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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Song, Chunyan Li, Jing Ouyang, Gaoxiang Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China Beijing Normal University State Key Laboratory of Cognitive Neuroscience and Learning Beijing100875 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Autism Spectrum Disorder (ASD) is one of the most common developmental disorders characterized by impairment of social interaction and communication skills, as well as stereotype behaviors. The early diagnosis has been focused on the analysis of EEG and MRI, which requires sophisticated medical equipment and the data collection process is cumbersome. Based on the differences of appearance characteristics between ASD and Typical Development (TD) children, we propose an efficient and effective method to diagnose autistic patients via facial expression recognition and head pose estimation. We apply the Conformer network to facial expression and head pose, respectively, to extract both local features and global features. In addition, we process the extracted features with accumulative histogram and adopt Long Short-Term Memory (LSTM) for classification. We verify the performance of the proposed method on our self-collected ASD video dataset (ACVD) and achieve a classification accuracy of 97.59%. © 2022 IEEE.

关键词： Long short-term memory

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Class Constraint-based Discriminative Features Learning Algorithm for Palm Print and Palm Vein Fusion Recognition 7

Class Constraint-based Discriminative Features Learning Algo...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Zhang, Kunyan Wang, Huabin Yu, Chengnian Du, Mengli Tao, Liang Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China Chaohu University School of Information Engineering Hefei China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

In recent years, hand-based multimodal biometrics has received a lot of attention in the field of pattern recognition due to its convenience, stability and inclusion of a wider range of recognition features. However, Some existing multimodal hand feature extraction methods extract the features of different modalities separately and then fuse them directly, mostly ignoring the correlation between the different modalities. In this paper, a multimodal biometric feature learning algorithm combining palm vein and palm print is proposed. The pixel difference vectors in multiple directions are first extracted by calculating the difference between each pixel in both modalities and its linear neighbour pixels. Then a simple and efficient feature learning algorithm is used, and the pixel difference vectors are projected into a low-dimensional binary code in a supervised manner. Finally, we perform a feature-level fusion of the learned binary features of palm veins and palm prints for multimodal hand feature recognition. The experimental results show that the method performs better than existing finger recognition methods. © 2022 IEEE.

关键词： Pixels

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EEG Spectral Correlates of Rapid and Deep Slow Breathing States and classification using ML

EEG Spectral Correlates of Rapid and Deep Slow Breathing Sta...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Patnaik, Siddesh Pandey, Pankaj Arun, Ishita Yadav, Goldy Miyapuram, Krishna Prasad Lomas, Derek Electrical Engineering IIT Gandhinagar India Computer Science and Engineering IIT Gandhinagar India Centre for Cognitive and Brain Sciences IIT Gandhinagar India Delft University of Technology Vibe Research Lab Netherlands

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

One interpretation of breathing exercise is to enforce mind-body harmony, when someone feels well and healthy, different organs of our body function harmoniously. One dysfunctional organ may disturb the resonating mechanism across multiple organs. There are different breathing techniques, and recent scientific evidence encourages understanding the neural correlates of breathing. This research investigates breathing exercises at two paces: Rapid and Deep Slow using neural signals. We collect Electroencephalography (EEG) recordings of 14 participants performing breathing tasks. EEG signals are primarily decomposed in frequency bands that designate different cognitive functions. We extract six primary frequency bands, including delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (21-30 Hz), and gamma (30-40 Hz). Two different techniques are utilized to report the findings encompassing power spectral analysis and employing machine learning classifiers to discriminate features among different stages of inhalation and exhalation with the significance of different frequencies bands. Lowered beta power in Slow Deep breathing is observed compared to Rapid Breathing, which may suggest increased relaxation, calmness, and anxiety reduction. Differences between the two conditions observed in the frontoparietal cortex may be attributed to differences in voluntary control between the two tasks. We observed classification accuracy of 72 % using low beta between Rapid and Deep Slow breathing using Decision Tree. Several interesting findings are observed in different scalp regions suggesting future direction for further investigation. This study contributes to the understanding of neural signatures for different breathing practices. The implication of this research in health care is to design personalized therapies and to design better breathing mobile applications for daily use. © 2022 IEEE.

关键词： Electroencephalography

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Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems

arXiv

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arXiv 2024年

作者： Lu, Bo-Han Lin, Yi-Hsuan Lee, En-Shiun Annie Tsai, Richard Tzong-Han Department of Computer Science and Information Engineering National Central University Taiwan Department of Computer Science Faculty of Arts and Science University of Toronto Canada Computer Science Program Faculty of Science Ontario Tech University Canada Center for GIS Research Center for Humanities and Social Sciences Academia Sinica Taiwan

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive experiments involve translation tasks across various writing systems of Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find that the use of a limited monolingual corpus still further improves the model’s Taiwanese Hokkien capabilities. We then utilize our translation model to standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting in further performance improvements. Additionally, we introduce an evaluation method incorporating back-translation and GPT-4 to ensure reliable translation quality assessment even for LRLs. The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2. © 2024, CC BY-NC-SA.

