學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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A Hierarchical Task Assignment for Manual Image Labeling
(2019-01-01)
Chang, Chia-Ming
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Mishra, Siddharth Deepak
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Igarashi, Takeo
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傳播與科技學系
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Department of Communication and Technology
Manual image labeling (selecting an appropriate "category" for an image) is very tedious and time consuming especially when selecting labels from a large number of categories. In this study, we propose a hierarchical assignment of labeling tasks where the labelers recursively classify images in a category group into sub category groups, working on a single level at a time. This significantly makes each labeler's task easier, reducing the number of choices from 1,000 to 27 on average. In the user study, we compared our hierarchical assignment to a normal (non-hierarchical) assignment for a labeling task. The results show that the hierarchical assignment requires less total time to complete the labeling task. In addition, the learning effect in the labeling process is more profound in the hierarchical assignment.
An Interpretable Compression and Classification System: Theory and Applications
(2020-01-01)
Tseng, Tzu-Wei
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Yang, Kai-Jiun
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Kuo, C-C Jay
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Tsai, Shang-Ho
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電子工程學系及電子研究所
This study proposes a low-complexity interpretable classification system. The proposed system contains main modules including feature extraction, feature reduction, and classification. All of them are linear. Thanks to the linear property, the extracted and reduced features can be inversed to original data, like a linear transform such as Fourier transform, so that one can quantify and visualize the contribution of individual features towards the original data. Also, the reduced features and reversibility naturally endure the proposed system ability of data compression. This system can significantly compress data with a small percent deviation between the compressed and the original data. At the same time, when the compressed data is used for classification, it still achieves high testing accuracy. Furthermore, we observe that the extracted features of the proposed system can be approximated to uncorrelated Gaussian random variables. Hence, classical theory in estimation and detection can be applied for classification. This motivates us to propose using a MAP (maximum a posteriori) based classification method. As a result, the extracted features and the corresponding performance have statistical meaning and mathematically interpretable. Simulation results show that the proposed classification system not only enjoys significant reduced training and testing time but also high testing accuracy compared to the conventional schemes.
Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction
(2020-10-01)
Huang, Guan-Hua
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Lin, Chih-Hsuan
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Cai, Yu-Ren
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Chen, Tai-Been
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Hsu, Shih-Yen
We analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over-sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state-of-the-art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.
Handwritten Signature Verification by Using a Six-Axis Motion Sensor and SVM
(2019-01-01)
Cheng, Chang-Chieh
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Chen, Yi-Chi
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Ching, Yu-Tai
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資訊工程學系
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資訊技術服務中心
Signature verification, if we consider the muscle memory, is a biometric for identification technology. To access muscle memory, we use a motion sensor that consists of accelerometer and gyroscope to implement a signature verification system. The motion sensor records six motion values including three-axis accelerations and angular velocities while name signing. 14 features of signature are extracted from the sequence of accelerations and angular velocities. A support vector machine (SVM) is then applied to verify the signatures. The proposed method was applied to verify the Chinese signatures. The SVM is trained by the training data from each person. The true positive rate of the proposed method can reach to 95.66%. Fake signatures generated by tracing from true signatures can also be recognized by the proposed method.
Predicting Smartphone Users' General Responsiveness to IM Contacts Based on IM Behavior
(2019-01-01)
Lee, Hao-Ping
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Chung, Yu-Lin
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Dingier, Tilman
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Chen, Chia-Yu
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Lin, Chih-Heng
History of conversations through instant messaging (IM) contains abundant information about the communication patterns of the dyad, including conversation partners' mutual responsiveness to messages. We have, however, not seen many examinations of using such information in modeling mobile users' responsiveness in IM communication. In this paper, we present an in-the-wild study, in which we leverage participants' IM messaging logs to build models predicting their general responsiveness. Our models based on data from 33 IM user achieved an accuracy of up to 71% (AUROC). In particular, we show that 90-day IM-communication patterns, in general, outperformed their 14-day equivalent in our prediction models, indicating better coherence between long-term IM patterns with their general communication experience.
FAE: Autoencoder-Based Failure Binning of RTL Designs for Verification and Debugging
(2019-01-01)
Shen, Cheng-Hsien
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Liang, Aaron C-W
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Hsu, Charles C-H
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Wen, Charles H-P
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電機工程學系
As the Register Transfer Level (RTL) designs are more complicated, debugging becomes a major bottleneck in the design process. To make debugging more efficient, failure binning aims at grouping failure traces caused by the same error source together so that designers can focus on one bug at one time. However, as there are multiple bugs in a design, behaviors exhibited by failure traces are diverse and severely confuse designers. One error source may result in different appearances subject to different activation conditions. In addition, different error sources may also exhibit similar appearances among the limited number of failure traces. In this work, we propose an autoencoder-based failure binning engine name FAE for debugging RTL designs more efficiently. The autoencoders extract meaningful representations from the sparse and high-dimensional feature space to the latent space with good properties for clustering. Superior to prior works, FAE provides confidence ranks between bins and in a bin to clearly guide designers during debugging. Experimental results show that FAE can drive bins of higher purity under an acceptable number of bins than prior works, dropping only few less-informative failures. Evaluated by three common metrics for clustering, FAE also achieves averagely 13.1% improvement in purity, 25.0% improvement in NMI and 18.2% improvement in ARI, respectively. As a result, the proposed autoencoder-based engine, FAE, applies machine learning to extract useful information from diverse failure traces and is effective on failure binning with more focused debugging.
