高阶英汉学习词典用法信息标示的现状与问题

用法既指语言的所有使用规则,又指语言运用的好与坏、正与误、标准与非标准。高阶英汉学习词典普遍采用用法标签标示词语或义项的用法信息,部分词典采用用法说明。为顺应语言研究及学习词典编纂的发展趋势,高阶英汉学习词典的编纂者尚需明示用法标签的含义、拓展用法信息的标示方式,并针对中国英语学习者的特殊疑难点更新标示内容。

论高阶英语学习词典用法信息的嬗变与发展趋势

用法信息指词典对词的拼写、语法特征和得体性等问题所提供的指导,包含丰富的语义信息和使用规则,对语言的理解与表达均有重要的指导作用。本文介绍用法信息的传统呈现方式,然后以高阶英语学习词典为研究对象,分析其中用法信息的嬗变轨迹并总结其发展趋势,以期对未来的用法信息研究与设计有一定的帮助。

浅谈语用信息在双语词典中的处理

语言学与词典编纂的关系密不可分,语用学是语言学的一个较新领域,词典应该既充分描述语言知识,又反映语用知识。语言学中的许多研究成果,尤其是语用学的研究成果大量应用在词典编纂中。国外著名单语词典处理词语的语用信息的正确做法通常有三种,国内双语词典在这个方面存在着许多不足之处,作者提出了改进建议。

侵犯公民个人信息罪的司法适用探究——以两高最新司法解释为视角

2017年5月发布的《关于办理侵犯公民个人信息刑事案件适用法律若干问题的解释》使《刑法修正案(九)》中有关侵犯公民个人信息罪的司法适用更为明确、具体、周延。其中,"公民个人信息"的内涵应采用有效关联说,把公民的身份识别信息和特定活动信息包含在内;违反"国家有关规定"应以客观目的解释为基础,以维护罪刑法定原则的权威。这一司法解释使侵犯公民个人信息罪的入罪标准逐渐全面,同时采取多层次认定模式,使入罪与出罪的方式更加灵活。

外研社建宏《英汉多功能词典》透视

外研社建宏《英汉多功能词典》透视胡文仲北京外国语大学教授一书在手排难解忧外研社建宏《英汉多功能词典》是外语教学与研究出版社与台湾建宏出版社最近推出的一部令人耳目一新的词典。我认为这部词典的最大特点是编者充分考虑到中国学生的需要。一部词典在手，可以解决...

Digital Rights Management：Model,Technology and Application

with rapid achievement of current information technology and computing ability and applications,much more digital content such as films,cartoons,design drawings,office documents and software source codes are produced in daily work,however to protect the content being copying,shared or deliberately stolen by inside or outside,digital rights management(DRM) became more and more important for digital content protection.In this paper,we studied various DRM model,technology and application,and first proposed DRM Security Infrastructure(DSI),in which we defined encryption,hash,signature algorithm,watermarking algorithms,authentication,usage control,trusted counter,conditional trace,secure payment,and based on the DSI we then proposed a whole classification approach and architecture of all kinds of DRMs,in which we proposed 6 typical classes of copyrights and content protection DRMs architecture:(1) Software-oriented DRM,(2) e Book-oriented DRM,(3) Video-oriented DRM,(4) Image-Oriented DRM(5) Unstructured data oriented DRM,(6) Text-oriented DRM.Based on the above DSI,we then proposed a dynamic DRM model selection method for various DRM application,which can be adapted dynamically for different technology of different applications,which can provide awhole solution for variant DRM development in a rapid and customized mode.The proposed DRM method,technology and application in this paper provided a common,flexible and extendable solution for variant DRM scenes,and can support rapid and customized development.Moreover,we proposed an opinion that the future life will enter into a new era that the content usage and consumption will not again adopt DRM technology rather than with law,liberty and morality.

Mining association rules in incomplete information systems

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

Multi-factors decision-making entropy method and its application in engineering management

In order to meet the strict requirements for information in engineering management, the positive interval （0, 1 ] in Shannon information entropy is extended to the real number interval [ - 1, 1 ]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up for. The multi-factors engineering decision-making methods are proposed, and some critical problems are solved in the practical engineering management decision-making process.

A Precise Information Extraction Algorithm for Lane Lines

Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.

Adaptive and Dynamic Mobile Phone Data Encryption Method

To enhance the security of user data in the clouds,we present an adaptive and dynamic data encryption method to encrypt user data in the mobile phone before it is uploaded.Firstly,the adopted data encryption algorithm is not static and uniform.For each encryption,this algorithm is adaptively and dynamically selected from the algorithm set in the mobile phone encryption system.From the mobile phone's character,the detail encryption algorithm selection strategy is confirmed based on the user's mobile phone hardware information,personalization information and a pseudo-random number.Secondly,the data is rearranged with a randomly selected start position in the data before being encrypted.The start position's randomness makes the mobile phone data encryption safer.Thirdly,the rearranged data is encrypted by the selected algorithm and generated key.Finally,the analysis shows this method possesses the higher security because the more dynamics and randomness are adaptively added into the encryption process.

