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A novel simulation framework for crowd transportations 一种新的人群运输仿真框架
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-07-2021-0019
Zhihui Li;Hongbo Sun
Purpose – With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people's daily life, but at the same time bring traffic congestion. How to relieve traffic congestion and improve its capacity is a hot research area. This paper aims to propose a new simulation framework for crowd transportations to ease traffic congestion. Design/methodology/approach – This paper establishes related simulation models such as vehicles, traffic lights and advisers. Then the paper describes their relationships, gives their interaction mechanism and solidifies the above into a software implementation framework. Findings – This paper proposes a simulation framework for crowd transportations. Originality/value – In this framework, traffic lights are used as a control method to control the road network and road conditions are used as an Affecter to influence individual behavior. The vehicle passing rate is defined by the correlation between endowment and the start time of the traffic lights. In this framework, members are related, dynamically adjusted according to road conditions and dynamically optimized member decisions. The optimal path is dynamic and real-time adjustments are made for each step forward. It is different from the traditional optimal path in which there is only one fixed one and it is different from the macroscopic optimal path that does not exist.
目的随着现代经济的发展,车辆不再是人们的奢侈品,极大地方便了人们的日常生活,但同时也带来了交通拥堵。如何缓解交通拥堵,提高通行能力是目前研究的热点。本文旨在提出一种新的人群交通模拟框架,以缓解交通拥堵。设计/方法论/方法本文建立了相关的仿真模型,如车辆、红绿灯和顾问。然后描述了它们之间的关系,给出了它们的交互机制,并将其固化为一个软件实现框架。发现本文提出了一个用于人群运输的仿真框架。独创性/价值在这个框架中,红绿灯被用作控制道路网络的控制方法,道路状况被用作影响个人行为的因素。车辆通过率由禀赋和红绿灯启动时间之间的相关性来定义。在这个框架中,成员是相关的,根据路况进行动态调整,并动态优化成员决策。最佳路径是动态的,每向前走一步都会进行实时调整。它不同于只有一个固定路径的传统最优路径,也不同于不存在的宏观最优路径。
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引用次数: 0
Research on collaborative governance mechanism of academic ecological environment under the background of crowd intellectual thinking 群体智力思维背景下学术生态环境协同治理机制研究
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-07-2021-0020
Yi He;Linlin Ma;Yanan Wang
Purpose – This paper aims to use intellectual thinking to solve the problem of how to carry out collaborative governance of the academic ecological environment. Design/methodology/approach – This paper selected academic ecosystem, academic ecological environment and academic ecological governance as three keywords to collect literature data. The hot issues on the academic ecological environment and its governance system in China are analyzed, with visualization software such as Citespace, China National Knowledge Infrastructure (CNKI) and other literature metrology tools. Findings – The previous research literature cannot fully explain the governance mechanism of the academic ecological environment. In this paper, the authors use the way of group cooperation thinking in the crowd science category, to build a collaborative governance framework of the academic ecological environment from the national level, the institutional level and the individual level, taking full advantage of the in-depth analysis on crowd intellectual thinking. Originality/value – The authors use the way of group cooperation thinking in the crowd science category, to build a collaborative governance framework of academic.
目的运用智力思维解决如何开展学术生态环境协同治理的问题。设计/方法/途径本文选取学术生态系统、学术生态环境和学术生态治理三个关键词进行文献资料收集。运用可视化软件Citespace、中国知网(CNKI)等文献计量工具,分析了中国学术生态环境及其治理体系的热点问题。以往的研究文献并不能完全解释学术生态环境的治理机制。本文运用群体科学范畴的群体合作思维方式,充分利用对群体智力思维的深入分析,从国家层面、制度层面和个人层面构建了学术生态环境的协同治理框架。作者运用群体科学范畴内的群体合作思维方式,构建了一个协同治理的学术框架。
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引用次数: 3
Crowd modelling: aggregating non-expert views as a method for theorizing 人群建模:聚集非专家观点作为理论化方法
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-04-2021-0015
Octavio González Aguilar
Purpose – This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may challenge current scientific theories or lead research in new phenomena. Design/methodology/approach – This paper describes a case study of theorizing by using a crowd-based method. The first section of the paper introduces what do the authors know about crowdsourcing, crowd science and the aggregation of non-expert views. The second section details the case study. The third section analyses the aggregation. Finally, the fourth section elaborates the conclusions, limitations and future research. Findings – This document answers to what extent the crowd-based method produces similar results to theories tested and published by experts. Research limitations/implications - From a theoretical perspective, this study provides evidence to support the research agenda associated with crowd science. The main limitation of this study is that the crowded research models and the expert research models are compared in terms of the graph. Nevertheless, some academics may argue that theory building is about an academic heritage. Practical implications - This paper exemplifies how to obtain an expert-level research model by aggregating the views of non-experts. Social implications - This study is particularly important for institutions with limited access to costly databases, labs and researchers. Originality/value – Previous research suggested that a collective of individuals may help to conduct all the stages of a research endeavour. Nevertheless, a formal method for theorizing based on the aggregation of non-expert views does not exist. This paper provides the method and evidence of its practical implications.
