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Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.

Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue. Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index &#x0028 WGI &#x0029 is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve &#x0028 ROC AUC &#x0029 and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20 &#x0025 or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

Cleft palate repair is a challenging procedure for cleft surgeons to teach, and in research, it can be difficult to evaluate different techniques and develop new treatments. In this study, a congenital cleft palate New Zealand rabbit model has been described and could be beneficial in future studies concerning cleft palate repair. Pregnant New Zealand rabbits received 1.0 mg dexamethasone injection intramuscularly once a day from the 13th gestation day (GD13) to GD16. On GD31. Newborn rabbits were delivered by cesarean sections, fed with a standardized gastric tube feeding method, and divided into two groups. The rate of survival and the incidence of cleft palate was calculated. Weight, appearance, behavior, maxillary occlusal view, and regional anatomic and histological comparisons were recorded within 1 month after birth. Infants from the two groups with similar physiological conditions were selected for continuous maxillofacial and mandibular Micro-CT scan and three-dimensional reconstruction analysis. Ten pregnant rabbits gave birth to 48 live infants. The survival and cleft palate rates were 65.6% and 60.4% respectively. Both groups survived over 1 month with no difference in weight, appearance, and behavior. The cleft type was stable, and anatomical defects, histological characteristics, and nasal-maxillary abnormalities of the cleft were similar to those of humans. There was no statistically significant difference in maxillary and mandible development between the two groups within one month after birth. This congenital cleft palate model is considered to have more research possibilities with efficient cleft induction, reliable feeding methods, stable anatomical defects, and maxillofacial development similar to those seen in humans.

A large number of cloud services provided by cloud data centers have become the most important part of Internet services. In spite of numerous benefits, cloud providers face some challenging issues in accurate large-scale task time series prediction. Such prediction benefits providers since appropriate resource provisioning can be performed to ensure the full satisfaction of their service-level agreements with users without wasting computing and networking resources. In this work, we first perform a logarithmic operation before task sequence smoothing to reduce the standard deviation. Then, the method of a Savitzky-Golay (S-G) filter is chosen to eliminate the extreme points and noise interference in the original sequence. Next, this work proposes an integrated prediction method that combines the S-G filter with Long Short-Term Memory network models to predict task time series at the next time slot. We further adopt a gradient clipping method to eliminate the gradient exploding problem. Furthermore, in the process of model training, we choose optimizer Adam to achieve the best results. Experimental results demonstrate that it achieves better prediction results than some commonly-used prediction methods.

In this paper, a novel method of point clouds segmentation based on Euclidean clustering and multi-plane extraction is newly proposed. To cope with overhanging objects, such as tree branches, a hybrid elevation map assisted with Euclidean clustering is designed. By clustering the 3D point clouds falling into the grid cell, the obstacles above a free space are checked and the corresponding traversable regions below are identified. Furthermore, the time consumption is reduced for the segementation by using the multi-resolution grids method. In addition, the multiplane extraction method based on RANSAC is well adapted to non-flat terrain. In the simulation, a variety of virtual environments are built on Gazebo platform to demonstrate the performance of the proposed algorithm. Moreover, it is also evaluated in the field environments. The results show that the accuracy as well as efficiency of point clouds segmentation achieves superior performance over existing approaches.

Deltamethrin (DM) has become one of the most widely used insecticides in the world due to its low toxicity, high efficiency and low persistence in soil. However, it is still unknown whether DM exposure has any effects on the Hypothalamic-Pituitary-Thyroid (HPT) axis in adolescent mice. In this study, the open field test and circadian activity test showed that DM exposure increased activity. There was no significant difference between the groups in the light/dark box test and nest building test. Forced swimming test showed that after 6 and 12 mg kg−1 DM exposure 28 days, the immobility time was increased and the swimming time was reduced. After 6 mg kg−1 DM treatment, the thyroid stimulating hormone (TSH) content increased, and thyrotropin releasing hormone (TRH), triiodothyronine (T3) and thyroxine (T4) decreased. After exposure to 6 and 12 mg kg−1 DM, mRNA levels of HPT axis-related genes were destroyed. The histological examination showed that, the DM groups mice thyroid tissues appeared expanded thyroid follicles, scanty colloid and hyperplastic thyroid cells. Western blot results showed that the expression level of tyrosine hydroxylase (TH) protein decreased and the content of dopamine transporter (DAT) protein increased in DM treated mice striatum. Collectively, our results indicated that DM exposure could induce thyroid dysfunction and behavioral disorders in adolescent mice.

An imbalanced class problem occurs within abundant real-world applications, e.g., fraud detection, text classification, and cancer diagnosis. Beside balancing the imbalanced data distribution to deal with imbalanced data problems, another significant way to solve the bias-to-majority problem is via proper feature selection. This work is intended to use a feature selection method that can choose a subset of features and make ROC AUC and F-measure results in order to achieve high performance on a minority class. In this paper, a weighted Gini index(WGI) feature selection method is proposed. In order to evaluate the proposed method, a comparison result among Chi-square, F-statistic and Gini index feature selection is shown, and Xgboost is the classifier that is used to test the performance of the subset of features. Experimental results indicate that F-statistic contains the best performance when a few features are selected. However, when the number of selected features increases, WGI feature selection achieves the best results. A comparison between the average results from ROC AUC and F-measure are also presented. It shows that ROC AUC always contains a good performance, even if only a few features are selected, and only changes slightly as the subset of features expands. However, the performance of F-measure achieves a good performance after 60% of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale dataset from Santander Bank. The results show that our approach can achieve higher Area Under the Curve (AUC) and less computational time. We also compare it with filter-based feature selection approaches, i.e., Chi-Square and F-statistic. The results show that it outperforms them but needs slightly more computational efforts.