On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation management.Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced data.However,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update module.This paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system.Here,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance.Computation of user reputation scores relies on their behavioral history,actively discouraging undesirable conduct.Additionally,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and reputation.Our scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior.This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

P2P transaction method for distributed energy prosumers based on reputation value

Adding a reputation incentive system to peer-to-peer(P2P)energy transactions can encourage prosumers to regulate their trading behavior,which is important for ensuring the efficiency and reliability of P2P transactions.This study proposed a P2P transaction mechanism and game optimization model for prosumers involved in distributed energy sources considering reputation-value incentives.First,the deviation of P2P transactions and the non-consumption rate of distributed renewable energy in P2P transactions were established as indicators to quantify the influencing factors of the reputation value,and a reputation incentive model of P2P transactions for prosumers was constructed.Then,the penalty coefficient was applied to the cost function of the prosumers,and a non-cooperative game model of P2P transactions based on the complete information of multi-prosumers was established.Furthermore,the Nash equilibrium problem was transformed into a nonlinear optimization problem by constructing the modified optimal reaction function,and the Nash equilibrium solution of the game was obtained via a relaxation algorithm.Finally,the modified IEEE 33-node test system based on electricity market P2P and an IEEE 123-node test system were used to analyze and verify the cost and P2P participation of prosumers considering the reputation value.The results show that the addition of the reputation incentive system can encourage prosumers to standardize their interactive transaction behavior and actively participate in P2P transactions.It can also improve the operation efficiency of the power grid and promote the perfection of the P2P transaction mechanism.

Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation

Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manageability of authorization through the token.However,traditional access control policies lack the ability to dynamically adjust based on user access behavior.Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility.As a result,this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control.The TBAC system divides the access control process into three stages:policy upload,token request,and resource request.The user reputation evaluation module evaluates the user’s token reputation and resource reputation for the token request and resource request stages of the TBAC system.The proposed system is implemented using the Hyperledger Fabric blockchain.The TBAC system is evaluated to prove that it has high processing performance.The user reputation evaluation model is proved to be more conservative and sensitive by comparative study with other methods.In addition,the security analysis shows that the TBAC system has a certain anti-attack ability and can maintain stable operation under the Distributed Denial of Service(DDoS)attack environment.

A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

As 5G becomes commercial,researchers have turned attention toward the Sixth-Generation(6G)network with the vision of connecting intelligence in a green energy-efficient manner.Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency.However,designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients'contributions during the learning process.In this paper,we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning.The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning.Meanwhile,clients’contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks,which satisfies availability,fairness,and additivity.The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.

How Do Young Children Manage Their Reputation among Peers?

Young children’s reputation management is closely related to their social development.The purpose of our study is to examine the interaction between theory of mind and partner choice on children’s reputation management.Participants consisted of 270 children who were 3 to 5 years old.First,we measured participants’theory of mind capabilities using the unexpected location task and unexpected content task and then randomly divided the participants into the control group,non-partner–choice group,and partner-choice group.We measured reputation management by comparing children’s willingness to share and sharing behavior between these groups.Thefindings are as follows:(1)Children from ages 3 to 5 demonstrated reputation management,and their reputation management followed a significant developmental trend.The reputation management of 4-to 5-year-old children was significantly better than that of 3-year-old children.(2)Scores on the theory of mind tasks positively predicted children’s reputation management.(3)Partner choice affected children’s reputation management.In the partner-choice group,children’s reputation management was more apparent.(4)Partner choice did moderate the relationship between theory of mind and children’s reputation management.In the partner-choice group,theory of mind had a stronger predictive effect on children’s reputation management.

Roles of Corporate Reputation and Service Recovery in Food Safety Crises:A Case Study of Food Hygiene Incidents in Hot Pot Restaurants

Food hygiene incidents in hot pot restaurants were studied,and a relationship model between corporate reputation and consumers behavioral intention was established.Hot pot consumers of hot pot restaurants were surveyed through questionnaires.The results show that corporate reputation positively affects service recovery and repurchase intention,and service recovery positively influences repurchase intention,while corporate reputation affects repurchase intention through service recovery.In other words,under the situation of enterprise crises,the service recovery of an enterprise can restore its image and reputation.Therefore,when an enterprise has a crisis,it should positively respond to the crisis incident,and take timely crisis recovery to maintain its positive image.

