The transition to sustainable systems often faces significant challenges, with polarisation emerging as a critical obstacle due to divergent views on sustainability. Understanding the underlying drivers of polarisation and promoting inclusivity in decision-making processes is essential for fostering collective action. This study uses the agent-based HUMAT framework to develop a dialogue tool to explore the complexities of community opinion dynamics such as polarisation and potential pathways to reconciliation in divided communities. By simulating scenarios such as unsatisfied, divided, and reconciled communities, the tool provides key insights into how individual characteristics—such as assertiveness and openness to change—affect community cohesion, satisfaction, and collective decision-making outcomes. Moreover, the study examines intervention strategies that reduce polarisation, offering actionable pathways toward consensus. The visual representation of dynamics across different scenarios has the potential to engage stakeholders in the decision-making process, encouraging deeper reflection on community collaboration and participation. While limitations such as simplified assumptions and the lack of empirical calibration limit its predictive accuracy, the dialogue tool shows strong potential for both educational and policy applications. It offers valuable insights into social influences, conformity, and opinion dynamics, making it a promising tool for fostering inclusive, informed decision-making processes and enhancing community engagement in sustainability transitions.

When designing rule-based dialogue systems, the need for elaborate design by the designer is a challenge for dialogue systems. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data, and more specifically on opinion dialogue where the system models the user. In opinion dialogue, there has not been much study of topic transition methods for modeling users while maintaining their motivation for dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user's personality characteristics. The content of questions can give the user a sense of the system's intention to understand them. We also reported the possibility that the system's rational intention contributes to the user's motivation for dialogue with the system.

Sociology and History as consolidated scientific and academic projects have maintained a peculiar and paradoxical relationship. The growing disciplinary and subdisciplinary specialization of these two sciences poses relevant epistemological and methodological challenges to face potential situations of isolation, fragmentation and enable the apology of an interdisciplinary perspective characterized by the historicity of social structures, actions and senses.Several types of reasons shape this diffuse process of approach/distinction between Sociology and History, namely, epistemological and methodological, disciplinary closure, academic, professional, configuring what can be called contained dialogues. This paper, starting from the sociological perspective of the authors, seeks to contribute to the reflection on the relevance of a scientific project aimed at affirming an interdisciplinary perspective that may foster the heuristic potentialities of both Sociology and History in the analysis of the complexity of social reality and human action.

Dialogue State Tracking is an essential component in multi-domain dialogue systems, aiming to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample has limitations and introduces noise. In this paper, we propose an External Slot Relation Memory-based Dialogue State Tracking model (ER-DST). By utilizing an external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Experimental results on the MultiWOZ2.0 and MultiWOZ2.1 benchmarks demonstrate significant improvements while reducing the time complexity to O(n).

Prominent among religions that claim direct or indirect lineage to Abraham are Judaism, Christianity and Islam. The three religions are known as monotheistic. Although, their interpretations of monotheism are closely similar, they have salient differences. Such differences have spurred unhealthy arguments, rivalry and hatred between some Christians and Muslims. Thus, this paper highlights some of the basic differences between Islam and Christianity traceable to scriptural differences and interpretations and the causes of disagreement between Christianity and Islam with a view to offering recommendations that would foster goodwill and engender better relationship between Christians and Muslims. The paper adopted a comparative and analytical system of research with library information and experiences as the major data gathering tools. The study finds that misinformation, ignorance, deliberate distortions, media irresponsibility among others is the major causes of disharmony among Christians and Muslims. The paper recommends mutual love and respect, constant dialogue, education and other factors that would improve the attitude of Christians and Muslims towards one another.

With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve lots of personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. To reduce time and space consumption, we further propose a simple and effective PPR method for the dialogue domain. Experiments demonstrated the superiority of the proposed methods. More importantly, we achieve new SOTA results on SAMSum and AMI datasets.

Multi-stakeholder participation processes in watershed management face challenges due to limited monitoring and baseline data, resulting in a lack of awareness among stakeholders about the current state of the watershed. This knowledge gap often leads to conflicts of interest, where the broader impacts of individual decisions are overlooked. To overcome these limitations, this paper explores the design and implementation of a Serious Game (SG) aimed at co-producing a watershed management plan at the basin scale within the specific context of the Campoalegre River basin in Colombia. By providing an interactive platform, the SG facilitates collaboration between local actors, who may be unfamiliar with existing watershed plans, and decision-makers. The goal is to create a participatory space where stakeholders can comprehend the watershed management plan structure and prioritize actions based on various climatic, social, and economic conditions. Following the application of the SGs, stakeholders demonstrated an improved understanding of the basin, fostering increased participation, open debate, and the proposal of actions. These outcomes serve as valuable inputs for the implementation of water management planning policies, showcasing the potential of SGs in bridging knowledge gaps, and fostering effective multi-stakeholder engagement.

