https://waseda.repo.nii.ac.jp/?action=repository_action_common_download&item_id=12133&item_no=1&attribute_id=162&file_no=1

言語運用能力(Communicative Competence)

Thank you very much for downloading the uses of argument. Maybe you have knowledge that, people have search numerous times for their chosen novels like this the uses of argument, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their computer. the uses of argument is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the the uses of argument is universally compatible with any devices to read.

The Uses Of Argument

Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

Hand and Mind: What Gestures Reveal about Thought

Adaptive control processes

Many researchers use Wizard of Oz (WoZ) as an experimental technique, but there are methodological concerns over its use, and no comprehensive criteria on how to best employ it. We systematically review 54 WoZ experiments published in the primary HRI publication venues from 2001 -- 2011. Using criteria proposed by Fraser and Gilbert (1991), Green et al. (2004), Steinfeld et al. (2009), and Kelley (1984), we analyzed how researchers conducted HRI WoZ experiments. Researchers mainly used WoZ for verbal (72.2%) and non-verbal (48.1%) processing. Most constrained wizard production (90.7%), but few constrained wizard recognition (11%). Few reported measuring wizard error (3.7%), and few reported pre-experiment wizard training (5.4%). Few reported using WoZ in an iterative manner (24.1%). Based on these results we propose new reporting guidelines to aid future research.

Wizard of Oz studies in HRI: a systematic review and new reporting guidelines

Human-Robot Interaction using free hand gestures is gaining more importance as more untrained humans are operating robots in home and office environments. The robot needs to solve three problems to be operated by free hand gestures: gesture (command) detection, action generation (related to the domain of the task) and association between gestures and actions.

/pdf/unsupervised-simultaneous-learning-of-gestures-actions-and-1al06r2uh7.pdf

Unsupervised simultaneous learning of gestures, actions and their associations for Human-Robot Interaction

Group discussions are used widely when generating new ideas and forming decisions as a group. Therefore, it is assumed that giving social influence to other members through facilitating the discussion is an important part of discussion skill. This study focuses on influential statements that affect discussion flow and highly related to facilitation, and aims to establish a model that predicts influential statements in group discussions. First, we collected a multimodal corpus using different group discussion tasks; in-basket and case-study. Based on schemes for analyzing arguments, each utterance was annotated as being influential or not. Then, we created classification models for predicting influential utterances using prosodic features as well as attention and head motion information from the speaker and other members of the group. In our model evaluation, we discovered that the assessment of each participant in terms of discussion facilitation skills by experienced observers correlated highly to the number of influential utterances by a given participant. This suggests that the proposed model can predict influential statements with considerable accuracy, and the prediction results can be a good predictor of facilitators in group discussions.

Predicting Influential Statements in Group Discussions using Speech and Head Motion Information

This paper focuses on the computational analysis of the individual communication skills of participants in a group. The computational analysis was conducted using three novel aspects to tackle the problem. First, we extracted features from dialogue (dialog) act labels capturing how each participant communicates with the others. Second, the communication skills of each participant were assessed by 21 external raters with experience in human resource management to obtain reliable skill scores for each of the participants. Third, we used the MATRICS corpus, which includes three types of group discussion datasets to analyze the influence of situational variability regarding to the discussion types. We developed a regression model to infer the score for communication skill using multimodal features including linguistic and nonverbal features: prosodic, speaking turn, and head activity. The experimental results show that the multimodal fusing model with feature selection achieved the best accuracy, 0.74 in R2 of the communication skill. A feature analysis of the models revealed the task-dependent and task-independent features to contribute to the prediction performance.

Estimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets

This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant's nonverbal activity and group activity. This feature representation enables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent co-occurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from group meetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the co-occurrence features improve the accuracy from 2 % up to 17 %.

/pdf/personality-trait-classification-via-co-occurrent-multiparty-3pzeuoeae0.pdf

Personality Trait Classification via Co-Occurrent Multiparty Multimodal Event Discovery

This paper presents an approach (SOINN-DTW)for recognition of motion (gesture) that is based on the Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping (DTW). Using SOlNN's function of eliminating noise in the input data and representing the distribution of input data, SOINN-PTW method approximates the output distribution of each state in a self-organizing manner corresponding to the input data. The proposed SOINN-DTW method enhances Stochastic Dynamic Time Warping Method (Nakagawa. 1986). Results of experiments show that SOINN-DTW outperforms HMM, CRF. and HCRF in motion data.

Shogo Okada

Papers

Unsupervised simultaneous learning of gestures, actions and their associations for Human-Robot Interaction

Predicting Influential Statements in Group Discussions using Speech and Head Motion Information

Estimating communication skills using dialogue acts and nonverbal features in multiple discussion datasets

Personality Trait Classification via Co-Occurrent Multiparty Multimodal Event Discovery

Motion recognition based on Dynamic-Time Warping method with Self-Organizing Incremental Neural Network