Satoshi Yamada, D. Kitakoshi, Akihiro Yamashita, Kentarou Suzuki, Masato Suzuki
This study aimed to examine the application of an Intelligent Dialogue Agent (IDA) in preventive care frameworks for healthy older adults. Introducing the agent increases familiarity with the frameworks, encourages performance of preventive care exercises, and helps older adults turn using the frameworks into a habit. We used a questionnaire to collect data on older adults' impressions of Information Technology (IT) devices, smart speakers in particular (main components of the IDA), and interviewed the participants after they actually used the smart speaker in order to discuss required functions and expected roles in developing the IDA. Results from the questionnaire and interview revealed promising characteristics of smart speakers and problems concerning Japanese speech recognition.
{"title":"Development of an Intelligent Dialogue Agent with Smart Devices for Older Adults: A Preliminary Study","authors":"Satoshi Yamada, D. Kitakoshi, Akihiro Yamashita, Kentarou Suzuki, Masato Suzuki","doi":"10.1109/TAAI.2018.00020","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00020","url":null,"abstract":"This study aimed to examine the application of an Intelligent Dialogue Agent (IDA) in preventive care frameworks for healthy older adults. Introducing the agent increases familiarity with the frameworks, encourages performance of preventive care exercises, and helps older adults turn using the frameworks into a habit. We used a questionnaire to collect data on older adults' impressions of Information Technology (IT) devices, smart speakers in particular (main components of the IDA), and interviewed the participants after they actually used the smart speaker in order to discuss required functions and expected roles in developing the IDA. Results from the questionnaire and interview revealed promising characteristics of smart speakers and problems concerning Japanese speech recognition.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The machine reading comprehension problem aims to extract crucial information from the given document to answer the relevant questions. Although many methods regarding the problem have been proposed, the similarity distraction problem inside remains unsolved. The similarity distraction problem addresses the error caused by some sentences being very similar to the question but not containing the answer. Named entities have the uniqueness which can be utilized to distinguish similar sentences to prevent models from being distracted. In this paper, named entity filters (NE filters) are proposed. NE filters can utilize the information of named entities to alleviate the similarity distraction problem. Experiment results in this paper show that the NE filter can enhance the robustness of the used model. The baseline model increases 5% to 10% F1 score on two adversarial SQuAD datasets without decreasing the F1 score on the original SQuAD dataset. Besides, by adding the NE filter, other existing models increases 5% F1 score on the adversarial datasets with less than 1% loss on the original one.
{"title":"Named Entity Filters for Robust Machine Reading Comprehension","authors":"Yuxing Peng, Jane Yung-jen Hsu","doi":"10.1109/TAAI.2018.00048","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00048","url":null,"abstract":"The machine reading comprehension problem aims to extract crucial information from the given document to answer the relevant questions. Although many methods regarding the problem have been proposed, the similarity distraction problem inside remains unsolved. The similarity distraction problem addresses the error caused by some sentences being very similar to the question but not containing the answer. Named entities have the uniqueness which can be utilized to distinguish similar sentences to prevent models from being distracted. In this paper, named entity filters (NE filters) are proposed. NE filters can utilize the information of named entities to alleviate the similarity distraction problem. Experiment results in this paper show that the NE filter can enhance the robustness of the used model. The baseline model increases 5% to 10% F1 score on two adversarial SQuAD datasets without decreasing the F1 score on the original SQuAD dataset. Besides, by adding the NE filter, other existing models increases 5% F1 score on the adversarial datasets with less than 1% loss on the original one.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133144308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evaluation functions are crucial for building strong computer players in two-player games, such as chess, Go, and shogi. Although a linear combination of a large number of features has been popular representation of an evaluation function in shogi, deep neural networks (DNNs) are recently considered to be more promising by the success of AlphaZero in multiple domains, chess, Go, and shogi. This paper shows that three loss functions, loss in comparison training, temporal difference (TD) errors and cross entropy loss in win prediction, are effective for the training of evaluation functions in shogi, presented in deep neural networks. For the training of DNNs in AlphaZero, the main loss function only consists of win prediction, though it is augmented with move prediction for regularization. On the other hand, for training in traditional shogi programs, various losses including loss in comparison training, TD errors, and cross entropy loss in win prediction, have contributed to yield accurate evaluation functions which are the linear combination of a large number of features. Therefore, it is promising to combine these loss functions and to apply them to the training of modern DNNs. In our experiments, we show that training with combinations of loss functions improved the accuracy of evaluation functions represented by DNNs. The performance of trained evaluation functions is tested through top-1 accuracy, 1-1 accuracy, and self-play.
