In mobile crowdsensing (MCS), the battery of participants is often limited. When participants perform too many sensing tasks resulting in insufficient remaining energy, they will exit the MCS system. This article mainly addresses the energy balancing problem to prolong the system lifespan. By this means, it can ensure adequate participants and promote the completion of tasks. First, it formulates a discrete time optimization model, which transforms abovementioned problem into the online control of task admission and allocation. In addition, this model uses remaining energy variance of the participants to measure the degree of balance. Next, an online energy balancing strategy (OEBS) is proposed based on the Lyapunov optimization, which can realize energy balance without utility loss. Finally, an approximate optimal policy is presented based on the linear programming and genetic algorithm to solve abovementioned optimization problem. Experiments show that OEBS effectively maintains adequate participants, prolongs the lifespan of MCS system and maximizes the system utility even when there are few participants with multiple tasks. Specifically, the lifespan in OEBS is longer than that in utility optimization algorithm (UOA) and LP-relaxation algorithm significantly. The total utility in OEBS is more than that in UOA. OEBS can maximizes average utility of system by adjusting${\bm{V}}$. In addition, the energy balancing ability of OEBS is always effective as${\bm{V}}$changes.

Text-to-SQL emerges to play an important role in interactive data analysis, which provides a friendly interface for converting natural language into relational database language (i.e., SQL). In order to translate a user’s query into an executable SQL statement, semantic parsing is essential to the transformation process. In particular, existing efforts provide some feasible solutions, and state-of-the-art models mainly adopt the sketch-based paradigm such that template values are to be filled. To this end, most methods extract values based on column representations. However, if the query contains multiple values that belong to different columns, these methods may fail to extract the values accurately. Moreover, it can be difficult to infer the right values when the query does not explicitly mention the corresponding column names. To bridge the gap, we propose a novel neural architecture, namely, ER-SQL for learning enhanced representations for Text-to-SQL. Based on pre-trained model BERT, ER-SQL uses column contents to better extract features of columns. Moreover, ER-SQL harnesses the column representations to latently reformulate the query. To verify the effectiveness of ER-SQL, comprehensive experiments demonstrate that ER-SQL achieves better results than existing models on the benchmark dataset WikiSQL, as well as on a representative Chinese dataset TableQA.

The evolutionary multi-objective optimization algorithm was used to optimize the clustering and splitting of nodes in the construction of inverted spatial objects index tree(ITSR) in this paper. Considering the objective factors including object's coverage, overlap, group center distance, directory rectangle perimeter and the word similarity between tree nodes, etc., a multi-objective optimization model for solving the optimal ITSR construction is established. To solve the model, this paper find a novel method of constructing high efficiency inverted text-spatial R-tree index. The experimental results show that the algorithm supports the multi-objective optimal clustering of ITSR with high efficiency and accuracy .

MOTIVATION: Small non-coding RNAs (ncRNAs), especially microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), play key roles in many biological processes. However, only a few tools can be used to develop the optimal primer or probe design for the expression profile of small ncRNAs. Here, we developed sRNAPrimerDB, the first automated primer designing and query web service for small ncRNAs. RESULTS: The primer online designing module of sRNAPrimerDB is composed of primer design algorithms and quality evaluation of the polymerase chain reaction (PCR) primer. Five types of primers, namely, generic or specific reverse transcription primers, specific PCR primers pairs, TaqMan probe, double-hairpin probe and hybridization probe for different small ncRNA detection methods, can be designed and searched using this service. The quality of PCR primers is further evaluated using melting temperature, primer dimer, hairpin structure and specificity. Moreover, the sequence and size of each amplicon are also provided for the subsequent experiment verification. At present, 531 306 and 2 941 669 primer pairs exist across 223 species for miRNAs and piRNAs, respectively, according to sRNAPrimerDB. Several primers designed by sRNAPrimerDB are further successfully validated by subsequent experiments. AVAILABILITY AND IMPLEMENTATION: sRNAPrimerDB is a valuable platform that can be used to detect small ncRNAs. This module can be publicly accessible at http://www.srnaprimerdb.com or http://123.57.239.141. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.