目的——本文旨在介绍一种基于人群的理论化方法。目的不是为了实现科学的理论。相反,目的是实现一个可能挑战当前科学理论或领导新现象研究的模型。设计/方法论/方法——本文描述了一个使用基于人群的方法进行理论化的案例研究。论文的第一部分介绍了作者对众包、众科学和非专家观点聚合的了解。第二部分详细介绍了案例研究。第三部分对聚合进行分析。最后,第四部分阐述了研究结论、局限性和未来的研究方向。研究结果——这份文件回答了基于人群的方法在多大程度上产生了与专家测试和发表的理论相似的结果。研究局限性/影响-从理论角度来看,本研究提供了支持与人群科学相关的研究议程的证据。本研究的主要局限性在于,拥挤研究模型和专家研究模型在图方面进行了比较。然而,一些学者可能会认为,理论建设是一种学术遗产。实际意义——本文举例说明了如何通过汇集非专家的观点来获得专家级的研究模型。社会影响——这项研究对于访问昂贵数据库、实验室和研究人员有限的机构来说尤其重要。独创性/价值-先前的研究表明,个人的集体可能有助于进行研究工作的所有阶段。然而,基于非专家观点的集合进行理论化的正式方法并不存在。本文提供了其实际意义的方法和证据。
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引用次数: 0
Crowd intelligence evolution based on complex network 基于复杂网络的群体智能进化
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-03-2021-0008
Jianran Liu;Wen Ji
PurposeIn recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks.Design/methodology/approachThis paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence.FindingsThe authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout.Practical implicationsThe simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed.Originality/valueBased on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.
目的近年来,随着计算能力的提高,人工智能逐渐可以被视为智能体并与人类互动,这种互动网络变得越来越复杂。因此,有必要对这种复杂的交互网络进行建模和分析。本文旨在利用视觉复杂网络对人群智能的进化进行建模和演示。设计/方法论/方法本文利用复杂网络对群体智能的协同进化行为和自组织系统进行建模和观察。发现利用复杂网络构建群体智能中的合作行为和自组织系统。通过构建节点之间的交互关系来确定节点的进化模式,并通过兵力布局来观察全局进化状态。仿真结果表明,状态进化图可以通过兵力布局和智能体的链接模式,有效地模拟人群智能的分布、交互和进化。独创性/价值基于复杂网络,构建了群体智能中的互动行为和组织系统,并将进化过程可视化。
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引用次数: 0
An embedded bandit algorithm based on agent evolution for cold-start problem 冷启动问题的基于智能体进化的嵌入式强盗算法
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-03-2021-0005
Rui Qiu;Wen Ji
Purpose – Many recommender systems are generally unable to provide accurate recommendations to users with limited interaction history, which is known as the cold-start problem. This issue can be resolved by trivial approaches that select random items or the most popular one to recommend to the new users. However, these methods perform poorly in many cases. This paper aims to explore the problem that how to make accurate recommendations for the new users in cold-start scenarios. Design/methodology/approach – In this paper, the authors propose embedded-bandit method, inspired by Word2Vec technique and contextual bandit algorithm. The authors describe user contextual information with item embedding features constructed by Word2Vec. In addition, based on the intelligence measurement model in Crowd Science, the authors propose a new evaluation method to measure the utility of recommendations. Findings – The authors introduce Word2Vec technique for constructing user contextual features, which improved the accuracy of recommendations compared to traditional multi-armed bandit problem. Apart from this, using this study's intelligence measurement model, the utility also outperforms. Practical implications – Improving the accuracy of recommendations during the cold-start phase can greatly raise user stickiness and increase user favorability, which in turn contributes to the commercialization of the app. Originality/value – The algorithm proposed in this paper reflects that user contextual features can be represented by clicked items embedding vector.
