The double digest problem (DDP) is a fundamental problem in bioinformatics, and it has been proven to be an NP-hard problem. As a type of promising combinational optimization method inspired by evolutionary theory, genetic algorithms have attracted much attention during the past four decades, and some genetic algorithms have been successfully applied to solve the DDP. To further enhance the ability to solve the DDP, it is of interest to couple the insights from classical genetic algorithms with the parallel capability of quantum computation. Thus, in this paper, we propose a quantum inspired genetic algorithm (QIGA) for the DDP. In our QIGA, the binary Q-bit representation is converted to mapping sequences, and on this basis, DNA fragments are reordered. The solution of the DDP is a permutation, selected by the QIGA, of all the DNA fragments. The effectiveness and efficiency of our proposal can be inferred from the simulation results for the QIGA on a classical computer. As far as we know, this is the first attempt to address the DDP using a QIGA. Compared with classical genetic algorithms for the DDP, our QIGA slightly accelerates the time require to solve the problem, and we believe that it will achieve superior performance when a quantum computer with the required scale is available.

Image recognition has long been one of the research hotspots in computer vision tasks. The development of deep learning is rapid in recent years, and convolutional neural networks usually need to be designed with fixed resources. If sufficient resources are available, the model can be scaled up to achieve higher accuracy, for example, VggNet, ResNet, GoogLeNet, etc. Although the accuracy of large-scale models has been improved, the following problems will occur with the expansion of model scale: (1) There may be over-fitting; (2) increasing model parameters; (3) slow model convergence. This paper proposes a design method for a modular convolutional neural network model which solves the problem of over-fitting and large model parameters by connecting multiple modules in parallel. Moreover, each module contains several submodules (three submodules in this paper) and fuses the features extracted from the submodules. The model convergence can be accelerated by using the fused features (the fused features contain more image information). In this study, we add a gate unit based on the attention mechanism to the model, which aims to optimize the structure of the model (select the optimal number of modules), allowing the model to select an optimum network structure by learning and dynamically reducing FLOPs (floating-point operations per second) of the model. Compared to VggNet, ResNet, and GoogLeNet, the structure of the model proposed in this paper is simple and the parameters are small. The proposed model achieves good results in the Kaggle datasets Cats-vs.-Dogs (99.3%), 10-Monkey Species (99.26%), and Birds-400 (99.13%).

Marmol et al.'s ant algorithm based trust model is improved from several aspects: new interactive ant algorithm, new node types, more reasonable meta-assumption on node behaviors, new trust evaluation function, new penalty mechanism, and so on. Simulations on identifying malicious nodes and electing cluster head show that the proposal is effective and can observably reduce the packet drop ratios.

As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we address the possibility of introducing economic levers, say, dynamic pricing mechanism, and so forth, for regulating IFA in NDN.

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model’s adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling.

Key management techniques for secure wireless-sensor-networks-based applications must minimally incorporate confidentiality, authenticity, integrity, scalability, and flexibility. Signcryption is the proper primitive to do this. However, existing signcryption schemes are heavyweight and not suitable for resource-limited sensors. In this paper, we at first propose a braid-based signcryption scheme and then develop a key establishment protocol for wireless sensor networks. From the complexity view, our proposal is 2¹⁵times faster than RSA-based ones. As far as we know, our proposal is the first signcryption scheme based on noncommutative algebraic structures.