ETRI Journal最新文献

{"title":"Multiobjective, trust-aware, artificial hummingbird algorithm-based secure clustering and routing with mobile sink for wireless sensor networks","authors":"Anil Kumar Jemla Naik,&nbsp;Manjunatha Parameswarappa,&nbsp;Mohan Naik Ramachandra","doi":"10.4218/etrij.2023-0330","DOIUrl":"10.4218/etrij.2023-0330","url":null,"abstract":"<p>Wireless sensor networks (WSNs) are composed of numerous nodes distributed in geographical regions. Security and energy efficiency are challenging tasks due to an open environment and a restricted battery source. The multiobjective trust-aware artificial hummingbird algorithm (M-TAAHA) is proposed to achieve secure and reliable transmission over a WSN with a mobile sink (MS). The M-TAAHA selects secure cluster head (SCH) nodes based on trust, energy, interspace between sensors, interspace between SCH and MS, and the CH balancing factor. A secure route is found by M-TAAHA with trust, energy, and interspace between SCH and MS. The M-TAAHA avoids the malicious nodes to improve data delivery and avoid unwanted energy consumption. The M-TAAHA is analyzed using energy consumption, alive nodes, life expectancy, delay, data packets received in MS, throughput, packet delivery ratio, and packet loss ratio. Existing techniques (LEACH-TM, EATMR, FAL, Taylor-spotted hyena optimization [Taylor-SHO], TBEBR, and TEDG) are used for comparison with the M-TAAHA. Findings show that the energy consumption of the proposed M-TAAHA for 1000 rounds is 0.56 J (1.78 × smaller than that of the Taylor-SHO).</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"950-964"},"PeriodicalIF":1.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197530","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}

{"title":"Amazon product recommendation system based on a modified convolutional neural network","authors":"Yarasu Madhavi Latha,&nbsp;B. Srinivasa Rao","doi":"10.4218/etrij.2023-0162","DOIUrl":"10.4218/etrij.2023-0162","url":null,"abstract":"<p>In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60 000 customer reviews with 14 806 neutral reviews, 19 567 negative reviews, and 25 627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency–inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 4","pages":"633-647"},"PeriodicalIF":1.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197526","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}

{"title":"MixFace: Improving face verification with a focus on fine-grained conditions","authors":"Junuk Jung,&nbsp;Sungbin Son,&nbsp;Joochan Park,&nbsp;Yongjun Park,&nbsp;Seonhoon Lee,&nbsp;Heung-Seon Oh","doi":"10.4218/etrij.2023-0167","DOIUrl":"10.4218/etrij.2023-0167","url":null,"abstract":"<p>The performance of face recognition (FR) has reached a plateau for public benchmark datasets, such as labeled faces in the wild (LFW), celebrities in frontal-profile in the wild (CFP-FP), and the first manually collected, in-the-wild age database (AgeDB), owing to the rapid advances in convolutional neural networks (CNNs). However, the effects of faces under various fine-grained conditions on FR models have not been investigated, owing to the absence of relevant datasets. This paper analyzes their effects under different conditions and loss functions using K-FACE, a recently introduced FR dataset with fine-grained conditions. We propose a novel loss function called MixFace, which combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness was experimentally demonstrated using various benchmark datasets.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 4","pages":"660-670"},"PeriodicalIF":1.3,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140147966","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}

{"title":"Violent crowd flow detection from surveillance cameras using deep transfer learning–gated recurrent unit","authors":"Elly Matul Imah,&nbsp;Riskyana Dewi Intan Puspitasari","doi":"10.4218/etrij.2023-0222","DOIUrl":"10.4218/etrij.2023-0222","url":null,"abstract":"<p>Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short-term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1-score. The use of ResNet50V2 for feature extraction can improve model performance.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 4","pages":"671-682"},"PeriodicalIF":1.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140147864","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}

{"title":"CR-M-SpanBERT: Multiple embedding-based DNN coreference resolution using self-attention SpanBERT","authors":"Joon-young Jung","doi":"10.4218/etrij.2023-0308","DOIUrl":"https://doi.org/10.4218/etrij.2023-0308","url":null,"abstract":"<p>This study introduces CR-M-SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding-based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost-effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR-M-SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high-recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"35-47"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987396","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}

