Manzai, Japanese duo stand-up comedy, has a social aspect because it is based on social codes. This study focuses on ‘boyaki (blurting out) manzai’ in postwar Japan and analyzes its content from two perspectives (first, from the social context of then, and second, from the social context of now), in order to clarify its relationship with social norms. In boyaki manzai, the new culture (particularly the fashion and behavior of young women) that was introduced to the country after World War II was the subject of ridicule. However, the content of the manzai of then does not evoke laughter in the present social context. From the modern perspective, it is evident that the statements in boyaki manzai contain gender discrimination based on male-centric principles, and thus, it is sometimes perceived negatively. This study argues that the acceptable range of laughter changes with social norms.

To reveal methods of making effective pauses and communicating as a character in a comedy skit, we have analyzed strategies for creating a histrionic, i.e., exaggerated and overly theatrical, comic performance that is often observed in comedy skits. We compared a manzai performance, which is considered to be a realistic comedy skit, with a comedy skit that had a histrionic performance by the comedy duo ``Sandwich Man'' doing the same material, in order to investigate the differences in the inter-utterance structure and posture-configuration structure of the two comedy styles. The results for the inter-utterance structure indicated that there were differences in the pauses between utterances, but there were no differences in the speech rates (sec/mora) between the comedy styles. Additionally, we found that in the posture-configuration structure, one of the performers turned his face toward his partner's face for a longer time in the histrionic comedy skit than that in the manzai skit. Also, in the histrionic comedy skit, the sum of the performers' shoulder-widths was shorter as seen by the audience than that in the manzai performance. Therefore, we concluded that utterance pauses and the performers' shoulder-widths are important factors in creating a histrionic comic show.

This paper reports the integration of control systems for the Manzai robots using RT-Middleware. The conventional Manzai robot systems have been implemented differently in each control system. It is desired that the integrated control system for the Manzai robot system that is separated from the part of hardware-dependent systems. User interfaces parts are developed as Web applications. The user interfaces for the Manzai robot system are communalized. Therefore, the scalability problems of the interfaces of the systems caused by dependency of the robot hardware are resolved. In addition, we improve scalability by the development of RT-components for each type of rendering of the Manzai for the performance. We show the feasibility and effectiveness of the methods through the implementation results.

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本研究は、

テレビやウェブサイトなどで目にするお笑いコンテンツには、可笑しくて笑えるものが多々ある一方で、笑わそうとしているにも関わらず、可笑しくないものもあり、芸人の

笑いに繋がる面白さはどのようにして生み出されるのか、また、それには法則性があるのか。一連の流れの中で連続的にボケが放たれ、笑いが発生する

どのタイミングで笑いが起きたのか、どの部分が面白さに繋がり、またその前後セリフや行動などを具体的に知ることが重要であると考え、

感情は身体の状態に影響を及ぼす．故に感情を適切に評価することは重要である．心理的バイアスの影響を受けずに感情を客観的に評価する方法として，脳・自律神経生理信号を利用できる可能性がある．本研究は，「お笑い」により誘発されるポジティブな感情（面白い等）を反映する生理指標を同定することを目的とする．20代健常男性14名に6種類の

口腔の状態を良好に保つことは全身的な健康の維持においても重要である．口腔の状態に悪影響をもたらす一因として，睡眠時ブラキシズム（SB）がある．SBとは睡眠中に起こる歯ぎしりやくいしばりであり，咬耗，歯の破折，歯周病の増悪などを引き起こす．SBの診断には咬筋筋電図を用いる方法があるが，歯科臨床の現場で用いられることはほとんどない．先行研究において，SB発生前には交感神経が優位になり，心拍数が上昇することが報告されている．そこで本研究では，SBの発生に関連した心拍変動特性を明らかにし，心拍変動を用いてSB発生の予測モデルを構築することを目的とした．ここでは，ウェアラブル心拍計を用いて計測された男性健常者20名（38±11歳）の睡眠時の心拍変動時系列，および，脳波を用いて評価された睡眠段階とSB発生時刻を解析した．５分ごとに心拍変動指標を計算し，それらを特徴量として入力するニューラルネットワークモデルを用いて，SB発生の有無を予測した．ニューラルネットワークモデルの予測精度を，PR (Precision-Recall)曲線を用いて評価した．PR曲線の平均AUCは0.91であったことから，心拍変動のみを用いても，SB発生を高い精度で予測できる可能性が示された．また，SB発生に伴いmeanNNの減少と，LFの増加傾向が見られたことから，SB発生と交感神経の過剰亢進の関連性が示唆された．

The advantages of non-invasive, real-time and convenient, computer audition-based heart sound abnormality detection methods have increasingly attracted efforts among the community of cardiovascular diseases. Time-frequency analyses are crucial for computer audition-based applications. However, a comprehensive investigation on discovering an optimised way for extracting time-frequency representations from heart sounds is lacking until now. To this end, we propose a comprehensive investigation on time-frequency methods for analysing the heart sound, i. e., short-time Fourier transformation, Log-Mel transformation, Hilbert-Huang transformation, wavelet transformation, Mel transformation, and Stockwell transformation. The time-frequency representations are automatically learnt via pre-trained deep convolutional neural networks. Considering the urgent need of smart stethoscopes for high robust detection algorithms in real environment, the training, verification, and testing sets employed in the extensive evaluation are subject-independent. Finally, explainable artificial intelligence approaches are used to reveal the reasons for the performance differences of six time-frequency representations in heart sound abnormality detection.