Field analysis of metallogenic information and its application

Focusing on the issue to deal with inadequate extraction of metallogenic information especially geological information,a new method of extracting metallogenic information based on field model,i.e.the field analysis method of metallogenic information,was proposed.In addition,a case study by using the method of the extraction of metallogenic information from the west Guangxi and southeast Yunnan district as an example was performed.The representation method for the field models of metallogenic information,including the metallogenic influence field model and the metallogenic distance field model,was discussed by introducing the concept of the field theory,based on the characteristic analysis of the distance gradualness and the influence superposition of metallogenic information.According to the field theory superposition principle and the spatial distance analysis method,the mathematical models for the metallogenic influence field and the metallogenic distance field of point,line and area geological bodies were derived out by using parameter equation and calculus.Based on the metallogenic background analysis,the metallogenic information field models of synsedimentary faults and manganese sedimentary basins were built.The relationship between the metallogenic information fields and the manganese mineralization distribution was also investigated by using the method of metallogenic information field analysis.The instance study indicates that the proposed method of metallogenic information field analysis is valid and useful for extracting the ore-controlling information of synsedimentary faults and manganese sedimentary basins in the study area,with which the extraction results are significant both statistically and geologically.

SERVICE COMMUNITY CONSTRUCTION METHOD OF INTERNET OF THINGS BASED ON SEMANTIC SIMILARITY

Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.

Assessing Suitability of Rural Settlements Using an Improved Technique for Order Preference by Similarity to Ideal Solution

Land suitability assessment is a prerequisite phase in land use planning; it guides toward optimal land use by providing information on the opportunities and constraints involved in the use of a given land area. A geographic information system-based procedure, known as rural settlement suitability evaluation(RSSE) using an improved technique for order preference by similarity to ideal solution(TOPSIS), was adopted to determine the most suitable area for constructing rural settlements in different geographical locations. Given the distribution and independence of rural settlements, a distinctive evaluation criteria system that differed from that of urban suitability was established by considering the level of rural infrastructure services as well as living and working conditions. The unpredictable mutual interference among evaluation factors has been found in practical works. An improved TOPSIS using Mahalanobis distance was applied to solve the unpredictable correlation among the criteria in a suitability evaluation. Uncertainty and sensitivity analyses obtained via Monte Carlo simulation were performed to examine the robustness of the model. Daye, a resource-based city with rapid economic development, unsatisfied rural development, and geological environmental problems caused by mining, was used as a case study. Results indicate the following findings: 1) The RSSE model using the improved TOPSIS can assess the suitability of rural settlements, and the suitability maps generated using the improved TOPSIS have higher information density than those generated using traditional TOPSIS. The robustness of the model is improved, and the uncertainty is reduced in the suitability results. 2) Highly suitable land is mainly distributed in the northeast of the study area, and the majority of which is cultivated land, thereby leading to tremendous pressure on the loss of cultivated land. 3) Lastly, 12.54% of the constructive expansion permitted zone and 8.36% of the constructive expansion conditionally permitted zone are situated in an unsuitable area, which indicates that the general planning of Daye lacks the necessary verification of suitability evaluation. Guidance is provided on the development strategy of rural settlement patches to support decision making in general land use planning.

Energy Efficient Switch Policy for Small Cells

Switch policy is essential for small cells to properly serve variable number of users in an energy efficient way.However,frequently switching small cell base stations(SBSs) may increase the network operating cost,especially when there is an nonnegligible start-up energy cost.To this end,by observing the variety of user number,we focus on the design of a switch policy which minimize the cumulative energy consumption.A given user transmission rate is guaranteed and the capability of SBSs are limited as well.According to the knowledge on user number variety,we classify the energy consumption problem into two cases.In complete information case,to minimize the cumulative energy consumption,an offline solution is proposed according to critical segments.A heuristic algorithm for incomplete information case(HAIIC) is proposed by tracking the difference of cumulative energy consumption.The upper bound of the Energy Consumption Ratio(ECR) for HAIIC is derived as well.In addition,a practical Q-learning based probabilistic policy is proposed.Simulation results show that the proposed HAIIC algorithm is able to save energy efficiently.

Optimization model of unascertained measurement for underground mining method selection and its application

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.

Context-Aware Scheduling Algorithm in Smart Home System

The main idea of pervasive computing is to make computing exist everywhere in the physical world.The smart home system is an important realisation of pervasive computing whose aim is to provide system users with an intelligent life experience.The key technique used to realise this is context awareness.Contexts in the living space can provide large amounts of information regarding users’behaviours and habits.Together with an information system,it can automatically execute many common operations of applications,instead of users,and can make the applications"smart".However,since contexts in the environment are diverse and sensitive,it is difficult to choose the ones that are most useful to the users’current activity.A proper scheduling strategy should first consider the users’demand.This paper proposes a context-aware scheduling algorithm that is based on correlation,with the purpose of improving the utilization rate of context collections.Experiments show that with the priority based on correlation in low-level contexts,the scheduling of reasoning tasks can reduce the cost of transmission.

Improvement of Euler＇s Method Using Particle Swarm Optimization

Many problems in applied mathematics lead to ordinary differential equation. In this paper, a considerable refinement and improvement of the Euler＇s method obtained using PSO （particle swarm optimization） was presented. PSO is a technique based on the cooperation between particles. The exchange of information between these particles allows to resolve difficult problems. This approach is carefully handled and tested with an illustrated example.

Research on Tourism E-commerce based on Data Mining

This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user database, the shopping cart）, access to relevant user preference information for tourism commodity. Based on these models, the paper presents recommended strategies for the site registered users, and has had the corresponding formulas for calculating the current user of certain items recommended values and the corresponding recommendation algorithm, and the system can get a recommendation for user.