Reputation Value Research Articles

Гражданская и этническая идентичность россиян: роль ценностей в их формировании

Background. Person's civil and ethnic identity, which are components of social identity, develop during socialization under the influence of a number of factors. J. Berry identifies cultural, political, economic and historical factors as the basis for identity formation. The scientific novelty of the work lies in the study of the role of the cultural factor, namely the structure of values, in the formation of a positive civic and ethnic identity of Russians. Objectives. The purpose of this work is to study the structure of the ethnic and civic identity of Russians as well as the influence of individual values on their formation. Study Participants. The study was conducted with the participation of 1014 respondents, ethnic Russians, aged from 19 to 83 years (Mage = 41 years; SD = 13.04; 527 women and 487 men). Methods. J. Finney's MEIM-R methods were used to measure ethnic identity. S. Schwartz's PVQ-R value structure questionnaire was also applied. Civic identity was measured using the methodology from the International Social Survey Program, adapted into Russian by L.K. Grigoryan. Results. It was found that the civic identity of Russians as the individual’s awareness of belonging to the citizens of the country is more pronounced than ethnic identity. Nationalism is expressed more than the ideology of patriotism. The affective component of ethnic identity is more pronounced than the cognitive one. Among the surveyed respondents, the values of independence, security, reputation and benevolence prevail. The values of security, benevolence and universalism increase the level of civic identity in Russians. The key positive regressor in relation to both civic and ethnic identity is the value “Tradition”. Conclusions. Socially oriented values (preservation and self-overcoming) have a positive impact on the formation of positive civic and ethnic identity of Russians. The values of personal orientation (openness to change and self-affirmation) have a negative impact on their formation.

Consumer evaluation mechanisms on e-commerce platforms: reputation management and analysis of influencing factors

Abstract This paper proposes a regression algorithm to statistically relate the relationship between the relationship variables and proposes a multiple linear regression model to conduct a linear correlation study between a dependent variable and multiple independent variables. The factors that affect the value of merchant reputation in the content of consumer evaluation are regressed and analyzed, and a model for evaluating merchant reputation is proposed based on the regression results. In the merchant reputation evaluation model, the Bayesian algorithm and multi-source data fusion method are used to calculate the merchant weight, the Euclidean distance is used to calculate the platform weight, and the judgment matrix is constructed to calculate the merchant e-commerce reputation score. In the model performance test, 10 Taobao merchants are randomly selected for reputation calculation. The highest merchant reputation scores of 4.582 and 4.584 can be obtained for merchants S9 and S10, and the index scores are more accurate. The original Taobao reputation scores of merchants S4, S8, and S9 are all 4.97. At the same time, the model in this paper calculates them to be 4.404, 4.304, and 4.582, respectively, which are more reflective of the real reputation level of merchants.

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

Research on incentive mechanisms for anti-heterogeneous federated learning based on reputation and contribution

<abstract> <p>An optimization algorithm for federated learning, equipped with an incentive mechanism, is introduced to tackle the challenges of excessive iterations, prolonged training durations, and suboptimal efficiency encountered during model training within the federated learning framework. Initially, the algorithm establishes reputation values that are tied to both time and model loss metrics. This foundation enables the creation of incentive mechanisms aimed at rewarding honest nodes while penalizing malicious ones. Subsequently, a bidirectional selection mechanism anchored in blockchain technology is developed, allowing smart contracts to enroll nodes with high reputations in training sessions, thus filtering out malicious clients and enhancing local training efficiency. Furthermore, the integration of the Earth Mover's Distance (EMD) mechanism serves to lessen the impact of non-IID (non-Independent and Identically Distributed) data on the global model, leading to a reduction in the frequency of model training cycles and an improvement in model accuracy. Experimental results confirm that this approach maintains high model accuracy in non-IID data settings, outperforming traditional federated learning algorithms.</p> </abstract>

Employer Branding Phenomenon: A Systematic Review Conceptualization at Higher Education Institutions

This research aims to uncover the dimensions of employer branding (EB) that attract and retain employees working at the university, both lecturers and education personnel. This research uses a systematic literature review with the PRISMA diagram. The database used comes from Scopus and Emerald with journal criteria, open access, English language, obtained 6 articles to be analyzed. The dimensions used are social values, development values, diversity, economic values, interest values, reputation values, working environment values, and work life balance. Some use Reward strategy, People orientedness, Leadership development to measure employer branding. In addition, it uses the EB dimension of employee retention and employee attraction. This study examines the object of employer branding research in higher education related to exploring the dimensions of the perspective of academic workers including lecturers and education personnel. In addition, it finds the grand theory used in constructing employer branding.

A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.

