Spatial information and the legibility of urban form: Big data in urban morphology

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, conceptualize proposed designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.     

基于直觉模糊信息的多属性排序问题的软集决策方法

针对属性信息具有模糊性的一类多属性方案排序问题,提出一种基于直觉模糊软集的决策方法。直觉模糊软集结合软集和直觉模糊集的优势,对对象近似描述时没有任何限制,并且能够更客观地表达事物的模糊性本质。首先定义直觉模糊软集的综合精确度、综合犹豫度和综合得分值,以及直觉模糊软集水平集的选择值和接受水平。在此基础上,提出关于多属性方案排序的选择值准则、精确度准则、犹豫度准则以及得分值准则。然后,提出一种组合使用各排序准则的多属性方案排序方法。最后,通过数值算例及讨论证实该方法的可行性和有效性。这种方法充分考虑决策者的直觉信息及主观偏好,能够有效完成多属性方案的优选和排序决策。     

Heterogeneity in consumer preference toward mega-sport event travel packages: Implications for smart tourism marketing strategy

Consumers’ decision-making processes and behaviors are often considered heterogeneous depending on various factors (i.e., decision strategy, spatial location, and individual characteristics). To better understand consumers’ decision-making process in the context of mega-sport events, the current study explores two potential decision biases inducing consumers’ heterogeneity – 1) a simplified decision rule model and 2) spatial variability. More specifically, this study conducted two sequential analyses that explore spatially varying preferences toward the mega-sport event travel package considering the effect of the simplified decision rule. Results revealed that the simplified decision rule model, including a selective evaluation of product attributes, explained better than the full model, considering all product attributes. This study also observed spatially varying preferences toward mega-sport event packages without any meaningful differences in psychological constructs. The findings contribute to the literature on consumer decision bias and spatial variability in tourists’ behaviors. Theoretical and managerial implications are provided in the conclusion.     

Multi-stage Internet public opinion risk grading analysis of public health emergencies: An empirical study on Microblog in COVID-19

In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method – AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice.     

Information management hits and misses in the COVID19 emergency in Brazil

Brazilian Federal Government created an emergency aid to face the COVID19 emergency. This aid provides monthly payments to low-income or unemployed citizens, informal workers, or individual micro-entrepreneurs. An intricate set of criteria made too complex the identification of all citizens eligible for emergency aid, considering there is no an integrated database to which they could apply these criteria. Consequently, lots of people who fulfilled the criteria were not able to receive the aid, and lots of people who were not supposed to get it ended up receiving it. In this context, the goal of this opinion paper is to discuss the process effectiveness and which issues related to information management hindered the positive program impact. Additionally, a less complex but relevant case of Rio Grande do Sul State is discussed. Both cases – the Federal Government and the state government – show the importance of effective information management to face very demanding situations and avoid the high social price to be paid by those who need this aid the most.     

基于累积前景理论的动态随机多准则决策方法

针对准则权重完全未知、准则值为离散型随机变量的动态随机多准则决策问题,提出了一种基于累积前景理论和集对分析的决策方法。该方法引入决策者的风险态度,首先根据分布函数计算不同时期的前景值,然后由二项分布法确定时间序列权重,基于离差最大化思想构建优化模型,确定准则权重,并引入集对分析中的同一度、对立度和集对势的概念,利用综合集对势的大小对备选方案排序。最后通过一个风险投资问题验证了该方法的有效性。     

基于灰关联寻优的协同研制供应链风险评估研究

研究不完全信息下大型客机协同研制供应链风险的评估方法。针对大型客机供应链风险评估的不完全特征,建立了考虑接近关联度和相似关联度最大的不完全信息推测模型;依据最大关联度的思想,提出了群体评估者定权的方法;基于灰靶决策框架,提出了考虑风险最大区分思想下的属性权重测算的极大熵方法。将所提出理论应用到大型客机“主-供”模式协同研制“初创期”的供应链风险评估实践,并得到合理的评估结果。     

科研数据造假判定实用情报策略与技术的宏观考量

[方法/过程]本研究对科学知识生产规律、科研数据发表行为惯例、学术不端防控研究现状、科研数据造假判定的实践困境及其原因进行宏观分析。[结果/结论]提出了基于科研数据造假判定信息约束分析、分类计量与风险模型构建的实用情报策略,从而以风险评估方式提示科研数据造假的可能性,文章还探讨了其技术实现的可能路径。[目的/意义]研究结果对完善科研诚信信息采集、计量情报工程体系具有参考意义。     

Identifying underlying influential factors in information diffusion process on social media platform: A hybrid approach of data mining and time series regression

