An attention-based deep learning model for credibility assessment of online health information

With the surge of searching and reading online health-based articles, maintaining the quality and credibility of online health-based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health-related articles' credibility. The paper proposed an Attention-based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed “Pubhealth,” which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1-score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short-term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN-CNN. The experimental results showed that the proposed model produced state-of-the-art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1-score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles.     

An improved Urdu stemming algorithm for text mining based on multi-step hybrid approach

Stemming is the basic operation in Natural language processing (NLP) to remove derivational and inflectional affixes without performing a morphological analysis. This practice is essential to extract the root or stem. In NLP domains, the stemmer is used to improve the process of information retrieval (IR), text classifications (TC), text mining (TM) and related applications. In particular, Urdu stemmers utilize only uni-gram words from the input text by ignoring bigrams, trigrams, and n-gram words. To improve the process and efficiency of stemming, bigrams and trigram words must be included. Despite this fact, there are a few developed methods for Urdu stemmers in the past studies. Therefore, in this paper, we proposed an improved Urdu stemmer, using hybrid approach divided into multi-step operation, to deal with unigram, bigram, and trigram features as well. To evaluate the proposed Urdu stemming method, we have used two corpora; word corpus and text corpus. Moreover, two different evaluation metrics have been applied to measure the performance of the proposed algorithm. The proposed algorithm achieved an accuracy of 92.97% and compression rate of 55%. These experimental results indicate that the proposed system can be used to increase the effectiveness and efficiency of the Urdu stemmer for better information retrieval and text mining applications.     

Fast insertion methods of a double‐array structure

A double‐array is a compact and fast data structure for a trie, but it degrades the speed of insertion for a large set of keys. In this paper, two kinds of methods for improving insertion are presented. The basic functions for retrieval, insertion and deletion are implemented in the C language. Comparing with the original double‐array for large sets of keys, the improved double‐array is about six to 320 times faster than that for insertion. Copyright © 2001 John Wiley & Sons, Ltd.     

A fast and compact elimination method of empty elements from a double‐array structure

A double‐array is a well‐known data structure to implement the trie. However, the space efficiency of the double‐array degrades with the number of key deletions because the double‐array keeps empty elements produced by the key deletion. This paper presents a fast and compact elimination method of empty elements using properties of the trie nodes that have no siblings. The present elimination method is implemented by C language. From simulation results for large sets of keys, the present elimination method is about 30–330 times faster than the conventional elimination method and maintains high space efficiency. Copyright © 2003 John Wiley & Sons, Ltd.     

一种基于listwise的在线学习书目排序检索算法

高效检索是数字图书馆的核心业务之一,其中排序是高效信息检索的核心问题。给定一系列的书目列表,利用排序模型生成目标书目的排序列表。将学习排序算法应用于信息检索领域时,常用方法是通过最小化pairwise损失函数值来优化排序模型。然而,已有结论表明,pairwise损失值最小化不一定能得到listwise算法的最佳排序性能。并且将在线学习排序算法与listwise算法相结合也非常困难。提出了一种基于listwise的在线学习排序算法,旨在保证listwise算法性能优势的前提下,实现在线学习排序算法,从而降低检索复杂度。首先解决将在线学习排序算法与listwise算法相结合的问题;然后通过最小化基于预测列表和真实列表定义的损失函数来优化排序模型;最后提出基于online-listwise算法的自适应学习率。实验结果表明,所提出算法具有较好的检索性能和检索速度。     

The effectiveness of refutation with logic vs. indignation in restoring the credibility of and trust in a government organization: A heuristic‐systematic model of crisis communication processing

Across two experiments, this study examines the relative effectiveness of refutational messages employing different rhetorical modes in mitigating the reputational damage from an activist’s accusation of which validity can be contentious. Experiment 1 reveals that an appeal to logic generates more positive evaluations than an appeal to indignation in the message evaluations of transparency and reliability. Experiment 2 shows that an appeal to logic yields more positive attitude, higher perceptions of credibility and trust in the organization than an appeal to indignation when crisis involvement is high. The results of this study support the theoretical proposition of a dual‐process model of persuasion—individuals’ processing of crisis discourse is greatly affected by their level of involvement with the crisis.     

