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Roger Chen

課程管理系統的資料探勘：以Moodle為例 - 0 views

pulipuli.blogspot.com/...moodle.html

data mining papers

shared by Roger Chen on 19 May 08 - Cached
  • Roger Chen on 19 May 08
     
    Data mining in course management systems: Moodle case study and tutorial by: Cristobal Romero, Sebastian Ventura, Enrique Garcia Computers & Education, Vol. In Press(2007), Corrected Proof
Roger Chen

Michael Nielsen » Open science - 0 views

michaelnielsen.org/blog

academia thinking

shared by Roger Chen on 14 May 08 - Cached
  • Scientific papers represent only a tiny fraction of the useful knowledge that scientists have to share with the world:
Roger Chen

什么是"同行评议"？ 学海拾贝 学海拾贝 图谋博客 - 0 views

libseeker.bokee.com/viewdiary.41378104.html

academia research

shared by Roger Chen on 12 Mar 09 - No Cached
  • 目前国内讨论较多的是狭义的同行评议，即作者投稿以后，由刊物主编或纳稿编辑邀请具有专业知识或造诣的学者，评议论文的学术和文字质量，提出意见和判定，主编按评议的结果决定是否适合在本刊发表。一般的科技期刊是请2-3位评审人，也有更多的。这些评议人的评议结果，将直接影响到主编对被评议的文章所做出的最终评定。除此以外，广义的同行评议不止于此，还包括文章发表以后由专门组成的审议小组 (Journal Clubs) 对文章发表后的交流和应用效果进行评议，其中也包括对投稿阶段的评审意见进行探讨，这在医学科学类期刊尤为普遍。近年来新兴的免费期刊(Open Access Journals) 开创的公开评议 (Open Review) 或有些论文网站 (Paper Database) 采纳的自荐评议，也属于广义的同行评议。
  • 同行评议成为科技期刊出版的基石，公认是20世纪中叶以后的事。这与二战后科技进步引发的论文数量的激增和期刊种类和数量的翻番不无关系。可以肯定地说，没有同行评议就没有庞大的科技期刊出版业；同行评议的质量是期刊出版质量的先决条件和重要保障之一。
  • 传统的同行评议，通常强调两个基本点。一是匿名；二是独立思考判断。主编邀请的评议人大都是与论文内容相关的科研人士，希望听到的是他们依据各自的专业知识和经验，对论文做出独立的判断和评定。既要实事求是，又要开诚布公。
  • ...3 more annotations...
  • 同行评议的具体操作形式，可以根据作者与评议人之间的了解程度，大致分为三种。一种是单隐 (Single-Blind Review或 Single Masked Review )；二是双隐 (Double-Blind Review 或 Double Masked Review)；三是公开评议(Open Review)。
  • 目前除了主编邀请评议人外，也有作者推荐评议人的。而且目前采取这种运作方式的科技期刊呈上升趋势。越来越多的期刊在投稿阶段，邀请作者向编辑部建议自己论文候选的审稿人（3-5人不等），也有的刊物同时邀请作者指出应该避嫌的评议人或绝对应该回避的人名单。
  • 出于种种原因，目前学者们对同行评议有不少意见。抱怨较多的是评议过程马拉松，审定意见有偏颇，评议人固执己见，无中生有，甚至借机偷窃他人论点等等。这些弊病除了评议人自检自律以外，主编的督察和作者的申辩都会起到积极的监督作用。另外值得一提的是，现阶段的评议手段对抄袭和一稿多投几乎一愁莫展，而这恰恰是目前科技期刊发展的重要隐患之一
Roger Chen

科技日报- 中科院院士王鼎盛抨击影响因子崇拜 - 0 views

www.stdaily.com/...content_829830.htm

academia papers

shared by Roger Chen on 27 Jun 08 - Cached
  • 根据SCI数据，王鼎盛分析全世界200多种发表研究论文的物理期刊时发现，期刊总的引用＝1.5×论文数量×影响因子×半衰期（寿命）。
  • 因此即使以总的引用作为期刊影响力的衡量标准，期刊寿命也与影响因子有同等重要的地位。然而，期刊寿命常常会被忽视。王鼎盛认为，搞科学的人应该更看重期刊的寿命。
  • 国人对研究论文评价时的期刊崇拜，和对评价期刊时的影响因子崇拜，不但有碍我国物理期刊的提高，而且助长了科研中的浮躁情绪。中国物理界更该提倡追求影响的持续年限，并进而追求经典。
Roger Chen