Blockchain-Based Lightweight Authentication Mechanisms for Industrial Internet of Things and Information Systems

The industrial internet of things (IIoT) necessitates robust cross-domain authentication to secure sensitive on-site equipment data. This paper presents a refined reputation-based lightweight consensus mechanism (LRBCM) tailored for IIoT's distributed network structures. Leveraging node reputation values, LRBCM streamlines ledger consensus, minimizing communication overhead and complexity. Comparative experiments show LRBCM outperforms competing mechanisms. It maintains higher throughput as the number of participating nodes increases and achieves a throughput approximately 10.78% higher than ReCon. Moreover, runtime analysis demonstrates LRBCM's scalability, surpassing ReCon by approximately 12.79% with equivalent nodes and transactions. In addition, as a combination of LRBCM, the proposed distributed lightweight authentication mechanism (ELAM) is rigorously evaluated against the security of various attacks, and its resilience is confirmed. Experiments show that ELAM has good efficiency while maintaining high security.

Wireless sensor network security defense strategy based on Bayesian reputation evaluation model

AbstractIn order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed.

Mental health and access to care in the Montagnard migrant community: Examining perspectives across four generations in North Carolina

Background The Montagnards are a diverse group of indigenous tribes from the Central Highlands of Vietnam. With thousands now resettled in the United States, Montagnard migrant communities face unique mental health challenges stemming from decades of trauma, war, and persecution. Research has demonstrated that health challenges facing migrant communities are often compounded by sociocultural, political, and economic factors associated with resettlement, and by a lack of access to health care. Methods In this qualitative study, framework analysis was used to assess mental health care access across multiple generations of Montagnards in North Carolina. Semi-structured interviews and mental health screenings were conducted with twenty-six participants. Interviews were transcribed and analyzed using Dedoose software. Results The results show that some Montagnards, especially elders, have an understanding of emotional, psychological, and social wellbeing that differs from the standard concept of “mental health” as defined by the CDC. Pervasive negative beliefs about mental illness, alongside cultural values of strength and family reputation, lead some Montagnards to avoid discussing mental health publicly. Barriers such as the cost of treatment and challenges with communication and language limit access to care and disproportionately affect older community members. However, only younger Montagnards showed symptoms of mental illness on the diagnostic screenings. Conclusions Montagnard migrant communities in North Carolina do not have adequate access to mental health care. Community-based interventions are needed to improve mental, emotional, and social wellbeing, increase access to care, and provide culturally-responsive support to Montagnards.

Analysis of E-Commerce Transformation of Historic Brands: Take Holiland as an Example

With the improvement of living standards, after solving the problem of food and clothing, people's attention to snacks and refreshments has increased, which has promoted the rise of China's dessert market; At the same time, the wide application of e-commerce provides tools for industry upgrading. Through literature analysis and case analysis, this paper studies how the famous Chinese dessert brand 'Holiland' has transformed its historic brands with the new era through e-commerce transformation, regained business opportunities and occupied a large market share. This paper hopes to analyze the success of 'Holiland', and explores whether time-honored brands can improve the value of brand reputation through the rational use of e-commerce, and transform and upgrade to adapt to the current era of social Internet + electronics. After analysis, this paper finds that if time-honored brands can make reasonable use of e-commerce, they can attract the love of the whole new customer base --like the main force of consumption - the young people of the Z generation.

A Privacy-Preserving and Reputation-Based Truth Discovery Framework in Mobile Crowdsensing

In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this paper proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.

Improving the Effectiveness of Eigentrust in Computing the Reputation of Social Agents in Presence of Collusion.

The introduction of trust-based approaches in social scenarios modeled as multi-agent systems (MAS) has been recognized as a valid solution to improve the effectiveness of these communities. In fact, they make interactions taking place in social scenarios much fruitful as possible, limiting or even avoiding malicious or fraudulent behaviors, including collusion. This is also the case of multi-layered neural networks (NN), which can face limited, incomplete, misleading, controversial or noisy datasets, produced by untrustworthy agents. Many strategies to deal with malicious agents in social networks have been proposed in the literature. One of the most effective is represented by Eigentrust, often adopted as a benchmark. It can be seen as a variation of PageRank, an algorithm for determining result rankings used by search engines like Google. Moreover, Eigentrust can also be viewed as a linear neural network whose architecture is represented by the graph of Web pages. A major drawback of Eigentrust is that it uses some additional information about agents that can be a priori considered particularly trustworthy, rewarding them in terms of reputation, while the non pre-trusted agents are penalized. In this paper, we propose a different strategy to detect malicious agents which does not modify the real reputation values of the honest ones. We introduce a measure of effectiveness when computing reputation in presence of malicious agents. Moreover, we define a metric of error useful to quantitatively determine how much an algorithm for the identification of malicious agents modifies the reputation scores of the honest ones. We have performed an experimental campaign of mathematical simulations on a dynamic multi-agent environment. The obtained results show that our method is more effective than Eigentrust in determining reputation values, presenting an error which is about a thousand times lower than the error produced by Eigentrust on medium-sized social networks.