目的许多推荐系统通常无法向交互历史有限的用户提供准确的推荐,这被称为冷启动问题。这个问题可以通过选择随机项目或最流行的项目向新用户推荐的琐碎方法来解决。然而,这些方法在许多情况下表现不佳。本文旨在探讨如何在冷启动场景中为新用户提供准确的推荐。设计/方法论/方法在本文中,作者受Word2Vec技术和上下文土匪算法的启发,提出了嵌入式土匪方法。作者利用Word2Vec构建的项目嵌入特征描述了用户上下文信息。此外,基于人群科学中的智力测量模型,作者提出了一种新的评估方法来衡量推荐的效用。发现作者引入了Word2Vec技术来构建用户上下文特征,与传统的多武装土匪问题相比,该技术提高了推荐的准确性。除此之外,使用本研究的智力测量模型,效用也优于。实际意义在冷启动阶段提高推荐的准确性可以大大提高用户粘性和用户好感度,这反过来又有助于应用程序的商业化。独创性/价值本文提出的算法反映了用户上下文特征可以用点击项嵌入向量来表示。
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引用次数: 1
An intelligence optimization method based on crowd intelligence for IoT devices 一种基于群组智能的物联网设备智能优化方法
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-03-2021-0007
Ke Wang;Zheming Yang;Bing Liang;Wen Ji
Purpose – The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently. Design/methodology/approach – In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices. Findings – Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level. Originality/value – This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.
目的5G技术的快速发展带来了物联网(IoT)的扩展。物联网中大量设备独立工作,给管理带来困难。本研究旨在优化物联网的成员结构,使其成员更有效地工作。设计/方法/途径本文从人群科学的角度出发,将遗传算法与人群智能相结合,优化物联网的整体智能。根据相关工作,以计算、缓存和通信能力作为智能的基础,并以设备相关性和距离因素来衡量智能的提升水平。最后,他们使用遗传算法为物联网设备选择协作状态。实验结果表明,本文提出的智能优化方法可将物联网的智能水平提高10倍以上。原创性/价值本文是第一个基于人群智能科学背景解决物联网场景下设备协同问题的研究。该智能优化方法在物联网场景下效果良好,在其他人群网络场景下也有应用潜力。
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引用次数: 1
Integration of biosensor to a window-based control system for user emotion detection to static and dynamic visual contents of webpages 将生物传感器集成到基于窗口的控制系统中,对网页的静态和动态视觉内容进行用户情感检测
Q2 Decision Sciences Pub Date : 2021-11-01 DOI: 10.1108/IJCS-06-2021-0018
Fatima M. Isiaka;Awwal Adamu;Zainab Adamu
Purpose – Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users' emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues. Design/methodology/approach – Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users' stress levels. The method's novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage. Findings – The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods. Research limitations/implications – Graphical simulation and an example scenario are only provided for the control's system design. Originality/value – The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.
在web应用程序和浏览的用户体验中捕捉基本的情感在很多方面都很重要。通常,在线用户体验是通过诸如任务完成时间、调查和生成数据属性的综合测试等有形指标来研究的。在某些情况下,对用户情绪和行为的预测主要取决于任务完成时间和给定时间间隔内的点击次数。然而,这种方法通常是主观的,并且严重依赖于分布假设,使得结果容易出现记录错误。本文旨在提出一种新的方法-窗口动态控制系统-来解决上述问题。设计/方法/方法主要数据来自实验室实验,在此过程中,44名志愿者有同步的生理读数——皮肤电导反应、皮肤温度、眼动行为和生物传感器采集的用户活动属性。基于窗口的动态控制系统(PHYCOB I)集成到生物传感器中,从这些同步的生理读数中收集次要数据属性,并将它们用于两个目的:检测最佳情绪反应和用户的压力水平。该方法的新颖之处在于,它能够将生理读数和眼动记录结合起来,识别网页上隐藏的关联。分析结果表明,当进行模型比较时,控制系统检测基本情绪并在准确性和可靠性方面优于其他传统模型-即,在基于窗口的控制系统环境中,三种模型的平均可恢复自然结构的准确性和可靠性比传统方法更一致。研究局限/启示图形模拟和示例场景只提供了控制的系统设计。该模型的新颖之处在于它抗过拟合的能力,以及在处理特定网页内容时自动评估用户情感的能力。该程序可用于预测网页的哪些内容会引起用户的压力性情绪。
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引用次数: 1
Behavioral data assists decisions: exploring the mental representation of digital-self 行为数据有助于决策:探索数字自我的心理表征
Q2 Decision Sciences Pub Date : 2021-08-01 DOI: 10.1108/IJCS-03-2021-0011
Yixin Zhang;Lizhen Cui;Wei He;Xudong Lu;Shipeng Wang
Purpose – The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs' mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell. Design/methodology/approach – In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making. Findings – The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest. Originality/value – In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.
