Locally weighted linear regression for cross-lingual valence-arousal prediction of affective words

Chinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis

An increasing amount of research has recently focused on dimensional sentiment analysis that represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space. Compared to the categorical approach that represents affective states as distinct classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. However, dimensional sentiment resources with valence-arousal ratings are very rare, especially for the Chinese language. Therefore, this study aims to: (1) Build a Chinese valence-arousal resource called Chinese EmoBank, the first Chinese dimensional sentiment resource featuring various levels of text granularity including 5,512 single words, 2,998 multi-word phrases, 2,582 single sentences, and 2,969 multi-sentence texts. The valence-arousal ratings are annotated by crowdsourcing based on the Self-Assessment Manikin (SAM) rating scale. A corpus cleanup procedure is then performed to improve annotation quality by removing outlier ratings and improper texts. (2) Evaluate the proposed resource using different categories of classifiers such as lexicon-based, regression-based, and neural-network-based methods, and comparing their performance to a similar evaluation of an English dimensional sentiment resource.

Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model

Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and IA = 0.9744) for modeling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and IA = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and IA = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and IA = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI.

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988-2020), remote sensing images (e.g., MODIS, Landsat 5-8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R² = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148, RAE = 4.48-23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929-0.967), corrected classified instances (CCI: 96.45-98.35), area under the curve (AUC: 0.983-0.997), and Gini coefficients (0.966-0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.

A novel Hybrid Wavelet-Locally Weighted Linear Regression (W-LWLR) Model for Electrical Conductivity (EC) Prediction in Surface Water

Rivers are the most common and vital sources of water, which play a fundamental role in ecological systems and human life. Water quality assessment is a major element of managing water resources and accurate prediction of water quality is very essential for better management of rivers. The electrical conductivity (EC) is known as one of the most important water quality parameters to predict salinity and mineralization of water. The present study introduces a novel hybrid wavelet-locally weighted linear regression (W-LWLR) method to predict the monthly EC of the Sefidrud River in Iran. 240 monthly discharge (Q) and EC samples, over a period of 20 years, were collected. The data were divided into two frequency components at two decomposition levels using the mother wavelet Bior 6.8. To compare the performance of various methods, the standalone LWLR, support vector regression (SVR), wavelet support vector regression (W-SVR), autoregressive integrated moving average (ARIMA), wavelet ARIMA (W-ARIMA), multivariate linear regression (MLR), and wavelet MLR (W-MLR) were also used. The discrete wavelet transform (DWT) was coupled with the LWLR, SVR, and ARIMA to create the W-LWLR, W-SVR, W-ARIMA methods to predict the EC parameter. The comparisons demonstrated that the W-LWLR was more accurate and efficient than the LWLR, SVR, W-SVR, ARIMA, and W-ARIMA methods. The correlation coefficient (R) values were 0.973, 0.95, 0.565, 0.473, 0.425, 0.917 for the W-LWLR, W-SVR, LWLR, SVR, ARIMA, and W-ARIMA methods, respectively. Further, the root mean square error (RMSE) of W-LWLR was 89.78, while the corresponding values for W-SVR, LWLR, SVR, ARIMA, W-ARIMA, MLR, and W-MLR were 123.50, 319.95, 341.20, 350.153, 155.292, 351.774, and 157.856 respectively. The overall comparison metrics and error analysis demonstrated the superiority of the new proposed W-LWLR method for water quality prediction.

A Robot Learning Method with Physiological Interface for Teleoperation Systems

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environments, the manipulator’s performance and efficiency of the human-robot interaction in the tasks may degrade significantly. In this study, a novel method of human-centric interaction, through a physiological interface was presented to capture the information details of the remote operation environments. Simultaneously, in order to relieve workload of the human operator and to improve efficiency of the teleoperation system, an updated regression method was proposed to build up a nonlinear model of demonstration for the prescribed task. Considering that the demonstration data were of various lengths, dynamic time warping algorithm was employed first to synchronize the data over time before proceeding with other steps. The novelty of this method lies in the fact that both the task-specific information and the muscle parameters from the human operator have been taken into account in a single task; therefore, a more natural and safer interaction between the human and the robot could be achieved. The feasibility of the proposed method was demonstrated by experimental results.