• English & American cultural studies
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• v.18 no.1
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• pp.53-92
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• 2018

This essay aims to examine chick lit written within the Asian American context. For the most part, the chick lit genre has been typically regarded as a site to study contemporary white women's experiences and to debate the genres' credentials as feminist literature. Though some may disagree, there is general consensus that chick lit has fallen out of vogue after reaching its peak in the first decade of the new millenium.; nevertheless, it is being revisited by readers and critics alike as it has recently re-emerged as a location upon which to examine how race and gender inform notions of national belonging and female subject formation in the twenty-first century. To this end, this essay reads Kim Wong Keltner's The Dim Sum of All Things (2004). Keltner's protagonist Lindsey Owyang is yet another twentysomething "chick" looking for love, self, independence, and success in the huge megalopolis of San Francisco. What sets Lindsey apart from the chick prototype is that she is a third-generation ABC (American-born Chinese) and issues relevant to Asian America frequently make their way into Lindsey's narrative. Though it is generally considered as standing a "few notches above the standard chick-lit fare" (Stover n. pag), I would argue that meaningful reflections on many of the major pillars of Asian American literature, history, and cultural politics are glossed over in favor of cursory musings about the daily vicissitudes of Lindsey's life. This essay thus takes to task Ferriss's claim that a "serious" consideration of chick lit "brings into focus many of the issues facing contemporary women and contemporary culture - issues of identity, of race and class, of femininity and feminism, of consumerism and self-image" (2). I contend that a close examination of Keltner's The Dim Sum of All Things discloses that the chick lit format undermines a "serious consideration" of Asian American issues by presenting in particular a highly problematic representation of race and of Asian American femininity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.263-283
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• 2024

Users' comments after online shopping are critical to product reputation and business improvement. These comments, sometimes known as e-commerce reviews, influence other customers' purchasing decisions. To confront large amounts of e-commerce reviews, automatic analysis based on machine learning and deep learning draws more and more attention. A core task therein is sentiment analysis. However, the e-commerce reviews exhibit the following characteristics: (1) inconsistency between comment content and the star rating; (2) a large number of unlabeled data, i.e., comments without a star rating, and (3) the data imbalance caused by the sparse negative comments. This paper employs Bidirectional Encoder Representation from Transformers (BERT), one of the best natural language processing models, as the base model. According to the above data characteristics, we propose the F_MixBERT framework, to more effectively use inconsistently low-quality and unlabeled data and resolve the problem of data imbalance. In the framework, the proposed MixBERT incorporates the MixMatch approach into BERT's high-dimensional vectors to train the unlabeled and low-quality data with generated pseudo labels. Meanwhile, data imbalance is resolved by Focal loss, which penalizes the contribution of large-scale data and easily-identifiable data to total loss. Comparative experiments demonstrate that the proposed framework outperforms BERT and MixBERT for sentiment analysis of e-commerce comments.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• Journal of Popular Narrative
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• v.26 no.1
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• pp.175-209
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• 2020