As the prevailing online communications paradigm, social media platforms are considered to be the fastest medium for sharing and diffusing information. But what influences the spread of information through these platforms? The content of the post? The sentiments contained? Or the characteristics of user's behavior? To explore which factors promote the spread of information through social media, we developed a data analytics method that combines data mining with time series regression. We then applied this analytical framework to the L group Double 11 false advertising scandal, which blew up on the Sina microblog – a public hot trend that attracted the attention of millions of people. Our analysis reveals how three factors – user activity, emotional changes, and public attention – interact and the role they play in the spread of information. Among these factors, sentiment polarity and reposting are found to be the two main drivers of information diffusion. Emotional contagion accelerates the spread of information when the event first breaks (known as the accumulation period), while reposting does more to spread information once the event has gained some traction (the diffusion period). Surprisingly, the topic of public concentration in the event has a significant impact on the spread of the event in the accumulation period, but the effect shades away during the diffusion and convergence periods, i.e., the farther relations among topics are tied, the less public interest is abating on the event – a finding that is supported by cognitive load theory. However, although public attention shows little influence in the diffusion process, it does reveal how consumers shift their attention to different subtopics over time. Overall, our analysis sheds some light on how online events evolve and ‘go viral’. Notably, this study not only explores how underlying factors dynamically influence the information diffusion process, but also offers insights into how to manage information diffusion processes in practice.     

基于风险偏好的信息系统安全技术选择与配置策略研究

以IDSs和人工调查技术组合为例,通过构建博弈模型,分析了风险偏好对信息系统安全技术选择与配置的影响,认为组织风险偏好不仅影响着自身的策略也将影响对方的策略。研究结论显示风险厌恶型组织配置IDS数量并不总是高于风险中立型组织,组织风险偏好对其部署单IDS还是多IDSs甚至无直接影响。同时组织在黑客期望收益很低时对风险厌恶型黑客的人工调查率更高,而在黑客期望收益很高时其对风险中立型黑客的调查率更高。此外,黑客在组织人工调查成本较低时更倾向于入侵风险中立型组织,在人工调查成本很高时更愿意入侵风险厌恶型组织。     

基于区块链技术的社交网络舆情风险管理掣肘与纾解

【 目的/意义】通过构建区块链社交网络舆情风险管理纾解对策,对于创造良好网络环境,提升网络舆情治理 效能以及应对网络风险复杂局面提供可行性方案。【方法/过程】本研究立足区块链理念与技术,分析社交网络舆情 风险管理概念框架,指明社交网络舆情风险现存问题,进一步探究区块链技术下社交网络舆情治理与纾解机理,建 立社交网络舆情管理系统模型,并从技术推进、多方协作、媒体引导等层面探究防控社交网络舆情风险的密钥。【结 果/结论】区块链技术对社交网络舆情风险治理、提高舆情信息传播质量具有积极作用,为营造健康、安全共享等网 络社交生态系统提供诸多可能性。【创新/局限】本文构建的区块链社交网络舆情管理模型是一个初步的设想,没有 考虑信息存储的上限,下一步研究工作是提升区块链社交网络舆情管理系统的兼容性和数据采集的标准化,为区 块链社交网络舆情管控提供参考。     

Personal knowledge management: supporting individual knowledge worker performance

Based on an exploratory study of the work and learning processes of highly skilled and experienced knowledge workers, this paper explores an alternative perspective to knowledge management, one that focuses on how individual workers apply knowledge processes to support their day-to-day work activities – broadly characterized as problem solving – and learning practices. Developing the concept of Personal Knowledge Management, the paper presents an emergent model that links distinctive types of problem solving activities with specific cognitive, information, social and learning competencies, supported by an individual, social and organizational enablers.     

Collaborative risk method for information security management practices: A case context within Turkey

In this case study, a collaborative risk method for information security management has been analyzed considering the common problems encountered during the implementation of ISO standards in eight Turkish public organizations. This proposed risk method has been applied within different public organizations and it has been demonstrated to be effective and problem-free. The fundamental issue is that there is no legislation that regulates the information security liabilities of the public organizations in Turkey. The findings and lessons learned presented in this case provide useful insights for practitioners when implementing information security management projects in other international public sector organizations.     

基于UTAUT模型的旅游信息服务平台用户使用意愿研究

【目的/意义】旅游信息服务平台是现代旅游发展的关键支撑,研究旅游信息服务平台用户使用意愿与行为 及其影响因素,有助于平台运营商不断优化功能、提升服务水平。【方法/过程】基于UTAUT模型,综合考虑信任程 度、感知风险、产品或服务权威性等,构建旅游信息服务平台用户使用意愿影响因素的结构方程模型,并以“掌上高 铁”为例,设计了用户使用意愿影响因素测量量表,通过发放问卷方式收集数据,利用软件 R4.1.0对结构方程模型 进行了分析。【结果/结论】数据分析结论显示：付出期望、社会影响、便利条件、信任程度、产品或服务权威性对使用 意愿有显著的正向影响,感知风险对使用意愿有显著负向影响,绩效期望和使用意愿对使用行为有显著正向影响。 【创新/局限】本文新增了“产品或服务权威性”变量,构建了铁路旅游信息服务平台即“掌上高铁”软件用户使用行 为影响因素模型。     

