Digital Library[ Search Result ]
Geographical Adaptive Attention Model for Points of Interest Recommendation
Muyeon Jo, Sejin Chun, Jungkyu Han
http://doi.org/10.5626/JOK.2025.52.3.217
Geographical influence, stemming from the location of Points of Interest (POIs), plays a vital role in POI recommendation. Most current studies utilize geographical information such as distance and location to define and extract POI-specific geographical influences for personalized recommendations. These approaches primarily emphasize distance-based influence, which gauges user preferences based on proximity, while often overlooking area-based influence, which reflects preferences for regions with specific POI characteristics. This paper introduces a POI recommendation model based on an attention network that integrates both distance- and area-based influences. The model adaptively assesses how previously visited POIs impact the likelihood of visiting a target POI, taking into account regional characteristics and user preferences. Experiments conducted on real-world datasets indicate that the proposed method significantly outperforms baseline models, achieving improvements of approximately 6–12% in Prec@10, 8–10% in Recall@10, and 6–7% in HR@10.
Reference Image-Based Contrastive Attention Mechanism for Printed Circuit Board Defect Classification
http://doi.org/10.5626/JOK.2025.52.1.70
Effective classification of defects in Printed Circuit Boards (PCBs) is critical for ensuring product quality. Traditional approaches to PCB defect detection have primarily relied on single-image analysis or failed to adequately address alignment issues between reference and test images, leading to reduced reliability and precision in defect detection. To overcome these limitations, this study aimed to introduce a novel deep image comparison method that could incorporate contrastive loss functions to improve image alignment with a contrastive attention mechanism to focus the model on areas with a higher likelihood of defects. Experiments conducted on actual PCB data demonstrated that the proposed method achieved superior classification performance, even with limited data, highlighting its potential to significantly enhance the reliability of PCB defect detection and address existing challenges in the field.
BERT-based Two-Stage Classification Models and Co-Attention Mechanism for Diagnosing Dementia and Schizophrenia-related Disease
Min-Kyo Jung, Seung-Hoon Na, Ko Woon Kim, Byoung-Soo Shin, Young-Chul Chung
http://doi.org/10.5626/JOK.2022.49.12.1071
Noting the recently increasing number of patients, we present deep learning methods for automatically diagnosing dementia and schizophrenia by exploring the use of the novel two-stage classification and the co-attention mechanism. First, the two-stage classification consists of two steps-the perplexity-based classification and the standard BERT-based classification. 1) the perplexity-based classification first prepares two types of BERTs, i.e., control-specific and patients-specific BERTs, pretrained from transcripts for controls and patients as the additional pretraining datasets, respectively, and then performs a simple threshold-based classification based on the difference between perplexity values of two BERTs for an input test transcript; then, for ambiguous cases where the perplexity difference only does not provide sufficient evidence for the classification, the standard BERT-based classification is performed based on a fine-tuned BERT. Second, the co-attention mechanism enriches the BERT-based representations from a doctor’s transcript and a client’s one by applying the cross-attention over them using the shared affinity matrix, and performs the classification based on the enriched co-attentive representations. Experiment results on a large-scale dataset of Korean transcripts show that the proposed two-stage classification outperforms the baseline BERT model on 4 out of 7 subtasks and the use of the co-attention mechanism achieves the best F1 score for 4 out of 8 subtasks.
OANet: Ortho-Attention Net Based on Attention Mechanism for Database Performance Prediction
Chanho Yeom, Jieun Lee, Sanghyun Park
http://doi.org/10.5626/JOK.2022.49.11.1026
Various parameters in a database can be modified, which are called knobs. Since the performance of the database varies according to the settings of the knobs, it is important to tune the knobs of the database. And when tuning, a model that can reliably and quickly predict database performance according to the knob setting is needed. However, even when the knob setting is the same, the results may be different if the workload performing the benchmark is different. Therefore, in this paper, we propose an OANet using the attention mechanism so that the relationship between the knob and the workload can also be considered. Through experiments, the performance prediction results of the database were compared to various machine learning techniques, and the superiority of the model was confirmed by showing the highest score.
Automatic Classification of Pneumonia Based on Ensemble Deep Learning Model Using Intensity Normalization and Multiscale Lung-Focused Patches on Chest X-Ray Images
Yoon Jo Kim, Jinseo An, Helen Hong
http://doi.org/10.5626/JOK.2022.49.9.677
It is difficult to classify normal and pneumonia in pediatric chest X-ray (CXR) images due to irregular intensity values. In addition, deep learning model has a limitation in that it can misclassify CXR by incorrectly focusing on the outer part of the lung. This study proposed an automatic classification of pneumonia based on ensemble deep learning model using three intensity normalizations and multiscale lung-focused patches on CXR images. First, to correct for irregular intensity values in internal lungs, three intensity normalization methods were performed respectively. Second, to focus on internal lungs, regions of interest were extracted by segmenting lung regions. Third, multiscale lung-focused patches were extracted to train the characterization of pneumonia. Finally, ensemble modeling with attention module was performed to improve the classification performance. In the experiment, the method using large patches of CLAHE images showed an accuracy of 92%, which was 5% higher than that of original images. Furthermore, the proposed method using an ensemble of large and middle patches showed the best performance with an accuracy of 93%.
