Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Evaluation Model for Capability of Enterprise Agent Coalition Based on Information Fusion and Attribute Reduction

For the issue of evaluation of capability of enterprise agent coalition,an evaluation model based on information fusion and entropy weighting method is presented. The attribute reduction method is utilized to reduce indicators of the capability according to the theory of rough set. The new indicator system can be determined. Attribute reduction can also reduce the workload and remove the redundant information,when there are too many indicators or the indicators have strong correlation. The research complexity can be reduced and the efficiency can be improved. Entropy weighting method is used to determine the weights of the remaining indicators,and the importance of indicators is analyzed. The information fusion model based on nearest neighbor method is developed and utilized to evaluate the capability of multiple agent coalitions,compared to cloud evaluation model and D-S evidence method. Simulation results are reasonable and with obvious distinction. Thus they verify the effectiveness and feasibility of the model. The information fusion model can provide more scientific,rational decision support for choosing the best agent coalition,and provide innovative steps for the evaluation process of capability of agent coalitions.

Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Sensing Attributes of an Agile Information System

Information Systems (IS) agility is a current topic of interest in the IS industry. The study follows up on the work on the definition of the construct of IS agility which is defined as the ability of IS to sense a change in real time;diagnose it in real time;and select and execute an action in real time. It explores the attributes for sensing in an Agile Information System. A set of attributes was initially derived using the practitioner literature and then refined using interviews with practitioners. Their importance and validity were established using a survey of the industry. All attributes derived from this study were deemed pertinent for sensing in an agile information system. Dimensions underlying these attributes were identified using Exploratory Factor Analysis. This list of attributes can form the basis for assessing and establishing sensing mechanisms to increase IS agility.

Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute reduction.The information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is inefficient.To fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information granulation.The first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence classes.The second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information granulation.Experimental results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the system.The strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.

Attribute Weighted Naïve Bayes Classifier

The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.

Why Is nice and Adj So Much More Frequent than Adj and nice?——From the Perspective of Humans’ Social and Limited-Processing-Capacity Attributes

This paper investigates the phenomenon of imbalance between the frequencies of the nice and Adj and Adj and nice patterns from the perspective of humans’ social and limited-processing-capacity attributes. Humans’ social attribute requires that language users stay informative with minimal effort in communication, resulting in the from-the-least-to-the-most-informative information organization in discourse. Their limited-processing-capacity attribute requires that they resort to the production biases of "easy first" and "plan reuse" in order to achieve communicative efficiency in real-time production. The analysis of the occurrences of the nice and Adj pattern and native speakers’ judgment of the degree of informativeness of nice in these occurrences suggest that nice is largely delexicalized. Such delexicalization makes nice and Adj consistent with the information organization and allows language users to stay informative with the use of the pattern, thus in line with the social attribute, but not Adj and nice. In the meantime, nice is not only highly frequent but also conceptually salient when it comes to the positive property(Panther & Thornburg, 2009), making nice and Adj easier to produce and more likely to be reused than Adj and nice, thus in line with the limited-processing-capacity attribute. The analysis of the unbalanced frequency of the two patterns suggests that human attributes should be considered when studying language form, and this should offer insights into English learning.

Visual Information Processing Based on Qualitative Mapping

Visual information processing is not only an important research direction in fields of psychology,neuroscience and artificial intelligence etc,but also the research base on biological recognition theory and technology realization.Visual information processing in existence,e.g.visual information processing facing to nerve calculation,visual information processing using substance shape distilling and wavelet under high yawp,ANN visual information processing and etc,are very complex in comparison.Using qualitative Mapping,this text describes the specific attributes in the course of visual information processing and the results are more brief and straightforward.So the software program of vision recognition is probably easier to realize.

Adopting Context Mediation in Information Integration to Resolve Semantic Heterogeneity in Distributed Environment

Ontology-based semantic information integration resolve the schema-level heterogeneity and part of data level heterogeneity between distributed data sources. But it is ubiquitous that schema semantics of information is identical while the interpretation of it varies with different context, and ontology-based semantic information integration can not resolve this context heterogeneity. By introducing context representation and context mediation to ontology based information integration, the attribute-level context heterogeneity can be detected and reconciled automatically, and hence a complete solution for semantic heterogeneity is formed. Through a concrete example, the context representation and the process in which the attribute-level context heterogeneity is reconciled during query processing are presented. This resolution can make up the deficiency of schema mapping based semantic information integration. With the architecture proposed in this paper the semantic heterogeneity solution is adaptive and extensive.