面向车联网的车辆节点信誉评估方法

车联网技术的发展促使了交通信息的交互与共享,能有效提升出行效率,但车联网的开放性使得交通实体容易受到恶意车辆的攻击,会造成严重的后果。针对上述问题,文章提出了一种面向车联网的车辆节点信誉评估方法。首先,提出了一种面向车联网的车辆节点分区区块链网络;然后,通过车辆节点之间的信任和基础设施对车辆的辅助信任来计算本地信誉值,借助深度学习算法动态计算全局信誉值,进而可基于全局信誉值确定最佳的数据共享节点;最后,对存储技术进行改进,利用分区区块链存储来保证信誉值、路况信息的不被篡改和可追溯性。仿真实验结果表明,本文所提的方法在通过信誉评估确定恶意节点的准确率和召回率都优于对比方法。

南丁格尔奖在我国护士和护生中的声誉度调查

目的:探讨南丁格尔奖在我国护士和护生中的声誉度,以更好地传承和弘扬南丁格尔精神。方法:采用便利抽样法和滚雪球抽样法对我国护士和护生进行南丁格尔奖声誉度抽样调查。结果:共调查1 557名护理人员,90.11%的调查者认为南丁格尔奖能对她从事的护理工作起到激励作用;93.26%的调查者会将南丁格尔精神作为职业信仰;93.71%的调查者愿意将南丁格尔奖获得者作为职业榜样。不同年龄、护龄、职称、学历护士和不同年级护生的南丁格尔奖声誉度不同(P<0.05)。结论:在职业范围内南丁格尔奖声誉度高。低年资、本科学历护士和本科一年级护生对南丁格尔奖声誉度的总体评价稍低,高年资护士和研究生的总体评价高。新时代南丁格尔精神传承中应重点关注低年资本科护士群体和本科一年级护生。

商会关系、声誉机制与代理成本

以中国证券市场2004—2021年A股上市公司为研究对象,研究企业家(董事长或总经理)商会关系资本与企业代理成本之间的关系,并进一步考察企业家个人声誉机制对上述关系的潜在影响。研究结果表明:企业家商会关系能够降低企业代理成本,且企业家商会任职层级越高、任职种类越多,抑制作用越显著;当企业家拥有人大代表或政协委员的政治身份以及兼任其他上市公司董事的社会身份等个人声誉时,商会关系对企业代理成本的抑制作用较弱;渠道检验发现,激励机制和监督机制是企业家商会关系作用于企业代理成本的重要渠道;进一步分析发现,企业家行业协会关系、国家及省级商会关系对代理成本的抑制作用更加明显,在非国有企业中企业家商会关系对代理成本的抑制作用更加明显。研究结论丰富和拓展了关系社会资本的微观经济后果及作用机制,为深入理解商会关系文化对微观企业决策行为的作用机制提供了初步的理论分析框架和经验证据。

基于模糊认知图的高铁企业国际声誉形成机制仿真分析

为探究高铁企业国际声誉形成机制的动态演化过程,采用一种结合了结构方程模型和模糊认知图的方法进行仿真分析。预测分析结果表明:宏观因素、企业能力和被感知的能力均与国际声誉呈正相关,与国际声誉之间的相关性大小排序为宏观因素>企业能力>被感知的能力。诊断分析结果表明:宏观因素是引起国际声誉变化的最根本原因。混合分析结果证明了以宏观因素为核心采取综合干预措施,对国际声誉的提升效果最好。基于分析结果,提出了高铁企业提升国际声誉的对策。这种混合方法模拟了因素间的动态交互作用,并在不同假设情景下进行仿真分析,揭示了高铁企业国际声誉形成机制的动态演化过程。研究结果有助于拓展国际声誉形成机制的知识体系,也可为高铁企业采取针对性策略提升国际声誉提供实践参考。

