## 2 Overview of Research Directions Based on the Spec Issue Papers
[12] proposed a method to evaluate user satisfaction during interaction between a voice-based intelligent systems (VIS) and a user-centered intelligent system. As a user satisfaction evaluation method, a VIS comprising four types of design parameters was developed and user satisfaction was measured using Kansei words (KWs). The questionnaire scores collected through KWs were analyzed using exploratory factor analysis and ANOVA was used to analyze differences in emotion. On the \"pleasurability\" and \"reliability\" axes, it was confirmed that among the four design parameters, \"sentence structure of the answer\" and \"number of trials to acquire the right answer for a question\" affect the emotional satisfaction of users. [PERSON] et al. [13] proposed a real-time dynamic hand gesture recognition system with gesture spotting function. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. The introduced gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results shows that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result. [PERSON] et al. [14] used the Toy-data problem in their paper to illustrate the regulated activation network modeling and recall procedure to report the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The results show how regulation enables contextual awareness among abstract nodes during the recall process. The authors show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure. [PERSON] et al. [15] analyze the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). [PERSON] et al. [16] propose an encoding method that enables typical homomorphic encryption schemes to operate on real-valued numbers of arbitrary precision and size. The approach is evaluated on two real-world scenarios relying on EEG signals: seizure detection and prediction of predisposition to alcoholism. The results show that the prediction performance of the models operating on encoded and encrypted data is comparable to that of standard models operating on plaintext data. [PERSON] et al. [17] implemented different modes of social gaze behavior in a companion robot, F-2, to evaluate the impression of the gaze behaviors on humans in three communicative situations. The authors extended the computer model of the robot in order to simulate realistic gaze behavior in the robot and create the impression of the robot changing its internal cognitive states. They used an iterative approach, extending the applied cognitive architecture in order to simulate the balance between different behavioral reactions and to test it in the experiments. [PERSON] and [PERSON] [18] examine the process of creating successful, engaging, interactive, and activity-based online educational materials, while taking the cognitive aspects of learners into account [19].