As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of artificial intelligence systems in critical domains. Explainable artificial intelligence (XAI) aims to provide a suite of machine learning techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical machine learning development process. We classify the various XAI approaches and, using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy.

Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.

The rapid development of multimodal generative vision models has drawn scientific curiosity. Notable advancements, such as OpenAI’s ChatGPT and Stable Diffusion, demonstrate the potential of combining multimodal data for generative content. Nonetheless, customising these models to specific domains or tasks is challenging due to computational costs and data requirements. Conventional fine-tuning methods take redundant processing resources, motivating the development of parameter-efficient fine-tuning technologies such as adapter module, low-rank factorization and orthogonal fine-tuning. These solutions selectively change a subset of model parameters, reducing learning needs while maintaining high-quality results. Orthogonal fine-tuning, regarded as a reliable technique, preserves semantic linkages in weight space but has limitations in its expressive powers. To better overcome these constraints, we provide a simple but innovative and effective transformation method inspired by Möbius geometry, which replaces conventional orthogonal transformations in parameter-efficient fine-tuning. This strategy improved fine-tuning’s adaptability and expressiveness, allowing it to capture more data patterns. Our strategy, which is supported by theoretical understanding and empirical validation, outperforms existing approaches, demonstrating competitive improvements in generation quality for key generative tasks.