Korean Dependency Parsing using Subtree Linking based on Machine Reading Comprehension
Jinwoo Min, Seung-Hoon Na, Jong-Hoon Shin, Young-Kil Kim, Kangil Kim
http://doi.org/10.5626/JOK.2022.49.8.617
In Korean dependency parsing, biaffine attention models have shown state-of-the-art performances; they first obtain head-level and modifier-level representations by applying two multi-layer perceptrons (MLP) on the encoded contextualized word representation, perform the attention by regarding modifier-level representation as a query and head-level one as a key, and take the resulting attention score as a probability of forming a dependency arc between the corresponding two words. However, given two target words (i.e., candidate head and modifier), biaffine attention methods are basically limited to their word-level representations, not being aware of the explicit boundaries of their phrases or subtrees. Thus, without relying on semantically and syntactically enriched phrase-level and subtree-level representations, biaffine attention methods might be not effective in the case that determining a dependency arc is not simple but complicated such as identifying a dependency between “far-distant” words, where these cases may often require subtree or phrase-level information surrounding target words. To address this drawback, this paper presents the use of dependency paring framework based on machine reading comprehension (MRC) that explicitly utilizes the subtree-level information by mapping a given child subtree and its parent subtree to a question and an answer, respectively. The experiment results on standard datasets of Korean dependency parsing shows that the MRC-based dependency paring outperforms the biaffine attention model. In particular, the results further given observations that improvements in performances are likely strong in long sentences, comparing to short ones.
Graph Convolution Network Based Feature Map Fusion Method for Multi Scale Object Detection
Jaegi Hwang, Seongju Kang, Kwangsue Chung
http://doi.org/10.5626/JOK.2022.49.8.627
Feature Pyramid Network (FPN) is a feature map fusion technique used to solve the multi-scale problem of object detection. However, since FPN performs feature map fusion by focusing on adjacent resolutions, there is a problem in that semantic information included in non-adjacent layers is diluted. This paper, proposes a graph convolution network (GCN)-based feature map fusion technique for multi-scale object detection. The proposed GCN-based method dynamically fuses feature map information of all layers according to learnable adjacency matrix weights. The adjacency matrix weight is generated based on the multi-scale attention mechanism to adaptively reflect the scale information of the object. The feature map fusion process is performed through a matrix multiplication operation between adjacency matrix and a feature node matrix. The performance of the proposed method was verified by showing that it improves the multi-scale object detection performance in the PASCAL-VOC benchmark dataset compared to the existing FPN method.
A Cross Domain Adaptation Method based on Adversarial Cycle Consistence Learning for Rotary Machine Fault Diagnosis
http://doi.org/10.5626/JOK.2022.49.7.530
Research on data-based fault diagnosis models is being actively conducted in various industries. However, in the case of industrial equipment, various operating conditions occur, and it is difficult to secure sufficient training data. To solve this problem, a cross-domain adaptation technique can be utilized. In this study, we propose an adversarial consistency-maintaining transformation learning method that can maintain failure classification consistency even for the new untrained environmental data using the rotating body vibration data. The data generated through consistent learning creates a continuous invariant latent space between the new operating condition data distribution and the known data distribution and learns to maintain the failure classification performance through an adversarial learning network that shares the failure classification characteristic information. Therefore, the proposed method can provide a more stable and general classification performance by expanding the potential space to minimize the discrepancy between domain data. The experimental results of the proposed model showed about 88% accuracy for a real-machine dataset, and compared to the existing cross-domain adaptive learning methods, it showed a performance improvement of about 5-10%. According to the results of this study, it is expected to be an effective solution for the problem of equipment failure diagnosis at actual industrial sites.
Graph Embedding-Based Point-Of-Interest Recommendation Considering Weather Features
Kun Woo Lee, Jongseon Kim, Yon Dohn Chung
http://doi.org/10.5626/JOK.2022.49.3.221
As the Location-Based Services (LBS) grow rapidly, the Point-Of-Interest (POI) recommendation becomes an active research area to provide users appropriate information relevant to their locations. Recently, translation-based recommendation systems using graph embedding, such as TransRec, are attracting great attention. In this paper, we discovered some drawbacks of TransRec; it is limited in expressing the complex relationship between users and POIs, and the relation embedding is fixed without considering weather features. We propose WAPTRec, a graph embedding-based POI recommendation method considering the weather, that overcomes the drawback of TransRec. WAPTRec can rep resent the same POI embedding in different ways according to users by using a category projection matrix and attention mechanism. In addition, it provides better recommendation accuracy by utilizing the users’ movement history, category of POIs and weather features. Experiments using public datasets illustrated that WAPTRec outperformed the conventional translation-based recommendation methods.
Person Re-Identification Using an Attention Pyramid for Local Multiscale Feature Embedding Extracted from a Person’s Image
http://doi.org/10.5626/JOK.2021.48.12.1305
In this paper, a person re-identification scheme using the dual pyramid adapting attention mechanisms to extract more elaborate local feature embedding by excluding the noises caused by the unnecessary backgrounds in person’s image is proposed. With the dual pyramid of local and scale ones, the spatial attention is used to suppress the noise effects caused by unnecessary backgrounds, and the channel attention is used to emphasize the relatively important multiscale features when the local feature embedding is constructed. In the experiments, the proposed scheme was compared with other cases in which the attention module is not used for each pyramid to confirm the optimal configuration and compared based on the rank-1 accuracy with the state-of-the-art studies for the person re-identification. According to the experimental results, the proposed method showed a maximum rank-1 accuracy of 99.4%, which is higher by at least about 0.2% and at most by about 13.8% than previous works.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr