GTR: An SQL Generator With Transition Representation in Cross-Domain Database Systems.

Recent studies have focused on using natural language (NL) to automatically retrieve useful data from database (DB) systems. As an important component of autonomous DB systems, the NL-to-SQL technique can assist DB administrators in writing high-quality SQL statements and make persons with no SQL background knowledge learn complex SQL languages. However, existing studies cannot deal with the issue that the expression of NL inevitably mismatches the implementation details of SQLs, and the large number of out-of-domain (OOD) words makes it difficult to predict table columns. In particular, it is difficult to accurately convert NL into SQL in an end-to-end fashion. Intuitively, it facilitates the model to understand the relations if a "bridge" transition representation (TR) is employed to make it compatible with both NL and SQL in the phase of conversion. In this article, we propose an automatic SQL generator with TR called GTR in cross-domain DB systems. Specifically, GTR contains three SQL generation steps: 1) GTR learns the relation between questions and DB schemas; 2) GTR uses a grammar-based model to synthesize a TR; and 3) GTR predicts SQL from TR based on the rules. We conduct extensive experiments on two commonly used datasets, that is, WikiSQL and Spider. On the testing set of the Spider and WikiSQL datasets, the results show that GTR achieves 58.32% and 71.29% exact matching accuracy which outperforms the state-of-the-art methods, respectively.

Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis.

Long short-term memory (LSTM) neural networks and attention mechanism have been widely used in sentiment representation learning and detection of texts. However, most of the existing deep learning models for text sentiment analysis ignore emotion's modulation effect on sentiment feature extraction, and the attention mechanisms of these deep neural network architectures are based on word- or sentence-level abstractions. Ignoring higher level abstractions may pose a negative effect on learning text sentiment features and further degrade sentiment classification performance. To address this issue, in this article, a novel model named AEC-LSTM is proposed for text sentiment detection, which aims to improve the LSTM network by integrating emotional intelligence (EI) and attention mechanism. Specifically, an emotion-enhanced LSTM, named ELSTM, is first devised by utilizing EI to improve the feature learning ability of LSTM networks, which accomplishes its emotion modulation of learning system via the proposed emotion modulator and emotion estimator. In order to better capture various structure patterns in text sequence, ELSTM is further integrated with other operations, including convolution, pooling, and concatenation. Then, topic-level attention mechanism is proposed to adaptively adjust the weight of text hidden representation. With the introduction of EI and attention mechanism, sentiment representation and classification can be more effectively achieved by utilizing sentiment semantic information hidden in text topic and context. Experiments on real-world data sets show that our approach can improve sentiment classification performance effectively and outperform state-of-the-art deep learning-based methods significantly.

Analysis and design on intervehicle distance control of autonomous vehicle platoons.

This paper presents an intervehicle distance control (IDC) to solve the problem of autonomous vehicle platooning, motivated by future automated highway system (AHS) or smart road which is proposed as intelligent transportation system (ITS) technology. First the velocity and position control of the single vehicle is studied based on internal model compensator. And then the platooning problem on multiple vehicles is solved in the light of multiagent concept. Moreover, the platoon condition is derived for the corresponding scheme. Further we analyze the influence of controller parameters on the whole system, and propose the guidance for parameter design. Finally some simulations are used to verify the effectiveness of the proposed IDC scheme with an analysis on controller parameters.

How to balance the bioinformatics data: pseudo-negative sampling.

BACKGROUND: Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance of the minority class of positive samples. Therefore, how to balance the bioinformatic data becomes a very challenging and difficult problem. RESULTS: In this study, we propose a new data sampling approach, called pseudo-negative sampling, which can be effectively applied to handle the case that: negative samples greatly dominate positive samples. Specifically, we design a supervised learning method based on a max-relevance min-redundancy criterion beyond Pearson correlation coefficient (MMPCC), which is used to choose pseudo-negative samples from the negative samples and view them as positive samples. In addition, MMPCC uses an incremental searching technique to select optimal pseudo-negative samples to reduce the computation cost. Consequently, the discovered pseudo-negative samples have strong relevance to positive samples and less redundancy to negative ones. CONCLUSIONS: To validate the performance of our method, we conduct experiments base on four UCI datasets and three real bioinformatics datasets. According to the experimental results, we clearly observe the performance of MMPCC is better than other sampling methods in terms of Sensitivity, Specificity, Accuracy and the Mathew's Correlation Coefficient. This reveals that the pseudo-negative samples are particularly helpful to solve the imbalance dataset problem. Moreover, the gain of Sensitivity from the minority samples with pseudo-negative samples grows with the improvement of prediction accuracy on all dataset.

A novel Chinese herbal medicine clustering algorithm via artificial bee colony optimization.

Traditional Chinese medicine (TCM) has become popular and been viewed as an effective clinical treatment across the world. Accordingly, there is an ever-increasing interest in performing data analysis over TCM data. Aiming to cope with the problem of excessively depending on empirical values when selecting cluster centers by traditional clustering algorithms, an improved artificial bee colony algorithm is proposed by which to automatically select cluster centers and apply it to aggregate Chinese herbal medicines. The proposed method integrates the following new techniques: (1) improving the artificial bee colony algorithm by applying a new searching strategy of neighbour nectar, (2) employing the improved artificial bee colony algorithm to optimize the parameters of the cutoff distance dc, the local density ρi and the minimum distance δi between the element i and any other element with higher density in the cluster algorithm by fast search and finding of density peaks (called DP algorithm) to find the optimal cluster centers, in order to clustering herbal medicines in an accurate fashion with the guarantee of runtime performance. Extensive experiments were conducted on the UCI benchmark datasets and the TCM datasets and the results verify the effectiveness of the proposed method by comparing it with classical clustering algorithms including K-means, K-mediods and DBSCAN in multiple evaluation metrics, that is, Silhouette Coefficient, Entropy, Purity, Precision, Recall and F1-Measure. The results show that the IABC-DP algorithm outperforms other approaches with good clustering quality and accuracy on the UCI and the TCM datasets as well. In addition, it can be found that the improved artificial bee colony algorithm can effectively reduce the number of iterations when compared to the traditional bee colony algorithm. In particular, the IABC-DP algorithm is applied to cluster multi-dimensional Chinese herbal medicines and the result shows that it outperforms other clustering algorithms in clustering Chinese herbal medicines, which can contribute to a larger effort targeted at advancing the study of discovering composition rules of traditional Chinese prescriptions.