{"title":"Comparison of Loss Functions for Training of Deep Neural Networks in Shogi","authors":"Hanhua Zhu, Tomoyuki Kaneko","doi":"10.1109/TAAI.2018.00014","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00014","url":null,"abstract":"Evaluation functions are crucial for building strong computer players in two-player games, such as chess, Go, and shogi. Although a linear combination of a large number of features has been popular representation of an evaluation function in shogi, deep neural networks (DNNs) are recently considered to be more promising by the success of AlphaZero in multiple domains, chess, Go, and shogi. This paper shows that three loss functions, loss in comparison training, temporal difference (TD) errors and cross entropy loss in win prediction, are effective for the training of evaluation functions in shogi, presented in deep neural networks. For the training of DNNs in AlphaZero, the main loss function only consists of win prediction, though it is augmented with move prediction for regularization. On the other hand, for training in traditional shogi programs, various losses including loss in comparison training, TD errors, and cross entropy loss in win prediction, have contributed to yield accurate evaluation functions which are the linear combination of a large number of features. Therefore, it is promising to combine these loss functions and to apply them to the training of modern DNNs. In our experiments, we show that training with combinations of loss functions improved the accuracy of evaluation functions represented by DNNs. The performance of trained evaluation functions is tested through top-1 accuracy, 1-1 accuracy, and self-play.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The structured electronic medical record is the basis for computers to process and achieve the target of precise diagnosis and treatment automatically using the knowledge and features of the techniques such as machine learning and artificial intelligence (AI). Because of the increasing demands on improving the efficiency and the flexibility during the step or phase of classification and extraction, providing the expansion mechanism for the automatic adaption of new NER (Named Entity Recognition, NER) model training during the NER model training stage anytime when the new entities/tags shall be learned and classified and hence the related knowledge database (DB) shall be expanded automatically. The proposed method includes a training stage involving the step of adaptive improved NER model training for the chest x-ray medical reports/files and a test stage involving the step of the dependency parsing and the relation extracting to be perform sequentially, and thus the goals of automatic information extraction and structured medical report generation using the machine learning technique, and the optimization and accuracy improvement of the doctor's work and performance through referring to the structured medical report for diagnosis and treatment can be achieved.
{"title":"Adaptive Generation of Structured Medical Report Using NER Regarding Deep Learning","authors":"Cheng-Tse Wu, Hsiao-ko Chang, Ji-Han Liu, J. Jang","doi":"10.1109/TAAI.2018.00012","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00012","url":null,"abstract":"The structured electronic medical record is the basis for computers to process and achieve the target of precise diagnosis and treatment automatically using the knowledge and features of the techniques such as machine learning and artificial intelligence (AI). Because of the increasing demands on improving the efficiency and the flexibility during the step or phase of classification and extraction, providing the expansion mechanism for the automatic adaption of new NER (Named Entity Recognition, NER) model training during the NER model training stage anytime when the new entities/tags shall be learned and classified and hence the related knowledge database (DB) shall be expanded automatically. The proposed method includes a training stage involving the step of adaptive improved NER model training for the chest x-ray medical reports/files and a test stage involving the step of the dependency parsing and the relation extracting to be perform sequentially, and thus the goals of automatic information extraction and structured medical report generation using the machine learning technique, and the optimization and accuracy improvement of the doctor's work and performance through referring to the structured medical report for diagnosis and treatment can be achieved.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130090923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"[Title page i]","authors":"","doi":"10.1109/taai.2018.00001","DOIUrl":"https://doi.org/10.1109/taai.2018.00001","url":null,"abstract":"","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multi-task learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the version 1.0 of ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30% relatively on the evaluation set.
{"title":"Replay Spoofing Detection System for Automatic Speaker Verification Using Multi-Task Learning of Noise Classes","authors":"Hye-jin Shim, Jee-weon Jung, Hee-Soo Heo, Sung-Hyun Yoon, Ha-jin Yu","doi":"10.1109/TAAI.2018.00046","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00046","url":null,"abstract":"In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multi-task learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the version 1.0 of ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30% relatively on the evaluation set.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134244051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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