Robust zero-watermarking is a protection of copyright approach that is both effective and distortion-free, and it has grown into a core of research on the subject of digital watermarking. This paper proposes a revolutionary zero-watermarking approach for color images using convolutional neural networks (CNN) and a 2D logistic-adjusted Chebyshev map (2D-LACM). In this algorithm, we first extracted deep feature maps from an original color image using the pre-trained VGG19. These feature maps were then fused into a featured image, and the owner's watermark sequence was incorporated using an XOR operation. Finally, 2D-LACM encrypts the copyright watermark and scrambles the binary feature matrix to ensure security. The experimental results show that the proposed algorithm performs well in terms of imperceptibility and robustness. The BER values of the extracted watermarks were below 0.0044 and the …

The tertiary structures of proteins play a critical role in determining their functions, interactions, and bonding in molecular chemistry. Proteins are known to demonstrate natural dynamism under various physiological conditions, which enables them to adjust their tertiary structures and effectively interact with the surrounding molecules. The present study utilized the remarkable progress made in Generative Adversarial Networks (GANs) to generate tertiary structures that accurately mimic the inherent attributes of actual proteins, which includes the backbone conformation as well as the local and distal characteristics of proteins. The current study has introduced a robust model, ROD-WGAN hybrid, that is able to generate large-scale tertiary protein structures that greatly mimic those found in nature. We have made several noteworthy contributions in pursuit of this objective by integrating the ROD-WGAN model with 

Revealing the tertiary structure of proteins holds huge significance as it unveils their vital properties and functions. These intricate three-dimensional configurations comprise diverse interactions including ionic, hydrophobic, and disulfide forces. In certain instances, these structures exhibit missing regions, necessitating the reconstruction of specific segments, thereby resulting in challenges in protein design, which encompasses loop modeling, circular permutation, and interface prediction. To address this problem, we present two pioneering models: pix2pix generative adversarial network (GAN) and PLM-GAN. The pix2pix GAN model is adept at generating and inpainting distance matrices of protein structures, whereas the PLM-GAN model incorporates residual blocks into the U-Net network of the GAN, building upon the foundation of the pix2pix GAN model. To bolster the models’ performance, we introduce a novel loss function named the “missing to real regions loss” (L_MTR) within the GAN framework. Additionally, we introduce a distinctive approach of pairing two different distance matrices: one representing the native protein structure and the other representing the same structure with a missing region that undergoes changes in each successive epoch. Moreover, we extend the reconstruction of missing regions, encompassing up to 30 amino acids and increase the protein length by 128 amino acids. The evaluation of our pix2pix GAN and PLM-GAN models on a random selection of natural proteins (4ZCB, 3FJB, and 2REZ) demonstrated promising experimental results. Our models constitute significant contributions to addressing intricate challenges in protein structure design. These contributions hold immense potential to propel advancements in protein–protein interactions, drug design, and further innovations in protein engineering. Data, code, trained models, examples, and measurements are available on https://github.com/mena01/PLM-GAN-A-Large-Scale-Protein-Loop-Modeling-Using-pix2pix-GAN_.

Abstract

The optimal requirements selection set aims primarily at careful search for the best requirements set of the next release of software during development process. This procedure is widely defined as the next release problem (NRP), which is also classified as NP-hard dilemma. Several techniques, in literature, have been proposed to tackle NRP. However, in real examples, the earlier studies still immature as NRP still suffers interactions and restrictions that makes the problem more complicated. Although few interesting works have been presented, yet NRP, based on our study, could be further investigated and effectively tackled. In this research, therefore, NRP is devised as a multi-objective optimisation problem. Two clashing objectives (satisfaction and cost) and two constraints (interactions forms) are formulated. To tackle NRP effectively, a new hybrid genetic and artificial bee colony algorithm (HGABC) is introduced. HGABC combines features of genetic and artificial bee colony algorithms. Experimental study, using case studies and three criteria, have been conducted to show HGABC's power of generating non-dominated effective Pareto solutions versus the state-of-the-art algorithms. Results indicate that HGABC does not just outperform its rivals, yet also gives better Pareto solutions in terms of diversity and quality for almost all the instances of

Abstract

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.

—In the field of molecular chemistry, the functions, interactions, and bonds between proteins depend on their tertiary structures. Proteins naturally exhibit dynamism under different physiological conditions, as they alter their tertiary structures to accommodate interactions with other molecular partners. Significant advancements in Generative Adversarial Networks (GANs) have been leveraged to generate tertiary structures closely mimicking the natural features of real proteins, including the backbone and local and distal characteristics. Our research has led to the development of stable model RODWGAN, which is capable of generating tertiary structures that closely resemble those found in nature. Four key contributions have been made to achieve this goal: (1) Utilizing Ratio Of Distribution (ROD) as a penalty function in the Wasserstein Generative Adversarial Networks (WGAN), (2) Developing a GAN network architecture that fertilizes the residual block in generator, (3) Increasing the length of the generated protein structures to 256 amino acids, and (4) Revealing consistent correlations through Structural Similarity Index Measure (SSIM) in protein structures with varying lengths. These model represent a significant step towards robust deep-generation models that can explore the highly diverse set of protein molecule structures that support various cellular activities. Moreover, they provide a valuable source of data augmentation for critical applications such as molecular structure prediction, inpainting, dynamics, and drug design. Data, code, and trained models are available at https://github.com/mena01/Generating-Tertiary-ProteinStructures-Resembling-Nature-using-Advanced-WGAN.

Software-Defined Network (SDN) is an established networking paradigm that separates the control plane from the data plane. It has central network control, and programmability facilities, therefore SDN can improve network flexibility, management, performance, and scalability. The programmability and centralization of control planes in SDN have improved network functions but also exposed it to security challenges such as Distributed Denial of Service (DDoS) attacks that target both control and data planes. This paper proposes an effective detection technique against DDoS attack in SDN data plane and control plane. For the control plane, the technique detects DDoS attacks through a Deep Learning (DL) model using new features extracted from traffic statistics. A DL method (AE-BGRU) for DDoS detection uses Autoencoder (AE) with Bidirectional Gated Recurrent Unit (BGRU). The proposed features for the …