An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

The development of a single-item Food Choice Questionnaire

Based on the multi-item Food Choice Questionnaire (FCQ) originally developed by Steptoe and colleagues (1995), the current study developed a single-item FCQ that provides an acceptable balance between practical needs and psychometric concerns. Studies 1 (N = 1851) and 2 (2a (N = 3290), 2b (N = 4723), 2c (N = 270)) showed that the single-item FCQ scale has good convergent and discriminant validity. Generally, the results showed the highest correlations with the related multi-item dimensions (>0.40). Study 2 refined the scale. Only the items for convenience (Study 2a), sensory appeal (Study 2b) and mood (Study 2c) needed to be revised (as Study 1 showed a correlation between the multi-item and the single-item below the threshold of 0.60). The results also showed comparable predictive validity. Both methods revealed similar association patterns between food motives and consumption behaviours (Fisher’s z tests revealed agreements of 86.2% for Study 1, 92.9% for Study 2a and 100% for Studies 2b and 2c). Study 3 (N = 6062) showed an example of the added value of a context-specific application for the single-item FCQ. Different motives were shown to be relevant across contexts, and the context-specific motives had additional explained variance beyond the general multi-item FCQ. Studies 2b and 3 also showed the performance of the single-item FCQ in an international context. In sum, the results indicate that the single-item FCQ can be used as a flexible and short substitute for the multi-item FCQ. The study also discusses the conditions that should be considered when using the single-item scale.     

Time-series clustering – A decade review

Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.     

灰局势决策在水利工程开发方案优选中的应用

针对水利工程开发方案优选具有多目标、多层次等特点,建立灰局势决策模型并应用变异系数法确定评价指标权重。利用该模型对灌区改建扩建规划方案进行优选,结果表明这是一种有效的方法。     

提高催化裂化装置(FCCU)柴汽比措施

为了满足市场的需要,中石化天津分公司炼油厂根据FCCU自身特点采取了一系列提高柴汽比的措施,包括:调节反应系统和分馏的操作条件及采用MGD技术,以提高柴油产率。实施后,FCCU的柴汽比从0.5提高到0.9,创造了良好的经济效益。     

热镀锌板(卷)工程设计中的环境保护问题

对热镀锌板(卷)的生产工艺、主要生产设备进行了简要介绍，并就其污染源(工序)、生产过程中产生的污染物及其控制措施进行了评述，同时就设计和生产中宜注意的几个环保问题进行了分析。     

计算机网络安全与防火墙技术

随着信息技术的高速发展和网络信息量的飞速膨胀，同时应有的安全防范措施相对薄弱，因此，非法入侵、对数据进行篡改和窃听等事件时有发生。本文就保证信息安全阐述了网络安全的必要性，论述了网络防火墙安全技术的功能、主要技术、配置、安全措施和防火墙设计思路等。     

液力联轴器喷油着火事故的原因分析及预防

以矿用液力联轴器为研究对象 ,阐明其喷油着火的机理 ,分析其喷油着火的原因并作出事故树分析 ,针对现场使用的情况 ,提出预防措施及发展趋势 ,对矿井安全生产具有指导性作用。     

中国未来电力需求与核能

本文介绍了我国未来电力需求和能源结构，阐述了核能是我国满足经济发达地区电力需求的合理选择，并提出了我国“九五”核电计划和中期目标，以及发展核电中面临的问题和基本对策。