词性标注中生词处理算法研究

词性兼类是自然语言理解必须解决的一类非常重要的歧义现象,尤其是对生词的词性歧义处理有很大的难度。文章基于隐马尔科夫模型(HMM),通过将生词的词性标注问题转化为求词汇发射概率,在词性标注中提出了一种生词处理的新方法。该方法除了用到一个标注好的单语语料库外,没使用任何其他资源(比如语法词典、语法规则等),封闭测试正确率达97%左右,开放测试正确率也达95%左右,基本上达到了实用的程度。同时还给出了与其他同样基于HMM的词性标注方法的测试比较结果,结果表明本文方法的标注正确率有较大的提高。     

An open source and modular search engine for biomedical literature retrieval

This work presents the bioMine system, a full‐text natural language search engine for biomedical literature. bioMine provides search capabilities based on the full‐text content of documents belonging to a database composed of scientific articles and allows users to submit their search queries using natural language. Beyond the text content of articles, the system engine also uses article metadata, empowering the search by considering extra information from picture and table captions. bioMine is publicly released as an open‐source system under the MIT license.     

From French Wikipedia to Erudit: A test case for cross‐domain open information extraction

In this paper, we describe an open information extraction pipeline based on ReVerb for extracting knowledge from French text. We put it to the test by using the information triples extracted to build an entity classifier, ie, a system able to label a given instance with its type (for instance, Michel Foucault is a philosopher). The classifier requires little supervision. One novel aspect of this study is that we show how general domain information triples (extracted from French Wikipedia) can be used for deriving new knowledge from domain‐specific documents unrelated to Wikipedia, in our case scholarly articles focusing on the humanities. We believe that the present study is the first that focuses on such a cross‐domain, recall‐oriented approach in open information extraction. While our system's performance shows room for improvement, manual assessments show that the task is quite hard, even for a human, in part because of the cross‐domain aspect of the problem we tackle.     

ArA*summarizer: An Arabic text summarization system based on subtopic segmentation and using an A* algorithm for reduction

Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster-based, and graph-based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall-oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n-gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works.     

A new iterative algorithm for solving H∞ control problem of continuous‐time Markovian jumping linear systems based on online implementation

A new online iterative algorithm for solving the H_∞ control problem of continuous‐time Markovian jumping linear systems is developed. For comparison, an available offline iterative algorithm for converging to the solution of the H_∞ control problem is firstly proposed. Based on the offline iterative algorithm and a new online decoupling technique named subsystems transformation method, a set of linear subsystems, which implementation in parallel, are obtained. By means of the adaptive dynamic programming technique, the two‐player zero‐sum game with the coupled game algebraic Riccati equation is solved online thereafter. The convergence of the novel policy iteration algorithm is also established. At last, simulation results have illustrated the effectiveness and applicability of these two methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Two‐stage auxiliary model gradient‐based iterative algorithm for the input nonlinear controlled autoregressive system with variable‐gain nonlinearity

This article focuses on the parameter estimation problem of the input nonlinear system where an input variable‐gain nonlinear block is followed by a linear controlled autoregressive subsystem. The variable‐gain nonlinearity is described analytical by using an appropriate switching function. According to the gradient search technique and the auxiliary model identification idea, an auxiliary model‐based stochastic gradient algorithm with a forgetting factor is presented. For the sake of improving the parameter estimation accuracy, an auxiliary model gradient‐based iterative algorithm is proposed by utilizing the iterative identification theory. To further optimize the performance of the algorithm, we decompose the identification model of the system into two submodels and derive a two‐stage auxiliary model gradient‐based iterative (2S‐AM‐GI) algorithm by using the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms and show that the 2S‐AM‐GI algorithm has higher identification efficiency compared with the other two algorithms.     

Automatically solving two‐variable linear algebraic word problems using text mining

The teaching and learning of algebraic word problems is a basic component of elementary education. Recently, to facilitate its learning, a few approaches for automatically solving algebraic and arithmetic word problems have been proposed. These systems generally use either natural language processing (NLP) or a combination of NLP and machine learning. However, they have low accuracy due to their large feature sets, extracted using limited preprocessing techniques. In this research work, we propose a template‐based approach that was developed by following a two‐step process. In the first step, we predict an equation template from a training dataset using NLP and a classification mechanism. The next step is to instantiate the predicted template with nouns and numbers through reasoning. To validate the proposed methodology, a prototype system was implemented. We then compared the proposed system with the existing systems using their respective datasets and the proposed dataset. The experimental results show improvement in accuracy, with an average precision of 80.6% and average recall of 83.5%.     

