The Value of Grammar in English Teaching

Few students know that grammar is acquired naturally;it need not be taught.Grammar is often misunderstood in the language teaching.There are some students thinking it a waste of time to memorize the rules.They even claim that if the structures are taught,the lessons will be boring.This paper will help to realize how helpful and useful as well as beneficial learning English grammar well will be,not only for students,but also for teachers.     

基于产业链协作平台的商务智能架构及数据挖掘技术探讨

主要对产业链协作平台的商务智能架构及数据挖掘技术进行分析,通过总结当今企业的发展形势,以某企业为例,从商务智能架构和数据挖掘技术两方面进行阐述。相关人员应了解和掌握产业链协作平台,并进行商务智能系统的功能设计、商务智能系统的体系设计,使商务智能架构更好地为企业服务。在数据挖掘技术的研究中,要根据不同的环境选择合适的挖掘技术,更好地进行数据挖掘,为企业提供真实、准确的信息。     

中国参与东北亚投资规则制定所面临的问题及对策

在东北亚区域内,投资规则发展迅猛的同时出现规则间利益界定模糊等问题,阻碍了区域投资合作的发展。中国作为东北亚最大经济体,有能力和义务对其进行治理。本文梳理了中国在东北亚签订投资协定的实践历程,发现中国存在制定的投资规则间参差不齐,给予的条款投资自由化不高和不愿给予高投资标准等问题,并结合中国和东北亚区域实际提出中国应提升中日韩次区域投资规则并推动其他规则跟进,贯彻“负面清单＋准入前国民待遇”原则和加大对外开放力度。     

兴奋剂禁用清单中的兜底条款探讨

摘要：兴奋剂禁用清单中的兜底条款虽然能够弥补清单的漏洞,但因其具有模糊性,并要求运动员承担过度责任,法理上可能与“法无明文规定不为罚原则”以及“比例原则”相悖,然而药物创新和反兴奋剂规则的价值取向使得兜底条款又有其存在的必要性。因此,有必要解决国际体育仲裁院在兴奋剂案件中遇到的兜底条款的适用困难。采用规则分析方法与案例研究方法进行研究认为：应当以成文规则的形式规定兜底条款的适用条件,来增加该条款的强制性;应当鼓励反兴奋剂组织和制药公司的互动合作来扩大兜底条款的作用范围;应当在科学与法律层面加强对检测方法的有效性与合法性的把关。     

Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications

Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications.     

QPLSA: Utilizing quad-tuples for aspect identification and rating

Aspect level sentiment analysis is important for numerous opinion mining and market analysis applications. In this paper, we study the problem of identifying and rating review aspects, which is the fundamental task in aspect level sentiment analysis. Previous review aspect analysis methods seldom consider entity or rating but only 2-tuples, i.e., head and modifier pair, e.g., in the phrase “nice room”, “room” is the head and “nice” is the modifier. To solve this problem, we novelly present a Quad-tuple Probability Latent Semantic Analysis (QPLSA), which incorporates entity and its rating together with the 2-tuples into the PLSA model. Specifically, QPLSA not only generates fine-granularity aspects, but also captures the correlations between words and ratings. We also develop two novel prediction approaches, the Quad-tuple Prediction (from the global perspective) and the Expectation Prediction (from the local perspective). For evaluation, systematic experiments show that: Quad-tuple PLSA outperforms 2-tuple PLSA significantly on both aspect identification and aspect rating prediction for publication datasets. Moreover, for aspect rating prediction, QPLSA shows significant superiority over state-of-the-art baseline methods. Besides, the Quad-tuple Prediction and the Expectation Prediction also show their strong ability in aspect rating on different datasets.     

基于KMV和关联规则算法的行业供应链信用风险传染研究

从行业供应链视角,以钢铁行业和医药流通行业供应链中上市企业市场数据为研究对象,上市公司信用风险大小以KMV模型中的违约距离值代替,利用Apriori算法挖掘上市公司信用风险传染的关联规则。研究结论表明,医药流通行业供应链中企业信用风险传染频率和传染强度均高于钢铁行业供应链中企业;供应链中上下游企业整合以及信用技术引进是影响供应链企业信用风险传染重要影响因素。     

基于半结构化分割的Web热点数据挖掘算法

随着大数据信息技术的发展,数据在线监测和数据挖掘成为计算机信息领域研究的热点。通过对Web热点数据分割挖掘,提高信息热点追踪和Web数据分类能力。传统算法采用非结构化数据挖掘算法,无法有效对Web热点数据进行准确定位和分层挖掘。提出一种基于半结构化分割的Web热点数据挖掘算法。采用半结构化数据进行特征分割,基于优秀基因位进行差分进化,使寻优曲线不断趋于平缓,在多个节点上并行的运行比较脚本,采用半结构化分割,使得Web热点特征挖掘实现自适应寻优,得到Web热点数据的分配因子,提高了挖掘性能。仿真结果表明,该算法获得了良好的效率和精度,提高了Web热点数据挖掘的自适应寻优能力。     

高铁车载温度传感数据状态模式挖掘

通过对高铁车厢车载温度传感数据状态模式的准确挖掘,实现对高铁车厢温度的精确控制,提高高铁运行性能。传统方法中对车载温度传感器的数据状态模式挖掘采用的是半导体气敏传感器测量法进行数据采集,并采用神经网络控制法实现数据状态模式挖掘,方法受限于温度数据的均衡控制无法准确把握,控制效果不好。提出一种基于正态相关的高铁车载温度传感数据状态模式挖掘方法,首先进行温度传感数据采集的硬件系统设计,在原始数据采集的基础上,提取温度传感数据的正态相关状态信息特征,采用PID控制器进行列车车厢的温度调节,温度变化率、积分时间和微分时间通过线性组合的方式进行控制,得到温度传感数据的自相关控制状态方程。数据跟踪器设计成二阶离散数据跟踪器,以满足实际需要。实现了温度传感数据的状态模式挖掘改进。仿真结果表明,算法温度控制和功率控制的稳定性和准确性优越传统算法,车载温度智能调节和控制性能提高。     

立足学科建设 发挥学会优势——《中国肿瘤临床》内容质量建设实践

【目的】探索内容质量建设的有效途径,为国内期刊提供参考与借鉴。【方法】以《中国肿瘤临床》为例,介绍其立足学科建设、发挥学会优势、加强优秀选题与组稿等一系列举措,总结经验与体会。【结果】《中国肿瘤临床》的内容质量与学术影响力得到显著提升,分别于2013年与2015年获得中国科协精品科技期刊项目资助。【结论】内容质量是期刊发展的基石与核心,科技期刊应积极研究与开拓,调动一切有利资源,打造学术精品。