Time series classification (TSC) is essential in various application domains for understanding system dynamics. The adoption of deep learning has advanced TSC, but model performance is sensitive to hyperparameter configurations. Manual tuning of high-dimensional hyperparameters can be labor-intensive, leading to a preference for automatic hyperparameter optimization (HPO) methods. To the best of our knowledge, survey paper covering various studies on automatic hyperparameters optimization (HPO) of deep learning for TSC is scarce and even none. To address this gap, we present a systematic literature review to assist researchers in addressing the HPO problem for deep learning in TSC. It analyzes studies published between 2018 and June 2024. This review examines HPO methods, hyperparameters, and tools utilized in this context, based on a total of 77 primary studies sourced from academic databases. The findings indicate that Metaheuristic algorithms and Bayesian Optimization are commonly employed approaches, with a focus on hyperparameters related to deep learning architecture. This review provides insights that can inform the design and implementation of HPO strategies for deep learning models in time series analysis.

Visually induced motion sickness (VIMS) is an important safety issue in stereoscopic 3D technology. Accompanying subjective judgment of VIMS with objective measurement is useful to identify not only biomedical effects of dynamic 3D contents, but also provoking scenes that induce VIMS, duration of VIMS, and user behavior during VIMS. Heart rate variability and depth gaze behavior are appropriate physiological indicators for such objective observation. However, there is no information about relationship between subjective judgment of VIMS, heart rate variability, and depth gaze behavior. In this paper, we present a novel investigation of VIMS based on simulator sickness questionnaire (SSQ), electrocardiography (ECG), and 3D gaze tracking. Statistical analysis on SSQ data shows that nausea and disorientation symptoms increase as amount of dynamic motions increases (nausea: p<;0.005; disorientation: p<;0.05). To reduce VIMS, SSQ and ECG data suggest that user should perform voluntary gaze fixation at one point when experiencing vertical motion (up or down) and horizontal motion (turn left and right) in dynamic 3D contents. Observation of 3D gaze tracking data reveals that users who experienced VIMS tended to have unstable depth gaze than ones who did not experience VIMS.

Nowadays, a lot of learning references use English language to support learning activities. Consequently, students are required to have good ability in English. Multimedia learning to facilitate communication between sources of information and recipients of information. There is no particularly research that focuses on examining the issues related to evaluating a learning media based on content such as multimedia with only text, multimedia without cue and multimedia with the cue in multimedia learning. This study aims to evaluate and to compare user experience of English learning interface of a learning multimedia. We compared three contents of English learning interfaces namely, multimedia with only text, multimedia without cueing, and multimedia with cueing. Thirthy eight participants were asked to learn about present perfect tense with various content. This research is also conducted to find out which designs are more preferred by user based on user experience and usability. User Experience Questionnaire(UEQ) and System Usability Scale (SUS) shows that multimedia without cueing is better than multimedia with cueing and multimedia with only text in term of user experience and usability.

The growth of vehicles in Yogyakarta Province, Indonesia is not proportional to the growth of roads. This problem causes severe traffic jam in many main roads. Common traffic anomalies detection using surveillance camera requires manpower and costly, while traffic anomalies detection with crowdsourcing mobile applications are mostly owned by private. This research aims to develop a real-time traffic classification by harnessing the power of social network data, Twitter. In this study, Twitter data are processed to the stages of preprocessing, feature extraction, and tweet classification. This study compares classification performance of three machine learning algorithms, namely Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). Experimental results show that SVM algorithm produced the best performance among the other algorithms with 99.77% and 99.87% of classification accuracy in balanced and imbalanced data, respectively. This research implies that social network service may be used as an alternative source for traffic anomalies detection by providing information of traffic flow condition in real-time.