Collaborative spectrum sensing is proposed to improve the detection performance in cognitive radio (CR) networks. However, most of the current collaborative sensing schemes are vulnerable to the interference of the ma...Collaborative spectrum sensing is proposed to improve the detection performance in cognitive radio (CR) networks. However, most of the current collaborative sensing schemes are vulnerable to the interference of the malicious secondary users (SUs). In this paper we propose a reputation-based collaborative spectrum sensing scheme to improve the security of cooperative sensing by mitigating the impacts of misbehaviors. The fusion center calculates the reputation rating of each SU according to their history reports to weight their sensing results in the proposed scheme. We analyze and evaluate the performance of the proposed scheme and its advantages over previous schemes in expansibility and integrity. Simulation results show that the proposed scheme can minimize the harmful influence from malicious SUs.展开更多
We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains,with a core focus on establishing trust among participating stakeholders through a novel reputation-based appro...We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains,with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach.The prevailing consensus mechanisms,initially crafted for cryptocurrency applications,prove unsuitable for the unique dynamics of supply chain systems.Unlike the broad inclusivity of cryptocurrency networks,our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria.The delineation of roles for supply chain participants within the consensus process becomes paramount.While reputation serves as a well-established quality parameter in various domains,its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory.Moreover,recognizing the primary role of efficient block verification in blockchain-enabled supply chains,our work introduces a comprehensive reputation model.This model strategically selects a leader node to orchestrate the entire block mining process within the consensus.Additionally,we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model.Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism,contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.展开更多
This study investigates the effect of flexible tax enforcement on firms’excess goodwill using unique manually collected data on taxpaying credit rating in China from 2014 to 2021.We document that A-rated taxpayer fir...This study investigates the effect of flexible tax enforcement on firms’excess goodwill using unique manually collected data on taxpaying credit rating in China from 2014 to 2021.We document that A-rated taxpayer firms have less excess goodwill;A-rated firms reduce excess goodwill by 0.005 vis-a-vis non-Arated firms,which accounts for 100%of the mean value of excess goodwill.This finding holds after multiple robustness tests and an endogeneity analysis.Moreover,this negative effect is more pronounced in firms with low information transparency,that are non-state-owned and that are located in regions with low tax enforcement intensity.The channel test results suggest that taxpaying credit rating system as flexible tax enforcement reduces firms’excess goodwill through a reputation-based effect and not a governance-based effect.This study reveals that the taxpaying credit rating system in China as flexible tax enforcement can bring halo effect to A rating firms,thereby limiting irrational M&As and breaking goodwill bubble.展开更多
Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without ...Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models： first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCCtrust -a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and community context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users＇ interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.展开更多
基金the National Natural Science Foundation of China (No.60802058)the SMC-"Chen Xing" Young Scholar Foundation of Shanghai Jiaotong University
文摘Collaborative spectrum sensing is proposed to improve the detection performance in cognitive radio (CR) networks. However, most of the current collaborative sensing schemes are vulnerable to the interference of the malicious secondary users (SUs). In this paper we propose a reputation-based collaborative spectrum sensing scheme to improve the security of cooperative sensing by mitigating the impacts of misbehaviors. The fusion center calculates the reputation rating of each SU according to their history reports to weight their sensing results in the proposed scheme. We analyze and evaluate the performance of the proposed scheme and its advantages over previous schemes in expansibility and integrity. Simulation results show that the proposed scheme can minimize the harmful influence from malicious SUs.
基金made possible by NPRP(NPRP11S-1227-1701359)from the Qatar National Research Fund(a member of Qatar Foundation).
文摘We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains,with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach.The prevailing consensus mechanisms,initially crafted for cryptocurrency applications,prove unsuitable for the unique dynamics of supply chain systems.Unlike the broad inclusivity of cryptocurrency networks,our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria.The delineation of roles for supply chain participants within the consensus process becomes paramount.While reputation serves as a well-established quality parameter in various domains,its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory.Moreover,recognizing the primary role of efficient block verification in blockchain-enabled supply chains,our work introduces a comprehensive reputation model.This model strategically selects a leader node to orchestrate the entire block mining process within the consensus.Additionally,we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model.Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism,contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.
基金funded by a grant from the Natural Science Foundation of China(No.71762014)Soft Science Foundation in Gansu(No.23JRZA374).
文摘This study investigates the effect of flexible tax enforcement on firms’excess goodwill using unique manually collected data on taxpaying credit rating in China from 2014 to 2021.We document that A-rated taxpayer firms have less excess goodwill;A-rated firms reduce excess goodwill by 0.005 vis-a-vis non-Arated firms,which accounts for 100%of the mean value of excess goodwill.This finding holds after multiple robustness tests and an endogeneity analysis.Moreover,this negative effect is more pronounced in firms with low information transparency,that are non-state-owned and that are located in regions with low tax enforcement intensity.The channel test results suggest that taxpaying credit rating system as flexible tax enforcement reduces firms’excess goodwill through a reputation-based effect and not a governance-based effect.This study reveals that the taxpaying credit rating system in China as flexible tax enforcement can bring halo effect to A rating firms,thereby limiting irrational M&As and breaking goodwill bubble.
基金supported by the National High-Technology Research and Development 863 Program of China under Grant No. 2006AA01A123National Science Fund for Distinguished Young Scholars under Grant No.60525202+1 种基金Program for Changjiang Scholars and Innovative Research Team in University under Grant No.IRT0652Defense Advanced Research Foundation of the General Armaments Department of the PLA under Grant Nos.9140A06060307JW0403 and 9140A06050208JW0414.
文摘Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models： first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCCtrust -a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and community context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users＇ interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.