{"title":"2023 Reviewer List","authors":"","doi":"10.4218/etr2.12667","DOIUrl":"https://doi.org/10.4218/etr2.12667","url":null,"abstract":"&lt;p&gt;Al-Aboosi, Yasin, Univ. of Mustansiriyah&lt;/p&gt;&lt;p&gt;A, Revathi, SASTRA Deemed Univ.&lt;/p&gt;&lt;p&gt;A, UMAMAGESWARI, SRM Univ. - Ramapuram Campus&lt;/p&gt;&lt;p&gt;Ab. Rahman, Azamuddin, Universiti Malaysia Pahang Al-Sultan Abdullah&lt;/p&gt;&lt;p&gt;Abbasi, Muhammad Inam, Universiti Teknikal Malaysia Melaka&lt;/p&gt;&lt;p&gt;Abd El-Hafeez, Tarek, Minia Univ.&lt;/p&gt;&lt;p&gt;Abd Rahman, Mohd Amiruddin, Universiti Putra Malaysia&lt;/p&gt;&lt;p&gt;Abdullah-Al-Shafi, Md., Univ. of Dhaka&lt;/p&gt;&lt;p&gt;ABOLADE, Jeremiah, Pan African Univ.&lt;/p&gt;&lt;p&gt;Abraham, Bejoy, College of Engineering Muttathara&lt;/p&gt;&lt;p&gt;Afify, Heba M., Higher Inst. of Engineering in Shorouk Academy&lt;/p&gt;&lt;p&gt;Afzal, Muhammad Khalil, COMSATS Univ Islamabad&lt;/p&gt;&lt;p&gt;Ahire, Harshawardhan, Veermata Jijabai Technological Institute&lt;/p&gt;&lt;p&gt;Ahmad, Mushtaq, Nanjing Univ. of Aeronautics and Astronautics&lt;/p&gt;&lt;p&gt;Ahmadi, Mahmood, Univ. of Razi&lt;/p&gt;&lt;p&gt;Ahmed, Anas, Al Iraqia Univ.&lt;/p&gt;&lt;p&gt;Ahmed, Areeb, Mohammad Ali Jinnah Univ.&lt;/p&gt;&lt;p&gt;Ahmed, Irfan, NED Univ. of Engineering &amp; Technology&lt;/p&gt;&lt;p&gt;Ahmed, Nisar, Univ. of Engineering and Technology Lahore, Pakistan&lt;/p&gt;&lt;p&gt;Ahmed, Suhaib, Baba Ghulam Shah Badshah Univ.&lt;/p&gt;&lt;p&gt;Ahn, Jin-Hyun, Myongji Univ. - Yongin Campus&lt;/p&gt;&lt;p&gt;Ahn, Seokki, ETRI&lt;/p&gt;&lt;p&gt;Ahn, Sungjun, Electronic and Telecom Research Institute&lt;/p&gt;&lt;p&gt;Ajayan, J., SNS College of Technology&lt;/p&gt;&lt;p&gt;Ajib, Wessam, Univ Quebec&lt;/p&gt;&lt;p&gt;Akbar, Son, Universitas Ahmad Dahlan&lt;/p&gt;&lt;p&gt;Akhriza, Tubagus, Kampus STIMATA&lt;/p&gt;&lt;p&gt;Akioka, Sayaka, Meiji Univ.&lt;/p&gt;&lt;p&gt;Al-Ali, Ahmed Kamil Hasan, Queensland Univ. of Technology&lt;/p&gt;&lt;p&gt;Alfaro, Emigdio, Universidad César Vallejo&lt;/p&gt;&lt;p&gt;alghanimi, abdulhameed, Middle Technical Univ.&lt;/p&gt;&lt;p&gt;Al-Hadi, Azremi Abdullah, Universiti Malaysia Perlis&lt;/p&gt;&lt;p&gt;Ali, Dia M, Ninevah Univ.&lt;/p&gt;&lt;p&gt;ali, Tariq, PMAS Arid Agriculture Univ.&lt;/p&gt;&lt;p&gt;Al-kaltakchi, Musab, Mustansiriyah Univ.&lt;/p&gt;&lt;p&gt;Al-Kaltakchi, Musab T. S., Mustansiriyah Univ.&lt;/p&gt;&lt;p&gt;Almasoud, Abdullah, Prince Sattam Bin Abdulaziz Univ.&lt;/p&gt;&lt;p&gt;almufti, saman, Nawroz Univ.&lt;/p&gt;&lt;p&gt;Al-qaness, Mohammed A. A., Wuhan Univ.&lt;/p&gt;&lt;p&gt;Al-Waeli, Ali H. A., American Univ. of Iraq&lt;/p&gt;&lt;p&gt;amin, Farhan, Yeungnam Univ.&lt;/p&gt;&lt;p&gt;Aminzadeh, Hamed, Payame Noor Univ.&lt;/p&gt;&lt;p&gt;Anwar, Aqeel, Georgia Tech&lt;/p&gt;&lt;p&gt;Arafat, Muhammad Yeasir, Chosun Univ.&lt;/p&gt;&lt;p&gt;Arif, Mehmood, Khwaja Fareed Univ. of Engineering &amp; Information Technology&lt;/p&gt;&lt;p&gt;Asgher, Umer, National Univ. of Sciences and Technology&lt;/p&gt;&lt;p&gt;Ashraf, Umer, NIT Srinagar&lt;/p&gt;&lt;p&gt;Atrey, Pradeep, State Univ. of New York&lt;/p&gt;&lt;p&gt;Awais, Qasim, Fatima Jinnah Women Univ.&lt;/p&gt;&lt;p&gt;B, Srinivas, Maharaj Vijayaram Gajapathi Ram College of Engineering&lt;/p&gt;&lt;p&gt;Bahar, Ali Newaz, Univ.of Saskatchewan&lt;/p&gt;&lt;p&gt;Bahng, Seungjae, ETRI&lt;/p&gt;&lt;p&gt;Bakkiam David, Deebak, VIT Univ.&lt;/p&gt;&lt;p&gt;Becerra-Sánchez, Aldonso, Universidad Autónoma de Zacatecas&lt;/p&gt;&lt;p&gt;Bhaskar, D. R., Delhi Technological Univ.&lt;/p&gt;&lt;p&gt;Bhowmick, Anirban, VIT Univ.&lt;/p&gt;&lt;p&gt;Bilim, Mehmet, Nuh Naci Yazgan Univ.&lt;/p&gt;&lt;p&gt;Biswal, Sandeep, OPJU&lt;/p&gt;&lt;p&gt;bose, avishek, Oak Ridge National Laboratory&lt;/p&gt;&lt;p&gt;Bouwmans, Thierry, Universite de La Rochelle&lt;/p&gt;&lt;p&gt;Brahmbhatt, Viraj, Union College&lt;/p&gt;&lt;p&gt;bruzzese, roberto","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"154-158"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987395","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}