An event‐flow model of GUI‐based applications for testing

Graphical user interfaces (GUIs) are by far the most popular means used to interact with today's software. The functional correctness of a GUI is required to ensure the safety, robustness and usability of an entire software system. GUI testing techniques used in practice are resource intensive; model‐based automated techniques are rarely employed. A key reason for the reluctance in the adoption of model‐based solutions proposed by researchers is their limited applicability; moreover, the models are expensive to create. Over the past few years, the present author has been developing different models for various aspects of GUI testing. This paper consolidates all of the models into one scalable event‐flow model and outlines algorithms to semi‐automatically reverse‐engineer the model from an implementation. Earlier work on model‐based test‐case generation, test‐oracle creation, coverage evaluation, and regression testing is recast in terms of this model by defining event‐space exploration strategies (ESESs) and creating an end‐to‐end GUI testing process. Three such ESESs are described: for checking the event‐flow model, test‐case generation, and test‐oracle creation. Two demonstrational scenarios show the application of the model and the three ESESs for experimentation and application in GUI testing. Copyright © 2007 John Wiley & Sons, Ltd.     

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.     

An iterative design procedure for the synthesis of generalized dual‐bandpass filters

This article presents an iterative design technique to calculate the optimal position of transmission zeros in dual‐bandpass filters. In the proposed design technique, the characteristic function of the filter is linearized around the displacement of the transmission zeros. A linear system is then obtained and solved in combination with a damping technique used to guarantee the convergence. Design examples which demonstrate the efficiency of the proposed method are presented and an experimental verification at 4 GHz using open‐loop resonators validates the methodology. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

An iteration‐based hybrid parallel algorithm for tridiagonal systems of equations on multi‐core architectures

An optimized parallel algorithm is proposed to solve the problem occurred in the process of complicated backward substitution of cyclic reduction during solving tridiagonal linear systems. Adopting a hybrid parallel model, this algorithm combines the cyclic reduction method and the partition method. This hybrid algorithm has simple backward substitution on parallel computers comparing with the cyclic reduction method. In this paper, the operation count and execution time are obtained to evaluate and make comparison for these methods. On the basis of results of these measured parameters, the hybrid algorithm using the hybrid approach with a multi‐threading implementation achieves better efficiency than the other parallel methods, that is, the cyclic reduction and the partition methods. In particular, the approach involved in this paper has the least scalar operation count and the shortest execution time on a multi‐core computer when the size of equations meets some dimension threshold. The hybrid parallel algorithm improves the performance of the cyclic reduction and partition methods by 19.2% and 13.2%, respectively. In addition, by comparing the single‐iteration and multi‐iteration hybrid parallel algorithms, it is found that increasing iteration steps of the cyclic reduction method does not affect the performance of the hybrid parallel algorithm very much. Copyright © 2015 John Wiley & Sons, Ltd.     

《元朝秘史》电子文本检索系统的研制

本文概要地介绍13世纪《元朝秘史》的文献背景及原文所独有的复杂文本形式,通过对文本的内涵分析和版面分析,设计了关于《元朝秘史》电子检索系统的研制方案。其中主要解决了原文三行一体显示格式的还原问题,而且系统可以分别对原文汉字音写、汉语译文、汉字旁译、语音语法标注等不同部分进行检索和统计。检索输出结果包括研究者最重视的传统学术章节号、卷页码、在电子文本出现的具体位置。另外,系统对检索词采用了上下文检索技术,输出文本包括检索词的部分上下文内容。本系统基本满足历史、文学和语言研究的应用需求。     

基于混沌混合优化的回转干燥窑出料含水量软测量

回转干燥窑由于出料含水量难以在线测量,其过程控制一直是一大难题.本文在分析回转干燥窑干燥过程的基础上,提出了基于分布式RBF网络的出料含水量软测量模型及其优化学习算法,首先根据先验知识和聚类将输入空间划分成多个子空间,对于每个子空间的RBF网络,由FCM算法和分区校验熵确定隐含节点数,中心向量的学习采用混沌优化与FCM相结合的混合算法.设计了获取样本数据的实验,得到了出料含水量软测量模型,此模型已成功应用于干燥窑的推断控制中.     

An iterative Q‐learning scheme for the global stabilization of discrete‐time linear systems subject to actuator saturation

In this paper, we propose a model‐free algorithm for global stabilization of linear systems subject to actuator saturation. The idea of gain‐scheduled low gain feedback is applied to develop control laws that avoid saturation and achieve global stabilization. To design these control laws, we employ the framework of parameterized algebraic Riccati equations (AREs). Reinforcement learning techniques are developed to find the solution of the parameterized ARE without requiring any knowledge of the system dynamics. In particular, we present an iterative Q‐learning scheme that searches for a low gain parameter and iteratively solves the parameterized ARE using the Bellman equation. Both state feedback and output feedback algorithms are developed. It is shown that the proposed scheme achieves model‐free global stabilization under bounded controls and convergence to the optimal solution of the ARE is achieved. Simulation results are presented that confirm the effectiveness of the proposed method.