A mobile application was developed to promote revisits to Kenrokuen-garden (a famous Daimyo garden) using gamification theory in this study. The tourism industry was greatly affected by the movement restrictions during the Covid-19 pandemic. Micro-tourism, which means visiting local areas, gained attention during this period. Gamification design is incorporated into the app as a mechanism to maintain the motivation for the game. The tourists who have visited Kenrokuen, want to visit the place more than once due to the system (gamification theory). The tourists are also motivated to play the game at the same time. The garden is one of famous tourist destinations in Kanazawa City, Japan. In the developed app, a badge system was used to evaluate tourist behaviours based on the number of visits, number of steps taken, arrival rate of photo spots, and achievement rate in the four seasons. It was designed to motivate the repeat visits by giving badges as rewards. The design intent and the operational verification results of the developed app are reported in this paper.

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Smartphones can be used in portrait or landscape mode, and their operating systems detect the device orientation and automatically adjust the screen view orientation. However, the adjustment policy is pessimistic, and the delay between device rotation and screen view rotation is large. We believe that this delay may degrade the user experience. In this paper, we propose a method to predict device rotation in the near future based on acceleration using a support vector machine, and to quickly adjust the screen view orientation based on the prediction results. We then show that the method can predict device rotation with high or non-low accuracy (with low false positive and false negative probabilities) and that the method can adjust the screen view orientation with a short delay time through our experimental results on three to six subjects using a smartphone with the Android operating system installed with the proposed method.

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車載組込みソフトウェアは自動車に要求される機能高度化や安全性強化などのために，益々複雑になっている．本研究では，車載ネットワークを介したＥＣＵ間，あるいは，車外と車載ＥＣＵが共通のソフトウェアインタフェースで連携するためのサービス指向アーキテクチャのモデルを提案し，プロトタイプ開発について報告する．

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Following the enactment of the new barrier-free law in 2006, several barriers to passage have been improved in many public facilities and transportation systems such as railroad stations and trains. According to these improvements, the number of wheelchair users who use railroads have been increasing for the past 15 years. However, they still have many problems to be solved to realize the environment in which wheelchair users can move around independently and freely. As one of solution, a system that guides wheelchair users on barrier-free routes is proposed in this study.

In this report, an application for smartphones using augmented reality with Unity has been developed. This application can be used regardless of the operating system and functions installed in the smartphone, and can navigate seamlessly both indoors and outdoors. Finally, the status of reaching the target specification was evaluated for the implemented functions.

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本稿では、ゲーム内でのプレイヤーの自己表現と社会的交流の中でも、政治的、または社会的活動に注目し、これらの活動とそれに対するゲーム開発者の対応について事例調査を行った。その後、これらを踏まえて、ゲーム内でのプレイヤーの政治的、または社会的活動において、プレイヤー、開発者それぞれが持つ必要があるリテラシーについて考察した。その結果、プレイヤーが持つゲームリテラシーとして、ゲーム内で差別やいじめ、嫌がらせを行わない能力、そして利用規約に則ったゲームプレイを行う能力が必要であると論じた。また、ゲーム開発リテラシーとしては、プレイヤーのゲーム内での政治的、または社会的活動において制限・禁止する基準を決める能力、そして、制限・禁止するか曖昧な事例について迅速に、かつ厳格に決定を下す能力が必要であると論じた。

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The surge of social media use, such as Twitter, introduces new opportunities for understanding and gauging public mood across different cultures. However, the diversity of expression in social media presents a considerable challenge to this task of opinion mining, given the limited accuracy of sentiment classification and a lack of intercultural comparisons. Previous Twitter sentiment corpora have only global polarities attached to them, which prevents deeper investigation of the mechanism underlying the expression of feelings in social media, especially the role and influence of rhetorical phenomena. To this end, we construct an annotated corpus for multilingual Twitter sentiment understanding that encompasses three languages (English, Japanese, and Chinese) and four international topics (iPhone 6, Windows 8, Vladimir Putin, and Scottish Independence); our corpus incorporates 5,422 tweets. Further, we propose a novel annotation scheme that embodies the idea of separating emotional signals and rhetorical context, which, in addition to global polarity, identifies rhetoric devices, emotional signals, degree modifiers, and subtopics. Next, to address low inter-annotator agreement in previous corpora, we propose a pivot dataset comparison method to effectively improve the agreement rate. With manually annotated rich information, our corpus can serve as a valuable resource for the development and evaluation of automated sentiment classification, intercultural comparison, rhetoric detection, etc. Finally, based on observations and our analysis of our corpus, we present three key conclusions. First, languages differ in terms of emotional signals and rhetoric devices, and the idea that cultures have different opinions regarding the same objects is reconfirmed. Second, each rhetoric device maintains its own characteristics, influences global polarity in its own way, and has an inherent structure that helps to model the sentiment that it represents. Third, the models of the expression of feelings in different languages are rather similar, suggesting the possibility of unifying multilingual opinion mining at the sentiment level.

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The surge of social media use, such as Twitter, introduces new opportunities for understanding and gauging public mood across different cultures. However, the diversity of expression in social media presents a considerable challenge to this task of opinion mining, given the limited accuracy of sentiment classification and a lack of intercultural comparisons. Previous Twitter sentiment corpora have only global polarities attached to them, which prevents deeper investigation of the mechanism underlying the expression of feelings in social media, especially the role and influence of rhetorical phenomena. To this end, we construct an annotated corpus for multilingual Twitter sentiment understanding that encompasses three languages (English, Japanese, and Chinese) and four international topics (iPhone 6, Windows 8, Vladimir Putin, and Scottish Independence); our corpus incorporates 5,422 tweets. Further, we propose a novel annotation scheme that embodies the idea of separating emotional signals and rhetorical context, which, in addition to global polarity, identifies rhetoric devices, emotional signals, degree modifiers, and subtopics. Next, to address low inter-annotator agreement in previous corpora, we propose a pivot dataset comparison method to effectively improve the agreement rate. With manually annotated rich information, our corpus can serve as a valuable resource for the development and evaluation of automated sentiment classification, intercultural comparison, rhetoric detection, etc. Finally, based on observations and our analysis of our corpus, we present three key conclusions. First, languages differ in terms of emotional signals and rhetoric devices, and the idea that cultures have different opinions regarding the same objects is reconfirmed. Second, each rhetoric device maintains its own characteristics, influences global polarity in its own way, and has an inherent structure that helps to model the sentiment that it represents. Third, the models of the expression of feelings in different languages are rather similar, suggesting the possibility of unifying multilingual opinion mining at the sentiment level.

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