{"title":"Spoken-to-written text conversion for enhancement of Korean–English readability and machine translation","authors":"HyunJung Choi,&nbsp;Muyeol Choi,&nbsp;Seonhui Kim,&nbsp;Yohan Lim,&nbsp;Minkyu Lee,&nbsp;Seung Yun,&nbsp;Donghyun Kim,&nbsp;Sang Hun Kim","doi":"10.4218/etrij.2023-0354","DOIUrl":"https://doi.org/10.4218/etrij.2023-0354","url":null,"abstract":"<p>The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule-based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data-driven Korean spoken-to-written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"127-136"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987413","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}

{"title":"Towards a small language model powered chain-of-reasoning for open-domain question answering","authors":"Jihyeon Roh,&nbsp;Minho Kim,&nbsp;Kyoungman Bae","doi":"10.4218/etrij.2023-0355","DOIUrl":"https://doi.org/10.4218/etrij.2023-0355","url":null,"abstract":"<p>We focus on open-domain question-answering tasks that involve a chain-of-reasoning, which are primarily implemented using large language models. With an emphasis on cost-effectiveness, we designed <i>EffiChainQA</i>, an architecture centered on the use of small language models. We employed a retrieval-based language model to address the limitations of large language models, such as the hallucination issue and the lack of updated knowledge. To enhance reasoning capabilities, we introduced a question decomposer that leverages a generative language model and serves as a key component in the chain-of-reasoning process. To generate training data for our question decomposer, we leveraged ChatGPT, which is known for its data augmentation ability. Comprehensive experiments were conducted using the HotpotQA dataset. Our method outperformed several established approaches, including the <i>Chain-of-Thoughts</i> approach, which is based on large language models. Moreover, our results are on par with those of state-of-the-art <i>Retrieve-then-Read</i> methods that utilize large language models.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"11-21"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987394","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}

