Cognition hypothesis and second language performance: Comparison of written and oral task performance

This study sought to test Robinson's (1995, 2003b) cognition hypothesis by investigating the effect of cognitive task difficulty on ESL learners' written and oral task performance. Ten exchange students at the University of Hawai'I at Manoa were given four sets of picture-based narrative tasks: a simple writing task, a difficult writing task, a simple speaking task, and a difficult speaking task. The simple tasks contained a fewer characters or main foreground events, but not supporting background events, in cartoon-based stories; on the other hand, the complex tasks included more characters or both foregorund and background events. The results indicated that (a) the difficuly task was more successful than was the difficult speaking task in eliciting complex language production without deteriorating its accuracy; (b) the cognition hypothesis seems to be more relevant to language complexity than accuracy, and (c) accuracy of language production seems to be more susceptible to individual learners' ability to produce accurate sentences than cognitive taks difficulty. These findings, therefore, partially support and partially disconfirm Robinson's cognition hypothesis

Data Augmentation for Lyrics Emotion Estimation

Lyrics emotion estimation can allow us to realise song retrieval systems and song recommendation systems which are based on not only text retrieval nor melody matching but also emotions in lyrics or transitions of emotions within lyrics of a whole song. This requires lyrics emotion corpora of phrase. However, it is difficult to build large scale lyrics emotion corpora because emotions are labelled manually. In this paper, we propose a method to augment lyrics emotion corpora. As a result, we augmented a corpus consisting of 366 phrases into a larger corpus consisting of 2145 phrases. We also evaluate the proposed method using 2 convolutional neural networks trained on original corpus and augmented corpus respectively. We define the target emotion classes as Joy, Love, Anger, Sorrow and Anxiety. Mean accuracy of the model trained on the augmented corpus was 75.9% whilst the model trained on the original corpus performed 70.7%

Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks

Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model

Association Between Three-year Longitudinal Changes in Physical Strength in Children with Their Build, Health Habits, and Psychophysical Health Indexes.

In this study we examined the association between longitudinal changes in schoolchildren＇s physical strength with their build, health habits, and psychophysical health index scores. Students (n= 195) were followed for three years, from the fifth to the eighth grade. Setting as a baseline the students＇ results on the Ministry of Education, Culture, Sports, Science and Technology＇s new physical strength test, we extracted data on those students whose physical strength relatively improved (improved group: 28 boys, 53 girls) and on those whose strength relatively declined (declined group: 15 boys, 16 girls). Build, health habits, and psychophysical health index scores were compared between the two groups. It was found that, although there were no significant differences in eating habits or sleeping habits between the two groups, compared to the improved group, the declined group was more likely to be either obese or underweight, have short durations of intense exercise and total exercise, and longer duration of watching television or videos. The declined group also showed poorer psychological health status, such as lower self-efficacy and higher anxiety. 　These findings indicate that children with good exercise habits, such as consistently engaging in a adequate physical activities that include intense exercise, will have improved physical strength outcome over time, whereas those children with few regular exercise habits and whose strength will not improve over time, will show outcomes such as polarization of body weight (obesity and underweight tendencies) and poorer psychological health status

Proceedings 2011: Selected papers from the fifteenth college-wide conference for students in languages, linguistics & literature

Selected papers from the fifteenth annual college-wide conference for students in languages, linguistics & literature, at the University of Hawai`i at MānoaSelected papers from the fifteenth annual college-wide conference for students in languages, linguistics & literature, at the University of Hawai`i at MānoaCollege of Languages, Linguistics & Literature, University of Hawai‘i at Mano

The Roles of MicroRNAs in Glioblastoma Biology and Biomarker

MicroRNAs (miRNAs) are small, noncoding RNAs transcribed from DNA that are 18–24 nucleotides in length. A single miRNA has the capacity to regulate a large number of target messenger RNAs (mRNAs), and the main function of miRNAs is to downregulate gene expression. A large set of miRNAs is overexpressed or downregulated in various human cancers compared with normal tissues, and gene silencing by miRNAs enhances tumor malignancies

A study on new material for sports mouthguards ─ Physical properties of silicone-based materials vulcanized by microwave oven and application of mixed with crystallized quartz fillers ─

松本歯科大学大学院歯学独立研究科博士（歯学）学位申請論