APPM: Adaptive Parallel Processing Mechanism for Service Function Chains

Cai, Jun; Huang, Zhongwei; Liao, Liping; Luo, Jianzhen; Liu, Waixi

doi:10.1109/tnsm.2021.3052223

Cited by 54 publications

(57 citation statements)

References 35 publications

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“…However, player evaluation is often a multi-dimensional decision-making problem while multi-dimensional decision-making problems often involves multiple-type data fusion, weight assignment as well as the optimization issue [21][22][23][24][25][26][27][28] . Therefore, we will further study the multisource data integration problem with weighting.…”

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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“…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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“…In the future, we will introduce more academic indicators, such as user trust [20][21][22] and time context [23][24][25][26][27][28]. In addition, computational cost or time cost is a key concern when the dataset to be processed is big [29][30][31][32][33][34][35][36][37][38][39]. erefore, we will further optimize our proposed method to accommodate the big data applicable scenarios.…”

Section: Discussionmentioning

confidence: 99%

Coauthorship Network Mining for Scholar Communication and Collaboration Path Recommendation

Zhao

Zou

Wei

et al. 2021

Security and Communication Networks

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With the increasing penetration of interdisciplinary subjects, it is more difficult for researchers to complete a paper individually, showing that the division of labor can improve the level and efficiency of scientific research. Thus, collaboration among multiple scholars has become a trend in academic research. However, because the numbers of scholars and papers are increasing and cooperation between scholars has become more frequent in recent years, it is an increasingly challenging task to discover useful knowledge resources for researchers. Against the background of big data, how to help scholars quickly find interested target collaborators, encourage them to participate more actively in academic communication, and create high-quality achievements in scientific research has become a significant problem. Considering this challenge, this article proposes a framework of coauthorship strength, author contribution, and search (CCS，taking the first letter of the keyword), which is based on the coauthorship feature of Google Academics. In CSS, we combined the search algorithm to select the optimal connection path to help scholars find interested target scholars efficiently and to better solve practical application problems. Finally, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results of our approach show better search outcomes compared to other competitive approaches.

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APPM: Adaptive Parallel Processing Mechanism for Service Function Chains

Cited by 54 publications

References 35 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

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

Coauthorship Network Mining for Scholar Communication and Collaboration Path Recommendation

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