{"title":"KMSAV: Korean multi-speaker spontaneous audiovisual dataset","authors":"Kiyoung Park,&nbsp;Changhan Oh,&nbsp;Sunghee Dong","doi":"10.4218/etrij.2023-0352","DOIUrl":"https://doi.org/10.4218/etrij.2023-0352","url":null,"abstract":"<p>Recent advances in deep learning for speech and visual recognition have accelerated the development of multimodal speech recognition, yielding many innovative results. We introduce a Korean audiovisual speech recognition corpus. This dataset comprises approximately 150 h of manually transcribed and annotated audiovisual data supplemented with additional 2000 h of untranscribed videos collected from YouTube under the Creative Commons License. The dataset is intended to be freely accessible for unrestricted research purposes. Along with the corpus, we propose an open-source framework for automatic speech recognition (ASR) and audiovisual speech recognition (AVSR). We validate the effectiveness of the corpus with evaluations using state-of-the-art ASR and AVSR techniques, capitalizing on both pretrained models and fine-tuning processes. After fine-tuning, ASR and AVSR achieve character error rates of 11.1% and 18.9%, respectively. This error difference highlights the need for improvement in AVSR techniques. We expect that our corpus will be an instrumental resource to support improvements in AVSR.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"71-81"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987392","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}

{"title":"Special issue on speech and language AI technologies","authors":"Dong-Jin Kim,&nbsp;Hyung-Min Park,&nbsp;Harksoo Kim,&nbsp;Seung-Hoon Na,&nbsp;Gerard Jounghyun Kim","doi":"10.4218/etr2.12666","DOIUrl":"https://doi.org/10.4218/etr2.12666","url":null,"abstract":"&lt;p&gt;Recent advancements in artificial intelligence (AI) have substantially improved applications that depend on human speech and language comprehension. Human speech, characterized by the articulation of thoughts and emotions through sounds, relies on language, a complex system that uses words and symbols for interpersonal communication. The rapid evolution of AI has amplified the demand for related solutions to swiftly and efficiently process extensive amounts of speech and language data. Speech and language technologies have emerged as major topics in AI research, improving the capacity of computers to comprehend text and spoken language by resembling human cognition. These technological breakthroughs have enabled computers to interpret human language, whether expressed in textual or spoken forms, unveiling the comprehensive meaning of the intentions, nuances, and emotional cues expressed by writers or speakers.&lt;/p&gt;&lt;p&gt;&lt;i&gt;Electronics and Telecommunications Research Institute (ETRI) Journal&lt;/i&gt; is a peer-reviewed open-access journal launched in 1993 and published bimonthly by ETRI, Republic of Korea. It is intended to promote worldwide academic exchange of research on information, telecommunications, and electronics.&lt;/p&gt;&lt;p&gt;This special is devoted to all aspects and future research directions in the rapidly progressing subject of speech and language technologies. In particular, this special issue highlights recent outstanding results on the application of AI techniques to understand speech or natural language. We selected 12 outstanding papers on three topics of speech and language technologies. Below, we provide a summary of commitments to this special issue.&lt;/p&gt;&lt;p&gt;The first paper [&lt;span&gt;1&lt;/span&gt;] “Towards a small language model powered chain-of-reasoning for open-domain question answering” by Roh and others focuses on open-domain question-answering tasks that involve a chain of reasoning primarily implemented using large language models. Emphasizing cost effectiveness, the authors introduce EffiChainQA, an architecture centered on the use of small language models. They employ a retrieval-based language model that is known to address the hallucination issue and incorporates up-to-date knowledge, thereby addressing common limitations of larger language models. In addition, they introduce a question decomposer that leverages a generative language model and is essential for enhanced chain of reasoning.&lt;/p&gt;&lt;p&gt;In the second paper in this special issue [&lt;span&gt;2&lt;/span&gt;], “CR-M-SpanBERT: Multiple-embedding-based DNN Coreference Resolution Using Self-attention SpanBERT” by Jung, a model is proposed to incorporate multiple embeddings for coreference resolution based on the SpanBERT architecture. The experimental results show that multiple embeddings can improve the coreference resolution performance regardless of the employed baseline model, such as LSTM, BERT, and SpanBERT.&lt;/p&gt;&lt;p&gt;As automated essay scoring has evolved from handcrafted techniques to deep le","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 1","pages":"7-10"},"PeriodicalIF":1.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987387","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}