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.

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.

A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applications.This paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering packets.The proposed version employs a network-based reputation system to select the best and most secure path to a destination.To achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system.To minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation tables.Moreover,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast approaches.The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other protocols.Using the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing path.The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones.Furthermore,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

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.

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.

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.

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.

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.

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。

基于信誉分类的拜占庭容错共识算法

针对许可区块链场景下实用拜占庭容错(Practical Byzantine Fault Tolerance,PBFT)共识算法通信开销大、主节点选取随意以及吞吐量低等问题,通过引入并优化信誉评分模型(Reputation Scoring Model,RSM)。提出了一种基于信誉分类的拜占庭容错(Byzantine Fault Tolerance Based on Reputation Classification,RCBFT)共识算法。定义RSM,依据节点的历史共识行为所获得的信誉评分排序对参与节点进行动态分类以及分级管理,提出基于信誉分类的多层次节点架构;在可信节点层中随机选取节点来担任主节点,优化主节点选取机制;设计了缓冲节点层类型转换策略(Type Conversion Strategy for Nodes,TCSN),兼顾了环境等非主观因素导致低信誉评分的诚实节点不能参与共识的问题,使得诚实节点尽可能多地参与共识,而拜占庭节点快速下降到最差类型中限制共识权限;RCBFT共识算法还对传统三阶段共识协议进行优化,减少通信开销,在确保容错性的同时能够提高算法性能。实验分析表明,相较于PBFT共识算法,RCBFT共识算法能够提升交易吞吐量,降低通信开销与共识时延。

企业声誉在食品行业企业危机响应过程中的作用——基于两家上市公司的比较分析

基于信号理论与危机情境沟通理论的视角,将上市公司发布的危机公告作为研究对象,通过梳理、分析同仁堂的过期蜂蜜事件和酒鬼酒的甜蜜素事件,识别出不同企业声誉维度的作用效果及企业危机响应策略的差异。案例分析发现:危机会引发上市公司市场价值下降,影响企业正常的生产经营活动;美誉度增强了危机响应策略的保护效果,但关注度增加了危机响应难度;同仁堂公司针对产品伤害危机采取适配性的道歉策略和纠正策略,危机公告信息透明度较高,修复了与利益相关者的关系;酒鬼酒公司针对谣言选择否认策略,危机公告信息透明度较低,危机沟通效果不佳。通过案例分析得到的一般结论可为其他企业的危机管理提供借鉴。

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。

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。

退市新规提高了审计收费吗?

为切实提高资本市场的有效性,我国在注册制上市制度出台后,将作为配套制度的退市制度也进行了相应改革。退市新规进一步细化了相关指标,强化了审计师的工作职责,从而提高了审计工作的压力和风险,这是否会对审计收费产生影响?本文以2020年底出台的退市新规为研究契机,选取2016-2022年A股上市公司为样本,研究显示,退市新规通过加大有退市风险公司的审计风险和审计师声誉风险,使得审计师增加了额外的审计收费以弥补过高风险。在非国企、内控水平较差、机构投资者占比较少和媒体关注较少的公司中提高更大,随着审计收费的提高,审计效率上升,审计激进度缓解,审计质量提升。本文从退市制度变革角度丰富了审计收费影响因素的研究,同时为完善退市制度提供了审计市场层面的证据。

防御型治疗仅是医生的责任吗?

医患共享决策背景下,独辟患者角度分析其诊疗需求对医生防御型治疗行为影响.建立演化博弈模型,特别抓取患者风险规避程度这一显著特征内生化诊疗需求,分析医患行为变化,并探究医患策略随患者患病程度的演变规律.研究表明,患者风险规避程度低于特定阈值则诱发防御型治疗,且促使患者接受度提升.此外,在患病程度对患者拒绝行为促进作用下,应重点关注医生轻疾诊疗决策并降低防御型治疗经济激励.研究结论对改善患者风险规避程度,设置差异化声誉惩罚,制定重大疾病补贴政策,DRGs定额支付方式推广与医养结合服务项目延伸等具有借鉴价值.

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。

公共部门组织声誉何以影响公民政策遵从?——基于数字治理中公民信息采集意愿的调查实验研究

公民政策遵从关乎政策成败,治理现代化背景下亟须探索促进公民政策遵从的有效策略。公共部门组织声誉作为政策领域的“无形资产”,是否能够影响公民政策遵从意愿?当前学界尚未对以上问题做出回答。基于此,本文在已有研究基础上引入中介变量——政府信任和调节变量——组织声誉维度,构建公共部门组织声誉与公民政策遵从之间的关系模型,通过调查实验探究公共部门组织声誉与公民政策遵从之间的关系及其形成的内在机理。研究结果表明:其一,相较于带有负面声誉的公共部门,公众对享有正面声誉的公共部门表现出更高的遵从度;其二,组织声誉对公民政策遵从的影响并非仅限于直接产生,也可通过公民政府信任间接实现;其三,不同组织声誉维度下政府信任存在明显差异,说明组织声誉维度可作为一个调节政府可信度的机制。将公共部门组织声誉理论与公民政策遵从进行链接和对话,既丰富了公共部门组织声誉的微观研究,也为提升政府治理成效以及推进公民政策获得感提供了实践启示。

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。