Analogical reasoning over Multimodal Knowledge Graphs (MMKGs) couples abductive relation induction with inductive tail completion. However, existing approaches rely on static fusion mechanisms that overlook the inherent asymmetry of modal relevance: while visual cues elucidate concrete entities, they are often noisy or irrelevant for abstract concepts, where text and graph structure provide decisive signals. Furthermore, prior methods fail to enforce consistency between induced relations and the modality patterns implied by the analogical context. To bridge this gap, we introduce RMAR, a Relation-aware Multimodal Analogical Reasoning framework with two complementary paths. An explicit path estimates modality fingerprints to score compatibility during relation induction and guide fusion during tail completion. An implicit path employs adaptive gating to blend structural, textual, and visual signals conditioned on the specific query context. To address the limitations of current benchmarks, which overrepresent concrete entities, we release MCNetAnalogy, and its companion graph, MCNetKG, a rigorous dataset enriched with abstract concepts and actions. RMAR is backbone-agnostic and works with multimodal knowledge graph embedding (MKGE) and transformer-based (MPT) pipelines. Extensive experiments demonstrate that RMAR delivers consistent gains across both embedding-based and transformer-based backbones, achieving a 29% relative improvement on MCNetAnalogy. Ablation studies confirm that RMAR's relation-aware modulation is particularly effective when modal evidence is weak or ambiguous.

Water treatment plants face growing challenges in managing complex infrastructure assets due to fragmented data systems and poor interoperability between design and operational tools. Traditional workflows require manual re-entry of Building Information Modeling (BIM) data into asset management platforms, leading to inefficiencies and information loss. This paper introduces WAM-ONTO, a semantic framework designed to enhance water infrastructure asset management by leveraging building information modeling, ontologies, and rule-based reasoning. WAM-ONTO integrates the Industry Foundation Classes (IFC4) standard with Web Ontology Language (OWL2) to formally represent asset types, lifecycle properties, and spatial-functional relationships. The framework comprises 996 ontology classes structured into twelve domain-specific categories, enriched with 61 object properties and 89 data properties. Semantic reasoning is implemented using SWRL rules and SPARQL queries, enabling automated classification, risk profiling, and maintenance prioritization. The framework was validated through expert interviews with fourteen domain specialists, consistency checks using multiple reasoners, and performance tests demonstrating 94.2% domain coverage and 91% automated classification accuracy on real-world facility data. Expert validation achieved 96% consensus on practical utility and industry alignment. A Clean-in-Place system case study demonstrates WAM-ONTO’s ability to preserve design knowledge during BIM-to-operations transitions while enabling real-time decision support for maintenance planning and risk assessment.

Financial literacy empowers individuals to make informed and effective financial decisions, improving their overall financial well-being and security. However, for many people understanding financial concepts can be daunting and only half of US adults are considered financially literate. Data visualization simplifies these concepts, making them accessible and engaging for learners of all ages. This systematic review analyzes 37 research papers exploring the use of data visualization and visual analytics in financial education and literacy enhancement. We classify these studies into five key areas: (1) the evolution of visualization use across time and space, (2) motivations for using visualization tools, (3) the financial topics addressed and instructional approaches used, (4) the types of tools and technologies applied, and (5) how the effectiveness of teaching interventions was evaluated. Furthermore, we identify research gaps and highlight opportunities for advancing financial literacy. Our findings offer practical insights for educators and professionals to effectively utilize or design visual tools for financial literacy. • This systematic review provides the first structured synthesis of data visualization in financial literacy education, analyzing 37 studies by addressing five key research questions on its historical development, research motivations, financial education pedagogy, visualization technologies, and intervention evaluation. • We identify critical research gaps, including the need for more interactive and adaptive visualization tools, broader audience inclusion beyond students, and stronger empirical validation of visualization effectiveness. These findings offer practical guidance for educators, researchers, and tool developers to enhance financial literacy education.

