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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With the popularity of Web services adopted for supporting domain applications, recommending and composing appropriate services with respect to user requirements is a challenge. This paper proposes a dynamic programming and variable length genetic algorithm for the recommendation and composition of Web services. Generally, starting and ending services are determined leveraging the constructed service network model. Based on which, services are selected and composed, such that these services should be more appropriate on satisfying users' requirements. Experimental evaluation result shows that our technique is effective and can improve the accuracy of service recommendation.
Blockchain is gaining traction and can be termed as one of the furthermost prevalent topics nowadays. Although critics question about its scalability, security, and sustainability, it has already transformed many individuals' lifestyle in some areas due to its inordinate influence on industries and businesses. Granting that the features of blockchain technology guarantee more reliable and expedient services, it is important to consider the security and privacy issues and challenges behind the innovative technology. The spectrum of blockchain applications range from financial, healthcare, automobile, risk management, Internet of things (IoT) to public and social services. Several studies focus on utilizing the blockchain data structure in various applications. However, a comprehensive survey on technical and applications perspective has not yet been accomplished. In this paper, we try to conduct a comprehensive survey on the blockchain technology by discussing its structure to different consensus algorithms as well as the challenges and opportunities from the prospective of security and privacy of data in blockchains. Furthermore, we delve into future trends the blockchain technology can adapt in the years to come.
Index Terms- Blockchains, Future Trends of Blockchains, Security, Privacy
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs' outstanding capability to learn the input features with deep layers of neuron structures and iterative training process. However, these learned features are hard to identify and interpret from a human vision perspective, causing a lack of understanding of the CNNs' internal working mechanism. To improve the CNN interpretability, the CNN visualization is well utilized as a qualitative analysis method, which translates the internal features into visually perceptible patterns. And many CNN visualization works have been proposed in the literature to interpret the CNN in perspectives of network structure, operation, and semantic concept.
In this paper, we expect to provide a comprehensive survey of several representative CNN visualization methods, including Activation Maximization, Network Inversion, Deconvolutional Neural Networks (DeconvNet), and Network Dissection based visualization. These methods are presented in terms of motivations, algorithms, and experiment results. Based on these visualization methods, we also discuss their practical applications to demonstrate the significance of the CNN interpretability in areas of network design, optimization, security enhancement, etc.
In this paper, we present a new hybrid binary version of dragonfly and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Dragonfly Enhanced Particle Swarm Optimization Algorithm(HBDESPO). In the proposed HBDESPO algorithm, we combine the dragonfly algorithm with its ability to encourage diverse solutions with its formation of static swarms and the enhanced version of the particle swarm optimization exploiting the data with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBDESPO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. Further, we use a set of assessment indicators to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBDESPO algorithm to search the feature space for optimal feature combinations.
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