ETRI Journal最新文献_第8页

Dual-scale BERT using multi-trait representations for holistic and trait-specific essay grading 使用多特质表征的双量表 BERT，用于整体和特质作文评分

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-02-28 DOI: 10.4218/etrij.2023-0324

Minsoo Cho, Jin-Xia Huang, Oh-Woog Kwon

{"title":"Dual-scale BERT using multi-trait representations for holistic and trait-specific essay grading","authors":"Minsoo Cho, Jin-Xia Huang, Oh-Woog Kwon","doi":"10.4218/etrij.2023-0324","DOIUrl":"https://doi.org/10.4218/etrij.2023-0324","url":null,"abstract":"As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"82-95"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-based reranking for improving Korean morphological analysis systems 基于变换器的重排，用于改进韩国语形态分析系统

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-02-28 DOI: 10.4218/etrij.2023-0364

Jihee Ryu, Soojong Lim, Oh-Woog Kwon, Seung-Hoon Na

{"title":"Transformer-based reranking for improving Korean morphological analysis systems","authors":"Jihee Ryu, Soojong Lim, Oh-Woog Kwon, Seung-Hoon Na","doi":"10.4218/etrij.2023-0364","DOIUrl":"https://doi.org/10.4218/etrij.2023-0364","url":null,"abstract":"This study introduces a new approach in Korean morphological analysis combining dictionary-based techniques with Transformer-based deep learning models. The key innovation is the use of a BERT-based reranking system, significantly enhancing the accuracy of traditional morphological analysis. The method generates multiple suboptimal paths, then employs BERT models for reranking, leveraging their advanced language comprehension. Results show remarkable performance improvements, with the first-stage reranking achieving over 20% improvement in error reduction rate compared with existing models. The second stage, using another BERT variant, further increases this improvement to over 30%. This indicates a significant leap in accuracy, validating the effectiveness of merging dictionary-based analysis with contemporary deep learning. The study suggests future exploration in refined integrations of dictionary and deep learning methods as well as using probabilistic models for enhanced morphological analysis. This hybrid approach sets a new benchmark in the field and offers insights for similar challenges in language processing applications.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"137-153"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multistage interference cancellation for cyclic interleaved frequency division multiplexing 循环交错频分复用的多级干扰消除

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-02-26 DOI: 10.4218/etrij.2023-0274

G. Anuthirsha, S. Lenty Stuwart

引用次数: 0

Air quality index prediction using seasonal autoregressive integrated moving average transductive long short-term memory 利用季节性自回归综合移动平均线转导式长短期记忆进行空气质量指数预测

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-02-25 DOI: 10.4218/etrij.2023-0283

Subramanian Deepan, Murugan Saravanan

{"title":"Air quality index prediction using seasonal autoregressive integrated moving average transductive long short-term memory","authors":"Subramanian Deepan, Murugan Saravanan","doi":"10.4218/etrij.2023-0283","DOIUrl":"10.4218/etrij.2023-0283","url":null,"abstract":"We obtain the air quality index (AQI) for a descriptive system aimed to communicate pollution risks to the population. The AQI is calculated based on major air pollutants including O3, CO, SO2, NO, NO2, benzene, and particulate matter PM2.5 that should be continuously balanced in clean air. Air pollution is a major limitation for urbanization and population growth in developing countries. Hence, automated AQI prediction by a deep learning method applied to time series may be advantageous. We use a seasonal autoregressive integrated moving average (SARIMA) model for predicting values reflecting past trends considered as seasonal patterns. In addition, a transductive long short-term memory (TLSTM) model learns dependencies through recurring memory blocks, thus learning long-term dependencies for AQI prediction. Further, the TLSTM increases the accuracy close to test points, which constitute a validation group. AQI prediction results confirm that the proposed SARIMA–TLSTM model achieves a higher accuracy (93%) than an existing convolutional neural network (87.98%), least absolute shrinkage and selection operator model (78%), and generative adversarial network (89.4%).","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"915-927"},"PeriodicalIF":1.3,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite impulse response design based on two-level transpose Vedic multiplier for medical image noise reduction 基于两级转置吠陀乘法器的有限脉冲响应设计，用于医疗图像降噪

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-02-25 DOI: 10.4218/etrij.2023-0335

Joghee Prasad, Arun Sekar Rajasekaran, J. Ajayan, Kambatty Bojan Gurumoorthy

{"title":"Finite impulse response design based on two-level transpose Vedic multiplier for medical image noise reduction","authors":"Joghee Prasad, Arun Sekar Rajasekaran, J. Ajayan, Kambatty Bojan Gurumoorthy","doi":"10.4218/etrij.2023-0335","DOIUrl":"10.4218/etrij.2023-0335","url":null,"abstract":"Medical signal processing requires noise and interference-free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two-level transpose Vedic multiplier. The proposed architecture identifies the zero-noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero-noise segregation. This is performed independently for the zero- and nonzero-noise intervals. Finally, the finite impulse response is estimated as the sum of zero- and nonzero-noise inputs at any finite classification.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 4","pages":"619-632"},"PeriodicalIF":1.3,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inceptionv3-LSTM-COV: A multi-label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short-term memory Inceptionv3-LSTM-COV：基于 Inceptionv3 和长短期记忆的从化学构象识别 COVID 药物不良反应的多标签框架

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-02-25 DOI: 10.4218/etrij.2023-0288

