Information Counseling Inventory of Affective and Cognitive Reactions While Learning the Internet

Abstract

Library professionals in the information age are called upon to provide user-friendly information environments. To accomplish this, more detailed knowledge is needed about the information behavior of users. The concept “information counseling” reflects this new orientation and involves instruction activities such as: orienting, advising, and reassuring novice learners. The taxonomic approach transforms self-witnessing reports into classified segments of information behaviors at three levels of internalization and in three behavioral domains, including: affective, cognitive, and sensorimotor behavior. Reference librarians and instructors are invited to contribute to the taxonomy and make use of it in planning and designing programs and facilities. This behavioral inventory of information behaviors learning the Internet is presented with illustrative entries under each category for affective and cognitive behavior. Suggested uses for the inventory include promoting information self-counseling skills, helping learners overcome technophobia through generational self-witnessing reports, and creating better point-of-use instructions for novices using complex information systems.     

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

一种高效的多层和概化关联规则挖掘方法

通过对分类数据的深入研究,提出了一种高效的多层关联规则挖掘方法:首先,根据分类数据所在的领域知识构建基于领域知识的项相关性模型DICM(domain knowledge-based item correlation model),并通过该模型对分类数据的项进行层次聚类;然后,基于项的聚类结果对事务数据库进行约简划分;最后,将约简划分后的事务数据库映射至一种压缩的AFOPT树形结构,并通过遍历AFOPT树替代原事务数据库来挖掘频繁项集.由于缩小了事务数据库规模,并采用了压缩的AFOPT结构,所提出的方法有效地节省了算法的I/O时间,极大地提升了多层关联规则的挖掘效率.基于该方法,给出了一种自顶向下的多层关联规则挖掘算法TD-CBP-MLARM和一种自底向上的多层关联规则挖掘算法BU-CBP-MLARM.此外,还将该挖掘方法成功扩展至概化关联规则挖掘领域,提出了一种高效的概化关联规则挖掘算法CBP-GARM.通过大量人工随机生成数据的实验证明,所提出的多层和概化关联规则挖掘算法不仅可以确保频繁项集挖掘结果的正确性和完整性,还比现有同类最新算法具有更好的挖掘效率和扩展性.     

光纤近红外漫反射光谱技术在前胡族植物分类中的应用探讨

探讨了近红外光谱技术在前胡族植物分类中的可能性。用系统聚类分析获得的分类结果与传统的植物学分类比较一致,谱明本方法应用于前胡植物中三亚族的分类是基本可行的。     

基于多最小支持度的多层模糊关联规则挖掘

为了在事务数据库中发现关联规则,在现实挖掘应用中,经常采用不同的标准去判断不同项目的重要性,管理项目之间的分类关系和处理定量数据集这3个方法去处理问题,因此提出一个在定量事务数据库中采用多最小支持度,在项目集中获取隐含知识的多层模糊关联规则挖掘算法。该挖掘算法使用两种支持度约束和至上而下逐步细化的方法推导出频繁项集,同时可以发现交叉层次的模糊关联规则。通过实例证明了该挖掘算法在多最小支持度约束下推导出的多层模糊关联规则是易于理解和有意义的,具有很好的效率和伸缩性。     

VPN技术

针对VPN的分类问题，讨论了VPN所基于隧道技术，包括系统的组件，目前常用的协议，以及它们在不同网络操作系统下的client／server实现。最后，指出了VPN的整体实现要求和网络模型。     

论阅读理解中的推理——分类及其测试设计中的体现

如何有效培养学生阅读理解推理能力,使学生善于理解所读材料的隐含意义、深层含义是大学英语阅读教学中的难题。本文试图从认知心理学角度概述推理现象及其分类,阐述、分析推理分类在我国大学英语标准化测试中的体现,从而从理论上启发大学英语教学中阅读理解推理能力的培养。     

基于多Agent系统的自动协商机制及通用协商框架

自动协商是多Agent系统实现协作的关键环节。目前,对协商模型的研究大部分是在特定的应用系统中针对具体协商背景进行研究,协商模型的通用性较差。本文分析了多Agent系统协商机制的三个方面:Agent通信语言、通信方式和交互协议。在此基础上,给出了基于多Agent的通用协商框架GNF,该协商框架包括抽象协商过程、协商规则分类和协商协议。最后,给出了一个基于通用协商框架GNF的原型系统——采用Jess规则引擎实现的多Agent的商品交易系统。     

安全协议的攻击分类及其安全性评估

对安全协议的安全性进行全面评估是十分重要的,但难度非常大．目前大量的研究工作主要集中于分析开放网络环境下安全协议的一些特定安全属性,例如,秘密性和认证性等．为了更全面地评估安全协议的安全防护能力,从攻击者的能力和攻击后果两个角度,提出一种新的安全协议攻击分类,并分析了不同攻击类型的特点与机理．在此基础上,探讨了安全协议的一种安全性评估框架,有助于更客观地评价安全协议的实际安全防护能力和设计新的协议．