关键词： Neural machine translation

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Distinguishing pianos: The difference in similarity 7

Distinguishing pianos: The difference in similarity

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Mukherjee, Himadri Marciano, Matteo Dhar, Ankita Obaidullah, Sk. Md. Roy, Kaushik West Bengal State University Department of Computer Science India Music Program New York University Department of Arts and Humanities Abu Dhabi United Arab Emirates Aliah University Department of Computer Science and Engineering India

ISBN: (纸本)9781665421683

Notating a music piece is not a trivial task. It requires training and experience. This is challenging for new and inexperienced musicians. Automated transcription systems can be very useful in such cases. A piece generally consists of multiple instruments and it is essential to separate them prior to transcription. The challenge aggravates even more when multiple modulations or varieties of the same instrument are present. This is common nowadays and hence, it is essential to distinguish different varieties of the same instrument prior to transcription. In this paper, a line spectral frequency-based technique is used. A highest accuracy of 97.06% was obtained for a dataset composed of 6 different pianos. © 2022 IEEE.

关键词： Music

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Feasibility Study of Semi-supervised Intrusion Detection Systems for Industrial Control Systems 10

Feasibility Study of Semi-supervised Intrusion Detection Sys...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Loo, Fu-Nie Chen, Tung-Shou Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei National Taichung University of Science and Technology Department of Computer Science and Information Engineering No.129 Sec.3 Sanmin Rd. North Dist. Taichung City404 Taiwan National Chung Hsing University Department of Computer Science and Engineering 145 Xingda Rd. South Dist. Taichung City402202 Taiwan China Medical University Master Program for Digital Health Innovation No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung City406040 Taiwan

ISBN: (纸本)9798350394924

Industrial Control systems (ICS) automate industrial processes but also introduces cybersecurity threats. Intrusion Detection System (IDS) are crucial for detecting cyber-attacks on ICS, yet zero-day attacks are often inefficiently detecting with supervised learning. This study employs semi-supervised learning using one-class SVM, isolation forest, and Local Outlier Factor (LOF), to train IDS models. Utilizing dataset collected from a self-build virtual ICS environment, the study demonstrates the feasibility of these models in detecting common attack like Injection, ARP, and Man-in-the-Middle. © 2024 IEEE.

关键词： Zero-day attack

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device

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Procedia computer science 2023年 227卷 1153-1162页

作者： Jason A. Wijaya Pauw D. Andersen Nico Surantha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method.

关键词： sleep sleep quality actigraphy artificial intelligences fuzzy logic

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Study Case on Implementation Elevator 2 Floor System on PLC OMRON CP1E N30 9

Study Case on Implementation Elevator 2 Floor System on PLC ...

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9th International Conference on Education and Technology, ICET 2023

作者： Habibi, Muhammad Afnan Wibawa, Arif Fadlika, Irham Irfani, Mochamad Farhan Ali Firmansyah, Mohamad Farel Baskoro, Angger Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Dept. of Electrical Engineering Malang Indonesia National Yang Ming Chiao Tung University Dept. of Electrical Engineering and Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350358292

The swift progression of industrial technology underscores the need for adept professionals capable of skillfully implementing and managing intricate systems. Vocational education assumes a pivotal role in cultivating competent intermediaries proficient in translating theoretical knowledge into practical applications. At the core of industrial automation lies the programmable Logic Controller (PLC), a specialized digital computer that interfaces with sensors, switches, and actuators to execute real-time control programs, thus facilitating meticulous industrial processes. This study concentrates on the practical deployment of a two-floor elevator system utilizing the OMRON CP1E N30 PLC. Elevators, integral to contemporary structures, mandate precision for secure vertical conveyance. Leveraging input signals from elevator buttons, sensors, and switches, the PLC orchestrates efficient motor control and dependable door operations. This hands-on application underscores the PLC's significance within real-time control contexts. Nonetheless, Vocational Higher Education graduates frequently grapple with the challenge of transitioning to industry settings due to limited practical exposure. This partnership harmonizes with the center's objective of enriching vocational education by integrating sophisticated technologies like PLCs, equipping the workforce with the acumen to bridge the gap between theoretical foundations and the pragmatic demands of industry. © 2023 IEEE.

关键词： Elevators

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YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation

arXiv

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arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

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Twenty years of e-learning in health science: A bibliometric 7

Twenty years of e-learning in health science: A bibliometric

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Raharja, Dwi Risza Budi Khaeruddin Herasmara, Ridho Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index. © 2022 Elsevier B.V.. All rights reserved.

关键词： Publishing

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