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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Many approaches have been proposed to perform the analysis of network(graph) data correlated with Internet, social networks, communication networks, knowledge graph etc, but few of them have been applied in the coordinate system which is thought to be an efficient platform to processing graph data. In this survey, we provide a short yet structured analysis of network(graph) data research in the coordinate system. We first introduce the coordinate embedding and transforming of double-loop network with its symmetrical and regular structure. We then present two categories of approaches for Internet embedding in the coordinate system and the technology of graph embedding of social network. Finally, we draw our conclusions and discuss potential applications and future research direction.
An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.
The frequency assignment problem (FAP) is the assignment of frequencies to television and radio transmitters subject to restrictions imposed by the distance between transmitters. One of the graph theoretical models of FAP which is well elaborated is the concept of distance constrained labeling of graphs. Let
With the rapid development of Internet of Things (IoT) technologies, smart home systems are getting more and more popular in our daily life. Besides providing convenient functionality and tangible benefits, smart home systems also expose users to security risks. To enhance the functionality and the security, machine learning algorithms play an important role in a smart home ecosystem, e.g., ensuring biotechnology-based authentication and authorization, anomalous detection, etc. On the other side, attackers also treat learning algorithms as a tool, as well as a target, to exploit the security vulnerabilities in smart home systems. In this paper, we unify the system architectures suggested by the mainstream service providers, e.g., Samsung, Google, Apple, etc. Based on our proposed overall smart home system model, we investigate the application of learning algorithms in smart home IoT system security. Our study includes two angles. First, we discussed the functionality and security enhancing methods based on learning mechanisms; second, we described the security threats exposed by employing learning techniques. We also explored the potential solutions that may address the aforementioned security problems.
In this paper, we present a signcryption scheme called CP_ABSC based on Ciphertext-Policy Attribute Based Encryption (CP_ABE) [
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