Mathematical Foundations of Computing (MFC) publishes original research papers of the highest quality in all areas of mathematics and computer science which are relevant to applications in communications technology. For this reason, submissions from many areas of mathematics are invited, provided these show a high level of originality, new techniques, an innovative approach, novel methodologies, or otherwise a high level of depth and sophistication. Any work that does not conform to these standards will be rejected.
Areas covered include analysis of algorithms,automata, computational complexity,theoretical computer science,geometry in computer science,discrete algorithms,secure computing,privacy-aware computing,distributed computing and networking,computational probability,statistical computation and simulation,computational intelligence,computational social network,computational biology, coding theory, graph theory,computational learning theory,probability and statistics in computer science, combinatorial optimization in computer science,logic and semantics in computer science,numerical analysis in computer science, numerical algebra in computer science and symbolic computation / computer algebra, but are not restricted to these. This journal also aims to cover the algorithmic and computational aspects of these disciplines. Hence, all mathematics and computer science contributions of appropriate depth and relevance to the above mentioned applications in computer science are welcome.
More detailed indication of the journal's scope is given by the subject interests of the members of the board of editors.
All papers will undergo a thorough peer reviewing process unless the subject matter of the paper does not fit the journal; in this case, the author will be informed promptly. Every effort will be made to secure a decision in three months and to publish accepted papers within six months.
- AIMS is a member of COPE. All AIMS journals adhere to the publication ethics and malpractice policies outlined by COPE.
- MFC will publish four issues starting from 2018 in February, May, August and November.
- MFC is a joint publication of the American Institute of Mathematical Sciences and Qufu Normal University. All rights reserved.
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Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference.
For a positive integer
Within the academic circle the Traveling Salesman Problem (TSP), this is one of the most major NP-hard problems that have been a primary topic of discussion for years. Developing efficient algorithms to solve TSP have been the goal of many individuals, and so this has been addressed efficiently in this article. Here, a discrete heat transfer search (DHTS) is proposed to solve TSP. DHTS uses three distinct phases to update the city tours namely, conduction, convection, and radiation. Each phase performs a certain function as the conduction phase is a replica of the 2-Opt local search technique, the convection phase exchanges the random city with the finest city tour, and the radiation phase exchanges the random city among two separate city tours without compromising the basics of HTS algorithm. Bench test problems taken from TSPLIB successfully test the algorithm and demonstrate the fact that the proposed algorithm can attain results near the optimal values, and do so within an acceptable duration.
Arbiter-based physical unclonable function (APUF) is a classical kind of physical unclonable function (PUF). In APUF-based device authentication, the fairness of traditional APUF is insufficient due to setup time of arbiter. To solve this problem, in this paper we design an arbiter and conduct Monte Carlo simulations to test the performance of the new arbiter. In addition, we present some new evaluation metrics to evaluate the new arbiter quantitatively. Finally, we certify that the new arbiter can work continuously with both one stage racing paths and eight stages racing paths. The new arbiter has good performance in correct rate, stability and fairness. Particularly, it mitigates the setup time problem by reducing the Asymmetry.
Although massive real-time data collected from users can provide benefits to improve the quality of human daily lives, it is possible to expose users' privacy.
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