Vocal-based emotion recognition using random forests and decision tree

This paper proposes a new vocal-based emotion recognition method using random forests, where pairs of the features on the whole speech signal, namely, pitch, intensity, the first four formants, the first four formants bandwidths, mean autocorrelation, mean noise-to-harmonics ratio and standard deviation, are used in order to recognize the emotional state of a speaker. The proposed technique adopts random forests to represent the speech signals, along with the decision-trees approach, in order to classify them into different categories. The emotions are broadly categorised into the six groups, which are happiness, fear, sadness, neutral, surprise, and disgust. The Surrey Audio-Visual Expressed Emotion database is used. According to the experimental results using leave-one-out cross-validation, by means of combining the most significant prosodic features, the proposed method has an average recognition rate of \(66.28\%\), and at the highest level, the recognition rate of \(78\%\) has been obtained, which belongs to the happiness voice signals. The proposed method has \(13.78\%\) higher average recognition rate and \(28.1\%\) higher best recognition rate compared to the linear discriminant analysis as well as \(6.58\%\) higher average recognition rate than the deep neural networks results, both of which have been implemented on the same database.     

Data-Driven Texture Rendering on an Electrostatic Tactile Display

In this article, we present a data-driven texture rendering method applied to a tactile display based on electrostatic attraction. The proposed method was examined in two steps. First, accelerations occurring due to sliding a tool on three different surfaces were measured, and then the collected data were replayed on an electrostatic tactile display. The proposed data-driven texture rendering method was evaluated against a conventional method in which a standard input such as a square wave was used for texture representation. Second, data from the Penn Haptic Texture Toolkit were used to generate virtual textures on the same tactile display. Psychophysical experiments were carried out for both steps, during which subjects rated similarities among the rendered virtual textures and the real samples. Confusion matrices were created, and multidimensional scaling (MDS) analysis was performed to create a perceptual space for further examination and to extract underlying dimensions of the textures. The results show that the virtual textures generated using the data-driven method were similar to the real textures. Roughness and stickiness were the primary dimensions of texture perception. Together with the supporting results from the MDS analysis, this study showed that the data-driven method is a viable solution for realistic texture rendering with electrostatic attraction.     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

A decoupled source current reconstruction method for noisy and reactive near‐field to far‐field transformation

In this article, a decoupled source current reconstruction method (SRM) for noisy and reactive near‐field (NF) to far‐field (FF) transformation is introduced. It is shown that the traditional SRM for NF/FF transformation shows instability in the regions that the amounts of noise or reactive radiations are noticeable. Therefore, in these regions, equivalent currents should be determined from a Tikhonov SRM equation. However, this equation increases the computational cost of the SRM. To simplify the Tikhonov SRM equation, a Tikhonov radial field retrieval algorithm is also proposed. In this algorithm, the Tikhonov integral equation is decoupled by considering and retrieving the radial components of the electric field. Results of far‐field calculation with both the proposed Tikhonov SRM equation and Tikhonov radial field retrieval algorithm with three different antennas are presented and compared with those of the full‐wave simulation and measurements. The results show more accurate field transformation with the proposed algorithms.     

ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice husk–bark ash

In the present work, compressive strength of geopolymers made from seeded fly ash and rice husk–bark ash has been predicted by adaptive network-based fuzzy inference systems (ANFIS). Different specimens, made from a mixture of fly ash and rice husk–bark ash in fine and coarse forms and a mixture of water glass and NaOH mixture as alkali activator, were subjected to compressive strength tests at 7 and 28 days of curing. The curing regimes were different: one set of the specimens were cured in water at room temperature until 7 and 28 days and the other sets were oven-cured for 36 h at the range of 40–90°C and then cured at room temperature until 7 and 28 days. A model based on ANFIS for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of ANFIS models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk–bark ash in the ashes mixture, the percentage of coarse rice husk–bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters in the ANFIS models, the compressive strength of each specimen was predicted. The training and testing results in ANFIS models showed a strong potential for predicting the compressive strength of the geopolymeric specimens.     

Computer user training and attitudes: a study of business undergraduates

For many users, the first real encounter with a computer occurs when taking an introductory course to computers at a college. To the extent that these training courses impact user understanding and motivation, they are important determinants of the user attitudes towards computers and merit serious assessment. Using 327 business undergraduates at two universities in the US, this paper reports on the reactions of students to computers and computer-related tasks before and after an introductory course to computers. Responses to a 20-item scale were analysed to examine the pattern of attitude change experienced by students in their training course. Factor analysis revealed five constructs for describing patterns of computer user attitude: negative reaction to computers; positive reaction to computers; reaction to computers for children education; reaction to computer-mediated services; and reaction to computer games. Four factors show significant change in mean scores after the training courses. The attitudes changed for males more than females, indicating improvement in attitudes. The respondents' attitude to computer-mediated services remained unchanged. While the directions of changes indicate an overall improvement in respondents' reactions, many attitudes did not change significantly after having taken the training courses. This may be due to the content or the format of these courses.     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

From 2D to 3D geodesic-based garment matching

Multimedia Tools and Applications - A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of...     

Influence of drying and extraction methods on yield and chemical composition of the essential oil of Satureja hortensis

The aerial parts of Satureja hortensis, cultivated in Iran (Research Station of Alborz, Karaj), were collected at the full-flowering stage and dried by three different drying methods (sun-drying, shade-drying and oven-drying at 45 °C). The essential oils of every treatment were obtained by hydro-distillation of the aerial parts. In addition, the essential oil of shade-dried sample was obtained by two other distillation methods (water- and steam-distillation and direct steam-distillation). The oils were analyzed by capillary GC and GC–MS. Statistical analysis showed no significant difference between oil yield (w/w) of the oven-dried sample (1.06%) compared to shade-dried (0.94%) and sun-dried (0.87%). The oil content of the shade-dried sample, obtained by hydro-distillation, was higher (0.94%) than that of the steam-distilled (0.27%). Twenty-three components were identified in the oil of S. hortensis in the different drying methods, including carvacrol (46.0–48.1%) and γ-terpinene (37.7–39.4%) as main components. Seventeen compounds were characterized in the oils of different distillation methods, including carvacrol (12.3–46%) and γ-terpinene (37.7–70.4%). Although the drying methods had no significant effect on oil composition of S. hortensis, the distillation changed the percentage of main components sharply (significant at 1%). The steam-distillation method produced the lowest amount of carvacrol and highest amount of γ-terpinene. The results showed that extraction by hydro-distillation gave the best results for S. hortensis, based on oil yield and carvacrol percentage.