This study presents the synchronisation between two non-linear Lorenz chaotic systems using an adaptive intuitionistic fuzzy controller (AIFC). This problem is solved in the presence of external disturbances and uncertainties. Two classes of Lorenz systems have been formulated one as the master and the other as the slave. The master and slave systems have different initial conditions. Under different initial conditions, both systems diverge. This is called the butterfly effect that rules the behaviour of chaotic systems. AIFC is chosen as a different method that has not been used before to synchronise the chaotic systems. The scaling factors of the inputs and the output of the intuitionistic fuzzy controller (IFC) are tuned using the artificial bee colony (ABC) algorithm depending on a specified objective function. Then IFC stability is studied when it is applied to synchronise the Lorenz system. The simulation results achieved to assess the adequacy of the proposed control and to exhibit its dependability to synchronise the Lorenz system.

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises.

The eye-tracking has been widely used in multiple discipline studies in recent years. However, most of the studies focused on the analysis for static images or text but less for highly interactive operation application. In addition, the affective computing rose and development in recent years have changed completely the design of thinking pattern for the human–computer interaction. Therefore, this study hopes to integrate the affective computing into the 3D guide system which was developed for the real campus through the eye-tracking technology. The analysing user's gaze position and recognising attention emotion are according to the interest region which is stetted into the environment, and shows the feedback content corresponds to the area and the emotion. Through the most intuitive gaze analysis, the operation burden can be reduced and the user's interactive experience can be improved to achieve intuitive and user-friendly experience. The results can also apply for medical therapy on human attention training.

As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventional collaborative filtering techniques such as matrix decomposition, which consider user preferences as a linear combination of user and item latent features, have limited learning capacities, hence suffer from a cold start and data sparsity problems. To tackle these problems, the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features. Conversely, the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations. Therefore, the authors proposed a multi-model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. The MMDL approach combines deep autoencoder with a one-dimensional convolution neural network model that learns user and item features to predict user preferences. From detail experimentation on two real-world datasets, the proposed work exhibits substantial performance when compared to the existing methods.

Part-of-speech (POS) and dependency grammar (DG) are the basic components of natural language processing. However, current word vector models have not made full use of both POS information and DG information, and hence the models’ performances are limited to some extent. The authors first put forward the concept of POS vector, and then, based on continuous bag-of-words (CBOW), constructed four models: CBOW + P, CBOW + PW, CBOW + G, and CBOW + G + P to incorporate POS information and DG information into word vectors. The CBOW + P and CBOW + PW models are based on POS tagging, the CBOW + G model is based on DG parsing, and the CBOW + G + P model is based on POS tagging and DG parsing. POS information is integrated into the training process of word vectors through the POS vector to solve the problem of the POS similarity being difficult to measure. The POS vector correlation coefficient and distance weighting function are used to train the POS vector as well as the word vector. DG information is used to correct the information loss caused by fixed context windows. Dependency relations weight is used to measure the difference of dependency relations. Experiments demonstrated the superior performance of their models while the time complexity is still kept the same as the base model of CBOW.