Virus machines, which develop models of computation inspired by biological processes and the spread of viruses among hosts, deviate from the traditional methods. These virus machines are recognized for their computational power (functioning as algorithms) and their ability to tackle computationally difficult problems. In this paper, we introduce a new extension of the matrix-based representation of virus machines. In this way, hosts, the number of viruses and the instructions to control virus transmission are represented as vectors and matrices, describing the computations of virus machines by linear algebra operations. We also use our matrix representation to show invariants, useful in the proofs, of such machines. In addition, an explicit example is shown to clarify the computation and invariants using the representation. That is, a virus machine that computes the discrete logarithm, which relies on the presumed intractability of cryptosystems such the digital signature algorithm.

Abstract Virus machines are computing devices inspired by the transmission and replication of viruses. This model of computation has been proved to be as powerful as Turing machines, while using very simple semantics: instructions can open channels to let viruses travel between different hosts. The basic model is sequential, in the sense that only one instruction can be executed in each time step. This behaviour is, in principle, easy to follow by using pen and paper, but it can become harder when the model is big enough, as it happens with other models of computation. This paper introduces a base software for virus machines that simulates their behaviour and has an easy approach for both researchers and developers. Besides, apart from the simulator, the software has two other main purposes: on the one hand, a experimental validator has been introduced to help the researcher with both the design and the formal verification of such devices; on the other hand, it has included a tool to create a LaTeX graphic of a virus machine with the usual visuals.

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.

Spiking Neural P Systems (SNP) are well-established computing models that take inspiration from spikes between biological neurons; these models have been widely used for both theoretical studies and practical applications. Virus machines (VMs) are an emerging computing paradigm inspired by viral transmission and replication. In this work, a novel extension of VMs inspired by SNPs is presented, called Virus Machines with Host Excitation (VMHEs). In addition, the universality and explicit results between SNPs and VMHEs are compared in both generating and computing mode. The VMHEs defined in this work are shown to be more efficient than SNPs, requiring fewer memory units (hosts in VMHEs and neurons in SNPs) in several tasks, such as a universal machine, which was constructed with 18 hosts less than the 84 neurons in SNPs, and less than other spiking models discussed in the work.

Deep convolutional neural networks have shown advanced performance in accurately segmenting images. In this paper, an SNP-like convolutional neuron structure is introduced, abstracted from the nonlinear mechanism in nonlinear spiking neural P (NSNP) systems. Then, a U-shaped convolutional neural network named SNP-like parallel-convolutional network, or SPC-Net, is constructed for segmentation tasks. The dual-convolution concatenate (DCC) and dual-convolution addition (DCA) network blocks are designed, respectively, in the encoder and decoder stages. The two blocks employ parallel convolution with different kernel sizes to improve feature representation ability and make full use of spatial detail information. Meanwhile, different feature fusion strategies are used to fuse their features to achieve feature complementarity and augmentation. Furthermore, a dual-scale pooling (DSP) module in the bottleneck is designed to improve the feature extraction capability, which can extract multi-scale contextual information and reduce information loss while extracting salient features. The SPC-Net is applied in medical image segmentation tasks and is compared with several recent segmentation methods on the GlaS and CRAG datasets. The proposed SPC-Net achieves 90.77% DICE coefficient, 83.76% IoU score and 83.93% F1 score, 86.33% ObjDice coefficient, 135.60 Obj-Hausdorff distance, respectively. The experimental results show that the proposed model can achieve good segmentation performance.

P systems are non-deterministic, parallel and distributed models of computation inspired by the behaviour and structure of living cells. Spiking neural P systems synthesise the connections that exist between neurons in the human brain, using pulses as a form of transmission of information. Usually, when a spiking neural P system is defined to solve any problem, it is checked in several cases to know if it works for them. But this methodology is not sufficient to verify if the system always works in a correct way. In this work, we introduce a methodology to look for characteristics in computations of spiking neural P systems that can be used to formally verify that the model works as it is intended.

Virus machines are computational devices inspired by the movement of viruses between hosts and their capacity to replicate using the resources of the hosts. This behavior is controlled by an external graph of instructions that opens different channels of the system to make viruses capable of moving. This model of computation has been demonstrated to be as powerful as turing machines by different methods: by generating Diophantine sets, by computing partial recursive functions and by simulating register machines. It is interesting to investigate the practical use cases of this model in terms of possibilities and efficiency. In this work, we give the basic modules to create an arithmetic calculator. As a practical application, two pairing functions are calculated by means of two different virus machines. Pairing functions are important resources in the field of cryptography. The functions calculated are the Cantor pairing function and the Gödel pairing function.

Natural computing is a research area of computer science where different models of computation arise from the inspiration of real-life natural processes. In particular, virus machines are devices inspired by the transmission of viruses between different hosts, and how they replicate in the organism. This paradigm provides devices that can be seen as a network of hosts where the communication between them is controlled by a set of instructions that lead to the transmission of viruses. Virus machines can be seen as generating devices, computing devices and recognizing devices, depending on the possible input and the output of the systems. In this work, we present some machines generating basic sets, computing basic functions and we present recognizer virus machines, capable of solving decision problems in order to create a new complexity theory paradigm with virus machines.