Comparison Training for Computer Chinese Chess
(2020-06-01)
Chen, Jr-Chang
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Tseng, Wen-Jie
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Wu, I-Chen
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Wei, Ting-Han
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資訊工程學系
This paper describes the application of modified comparison training for automatic feature weight tuning. The final objective is to improve the evaluation functions used in Chinese chess programs. First, we apply n-tuple networks to extract features. N-tuple networks require very little expert knowledge through its large numbers of features, while simultaneously allowing easy access. Second, we propose a modified comparison training into which tapered eval is incorporated. Experiments show that with the same features and the same Chinese chess program, the automatically tuned feature weights achieved a win rate of 86.58% against the hand-tuned features. The above trained version was then improved by adding additional features, most importantly n-tuple features. This improved version achieved a win rate of 81.65% against the trained version without additional features.
Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task
(2020-06-01)
Ko, Li-Wei
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Komarov, Oleksii
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Lai, Wei-Kai
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Liang, Wei-Gang
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Jung, Tzyy-Ping
Objective. A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor. Approach. Fifteen healthy 22-28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth (R) module. Main results. The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers' reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 +/- 0.019 s, and the r(2) is 0.885 +/- 0.057 according to a within-session leave-one-trial-out cross-validation. Significance. The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.
Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot
(2020-01-01)
Phang, Chun-Ren
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Ko, Li-Wei
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交大名義發表
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生物資訊及系統生物研究所
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National Chiao Tung University
Conventional passive lower limb rehabilitation is suboptimal since the brain is not actively involved in the training. An autonomous motor imagery brain-computer interface (MI-BCI) could potentially improve rehabilitation outcomes. However, motor cortex regions associated with the individual feet are anatomically close to each other. This presents a difficulty in distinguishing the left and right foot MI during rehabilitation therapy. To overcome this difficulty, we extracted functional connectivity to measure the global cortical network via electroencephalography (EEG) signals. Fourteen spatial connections (P3-Fp1, P3-F3, P3-F7, P3-C3, T5-F7, T5-C3, T5-T3, Fp2-T5, Fp2-P3, T6-Fp2, T6-T4, Cz-Fp1, Cz-F7 and Fp2-F7) found across twelve subjects significantly differed between the left and right foot MI, evidencing nonlocalized brain activity during MI. Foot MI were distinguished using machine learning algorithms in terms of the time- and frequency-domain connectivities extracted from Pearson & x2019;s correlation, multivariate autoregression (MVAR), bandpass correlation, and partial directed coherence (PDC) models. The results showed that connectivity extracted by pairwise Pearson & x2019;s correlation could be distinguished with 86.26 & x00B1; 9.95 & x0025;, while in the frequency-domain, the gamma band presented the best classification accuracy of 73.55 & x00B1; 17.11 & x0025;. We attempted to simulate asynchronous real-time classification paradigms in order to evaluate the classification performance of connectivity features compared to common spatial pattern (CSP) and band power (BP). The results indicate correlation-connectivity has the best outcome, attaining an accuracy of 80.75 & x00B1; 9.51 & x0025; in asynchronous classification.
Big Data Platform Configuration Using Machine Learning
(2020-05-01)
Yeh, Chao-Chun
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Lu, Han-Lin
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Zhou, Jiazheng
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Chang, Sheng-An
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Lin, Xuan-Yi
By ensuring well-developed complex big data platform architectures, data engineers provide data scientists and analysts infrastructure with computational and storage resources to perform their research. Based on such supports, data scientists are provided an opportunity to focus on their domain problems and design the required intelligent modules (i.e., prepare the data; select, train, and tune the machine-learning modules; and validate the results). However, there are still gaps between system engineering and data scientist/engineering teams. Generally, system engineers have limited knowledge on the application domains and the purposes of an analytical program. On the contrary, both data scientists and engineers are usually unfamiliar with the configuration of a computational system, file system, and database. However, the performance of an application can be affected by a system's configuration, and the data scientists and engineers have little information and knowledge about which of the system's properties can affect the application's performance. As a typical example, for Internet-scale applications that have thousands of computing nodes or billions of Internet of Things devices, even a slight improvement may have an enormous influence on energy management and environmental protection issues. To bridge the gap between system engineering and data scientist/engineering teams, we proposed the concept of a configuration layer based on a big data platform, Hadoop. We built a configuration tuner, BigExplorer, to collect and preprocess data. Furthermore, we also created golden configurations for performance improvement. Based on the processed data, we used a semi-automatic feature engineering technique to provide more features for data engineers and developed the performance model using three different machine learning algorithms (i.e., random forest, gradient boosting machine, and support vector machine). Using the commonly used benchmarks of Word-Count, TeraSort, and Pig workloads, our configuration tuner achieved a significant performance improvement of 28%-51% for different workloads than using the rule-of-thumb configuration.

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