文化与经济双重情境作用下管理者声誉对长期主义的影响研究

运用机器学习与文本分析方法构建管理者长期主义变量,以中国A股上市公司为样本,基于跨期选择理论,探究管理者声誉对长期主义的塑造作用以及儒家文化与经济政策不确定性的调节效应。研究表明:管理者声誉与其长期主义导向显著正相关;在受儒家文化影响更加深远以及外部经济政策不确定性更强的企业中,管理者声誉对于长期主义的影响更为显著;管理者声誉能够通过塑造长期主义者,优化企业在减少违规行为、提升内控质量、提高信息透明度、促进社会责任承担等价值分配方面,以及促进技术创新、绿色创新、数字化转型、高质量发展等价值创造方面的表现。

基于信誉机制的改进PBFT共识算法

针对实用拜占庭容错共识算法(practical Byzantine fault tolerant,PBFT)通信开销大和缺乏奖惩机制的问题,提出一种基于信誉机制的改进PBFT共识算法RPBFT(reputed practical Byzantine fault tolerance)。首先,引入信誉机制对节点评分,将参与共识的节点分为收集器节点和普通共识节点,并对恶意节点进行惩罚。其次,收集器节点负责收集普通共识节点的投票消息,避免普通共识节点之间的通信,从而降低通信开销。最后,当普通共识节点中的拜占庭节点均无恶意行为时,通过增加收集所需的投票数量,减少一次投票收集过程,实现快速共识。实验结果表明,RPBFT能够有效地发现恶意节点并对其作出惩罚,同时具有更低的通信开销、平均共识时延以及更高的共识吞吐量。当节点总数为37时,与SBFT相比,RPBFT将平均共识时延降低25.2%以上,并将共识吞吐量提高39%以上。

CEO教育背景与内部控制信息披露质量

高学历水平通常意味着拥有更扎实的专业素养以及更理性的行为,而在愈发重视内部控制信息披露的资本市场中,作为核心管理者的首席执行官(CEO),其教育背景是否与内部控制信息披露水平具有重要联系?基于2007—2021年中国A股上市公司的相关数据,实证检验CEO教育背景对企业内部控制信息披露质量的影响。基准回归结果表明,高学历CEO能稳健地提升企业内部控制信息披露质量。进一步的机制分析结果表明,CEO学历水平有效提升了CEO能力,从而促进内部控制信息披露质量提升,而CEO能力所隐含的声誉也是重要机制之一。分项检验结果显示,高学历CEO主要提高了内部环境、控制活动及信息与沟通等方面的信息披露质量。异质性分析结果显示,对于代理问题成本高、规模小的企业,高学历CEO对企业内部控制信息披露质量的提升效应更为明显。研究结论丰富了CEO个人特征的经济后果研究和内部控制信息披露质量的影响因素研究,为经理人的治理作用增添了新的经验证据。

专利出海与中国企业对外直接投资

专利出海作为开放创新生态的重要内容,是推进高水平对外开放的新优势。本文选取2003—2021年中国专利出海上市企业为研究对象,探究专利出海对中国企业对外直接投资的影响及作用机制。研究结果显示,专利出海能够促进中国企业对外直接投资,其动态滞后效应在第2期达到最大。在考虑多维专利出海质量、内生性问题和进行一系列稳健性检验后,结论依然成立。机制检验结果表明,专利出海通过技术领先效应和声誉传递效应促进中国企业对外直接投资。异质性分析结果显示,专利出海对数字化企业、顺向投资企业和民营企业对外直接投资的促进作用明显,对中国企业向投资网络社区内东道国、高知识产权保护东道国的投资促进作用更强。本文为依托创新全球化布局推动企业对外投资提供了微观依据,对提升国际循环质量和实现更高水平开放型经济具有重要启示。

