Institute of Electronics, Information and Communication Engineers Acta Medica Okayama 0916-8532 E103.D 10 2020 Empirical Evaluation of Mimic Software Project Data Sets for Software Effort Estimation 2094 2103 EN Maohua Gan Okayama University Zeynep Yücel Okayama University Akito Monden Okayama University Kentaro Sasaki Okayama University To conduct empirical research on industry software development, it is necessary to obtain data of real software projects from industry. However, only few such industry data sets are publicly available; and unfortunately, most of them are very old. In addition, most of today's software companies cannot make their data open, because software development involves many stakeholders, and thus, its data confidentiality must be strongly preserved. To that end, this study proposes a method for artificially generating a “mimic” software project data set, whose characteristics (such as average, standard deviation and correlation coefficients) are very similar to a given confidential data set. Instead of using the original (confidential) data set, researchers are expected to use the mimic data set to produce similar results as the original data set. The proposed method uses the Box-Muller transform for generating normally distributed random numbers; and exponential transformation and number reordering for data mimicry. To evaluate the efficacy of the proposed method, effort estimation is considered as potential application domain for employing mimic data. Estimation models are built from 8 reference data sets and their concerning mimic data. Our experiments confirmed that models built from mimic data sets show similar effort estimation performance as the models built from original data sets, which indicate the capability of the proposed method in generating representative samples. No potential conflict of interest relevant to this article was reported.

Institute of Electronics, Information and Communication Engineers Acta Medica Okayama 0916-8532 E103.D 8 2020 An Algorithm for Automatic Collation of Vocabulary Decks Based on Word Frequency 1865 1874 EN Zeynep Yücel Okayama University Parisa Supitayakul Okayama University Akito Monden Okayama University Pattara Leelaprute Department of Computer Engineering, Faculty of Engineering, Kasetsart University This study focuses on computer based foreign language vocabulary learning systems. Our objective is to automatically build vocabulary decks with desired levels of relative difficulty relations. To realize this goal, we exploit the fact that word frequency is a good indicator of vocabulary difficulty. Subsequently, for composing the decks, we pose two requirements as uniformity and diversity. Namely, the difficulty level of the cards in the same deck needs to be uniform enough so that they can be grouped together and difficulty levels of the cards in different decks need to be diverse enough so that they can be grouped in different decks. To assess uniformity and diversity, we use rank-biserial correlation and propose an iterative algorithm, which helps in attaining desired levels of uniformity and diversity based on word frequency in daily use of language. In experiments, we employed a spaced repetition flashcard software and presented users various decks built with the proposed algorithm, which contain cards from different content types. From users' activity logs, we derived several behavioral variables and examined the polyserial correlation between these variables and difficulty levels across different word classes. This analysis confirmed that the decks compiled with the proposed algorithm induce an effect on behavioral variables in line with the expectations. In addition, a series of experiments with decks involving varying content types confirmed that this relation is independent of word class. No potential conflict of interest relevant to this article was reported.

Taylor and Francis Acta Medica Okayama 1044-7318 36 16 2020 Estimating Level of Engagement from Ocular Landmarks 1527 1539 EN Zeynep Yucel Department of Computer Science, Division of Industrial Innovation Sciences, Okayama University Serina Koyama Department of Computer Science, Division of Industrial Innovation Sciences, Okayama University Akito Monden Department of Computer Science, Division of Industrial Innovation Sciences, Okayama University Mariko Sasakura Department of Computer Science, Division of Industrial Innovation Sciences, Okayama University E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders. No potential conflict of interest relevant to this article was reported.

Public Library of Science Acta Medica Okayama 1932-6203 14 10 2019 Identification of social relation within pedestrian dyads e0223656 EN Zeynep Yucel Department of Computer Science, Okayama University Francesco Zanlungo Intelligent Robotics and Communication Laboratory, ATR Claudio Feliciani Research Center for Advanced Science and Technology,The University of Tokyo Adrien Gregorj Department of Computer Science, Okayama University Takayuki Kanda Intelligent Robotics and Communication Laboratory, ATR This study focuses on social pedestrian groups in public spaces and makes an effort to identify the type of social relation between the group members. As a first step for this identification problem, we focus on dyads (i.e. 2 people groups). Moreover, as a mutually exclusive categorization of social relations, we consider the domain-based approach of Bugental, which precisely corresponds to social relations of colleagues, couples, friends and families, and identify each dyad with one of those relations. For this purpose, we use anonymized trajectory data and derive a set of observables thereof, namely, inter-personal distance, group velocity, velocity difference and height difference. Subsequently, we use the probability density functions (pdf) of these observables as a tool to understand the nature of the relation between pedestrians. To that end, we propose different ways of using the pdfs. Namely, we introduce a probabilistic Bayesian approach and contrast it to a functional metric one and evaluate the performance of both methods with appropriate assessment measures. This study stands out as the first attempt to automatically recognize social relation between pedestrian groups. Additionally, in doing that it uses completely anonymous data and proves that social relation is still possible to recognize with a good accuracy without invading privacy. In particular, our findings indicate that significant recognition rates can be attained for certain categories and with certain methods. Specifically, we show that a very good recognition rate is achieved in distinguishing colleagues from leisure-oriented dyads (families, couples and friends), whereas the distinction between the leisure-oriented dyads results to be inherently harder, but still possible at reasonable rates, in particular if families are restricted to parent-child groups. In general, we establish that the Bayesian method outperforms the functional metric one due, probably, to the difficulty of the latter to learn observable pdfs from individual trajectories. No potential conflict of interest relevant to this article was reported.