Time and sequence awareness in similarity metrics for recommendation

Modeling the temporal context efficiently and effectively is essential to provide useful recommendations to users. In this work, we focus on improving neighborhood-based approaches where we integrate three different mechanisms to exploit temporal information. We first present an improved version of a similarity metric between users using a temporal decay function, then, we propose an adaptation of the Longest Common Subsequence algorithm to be used as a time-aware similarity metric, and we also redefine the neighborhood-based recommenders to be interpreted as ranking fusion techniques where the neighbor interaction sequence can be exploited by considering the last common interaction between the neighbor and the user.We demonstrate the effectiveness of these approaches by comparing them with other state-of-the-art recommender systems such as Matrix Factorization, Neural Networks, and Markov Chains under two realistic time-aware evaluation methodologies (per user and community-based). We use several evaluation metrics to measure both the quality of the recommendations – in terms of ranking relevance – and their temporal novelty or freshness. According to the obtained results, our proposals are highly competitive and obtain better results than the rest of the analyzed algorithms, producing improvements under the two evaluation dimensions tested consistently through three real-world datasets.     

企业合作创新风险的多层次模糊综合评价模型及应用

首先对企业合作创新风险内在本质和系统构成进行了阐述,并以此建立了企业合作创新的综合评价指标体系,进而结合企业合作创新风险评估的特点,构造了相应的多层次模糊综合评价模型,最后,通过使用实例检验了模型的有效性.     

基于证据理论的信息服务成熟度评价模型研究

本文在借鉴CMMI技术上,构建了信息服务成熟度模型。通过引入证据理论方法评价信息服务提供商的成熟度,其目的在于解决现有评价方法存在的判断矩阵一致性难以满足、以及专家对指标属性值和隶属度值难以确定等问题。经算例研究结果证明:本文提出的评价模型能够准确可靠的评价信息服务成熟度,亦可作为用户评价或选择信息服务提供商的重要工具。     

一种内部网络信息安全风险评估模型及技术实现

提出了一种基于ISO/IEC27000的内部网络信息安全风险评估模型,并具体设计了一个内部网络风险评估系统用于实现该模型,对模型在计算机上的实现进行了简要说明,分析了系统的开发模式和实现平台。     

A unifying and general account of fairness measurement in recommender systems

Fairness is fundamental to all information access systems, including recommender systems. However, the landscape of fairness definition and measurement is quite scattered with many competing definitions that are partial and often incompatible. There is much work focusing on specific – and different – notions of fairness and there exist dozens of metrics of fairness in the literature, many of them redundant and most of them incompatible. In contrast, to our knowledge, there is no formal framework that covers all possible variants of fairness and allows developers to choose the most appropriate variant depending on the particular scenario. In this paper, we aim to define a general, flexible, and parameterizable framework that covers a whole range of fairness evaluation possibilities. Instead of modeling the metrics based on an abstract definition of fairness, the distinctive feature of this study compared to the current state of the art is that we start from the metrics applied in the literature to obtain a unified model by generalization. The framework is grounded on a general work hypothesis: interpreting the space of users and items as a probabilistic sample space, two fundamental measures in information theory (Kullback–Leibler Divergence and Mutual Information) can capture the majority of possible scenarios for measuring fairness on recommender system outputs. In addition, earlier research on fairness in recommender systems could be viewed as single-sided, trying to optimize some form of equity across either user groups or provider/procurer groups, without considering the user/item space in conjunction, thereby overlooking/disregarding the interplay between user and item groups. Instead, our framework includes the notion of statistical independence between user and item groups. We finally validate our approach experimentally on both synthetic and real data according to a wide range of state-of-the-art recommendation algorithms and real-world data sets, showing that with our framework we can measure fairness in a general, uniform, and meaningful way.     

Adjusting to the digital: Societal outcomes and consequences

Innovation in digital technologies is central to contemporary debates about the need for policy and regulatory adjustment in response to the consequences of the centrality of these technologies in contemporary societies. Christopher Freeman's research in relation to changes in techno-economic paradigm and, specifically, in relation to the information and communication technology (ICT) paradigm, cautioned that assessments of these changes needed to go beyond market dynamics to examine social, cultural and political issues. In this paper several predominant themes in his work are foregrounded – the ambiguity of changes within the ICT paradigm; the role of guiding principles in influencing expectations about societal outcomes; and the importance of political factors in shaping the consequences of technological innovation. These three themes are then deployed in a discussion of recent innovations – two technical (5G mobile networks and artificial intelligence-as-a-service) and one institutional (proposals for changes in the international taxation regime in response to claims that the existing regime is inappropriate in the face of global online service provision). In each instance, the aim is to illustrate how following Freeman by giving attention to the themes operates as an important guide to analysis of adjustments to novel deployments of digital technology. The conclusion emphasizes the value of Freeman's contributions to shaping research agendas that acknowledge the need to humanize technology, to consider alternatives to taken-for-granted principles and practices, and to take into account the role of political power in tandem with concentrated economic power.