Pranab Das, Dilwar Hussain Mazumder

{"title":"Inceptionv3-LSTM-COV: A multi-label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short-term memory","authors":"Pranab Das, Dilwar Hussain Mazumder","doi":"10.4218/etrij.2023-0288","DOIUrl":"10.4218/etrij.2023-0288","url":null,"abstract":"Due to the global COVID-19 pandemic, distinct medicines have been developed for treating the coronavirus disease (COVID). However, predicting and identifying potential adverse reactions to these medicines face significant challenges in producing effective COVID medication. Accurate prediction of adverse reactions to COVID medications is crucial for ensuring patient safety and medicine success. Recent advancements in computational models used in pharmaceutical production have opened up new possibilities for detecting such adverse reactions. Due to the urgent need for effective COVID medication development, this research presents a multi-label Inceptionv3 and long short-term memory methodology for COVID (Inceptionv3-LSTM-COV) medicine development. The presented experimental evaluations were conducted using the chemical conformer image of COVID medicine. The features of the chemical conformer are denoted utilizing the RGB color channel, which is extracted using Inceptionv3, GlobalAveragePooling2D, and long short-term memory (LSTM) layers. The results demonstrate that the efficiency of the Inceptionv3-LSTM-COV model outperformed the previous study's performance and achieved better results compared to MLCNN-COV, Inceptionv3, ResNet50, MobileNetv2, VGG19, and DenseNet201 models. The proposed model reported the highest accuracy value of 99.19% in predicting adverse reactions to COVID medicine.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1030-1046"},"PeriodicalIF":1.3,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Suboptimal video coding for machines method based on selective activation of in-loop filter 基于选择性激活内环滤波器的次优机器视频编码方法

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-02-25 DOI: 10.4218/etrij.2023-0085

Ayoung Kim, Eun-Vin An, Soon-heung Jung, Hyon-Gon Choo, Jeongil Seo, Kwang-deok Seo

{"title":"Suboptimal video coding for machines method based on selective activation of in-loop filter","authors":"Ayoung Kim, Eun-Vin An, Soon-heung Jung, Hyon-Gon Choo, Jeongil Seo, Kwang-deok Seo","doi":"10.4218/etrij.2023-0085","DOIUrl":"10.4218/etrij.2023-0085","url":null,"abstract":"A conventional codec aims to increase the compression efficiency for transmission and storage while maintaining video quality. However, as the number of platforms using machine vision rapidly increases, a codec that increases the compression efficiency and maintains the accuracy of machine vision tasks must be devised. Hence, the Moving Picture Experts Group created a standardization process for video coding for machines (VCM) to reduce bitrates while maintaining the accuracy of machine vision tasks. In particular, in-loop filters have been developed for improving the subjective quality and machine vision task accuracy. However, the high computational complexity of in-loop filters limits the development of a high-performance VCM architecture. We analyze the effect of an in-loop filter on the VCM performance and propose a suboptimal VCM method based on the selective activation of in-loop filters. The proposed method reduces the computation time for video coding by approximately 5% when using the enhanced compression model and 2% when employing a Versatile Video Coding test model while maintaining the machine vision accuracy and compression efficiency of the VCM architecture.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 3","pages":"538-549"},"PeriodicalIF":1.4,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139981328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Framework for evaluating code generation ability of large language models 评估大型语言模型代码生成能力的框架

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-02-14 DOI: 10.4218/etrij.2023-0357

Sangyeop Yeo, Yu-Seung Ma, Sang Cheol Kim, Hyungkook Jun, Taeho Kim

{"title":"Framework for evaluating code generation ability of large language models","authors":"Sangyeop Yeo, Yu-Seung Ma, Sang Cheol Kim, Hyungkook Jun, Taeho Kim","doi":"10.4218/etrij.2023-0357","DOIUrl":"10.4218/etrij.2023-0357","url":null,"abstract":"Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, \u0000<math>\u0000 <mi>p</mi>\u0000 <mi>a</mi>\u0000 <mi>s</mi>\u0000 <mi>s</mi>\u0000 <mtext>-</mtext>\u0000 <mi>r</mi>\u0000 <mi>a</mi>\u0000 <mi>t</mi>\u0000 <mi>i</mi>\u0000 <mi>o</mi>\u0000 <mi>@</mi>\u0000 <mi>n</mi></math>, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the \u0000<math>\u0000 <mi>p</mi>\u0000 <mi>a</mi>\u0000 <mi>s</mi>\u0000 <mi>s</mi>\u0000 <mtext>-</mtext>\u0000 <mi>r</mi>\u0000 <mi>a</mi>\u0000 <mi>t</mi>\u0000 <mi>i</mi>\u0000 <mi>o</mi>\u0000 <mi>@</mi>\u0000 <mi>n</mi></math> metric.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"106-117"},"PeriodicalIF":1.4,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint streaming model for backchannel prediction and automatic speech recognition 用于后信道预测和自动语音识别的联合流模型

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-02-14 DOI: 10.4218/etrij.2023-0358

Yong-Seok Choi, Jeong-Uk Bang, Seung Hi Kim

{"title":"Joint streaming model for backchannel prediction and automatic speech recognition","authors":"Yong-Seok Choi, Jeong-Uk Bang, Seung Hi Kim","doi":"10.4218/etrij.2023-0358","DOIUrl":"10.4218/etrij.2023-0358","url":null,"abstract":"In human conversations, listeners often utilize brief backchannels such as “uh-huh” or “yeah.” Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human–machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single-task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single-task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"118-126"},"PeriodicalIF":1.4,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Named entity recognition using transfer learning and small human- and meta-pseudo-labeled datasets 利用迁移学习和小型人类与元伪标签数据集进行命名实体识别

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-02-14 DOI: 10.4218/etrij.2023-0321

Kyoungman Bae, Joon-Ho Lim

引用次数: 0