhiyuan Su Chia-Sui Wang

This study applies cultivation theory, social cognitive theory and problem-behavior theory as theoretical bases for the research model. A total of 470 valid questionnaires in the study. Both Cronbach's alpha and composite reliability were greater than the value of .70 and the item-to-total correlation coefficient was greater than the value of .50 for each construct. The research result showed that the more teenagers were exposed to TV violence, the more likely it was for them to develop violent tendencies. Furthermore, teenagers' experience of violence in the past would also positively influence their violent tendencies.

Value (mathematics)

10.1109/icea.2019.8858291

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Citations (1)

Anomaly Detection Based on Selection and Weighting in Latent Space

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (2021)

Yi-Wen Liao Alexander Bartler Bin Yang

With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in high safety and reliability in the production and manufacturing industry. Recently, autoencoders have been widely used as a backend algorithm for anomaly detection. Different techniques have been developed to improve the anomaly detection performance of autoencoders. Nonetheless, little attention has been paid to the latent representations learned by autoencoders. In this paper, we propose a novel selection-and-weighting-based anomaly detection framework called SWAD. In particular, the learned latent representations are individually selected and weighted. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD. On the benchmark datasets, the SWAD framework has reached comparable or even better performance than the state-of-the-art approaches.

Benchmark (surveying)

Anomaly (physics)

10.1109/case49439.2021.9551267

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Citations (14)

Alignment of Visual Senses and Acoustical Senses based on Emotion Recognitions

2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS) (2022)

Ja-Hwung Su Yi-Wen Liao Ming-Hung Kao Yung-Wen Tsai Chih-Jui Chang

Recently, due to the rapid advances on multimedia technology, multimedia data such as pictures and music has heavily invaded our daily life. By the mobile camera, users capture the interested scenes as the preferred pictures, and then browse these pictures in either computers or mobile devices. This incurs an interesting issue that, how to provide the users with a better browsing/listening atmosphere and experience by music. Actually, it can be viewed as the problem for aligning photos with music and also as an exciting research topic for cross-media retrieval. However, very few studies focus their attention on this topic. To aim at this issue, in this paper, we propose a novel cross-media alignment method that bridges visual pictures to harmonic music by visual emotions and acoustical emotions. In this method, the pictures and music are projected onto the emotion space first. Next, the emotion features are fuzzed to be near the human senses. Finally, the effective alignment method for associating pictures with music is performed. The results of subjective and objective evaluations on the real datasets reveal that, our proposed method can successfully provide the users with a rich experience on audiovisual presentations. Also, the proposed alignment algorithm is shown to be effective in terms of Normalized Discounted Cumulative Gain.

10.1109/scisisis55246.2022.10001938

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Artificial Intelligences on Automated Context-Brain Recognition with Mobile Detection Devices

Lecture notes in computer science (2023)

Ja-Hwung Su Weijiang Chen Mingcheng Zhang Yi-Wen Liao

Bridge (graph theory)

SIGNAL (programming language)

10.1007/978-981-99-5834-4_31

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