声誉对互联网医院使用意愿影响的多元路径分析——基于SEM和fsQCA的实证分析

目的:探究声誉对互联网医院使用意愿的影响,对优化互联网医院服务供给模式,提高患者就医体验,促进医院高质量发展都具有重要意义。方法:基于信息-解释-响应(I-I-R)理论构建声誉对互联网医院使用意愿影响框架模型,采用结构方程模型(SEM)和模糊集定性比较分析法(fsQCA)对互联网医院的使用意愿和组态路径进行实证分析。结果:医院社会责任、医院实力和医生声誉正向影响评估性态度,路径系数由强到弱依次为0.360、0.301和0.260;医院实力和医院社会责任正向影响情感性态度,路径系数分别为0.449和0.272;评估性态度和情感性态度正向影响使用意愿,路径系数分别为0.519和0.386。且以医院社会责任型和医生声誉+医院实力型为核心条件可构成两种模式下的六条有效路径。结论:医生声誉、医院实力和医院社会责任对提高互联网医院使用意愿最有效。医院应构建专业度高的医疗服务平台,提高知名度;应定期开展学习和培训,更新医疗设备,保持信息公开透明,积极承担社会责任,提高医院实力;医生应改善服务态度,提高线上互动性。

基于分组学习粒子群算法的众包软件项目调度

为解决众包软件项目调度问题中的开发者选择、任务分配和投入度确定3个强耦合子问题,引入开发者信誉度,考虑技能、工作时长、开发团队规模等约束,以项目完成质量和工期为目标建立数学模型。提出一种采用三段式混合编码的分组学习粒子群算法求解所建模型。所提算法根据适应度排序将种群划分为3组,不同分组的粒子数量随进化代数自适应变化,且各组根据不同的适应度采用不同的更新策略。将所提算法与10种具有代表性的算法在12个不同规模的众包软件项目调度算例中进行对比,结果表明,所提算法能够获得精度更高的调度方案。

高校入选“双一流”吸引了更高质量生源吗?——基于江苏省高考理科招生数据的分析

“双一流”是中国高等教育继“211工程”“985工程”之后的又一国家战略,对高等教育发展起着承前启后的重要作用。采用双重差分、事件分析等方法,在以录取分数及其位次表征生源质量的基础上,分析2014—2020年江苏省高考理科招生数据,系统考察“双一流”建设对高校生源质量影响。研究发现:(1)“一流大学”和“一流学科”建设均显著提高了生源的平均质量(录取位次),但存在异质性,表现为“一流大学”建设只影响了录取最低分及其位次,而“一流学科”建设同时对最高分和最低分产生了作用;(2)资源机制和声誉机制是“双一流”建设影响生源质量的重要渠道;(3)“双一流”建设通过优先发展部分高校和学科,在一定程度上改善了优质生源在高校和地区间的配置。

基于前景理论的即时物流骑手交通违规监管演化博弈分析

为治理即时物流骑手交通违规引发的道路交通安全问题,以前景理论替代期望效用理论,构建融入主观概率权重函数与价值函数的骑手违规配送监管演化博弈模型,分析了即时物流平台监管与骑手配送决策的互动关系与影响因素,进一步引入声誉激励机制构建骑手关于配送收入的决策行为影响模型,探究了平台对不同风险态度骑手违规配送治理的新思路。研究表明:骑手选择合规配送的比例与平台查处违规配送概率及罚金、骑手违规配送下交通事故发生概率及损失呈正相关,与骑手合规配送成本呈负相关,平台与骑手对高概率损失的风险偏好属性以及对积极监管与合规配送成本前景价值感知的低估,导致理想均衡稳定策略难以形成;不同风险态度骑手对平台声誉激励机制的反馈存在差异,平台通过引入声誉激励机制能够促进风险回避型骑手选择合规配送策略,而声誉激励机制对于风险偏好型骑手失效,此时平台通过设置合理额度的惩罚能够约束风险偏好型骑手选择违规配送策略。研究结果可为即时物流平台制定奖惩结合的监管策略提供理论参考。