UBC Academic Search - Another Impact Factor Metric - <i>W-index</i> - 0 views

weblogs.elearning.ubc.ca/...046689.html

academia papers

shared by Roger Chen on 19 Jun 08 - Cached
  • Roger Chen on 19 Jun 08
     
    A new index measuring a scientist's impact in his/her field has been developed called the Wu index or w-index. Developed by Qiang Wu from the University of Science and Technology of China in Hefei, it was published as The w-index: A significant improvement of the h-index in this week's Physics arxiv.
Roger Chen

SIGecom Exchanges - 0 views

www.sigecom.org/exchanges

e-commerce papers research

shared by Roger Chen on 25 Jun 08 - Cached
  • Roger Chen on 25 Jun 08
     
    Newsletter of the ACM Special Interest Group on E-commerce
Roger Chen

Good research: invent new problems or explain mysteries - 0 views

www.daniel-lemire.com/...-problems-or-explain-mysteries

academia papers research

shared by Roger Chen on 25 Jun 08 - Cached
  • There are at least 3 types of good research questions: 1) explain with a theoretical model a (puzzling) experimental observation 2) improve by at least an order of magnitude an existing technique 3) make up a new problem and be the first to propose a solution
  • So I submit to you Lemire’s first rule of good research: you must either be trying to explain puzzling experimental results, or be inventing new problems.
Roger Chen

TOCProceedings of the 2007 ACM conference on Recommender systems - 0 views

portal.acm.org/toc.cfm

papers recommender

shared by Roger Chen on 01 Jul 08 - Cached
  • Roger Chen on 01 Jul 08
     
    Proceedings of the 2007 ACM conference on Recommender systems, 2007, Minneapolis, MN, USA October 19 - 20, 2007
Roger Chen

Data & Knowledge Engineering (0169-023X) - ACM Guide to Computing Literature - 0 views

portal.acm.org/toc.cfm

data mining journals papers

shared by Roger Chen on 30 Jun 08 - Cached
  • Roger Chen on 30 Jun 08
     
    Data & Knowledge Engineering (0169-023X)
Roger Chen

Expert Systems with Applications: An International Journal (0957-4174) - 0 views

portal.acm.org/toc.cfm

data mining journals papers

shared by Roger Chen on 30 Jun 08 - Cached
  • Roger Chen on 30 Jun 08
     
    ACM Portal - Expert Systems with Applications: An International Journal
Roger Chen

Incredibly Dull: The KM Core Sample - 0 views

incrediblydull.blogspot.com/...km-core-sample.html

reading thinking

shared by Roger Chen on 30 Sep 08 - Cached
  • The Core Sample is -- like its name sake -- a snapshot of a point in time. It captures the various levels of "knowledge" and where they reside. The diagram also illustrates the rationalization and codification of knowledge as it rises through the layers.
  • Starting at the bottom, at the very core, are people. This is where true knowledge exists.
  • The next layer up is where that personal communication is expanded to allow people to "talk" to others they do not know or cannot meet in person.
  • ...3 more annotations...
  • The next layer up represents "knowledge capture". Here the knowledge is instantiated in documents of some kind: sample documents, lesson learned, case studies, white papers
  • Finally, in the top layer the captured knowledge and learnings are further refined into a defined set of templates, guidelines, and standard processes.
  • Collaboration strategies focus on the tacit knowledge layer. Methods like knowledge harvesting, lessons learned, and storytelling focus on the best practices layer. While ITIL, Six Sigma, ISO 9001, and other standardization methodologies focus on establishing institutionalized knowledge.
Roger Chen

Data Randomization - 0 views

research.microsoft.com/...view.aspx

data mining papers reference research

shared by Roger Chen on 04 Sep 08 - Cached
  • Roger Chen on 04 Sep 08
     
    Attacks that exploit memory errors are still a serious problem. We present data randomization, a new technique that provides probabilistic protection against these attacks by xoring data with random masks. Data randomization uses static analysis to partition instruction operands into equivalence classes: it places two operands in the same class if they may refer to the same object in an execution that does not violate memory safety. Then it assigns a random mask to each class and it generates code instrumented to xor data read from or written to memory with the mask of the memory operand's class. Therefore, attacks that violate the results of the static analysis have unpredictable results. We implemented a data randomization prototype that compiles programs without modifications and can preventmany attacks with low overhead. Our prototype prevents all the attacks in our benchmarks while introducing an average runtime overhead of 11% (0%to 27%) and an average space overhead below 1%.
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