A time-aware dynamic service quality prediction approach for services

Jin, Ying; Wei-guang, Guo; Zhang, Yiwen

doi:10.26599/tst.2019.9010007

Cited by 40 publications

(20 citation statements)

References 26 publications

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“…In addition, Mahalanobis Distance requires additional time cost to compute the covariance matrix of different dimensions; therefore, its time complexity is not very low. While time cost is critical for real world applications especially for the big data scenario [29][30][31][32][33][34][35] . Therefore, we would continuously refine our algorithm to further reduce its time costs so as to meet the quick response requirements from users.…”

Section: Discussionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

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Section: Discussionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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“…In this paper, we use a cross-validation method to verify the calculation results returned by the computing party, which causes the client to pay the calculation fee to the two computing parties, which is very expensive for the client. One future direction would be to combine the time prediction [42] with the measurement of the participant's behavioral uncertainty [43], to verify the computing party with a high reputation with a low verification probability, and reduce the computation cost to a computing party. Another future direction would be to extend our scheme to the rational delegation of computation like [23].…”

Section: Discussionmentioning

confidence: 99%

A rational delegating computation protocol based on reputation and smart contract

Chen

Wang

et al. 2021

J Cloud Comp

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The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. First, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Next, to satisfy the fairness of the rational delegating computation, we propose a rational delegating computation protocol based on reputation and smart contract. More specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. The reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Then, we conduct a comprehensive experiment to evaluate the proposed protocol. The simulation and analysis results show that the proposed protocol solves the complex traditional verification problem. We also conduct a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network. Meanwhile, we prove the fairness and correctness of the protocol.

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“…Recommender systems are actually a kind of decisionmaking problem that involves users, multiple influencing factors and so on [24][25][26][27][28][29] . In the future work, we will further discuss multi-factor recommendation problems for users.…”

Section: Discussionmentioning

confidence: 99%

Privacy-Aware Content-of-Interest Search and Recommendation in Internet of Things for Cross-Dressers

Sun

Cao

et al. 2021

IEEE Access

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With the continuous development and gradual progress of Internet of Things (IoT) in human society, people are becoming increasingly diverse in terms of user preferences and things choices. In this situation, several new cultures or social phenomena have been emerging including the so-called Crossdressing culture. As a special group of humans, cross-dressers are often very sensitive to their nonmainstream identities. Therefore, they are often confronted with more difficulties when using some modern information techniques such as Content-of-Interest (COI) search. Motivated by this fact, we introduce some advanced information retrieval and privacy protection techniques into the cross-dressing domain and further propose a privacy-aware COI search and recommendation solution for cross-dressers, named PCSR. First, PCSR uses fastText tool to transform the cross-dressers' input keywords and the candidate webpages into corresponding vectors with less private content associated with cross-dressers. Afterwards, we use vector similarity calculation techniques to make privacy-preserving COI search and recommendation. At last, we validate the effectiveness of PCSR through a set of experiments. We believe that our proposed PCSR solution can benefit the cross-dressers significantly when performing COI search and recommendation in IoT while protecting sensitive information of cross-dressers.

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A time-aware dynamic service quality prediction approach for services

Cited by 40 publications

References 26 publications

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

A rational delegating computation protocol based on reputation and smart contract

Privacy-Aware Content-of-Interest Search and Recommendation in Internet of Things for Cross-Dressers

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