不协调广义多尺度序决策信息系统的知识获取

针对不协调广义多尺度序决策信息系统中的最优尺度组合和IF-THEN知识获取问题,首先,提出了集值优势矩阵的概念,给出了其判断序决策信息系统是否协调的条件。然后,在不协调的广义多尺度序决策信息系统中,引入基于优势关系的广义决策概念,同时通过定义区间值优势关系的方法,构造了新的协调广义多尺度序决策信息系统,并设计了通过集值优势矩阵求最优尺度组合的算法,挖掘了隐藏在不协调广义多尺度序决策信息系统中的决策规则。最后,通过实验验证了所提广义决策最优尺度组合的有效性。

Generalized binary discernibility matrix for attribute reduction in incomplete information systems

To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.

历史主义视角下情报学的学科起源、结构与属性

文章从历史主义视角出发,基于文献述评分析了情报学的学科起源、学科边界、焦点研究问题与核心组成部分,将情报学(Information Science)定义为“在不断发展的技术环境中研究信息的管理、分析、处理、增长、关联、传播,以及个人、组织进行信息获取、共享、开发、利用以满足个性化需求的学科”,其核心组成部分包括图书馆信息学、信息计量学、信息学与社会信息学、情报研究与情报工程;分析了情报学的三大学科起源:欧洲的文献信息学派、苏联的科学交流学派、美国的信息科学学派;提出情报学是一门受信息技术驱动并广泛服务于经济社会发展的交叉学科、基础学科和应用学科,其核心目标是“解决人类信息处理能力的局限与信息无限增长之间的矛盾”,并提出相比于“信息资源管理”,信息科学是更加合适的一级学科名称。

碳信息披露的绿色创新效应研究

文章基于信号传递理论和合法性理论,利用2013—2022年A股制造业上市公司的相关数据,实证检验碳信息披露的绿色创新效应。研究发现:碳信息披露具有显著的绿色创新效应,主要表现为绿色专利数量和质量同步显著提升,且专利结构实现了优化;缓解融资约束和资本更新是碳信息披露绿色创新效应的传导机制;碳信息披露的绿色创新效应在民营企业、重污染企业、低碳排放地区和低环境规制强度地区更显著;唯有实质性绿色创新才能提升制造业企业经济绩效和环境绩效,且碳信息披露可以强化这一激励效应。研究结论为评估碳信息披露制度的运行效果提供了新视角,也为驱动制造业企业绿色发展提供了新思路。

平均近似精度的性质和应用

平均近似精度是粗糙集理论中新近提出的一个重要概念。首先分析平均近似精度的数学结构,给出平均近似精度一种新的解释;然后重点讨论平均近似精度的若干重要性质,相比传统方法,其能更有效地刻画粗糙集模型知识表示的能力;最后,探讨平均近似精度在不完备信息表和特征选择两方面的应用。这些研究成果丰富了粗糙集理论的内容,扩展了粗糙集理论在实际问题中的应用。

文本属性对视频弹幕阅读体验影响研究

弹幕作为近年新型的信息呈现方式,拥有广泛的用户群体。本文通过行为实验研究弹幕的文本属性对视频弹幕阅读的影响,为弹幕设置的优化提供参考与指导,有助于提高弹幕视频用户体验。本研究结合客观行为实验与主观问卷方法,以字符大小、字符颜色为自变量,以被试的再认成绩和主观满意度为因变量,设计5(字符大小)×2(字符颜色)混合实验。在行为实验结束后采用李克特7点量表对被试的弹幕视频主观满意度进行量化,并对行为与问卷结果数值进行统计分析,考察字符大小与颜色对弹幕阅读体验的影响。研究发现,弹幕视频中字符大小、颜色对弹幕阅读信息加工效果影响显著,且与用户满意度一致;弹幕的字符尺寸占屏比(字符对角线/屏幕对角线)为1.55%时阅读体验最优,白色字幕优于彩色字幕。

基于多异构属性和不完全权重信息的案例检索方法

针对多异构属性和不完全权重信息的案例检索问题,提出了一种基于后悔理论的案例检索方法。通过定义基于属性相似度和后悔理论(RT)的感知效用函数,并基于线性规划技术的多维偏好分析(LINMAP),构建了确定属性权重的数学规划模型。在此基础上,计算感知效用,并确定一组类似的历史案例,计算相似历史案例的综合效用,得到相似历史案例的排序,从而获得最适合的历史案例。最后,以瓦斯爆炸案例验证该方法的可行性与有效性。结果表明:该方法可以在计算案例相似性和综合效用时提供更客观、准确的结果。