The ability for computers to interpret and utilise computerised building regulations is crucial for automated compliance checking of buildings, enabling a faster and more objective building permit review. Since natural language regulations are often not designed to be represented formally, integrating formal representations into the regulation drafting process could drive this change. Through in-depth qualitative expert interviews with regulators worldwide, this study examines the drafting process of building regulations. It aims to establish a methodology for developing formal representations in parallel with natural language versions. To be able to do so, the first step is to understand the current workflow and its requirements. The interviewees in this study highlighted several benefits of parallel drafting, including improved regulation quality and reduced inconsistencies. However, they also acknowledged challenges such as the difficulty of modifying existing laws and the significant time and costs required for such changes. This study explores various strategies for integrating formal representations into the drafting process of building regulations. Notably, in many countries, significant progress has been made towards automated compliance checking. However, much of this progress has taken place independently of legislative bodies, limiting the potential of automation in the building permit process. Nonetheless, early efforts by legislators to adopt formal representations are emerging. This study aims to raise awareness of these developments and contribute to the broader understanding of how formal representations can enhance the regulation drafting process.

Current research for node classification focuses on dealing with either graph noise or label noise, but few studies consider both of them. In this paper, we propose a new robust node classification method to simultaneously deal with graph noise and label noise. To do this, we design a graph contrastive loss to conduct local graph learning and employ self-attention to conduct global graph learning. They enable us to improve the expressiveness of node representation by using comprehensive information among nodes. We also utilize pseudo graphs and pseudo labels to deal with graph noise and label noise, respectively. Furthermore, We numerically validate the superiority of our method in terms of robust node classification compared with all comparison methods.

Recent developments show that large transformer-based language models have the capability to generate coherent text and source code in response to user prompts. This capability can be used in the construction domain to interpret building regulations and convert them into a formal representation usable for automated compliance checking. While base-size models can already be taught to perform semantic parsing with decent quality, this paper shows how Intermediate Representations (IRs) can be used to improve the semantic parsing quality. With reversible IRs, the training time was reduced to almost a quarter of the initial duration, and through adding a hierarchical parsing step, improvements of up to 6.6% on F1 scores were reached. Furthermore, intermediate representations provide a novel and interpretable method towards a human-in-the-loop approach for translating building regulations into a formal representation.

Judgment summaries are beneficial for legal practitioners to comprehend and retrieve case law efficiently. Unlike summaries in general domains, e.g., news, judgment summaries often require a clear structure. Such a structure helps readers grasp the information contained in the summary and reduces information loss. To the best of our knowledge, none of the existing text summarizers can generate summaries aligned with the summary structure in the legal domain. Inspired by this observation, this paper introduces a Summary Structure-Enhanced (SSE) method to synthesize structured summaries for legal documents. SSE can easily be incorporated into the Encoder-Decoder framework, which is commonly adopted in state-of-the-art text summarizers. Experiments on the datasets of New Zealand and Chinese judgments show that the proposed method consistently improves the performance of state-of-the-art summarizers in terms of Rouge scores.

Graph Neural Networks (GNN) are an effective technique for node classification, but their performance is easily affected by the quality of the primitive graph and the limited receptive field of message-passing. In this paper, we propose a new self-attention method, namely Chain of Propagation Prompting (CPP), to address the above issues as well as reduce dependence on label information when employing self-attention for node classification. To do this, we apply the self-attention framework to reduce the impact of a low-quality graph and to obtain a maximal receptive field for the message-passing. We also design a simple pattern of message-passing as the prompt to make self-attention capture complex patterns and reduce the dependence on label information. Comprehensive experimental results on real graph datasets demonstrate that CPP outperforms all relevant comparison methods.

Abstract Various attempts and approaches have been made to teach individuals about the knowledge of best practice for earthquake emergencies. Among them, Immersive Virtual Reality Serious Games (IVR SGs) have been suggested as an effective tool for emergency training. The notion of IVR SGs is consistent with the concept of problem-based gaming (PBG), where trainees interact with games in a loop of forming a playing strategy, applying the strategy, observing consequences, and making reflection. PBG triggers reflection-on-action, enabling trainees to reform perceptions and establish knowledge after making a response to a scenario. However, in the literature of PBG, little effort has been made for trainees to reflect while they are making a response (i.e., reflection-in-action) in a scenario. In addition, trainees do not have the possibility to adjust their responses and reshape their behaviors according to their reflection-in-action. In order to overcome these limitations, this study proposes a game mechanism, which integrates spiral narratives with immediate feedback, to underpin reflection-in-action and reflective redo in PBG. An IVR SG training system suited to earthquake emergency training was developed, incorporating the proposed game mechanism. A controlled experiment with 99 university students and staff was conducted. Participants were divided into three groups, with three interventions tested: a spiral narrated IVR SG, a linear narrated IVR SG, and a leaflet. Both narrated IVR SGs were effective in terms of immediate knowledge gain and self-efficacy improvement. However, challenges and opportunities for future research have been suggested.

