Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Editoria

Crowdfunding Non-fungible Tokens on the Blockchain

Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while blockchains like Ethereum are ideal for smaller works of art, additional support is needed when the artwork is larger than is feasible to store on the blockchain. In this paper, we propose a fundraising mechanism that will help artists to gain financial support for their initiatives, and where the backers can receive a share of the profits in exchange for their support. We discuss our prototype implementation using the SpartanGold framework. We then discuss how this system could be expanded to support large NFTs with the 0Chain blockchain, and describe how we could provide support for ongoing storage of these NFTs

Fake Malware Generation Using HMM and GAN

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the goal to build fake malware data that can be used to effectively train the machine learning models. We use and compare different Generative Adversarial Networks (GAN) algorithms and Hidden Markov Models (HMM) to generate such fake samples obtaining promising results

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Experimental Verification of Optimized Multiscroll Chaotic Oscillators Based on Irregular Saturated Functions

Multiscroll chaotic attractors generated by irregular saturated nonlinear functions with optimized positive Lyapunov exponent are designed and implemented. The saturated nonlinear functions are designed in an irregular way by modifying their parameters such as slopes, delays between slopes, and breakpoints. Then, the positive Lyapunov exponent is optimized using the differential evolution algorithm to obtain chaotic attractors with 2 to 5 scrolls. We observed that the resulting chaotic attractors present more complex dynamics when different patterns of irregular saturated nonlinear functions are considered. After that, the optimized chaotic oscillators are physically implemented with an analog discrete circuit to validate the use of proposed irregular saturated functions. Experimental results are consistent with MATLAB™ and SPICE circuit simulator. Finally, the synchronization between optimized and nonoptimized chaotic oscillators is demonstrated