Table of Content

Models of Computation: Specification, Implementation and Challenges

Submission Deadline: 30 May 2022 (closed)

Guest Editors

Prof. Maode MA, Qatar University, Qatar

Prof. Gabriella.Casalino, University of Bari, Italy

Summary

In computer science, especially in computability theory and computational complexity theory, a model of computation is a model which describes how an output of a mathematical function is computed given an input. It describes how units of computations, memories, and communications are organized.

Nowadays, devices such as mobile devices, information-sensing Internet of things devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks are collecting data with sizes that exceed the capacity of traditional software to process. Using computation model encapsulated with probabilities, functions and theories, allows people to process huge amount of data with high accuracy and efficiency. A computational model can cope with complexity in ways that verbal arguments cannot, resulting in satisfactory answers for what would otherwise be ambiguous arguments. Furthermore, computational models can manage complexity at several levels of analysis, allowing data from various levels to be integrated and connected.

Given the above, the aim of this special issue is to introduce novel algorithms or implementations that exploit the models of computation to tackle multiple optimization problems. We invite high quality scientific contributions that explore the specification and practice of computation models. Real-world applications of the proposed algorithms are of significant interest.


Potential topics include but are not limited to the following:

1 Sequential models (finite state machines; Post–Turing machines and tag machines; Pushdown automata; Register machines; Random-access machines; Turing machines)

2 Functional models (Abstract rewriting systems; Combinatory logic; General recursive functions; Lambda calculus)

3 Concurrent models (Actor model; Cellular automaton; Interaction nets; Kahn process networks; Logic gates and digital circuits; Petri nets; Synchronous Data Flow)

4. Deep learning for data managements

5. Model-based knowledge transfer methods

6. Machine learning, deep learning, and optimization techniques for advanced information management



Keywords

Sequential models; functional models; concurrent models; deep learning; machine learning.

Published Papers


  • Open Access

    ARTICLE

    Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

    Zefeng Gu, Hua Chen
    Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2497-2514, 2023, DOI:10.32604/cmes.2023.024332
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract Link prediction, also known as Knowledge Graph Completion (KGC), is the common task in Knowledge Graphs (KGs) to predict missing connections between entities. Most existing methods focus on designing shallow, scalable models, which have less expressive than deep, multi-layer models. Furthermore, most operations like addition, matrix multiplications or factorization are handcrafted based on a few known relation patterns in several well-known datasets, such as FB15k, WN18, etc. However, due to the diversity and complex nature of real-world data distribution, it is inherently difficult to preset all latent patterns. To address this issue, we propose KGE-ANS, a novel knowledge graph embedding… More >

  • Open Access

    ARTICLE

    An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

    Jun Liu, Geng Yuan, Changdi Yang, Houbing Song, Liang Luo
    Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates many unreliable parameters and makes… More >

  • Open Access

    ARTICLE

    A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System

    Siguo Bi, Cong Wang, Jiajie Shen, Wang Xiang, Wei Ni, Xin Wang, Bochun Wu, Yi Gong
    Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 527-538, 2023, DOI:10.32604/cmes.2022.022298
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract The misreading problem of a passive ultra-high-frequency (UHF) radio frequency identification (RFID) tag is a frequent problem arising in the field of librarianship. Unfortunately, existing solutions are something inefficient, e.g., extra resource requirement, inaccuracy, and empiricism. To this end, under comprehensive analysis on the passive UHF RFID application in the librarianship scenario, a novel and judicious approach based on RFID localization is proposed to address such a misreading problem. Extensive simulation results show that the proposed approach can outperform the existing ones and can be an attractive candidate in practice. More >

  • Open Access

    ARTICLE

    Dark-Forest: Analysis on the Behavior of Dark Web Traffic via DeepForest and PSO Algorithm

    Xin Tong, Changlin Zhang, Jingya Wang, Zhiyan Zhao, Zhuoxian Liu
    Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 561-581, 2023, DOI:10.32604/cmes.2022.022495
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract The dark web is a shadow area hidden in the depths of the Internet, which is difficult to access through common search engines. Because of its anonymity, the dark web has gradually become a hotbed for a variety of cyber-crimes. Although some research based on machine learning or deep learning has been shown to be effective in the task of analyzing dark web traffic in recent years, there are still pain points such as low accuracy, insufficient real-time performance, and limited application scenarios. Aiming at the difficulties faced by the existing automated dark web traffic analysis methods, a novel method… More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Based on Multi-Channel Attention Residual Network

    Tongping Shen, Huanqing Xu
    Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 539-560, 2023, DOI:10.32604/cmes.2022.022312
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract For the problems of complex model structure and too many training parameters in facial expression recognition algorithms, we proposed a residual network structure with a multi-headed channel attention (MCA) module. The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples. The designed MCA module is integrated into the ResNet18 backbone network. The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights, and the multi-head structure focuses more on the local features of the pictures, which improves the efficiency of facial… More >

  • Open Access

    ARTICLE

    Certrust: An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

    Lei Yan, Maode Ma, Dandan Li, Xiaohong Huang, Yan Ma, Kun Xie
    Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2137-2162, 2023, DOI:10.32604/cmes.2022.022462
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract The low-intensity attack flows used by Crossfire attacks are hard to distinguish from legitimate flows. Traditional methods to identify the malicious flows in Crossfire attacks are rerouting, which is based on statistics. In these existing mechanisms, the identification of malicious flows depends on the IP address. However, the IP address is easy to be changed by attacks. Compared with the IP address, the certificate is more challenging to be tampered with or forged. Moreover, the traffic trend in the network is towards encryption. The certificates are popularly utilized by IoT devices for authentication in encryption protocols. DTLShps proposed a new… More >

    Graphic Abstract

    Certrust: An SDN-Based Framework for the Trust of Certificates against Crossfire Attacks in IoT Scenarios

  • Open Access

    ARTICLE

    An Unambiguity and Anti-Range Eclipse Method for PD Radar Using Biphase Coded Signals

    Jihong Yan, Weihan Ni, Jianshu Zhai, Haiyang Dong
    Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1337-1351, 2023, DOI:10.32604/cmes.2022.021567
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract Target detection is an important research content in the radar field. At present, efforts are being made to optimize the precision of detection information. In this paper, we use the high pulse repetition frequency (HPRF) transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection. In addition, We also apply Walsh matrix and genetic algorithm (GA) to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak, which make the results more accurate. In a radar receiver, data rearrangement of echo signals is performed, and then… More >

  • Open Access

    ARTICLE

    A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

    Hao Han, Wei Wang
    Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494
    (This article belongs to this Special Issue: Models of Computation: Specification, Implementation and Challenges)
    Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More >

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