Towards Generation of Visual Attention Map for Source Code

Program comprehension is a dominant process in software development and maintenance. Experts are considered to comprehend the source code efficiently by directing their gaze, or attention, to important components in it. However, reflecting the importance of components is still a remaining issue in gaze behavior analysis for source code comprehension. Here we show a conceptual framework to compare the quantified importance of source code components with the gaze behavior of programmers. We use "attention" in attention models (e.g., code2vec) as the importance indices for source code components and evaluate programmers' gaze locations based on the quantified importance. In this report, we introduce the idea of our gaze behavior analysis using the attention map, and the results of a preliminary experiment.Comment: 4 pages, 2 figures; APSIPA 2019 ACCEPTE

Modeling and Prediction of Driving Behaviors Using a Nonparametric Bayesian Method with AR Models

To develop a new generation advanced driver assistance system that avoids a dangerous condition in advance, we need to predict driving behaviors. Since a nonparametric Bayesian method with a two-level structure successfully predicted the symbolized behaviors only, we applied a nonparametric Bayesian method with linear dynamical systems to predicting the driving behavior. The method called the beta process autoregressive hidden Markov model (BP-AR-HMM) segments driving behaviors into states each of which corresponds to an AR model and it predicts future behaviors using the estimated future state sequence and the dynamical systems therein. Here, the segmentation as well as the parameters of the dynamical systems are determined using given training data in an unsupervised way. We carried out experiments with real driving data and found that the BP-AR-HMM predicted driving behaviors better than other methods

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

コウシジョウ デンキョク オ モチイタ オトガイ カブ カラ ノ ヒョウメン キンデンズ ケイソク オヨビ ボイン ニンシキ エノ ソノ オウヨウ

博士(Doctor)工学(Engineering)奈良先端科学技術大学院大学博第1053号甲第1053号博士(工学)奈良先端科学技術大学院大

Extraction of Hierarchical Behavior Patterns Using a Non-parametric Bayesian Approach

Extraction of complex temporal patterns, such as human behaviors, from time series data is a challenging yet important problem. The double articulation analyzer has been previously proposed by Taniguchi et al. to discover a hierarchical structure that leads to complex temporal patterns. It segments time series into hierarchical state subsequences, with the higher level and the lower level analogous to words and phonemes, respectively. The double articulation analyzer approximates the sequences in the lower level by linear functions. However, it is not suitable to model real behaviors since such a linear function is too simple to represent their non-linearity even after the segmentation. Thus, we propose a new method that models the lower segments by fitting autoregressive functions that allows for more complex dynamics, and discovers a hierarchical structure based on these dynamics. To achieve this goal, we propose a method that integrates the beta process—autoregressive hidden Markov model and the double articulation by nested Pitman-Yor language model. Our results showed that the proposed method extracted temporal patterns in both low and high levels from synthesized datasets and a motion capture dataset with smaller errors than those of the double articulation analyzer