National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

A Survey of Query Auto Completion in Information Retrieval

Abstract: In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prex consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prex to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benets of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the eld of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the eld, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.

Extended Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Data Focusing

Abstract: In this paper, an extended nonlinear chirp scaling (ENLCS) algorithm for focusing synthetic aperture radar data acquired at high resolution and highly squint angle is proposed. The whole processing of the ENLCS consists of the following three steps. First, a linear range walk correction is used to remove the linear component of target range cell migration (RCM) and to mitigate the range-azimuth coupling of the 2-D spectrum. Second, a bulk second range compression (SRC) is performed in the 2-D frequency domain for compensating the residual RCM, SRC term, and higher order range-azimuth coupling terms. Third, a modified azimuth NLCS (ANLCS) operation is applied to equalize the azimuth frequency modulation rate for azimuth compression. By adopting higher order approximation processing and by properly selecting the scaling coefficients, the proposed modified ANLCS operation has better accuracy and little image misregistration. The overall focusing procedure of the ENLCS algorithm only involves fast Fourier transform and complex multiplication, which means easier implementation and higher efficiency. The experimental results with simulated data prove the effectiveness of the proposed algorithm.

A Novel Ensemble Method for Imbalanced Data Learning

Abstract: Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.