Reputation Consensus-Based Scheme for Information Sharing in Internet of Vehicles

Low latency and low energy consumption are prerequisites for Internet of Vehicles (IoV) research; nevertheless, consensus mechanisms of legacy blockchain applied to IoV typically incur high delay and serious energy consumption due to the computational puzzle. In this paper, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to implement a novel reputation consensus mechanism for IoV based on vehicle reputation values and traffic environment, which can not only reduce the computational pressure but also ensure the fairness of consensus. Firstly, the information transmission vehicles (ITVs) would be picked following the reputation value and its associated environmental data that makes the choice more comprehensive and objective than other mechanisms. Secondly, the authenticity of sharing information is determined with the help of Bayesian inference, aided by the reputation value of message reporting vehicles (MRVs). Finally, simulation results show that the proposed scheme can improve consensus efficiency by picking ITVs in a short time and avoid the spreading of false information effectively in the IoV system.

Blockchain‐assisted twin migration for vehicular metaverses: A game theory approach

AbstractAs the fusion of automotive industry and metaverse, vehicular metaverses establish a bridge between the physical space and virtual space, providing intelligent transportation services through the integration of various technologies, such as extended reality and real‐time rendering technologies, to offer immersive metaverse services for vehicular metaverse users (VMUs). In vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide Units (RSUs) to obtain metaverse services. However, due to the mobility of vehicles and the limited service coverage of RSUs, VT migration is necessary to ensure continuous immersive experiences for VMUs. This process requires RSUs to contribute resources for enabling efficient migration, which leads to a resource‐trading problem between RSUs and VMUs. Moreover, a single RSU cannot support large‐scale VT migration. To this end, we propose a blockchain‐assisted game approach framework for reliable VT migration in vehicular metaverses. Based on the subject logic model, we first calculate the reputation values of RSUs considering the freshness of interaction between RSUs and VMUs. Then, a coalition game based on the reputation values of RSUs is formulated, and RSU coalitions are formed to jointly provide bandwidth resources for reliable and large‐scale VT migration. Subsequently, the RSU coalition with the highest utility is selected. Finally, to incentivize VMUs to participate in VT migration, we propose a Stackelberg model between the selected coalition and VMUs. Numerical results demonstrate the reliability and effectiveness of the proposed schemes.

Delegated Proof of Stake Consensus Mechanism Based on Community Discovery and Credit Incentive.

Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as "one ballot, one vote", a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving "one ballot, multiple votes", and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods.

Efficient Anonymous Authentication and Privacy-Preserving Reliability Evaluation for Mobile Crowdsensing in Vehicular Networks

Mobile crowdsensing (MCS) is widely applied in vehicular networks where several sensing vehicles complete the same sensing task. Recently, the privacy and reliability of sensing vehicles have aroused extensive attention of researchers in academia. Although the majority of existing schemes have achieved anonymous authentication with large computation and communication overheads, they do not take the reliability of sensing vehicles into account when selecting sensing vehicles. In this paper, we propose an efficient anonymous authentication and privacy-preserving reliability evaluation (AARE) scheme for MCS, which not only improves the efficiency of mutual authentication but also guarantees the reliability of sensing vehicles. Specifically, an efficient anonymous authentication method is proposed to achieve the anonymous authentication of sensing vehicles. Besides, even if a sensing vehicle passes anonymous authentication, it is difficult to fully guarantee its reliability. Then, a privacy-preserving reliability evaluation algorithm is adopted to evaluate the reliability of sensing vehicles. Meanwhile, in dynamic vehicular networks, the reliability of sensing vehicles is uncertain since there exist many attacks, thus, an accurate reputation update algorithm is designed. Subsequently, the privacy features, computation, and communication overheads in the proposed scheme are analyzed. Comparisons with the existing schemes show that the authentication efficiency of the proposed scheme is increased by 46%-74%, the communication overhead is reduced by 50%-80%, and the accuracy of reputation values of sensing vehicles which submit the reliable/unreliable sensing data is improved/reduced by 16%-22%/67%-74%.

