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关键词： 大数据背景下   矿山开采   信息资源整合管理  

摘要： 矿山开采中的资源信息结构十分复杂,为了实现对复杂数据信息的智能化管理和整合,设计了基于大数据背景下的矿山开采智能化信息资源整合管理系统。对系统硬件结构、自动化整合模块、查询定位模块以及智能化管理模块进行设计,并通过对比实验验证该系统与传统系统相比对资源的管理效率得到了明显的提高。

关键词： 工程档案   归档工作   立卷归档   档案管理   工程勘测设计   自我保护   信息技术   原始档案  

摘要： 工程原始档案是在工程勘测设计过程中产生的原始记录,其归档工作是设计企业设计流程的重要环节,也是企业提高设计成品质量的可靠保证。原始档案立卷归档的准确性、及时性是对企业及设计人员的一种自我保护,原始档案归档率的高低反映了企业生产设计的规范化水平和管理水平。随着信息技术的不断发展和信息化应用的不断深入,将信息资源转换成工程原始档案的方法研究也成为了档案管理的新课题。

关键词： 互联网+   课堂互动   高职课堂   信息资源  

摘要： 随着社会科技的不断进步,我国的互联网也得到了迅速的发展。尤其是近年来,在互联网+的背景下,信息技术对我国各方面都产生强烈的冲击,其中对教育事业的冲击作用更强,彻底地改变了很多我国传统的教育教学模式,使我国的教育事业发生了翻天覆地的变化。而对于高职学生的学习来说,信息技术的引入极大地促进了高职教育的发展。为了更好适应时代的发展与进步,学生必须掌握更多的信息和知识,这些都得益于互联网技术。目前传统的课堂教学方法已经远远不能满足学生的需求,其知识内容受到了理念以及文本知识的约束,进而极大地限制了学生获取知识的途径,限制了信息的传播,使信息的时效性和有效性受到了一定程度的影响,不能更好地适应目前我国教育事业的发展。然而,“互联网+”时代的到来,为我国的教育改革提供了一个很好的机会,所以应该积极地面对互联网时代带来的挑战,不断的进行角色转换,开发创新性教学课堂,使教学效果得到显著提升。

关键词： “双一流”   高校数字图书馆   信息服务  

摘要： 建设世界一流大学和一流学科(简称"双一流"),已经成为国家核心竞争力快速提升的必经之路,而信息资源是"双一流"建设不可或缺的资源保障,尤其是高校数字图书馆的数字资源,是实现"双一流"的重要前提。本文对我国现有的高校数字图书馆信息资源服务进行分析,在此基础上,提出了高校数字图书馆智能化信息资源服务模式,并从资源配置方面,对高校数字图书馆如何加强数字资源建设进行探析。

关键词： 核心矢量要素   数据整合   地图配图   经验总结  

摘要： 针对全球地理信息资源建设与维护更新项目的核心矢量要素数据生产,本文探讨了对交通、水系、地名、境界政区要素的数据整合、地图配图等内容,针对在数据生产过程遇到问题的解决方法进行总结,为核心矢量要素数据生产提供参考。

关键词： 信息资源管理   档案信息开发   思维导图  

摘要： 随着档案工作中心从“档案保管”转移到“档案利用”上,对档案信息开发人员的思维素养要求越来越高,本文就信息资源管理背景下,档案信息开发人员科学思维的培养问题提出一些解决之策。

关键词： 互联网   发明构思   检索模型   非专利   搜索引擎  

摘要： 发明构思是贯穿专利审查过程中的一条明线,无论是在前期的检索工作还是后期的实质审查中,都有着重要的指引作用。本文结合一些实际案例,对如何基于发明构思在互联网资源中获取非专利文献进行了初步探索,以期能够为同仁们带来一些非专利文献的检索启示。

关键词： search advertising   advertising budgets   differentiation   competitive strategy   auctions   bid jamming   game theory  

摘要： In this paper, we investigate advertisers' budgeting and bidding strategies across multiple search platforms. We develop a model with two platforms and budget-limited advertisers that compete for advertising slots across platforms. When platform reserve prices are low and exogenous, we find that symmetric advertisers pursue asymmetric budget allocation strategies and partially differentiate: one advertiser allocates a share of its budget to platform A higher than A's share of user traffic and a share of its budget to platform B lower than B's share of user traffic, whereas the second advertiser does the reverse. This partial differentiation balances two forces: a demand force arising from a desire to be present on both platforms to obtain more clicks and a strategic force driven by a desire to be budget dominant on at least one platform to obtain clicks at a lower cost. We then show that the benefit from differentiation for advertisers diminishes if platforms strategically increase their reserve prices. At reserve prices that maximize platform revenues, advertisers allocate their budgets proportional to each platform's share of user traffic, and platforms fully appropriate these budgets.

关键词： Live online search   community question answering   crowdsourcing   social computing   collaborative service   query transformation  

摘要： Search engines are still the most common way of finding information on the Web. However, they are largely unable to provide satisfactory answers to time- and location-specific queries. Such queries can best and often only be answered by humans that are currently on-site. Although online platforms for community question answering are very popular, very few exceptions consider the notion of users' current physical locations. In this article, we present CloseUp, our prototype for the seamless integration of community-driven live search into a Google-like search experience. Our efforts focus on overcoming the defining differences between traditional Web search and community question answering, namely the formulation of search requests (keyword-based queries vs. well-formed questions) and the expected response times (milliseconds vs. minutes/hours). To this end, the system features a deep learning pipeline to analyze submitted queries and translate relevant queries into questions. Searching users can submit suggested questions to a community of mobile users. CloseUp provides a stand-alone mobile application for submitting, browsing, and replying to questions. Replies from mobile users are presented as live results in the search interface. Using a field study, we evaluated the feasibility and practicability of our approach.

关键词： Mass spectrometry   Proteomics   Peptide   Mutations   Search engine   MS   MS   Sequencing   Variants   Cloud   Bolt  

摘要： Recent increases in mass spectrometry speed, sensitivity, and resolution now permit comprehensive proteomics coverage. However, the results are often hindered by sub-optimal data processing pipelines. In almost all MS/MS peptide search engines, users must limit their search space to a canonical database due to time constraints and q value considerations, but this typically does not reflect the individual genetic variations of the organism being studied. In addition, engines will nearly always assume the presence of only fully tryptic peptides and limit PTMs to a handful. Even on high-performance servers, these search engines are computationally expensive, and most users decide to dial back their search parameters. We present Bolt, a new cloud-based search engine that can search more than 900,000 protein sequences (canonical, isoform, mutations, and contaminants) with 41 post-translation modifications and N-terminal and C-terminal partial tryptic search in minutes on a standard configuration laptop. Along with increases in speed, Bolt provides an additional benefit of improvement in high-confidence identifications. Sixty-one percent of peptides uniquely identified by Bolt may be validated by strong fragmentation patterns, compared with 13% of peptides uniquely identified by SEQUEST and 6% of peptides uniquely identified by Mascot. Furthermore, 30% of unique Bolt identifications were verified by all three software on the longer gradient analysis, compared with only 20% and 27% for SEQUEST and Mascot identifications respectively. Bolt represents, to the best of our knowledge, the first fully scalable, cloud-based quantitative proteomic solution that can be operated within a user-friendly GUI interface. Data are available via ProteomeXchange with identifier PXD012700.