Multimodal Affect Models: An Investigation of Relative Salience of Audio and Visual Cues for Emotion Prediction

People perceive emotions via multiple cues, predominantly speech and visual cues, and a number of emotion recognition systems utilize both audio and visual cues. Moreover, the perception of static aspects of emotion (speaker's arousal level is high/low) and the dynamic aspects of emotion (speaker is becoming more aroused) might be perceived via different expressive cues and these two aspects are integrated to provide a unified sense of emotion state. However, existing multimodal systems only focus on single aspect of emotion perception and the contributions of different modalities toward modeling static and dynamic emotion aspects are not well explored. In this paper, we investigate the relative salience of audio and video modalities to emotion state prediction and emotion change prediction using a Multimodal Markovian affect model. Experiments conducted in the RECOLA database showed that audio modality is better at modeling the emotion state of arousal and video for emotion state of valence, whereas audio shows superior advantages over video in modeling emotion changes for both arousal and valence.

PREDIKSI PERUBAHAN TUTUPAN LAHAN DENGAN MODEL MARKOV CHAIN DAN ANN-MARKOV DI DAS KRUENG ACEH (Land cover change prediction using Markov Chain and ANN-Markov Model in Krueng Aceh Watershed)

Prediksi perubahan tutupan lahan yang baik akan menjadi pertimbangan dalam menentukan strategi pembangunan di masa depan. Terdapat banyak metode dalam melakukan prediksi perubahan tutupan lahan yang tergantung pada kebutuhan data, algoritma pemodelan yang dilakukan dan output apa saja yang diperlukan. Penelitian ini dilakukan untuk mengkaji model prediksi perubahan tutupan lahan dari tahun 2007 hingga 2020 di DAS Krueng Aceh. Pendekatan yang dilakukan menggunakan penginderaan jauh dan SIG. Model Markov Chain (MC) dan Artificial Neural Network-Markov (ANN-MC) digunakan untuk memahami dinamika spatio-temporal tutupan lahan. Akurasi dari citra penginderaan jauh yang diklasifikasikan diperoleh dari hasil interpretasi visual pada citra resolusi sedang Landsat OLI tahun 2020 dengan nilai Kappa Accuracy sebesar 84%. Kedua model prediksi menggunakan data tutupan lahan tahun 2007 (T1) dan 2017 (T2) untuk membuat probability perubahan yang digunakan dalam memprediksi tutupan lahan pada tahun 2020 (T3). Validasi kedua algoritma menunjukkan korelasi yang kuat dengan peta tutupan lahan 2020, hal tersebut membuktikan kehandalan model kedua simulasi (ANN=87,81% dan MC=88,69%).

Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).