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.

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.

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.

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.

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.