Virtual Reality (VR) systems have become an affordable mass market technology, along with 3D building design tools such as Building Information Modelling (BIM). This provides architects, designers and their clients with immersive experiences of evolving building designs. VR systems typically require physical controllers to manage 3D navigation and to invoke other functions within the virtual environment. A range of mid-air, contactless gesture detecting technologies exist which offer the potential of more intuitive interactions between a user and the 3D model they are experiencing. This research elicited a mid-air hand gesture set for 3D building navigation from interviews with architects and other design professionals. The gestures proposed by design professionals were evaluated against known usability factors to determine an initial set of gestures which were tested through a VR navigation task. An evaluation study was performed looking at the memorability, intuitiveness and comfort of the gestures. The data from this study suggests that the developed gesture set is effective.

A retrospective is a standard agile meeting practice designed for agile software teams to reflect and tune their process. Despite its integral importance, we know little about what aspects are focused upon during retrospectives and how reflection occurs in this practice. We conducted Case Study research involving data collected from interviews of sixteen software practitioners from four agile teams and observations of their retrospective meetings. We found that the important aspects focused on during the retrospective meeting include identifying and discussing obstacles, discussing feelings, analyzing previous action points, identifying background reasons, identifying future action points and generating a plan. Reflection occurs when the agile teams embody these aspects within three levels of reflection: reporting and responding, relating and reasoning, and reconstructing. Critically, we show that agile teams may not achieve all levels of reflection simply by performing retrospective meetings. One of the key contributions of our work is to present a reflection framework for agile retrospective meetings that explains and embeds three levels of reflection within the five steps of a standard agile retrospective. Agile teams can use this framework to achieve better focus and higher levels of reflection in their retrospective meetings.

The project manager has been a ubiquitous feature of traditional software development projects. However, agile software development (ASD) methods which emphasize selforganizing teams and rapid response to change have done away with the project manager's title. New job titles such as the scrum master and product owner have been introduced instead. It is unclear as to what extent the "project manager" is still encountered in the agile software industry. An online survey was posted out to agile special interest groups on popular social media platforms to discover the frequency of the job title "project manager" in agile projects. Analysis of the 97 responses from 31 countries around the world revealed that: a) the title of project manager is still widely used (67%); b) there is a correlation between the team size and presence of project manager such that there is a higher probability the project manager will be present in teams of 5-10 members and those over 25 members; and c) there is an inverse correlation between the co-location of a team and presence of project manager. Further research is needed to better understand why the project manager continues to be present on ASD projects and how their role may have changed.

A central role of Geographic Information Systems (GIS) is to allow the identification and visualisation of relevant spatial features from typically large volumes of data. This requires a querying system to provide both flexibility and usability. While standard GIS querying capabilities are often either very limited, or require a user to have knowledge in specialised areas, techniques in qualitative spatial reasoning have been developed that provide a powerful and intuitive method of representing and reasoning about spatial information. In this paper we present a method for querying about the qualitative distance between features using dynamic networks such as roads, bus or ferry services, and flight paths, rather than only using the Euclidean distance. Linguistic values are used to implement qualitative distances for the linguistic variable proximity. TreeSap GIS with qualitative querying support is presented to demonstrate how qualitative distance measures through a network can provide both a computationally practical solution and a mechanism through which non-experts benefit from powerful search tools.

The Java Standard API has grown enormously since Java's beginnings, now consisting of over 3,000 classes and 20,000 methods. The intent of this API is to provide high quality components that can be easily reused and so increase the Java developer's productivity - but does it? In this paper, we present a study that begins to answer this question. Specifically we take a corpus-based approach to help determine the "typical" usage of the Standard API. We find that, in an extensive corpus of open-source software, only about 50% of the classes in the Standard API are used at all, and around 21% of the methods are used. We discuss the implications this has for future development of both the API itself, and for tools to support the API.

Crafting a good user experience requires skills in several disciplines. Few people have this breadth of knowledge, and undergraduate computer science students are no exception. Encouraging computer science students to appreciate the ways that other disciplines contribute to Human Computer Interaction is important, yet difficult. Our students learn about this disciplinary interdependence through peer teaching as part of a group project. Each group contains students with complementary skills and we expect a transfer of knowledge. Here we discuss the educational theory behind the project, the project's essential elements and an evaluation of how it aids learning. The model we have developed could be easily adapted for other courses which draw on diverse skills.