동남권 대학 졸업자의 수도권 2차 유출 요인 연구

Not only in Korean society as a whole, but also in the Southeastern region, the outflow of young people to the metropolitan area has been intensified. To understand this phenomenon, we need to pay attention to their job values. According to previous studies, the job values of young people are influenced by the “self-development discourse.” This shows the probability that their development value will affect the outflow to the metropolitan area as a place where individual development is experienced. Since the Southeastern region has unique characteristics as industrial city, college graduates in this region are likely to be affected by this structural aspect, which provides a reason to study the youth of this region separately. In this study, by analyzing the Graduate Occupational Mobility Survey from 2013 to 2019, their job values were analyzed employing exploratory factor analysis, which rendered four factors (environment, development, task, and reputation). Using binomial logistic regression, the effect of each job value on the outflow of graduates from the Southeastern area to the metropolitan area was analyzed. It was found that the development value had a positive effect on the outflow to the metropolitan area, and the task and reputation values had negative effects. This suggests that the self-development discourse is working for graduates in the Southeastern region.

Fibonacci Group Consensus Algorithm Based on Node Evaluation Mechanisms

In response to challenges posed by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, where all nodes are involved in the consensus process, issues such as high communication overhead in the three-phase procedure, random selection of master nodes, and the absence of reward and penalty mechanisms are addressed. This leads to the proposal of a Speculative Practical Byzantine Fault Tolerance (SP-PBFT) consensus algorithm based on a node evaluation mechanism for speculative grouping. Firstly, the consensus protocol was optimized, and a timeout mechanism was proposed to divide the consensus process into an optimistic mode and a pessimistic mode, which reduced the communication overhead under the premise of resisting Byzantine node attacks. Secondly, a node evaluation mechanism was proposed to calculate the node reputation value according to the node’s historical behavior and give the corresponding reward or punishment, which reduced the possibility of malicious nodes participating in the consensus process. Finally, the Fibonacci grouping mechanism was used to reduce the number of nodes participating in the consensus process, fundamentally improving the consensus efficiency, avoiding the problem of centralization of the consensus process caused by the cumulative reputation value of nodes, and improving the enthusiasm of consensus nodes. Simulation experiments using Docker containers to simulate multiple nodes show that the SP-PBFT consensus algorithm proposed in this paper has better performance than the PBFT consensus algorithm and other improved algorithms in terms of consensus delay, throughput, fault tolerance and communication complexity.

Joint optimization of energy trading and consensus mechanism in blockchain-empowered smart grids: a reinforcement learning approach

Under the trend of green development, the traditional fossil fuel and centralized energy management models are no longer applicable, and distributed energy systems that can efficiently utilize clean energy have become the key to research in the energy field nowadays. However, there are still many problems in distributed energy trading systems, such as user privacy protection and mutual trust in trading, how to ensure the high quality and reliability of energy services, and how to motivate energy suppliers to participate in trading. To solve these problems, this paper proposes a blockchain-based smart grid system that enables efficient energy trading and consensus optimization, enabling electricity consumers to obtain high-quality, reliable energy services and electricity suppliers to receive rich rewards, and motivating all parties to actively participate in trading to maintain the balance of the system. We propose a reputation value assessment algorithm to evaluate the reputation of electricity suppliers to ensure that electricity consumers receive quality energy services. To minimize the cost, maximize the benefit for the electricity suppliers and optimize the system, we present an algorithm based on reinforcement learning DDPG to determine the power supplier, power generation capacity, and consensus mechanism between nodes to obtain power trading rights in each round. Simulation results show that the proposed energy trading scheme has good performance in terms of rewards.

ROMSS: a rational optional multi-secret sharing scheme based on reputation mechanism

The traditional threshold secret sharing scheme only allows the participants’ sub-secret shares to be used once in the reconstruction process. Several multi-secret sharing schemes have been proposed that are related to cloud computing, aiming to improve reconstruction efficiency. Rational secret sharing is a technique that combines secret sharing with game theory. In traditional rational multi-secret sharing, participants must reconstruct all secrets, resulting in unnecessary overhead. Rational participants will act dishonestly to maximize their own interests, leading to a prisoner’s dilemma and incomplete secret reconstruction. Additionally, when sharing multiple secrets, the Dealer must distribute the sub-secret shares of all secrets to the participants, increasing overhead. In this paper, we propose a rational optional multi-secret sharing scheme based on a reputation mechanism that selectively reconstructs secrets according to participants’ needs in the context of cloud computing. Our scheme introduces a reputation mechanism to evaluate participants’ reputation values to avoid their dishonest behaviors. Furthermore, we adopt a broadcast encryption matrix so that participants only need to receive a single sub-secret share to participate in multi-secret reconstruction. Our security analysis shows that the proposed scheme can effectively constrain the self-interested behavior of rational participants and reduce the overhead in the process, thus multi-secret sharing scheme can provide more efficient and secure solutions for secret sharing in key management and distributive storage for the cloud scenarios.