This paper examines how the cinematic representation of the Japanese military "comfort women" stimulates 'imagination' in the realm of everyday life and in the memory of the masses, creating a common awareness and affect. The history of the Japanese military "comfort women" was hidden for a long time, and it was not until the 1990s that it entered the field of public recognition. Such a transition can be attributed to the external and internal chronopolitics that made possible the testimony of the victims and the discourse of the "comfort women" issue. It shows the peculiar status of the comfort women history as 'politics of time'. In the same vein, the cinematic representations of the Japanese military "comfort women" can be found in similar chronopolitics. The 'comfort women' films have shown the dual time frame of the continuity and discontinuity of the 'silence'. In Korean film history, the chronotope of the reproduction of "comfort women" can be divided into four phases: 1) the fictional representations of "comfort women" before the 1990s 2) documentaries in the late 1990s as the work of testimony and history writing, 3) melodramatic transformation in the feature films in the 2000s, and 4) the diffusion of media and categories. The purpose of this article is to focus on the first phase and the third phase in which the issue of 'comfort women' is represented in the category of popular fiction films. While the "comfort women" representations before 1990 were strictly adhering to the framework of commercial movies and pursued the sexual exploitation of "comfort women" history, the recent films since the 2000s are experimenting with various attempts in the style of popular imagination. Especially, the emergence of 'comfort women' feature films in the 2000s, such as Spirit's Homecoming, I Can Speak, and Herstory, raise various questions as to whether we are "properly" aware of issues and how to remember and present the "cultural memory" of comfort women. Also, focusing on the cinematic representation strategies of the 2000s "comfort women", this article discusses the popular politics of melodrama, the representation of victims and violence, and the feature of 'comfort women' as meta-memory. As a melodramatic imagination and meta-memory for the historical trauma, the "comfort women" drama shows the historical, political, and aesthetic gateways to which the "comfort women" problem must pass. As we have seen in recent fiction films, the issue of "comfort women" goes beyond transnational relations between Korea and Japan; it demands a postcolonial task to dismantle the old colonial structure and explores a transnational project in which women's movements and human rights movements are linked internationally.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Communications of Mathematical Education
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• v.30 no.3
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• pp.353-374
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• 2016

Communication is one of 6 core competencies suggested newly in mathematics curriculum revised in 2015 in Korea. Also, it's importance has been emphasized through NCTM and CCSSI. By the subject of Mathematics in Context(MiC) textbook, this study planned to explore the communication elements according to the types of communication such as discourse, representation, operation. Namely, this study dealt with 316 questions in a total of 34 tasks relevant to function content in the MiC textbook, and this study explored the communication elements on the questions of each task. To accomplish this, this study first of all was to reconstruct and establish an analytic framework, on the basis of 'D.R.O.C type' of communication developed by Kim & Pang in 2010. In addition, based on the achievement standards of function domain in mathematics curriculum revised in 2015 in Korea, this study basically compared with the function content included in MiC textbook and Korean mathematics curriculum document. Also, it tried to explore the distribution of communication elements according to the types of communication.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2469-2475
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• 2015

A structured XML has been widely used to present services on various Web-services. The XML is also used for digital documents and digital signatures and for the representation of multimedia files in email systems. The XML document should be firstly parsed to access elements in the XML. The parsing is the most compute-instensive task in the use of XML documents. Most of the previous work has focused on hardware based XML parsers in order to improve parsing performance, while a little work has studied parsing techniques. We present the high performance parsing technique which can be used all of XML parsers and design hardware based XML parser using an FPGA. The proposed parsing technique uses element analyzers instead of the state machine and performs multibyte-based element matching. As a result, our parsing technique can reduce the number of clock cycles per byte(CPB) and does not need to require any preprocessing, such as loading XML data into memory. Compared to other parsers, our parser acheives 1.33~1.82 times improvement in the system performance. Therefore, the proposed parsing technique can process XML documents in real time and is suitable for applying to all of XML parsers.

• Korean Journal of Cognitive Science
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• v.26 no.1
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• pp.27-51
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• 2015

The present study tested the effect of a top-down influence on recalling the colors of Stroop words. Participants remembered the colors of 1, 2, 3 or 6 Stroop words. After 1 second of a memory delay, they were asked to recall the color of a cued Stroop word by selecting out its corresponding color on a color-wheel stimulus. The correct recall was defined when the participants chose a color that was within ${\pm}45^{\circ}$ from the exact location of Stroop word's color on the color-wheel. Otherwise, the recall was defined as incorrect. The analyses of the frequency distribution of the participants' responses in the error trials showed that the probability of choosing the color-name of the target Stroop word was higher than the probability of other five color-names on the color-wheel. Further analyses showed that increasing the number of Stroop words to manipulate memory load did not affect the probability of the Stroop interference. These results indicate that the top-down interference by Stroop manipulation may induce systematic distortion of the stored representation in visual working memory.