目的数字自我的行为决策是群体智能网络的重要研究内容之一。影响决策的因素和机制引起了许多研究者的关注。在影响决策的因素中,数字自我的心态起着重要的作用。探索数字自我心理对决策的影响机制,有助于理解群体智能网络的行为,提高众知网络的交易效率。设计/方法/途径本文采用行为模式感知层、多向感知层和记忆网络增强层自适应地探索数字自我的心理,从外部行为、心理多向因素和记忆单元三个方面生成数字自我的心理表征。作者使用心理表征来辅助行为决策。在真实开放数据集上的评估表明,该方法可以对用户的思维进行建模,并验证了思维对行为决策的影响,其性能优于通用基线方法对用户兴趣的建模。总的来说,作者利用数字自我的行为来挖掘和探索其思想,以帮助数字自我在CrowdIntell网络中进行决策和促进交易。这项工作是早期的尝试之一,它使用神经网络来模拟数字自我的心理表征。
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引用次数: 1
An anomaly detection approach based on hybrid differential evolution and K-means clustering in crowd intelligence 一种基于混合差分进化和K-means聚类的人群智能异常检测方法
Q2 Decision Sciences Pub Date : 2021-08-01 DOI: 10.1108/IJCS-07-2020-0013
Jianran Liu;Bing Liang;Wen Ji
Purpose – Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution. Design/methodology/approach – In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend. Findings – This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution. Practical implications – Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources. Originality/value – In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.
目的人工智能正在逐渐渗透到人类社会。在网络时代,人类与人工智能,甚至人工智能之间的互动变得越来越复杂。因此,有必要对人群智能网络的演化进行动态描述和干预。本文旨在检测智能进化早期的异常主体。设计/方法论/方法本文采用差分进化(DE)和K-means聚类方法对进化趋势异常的人群智能进行检测。发现本研究将群体智能的进化过程抽象为DE的求解过程,并使用K-means聚类来识别智能进化早期不利于进化的个体。实验表明,即使在实际应用的复杂人群智能交互环境中,我们提出的方法也能够尽早发现没有进化趋势的个体智能。因此,它可以避免时间和计算资源的浪费。独创性/价值本文将DE和K-means聚类相结合,分析了人群智能交互的演化过程。
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引用次数: 2
Evaluation and control of opinion polarization and disagreement: a review 意见两极分化和分歧的评估和控制:综述
Q2 Decision Sciences Pub Date : 2021-08-01 DOI: 10.1108/IJCS-02-2021-0003
Yuejiang Li;Hong Zhao
Purpose – The purpose of this paper is to review the recent studies on opinion polarization and disagreement. Design/methodology/approach – In this work, recent advances in opinion polarization and disagreement and pay attention to how they are evaluated and controlled are reviewed. Findings – In literature, three metrics: polarization, disagreement and polarization-disagreement index are usually adopted and there is a tradeoff between polarization and disagreement. Different strategies have been proposed in literature which can significantly control opinion polarization and disagreement based on these metrics. Originality/value – This review is of crucial importance to summarize works on opinion polarization and disagreement and to the better understanding and control of them.
目的——本文的目的是回顾最近关于意见两极分化和分歧的研究。设计/方法/方法——在这项工作中,回顾了意见两极分化和分歧的最新进展,以及对如何评估和控制它们的关注。研究结果——在文献中,通常采用三个指标:极化、分歧和极化分歧指数,极化和分歧之间存在权衡。文献中提出了不同的策略,可以根据这些指标显著控制意见两极分化和分歧。原创性/价值——这篇综述对于总结关于意见两极分化和分歧的工作以及更好地理解和控制它们至关重要。
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引用次数: 3
期刊
International Journal of Crowd Science
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