Abstract This paper establishes that model checking for successor invariant first order logic (FO) is fixed parameter tractable on graph classes that exclude a fixed graph H as a topological subgraph. While model checking for plain FO is well understood on sparse graph classes, extending these tractability results to successor invariant FO where a successor relation is added but formulas must be invariant under... This paper establishes that model checking for successor invariant first order logic (FO) is fixed parameter tractable on graph classes that exclude a fixed graph H as a topological subgraph. While model checking for plain FO is well understood on sparse graph classes, extending these tractability results to successor invariant FO where a successor relation is added but formulas must be invariant under its choice has been challenging. The authors extend prior results on planar and minor excluded graphs by proving tractability for the broader class of graphs excluding a topological subgraph. The proof utilises a decomposition theorem by Grohe and Marx, which constructs ak walk in a super graph while preserving structural sparsity, thereby enabling the simulation of the successor relation via FO interpretation. This allows application of existing FO model checking algorithms on bounded expansion classes. The work narrows the gap between plain and successor invariant FO, and also shows that model checking for order invariant FO is tractable on colored posets of bounded width. The results contribute to algorithmic meta theorems, highlighting how structural graph properties can be leveraged in logic based algorithm design Read More Read Less

Abstract This work centers on the optimization design of the green architectural landscape environment in traditional villages, utilizing the niche genetic algorithm. The aim is to investigate how optimization design techniques can enhance the green architectural landscape environment in these villages. By examining the cultural traits and environmental challenges faced by traditional villages and integrating both domestic and international research advancements, a method grounded in... This work centers on the optimization design of the green architectural landscape environment in traditional villages, utilizing the niche genetic algorithm. The aim is to investigate how optimization design techniques can enhance the green architectural landscape environment in these villages. By examining the cultural traits and environmental challenges faced by traditional villages and integrating both domestic and international research advancements, a method grounded in the niche genetic algorithm is introduced. The results from experiments indicate that this approach significantly improves the green architectural landscape environment in traditional villages, thus elevating the quality of life for residents and their adaptability to the environment. This study offers a novel method and strategy for the sustainable development of traditional villages. Read More Read Less

Abstract The rapid advancement of developments in artificial intelligence (AI), particularly in Generative Adversarial Networks (GANs), has paved the way for the creation of extremely realistic synthetic images that pose a challenge for digital forensics. Traditional image authentication techniques lack the pace to catch up with the growing sophistication of AI-synthesised images, calling for more innovative detection methods. This review examines the potential application of... The rapid advancement of developments in artificial intelligence (AI), particularly in Generative Adversarial Networks (GANs), has paved the way for the creation of extremely realistic synthetic images that pose a challenge for digital forensics. Traditional image authentication techniques lack the pace to catch up with the growing sophistication of AI-synthesised images, calling for more innovative detection methods. This review examines the potential application of deep learning technologies, specifically Convolutional Neural Networks (CNNs), in detecting AI-generated synthetic images. The paper discusses several conventional and deep learning based approaches, compares their performance, and indicates the significance of lightweight CNN models in maintaining computational efficiency without sacrificing accuracy. In addition, it elaborates on the implications of explainable AI in bringing transparency to detection models. The review also explores the importance of synthetic image data in computer vision tasks, as well as its challenges, including domain gaps and biases. Lastly, the application of digital forensics in preventing misinformation and malicious acts involving synthetic images is discussed. The results underscore the importance of reliable and interpretable deep learning based detection methods in preserving the integrity of digital forensic examinations. Read More Read Less