The present research is embedded in the professional development and research line and in the needs of secondary education and first-year university teachers. We focussed on evidencing the importance of teachers’ professional training to include some specific adaptation and skills in intercultural dialogue and understanding -often called Intercultural Competence- because of its direct impact on the sustainable development of human beings, groups, and ecosystems. We investigated the role played by each of the main competencies linked to the following intercultural dimensions: Professional Identity, Ethics and Axiology, Methodology, and Inclusive Education. We used an integrated methodology and a cross-study of data, performed after the obtention of a three-cornered evaluation of results collected in focus groups, interviews, and questionnaires. We were able to show the impact of intercultural dialogue and understanding in the education for a sustainable development pattern. This is fundamental to set up a new ecology of forms, knowledge, attitudes, and educational meanings, further used to update teachers and students’ training in sustainable ecology and cultural diversity. Progress made in these complementary competencies -Professional Identity, Ethics and Axiology, Methodology- were appraised by teachers participating in the present study; the latter showing an increased interest and demand for the intercultural competence, after increasing their proficiency in the other complementary competencies.

“A Picture is worth a thousand words”. Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation aims at generating three cause-and-effect captions (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after for a given image. However, such a task is challenging for machines owing to two limitations: existing approaches (1) directly utilize conventional vision-language transformers to learn relationships between input modalities, and (2) ignore relations among target cause-and-effect captions but consider each caption independently. We propose Cause-and-Effect BART (CE-BART) which is based on (1) Structured Graph Reasoner that captures intra- and inter-modality relationships among visual and textual representations, and (2) Cause-and-Effect Generator that generates cause-and-effect captions by considering the causal relations among inferences. We demonstrate the validity of CE-BART on VisualCOMET and AVSD benchmarks. CE-BART achieves SOTA performances on both benchmarks, while extensive ablation study and qualitative analysis demonstrate the performance gain and improved interpretability.

In an increasingly globalized and interconnected world, the challenge of maintaining social cohesion and cultivating a sense of belonging and inclusion within diverse societies has become a critical priority. Traditional notions of social cohesion, often centered on cultural homogeneity and the assimilation of minority groups, have proven inadequate in the face of rising social, cultural, and demographic complexities. This article proposes a transformative approach to redefining social cohesion through the lens of inclusion and belonging – an orientation that embraces diversity, promotes equity, and empowers marginalized communities as active agents of change. Drawing on a comprehensive review of interdisciplinary literature, the article examines the theoretical foundations and multifaceted dimensions of inclusive social cohesion. The analysis synthesizes insights from social identity theory, intergroup contact theory, the capabilities approach, and institutional theory to develop a holistic conceptual framework. This framework comprises four interrelated pillars: (1) promoting equitable access to resources and opportunities; (2) facilitating meaningful intercultural exchange and mutual understanding; (3) empowering marginalized communities and amplifying their voices; and (4) strengthening institutional responsiveness and adaptive governance. The article then presents a systematic review of innovative case studies from diverse global contexts to illustrate the transformative potential of this inclusive social cohesion approach. The case analyses highlight how these multi-dimensional initiatives have enhanced belonging, challenged structural inequalities, and fostered more equitable and resilient societies. Finally, the article outlines policy recommendations and future research directions to advance the agenda of inclusive social cohesion and catalyze positive societal transformations worldwide. By redefining social cohesion through the lens of inclusion and belonging, this article offers a groundbreaking contribution to the scholarly discourse and policy debates on managing diversity, promoting social integration, and building more inclusive and sustainable communities.

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation

This study investigates how a range of economic, environmental, and social indicators have influenced the concept of inclusive growth in 19 member countries of the Asian Cooperation Dialogue over the period spanning from 1995 to 2021. To analyze these relationships, the research employs the Driscoll Kraay's Standard Errors regression technique, designed to account for factors such as cross-sectional dependence, hetero-scedasticity, and autocorrelation within the data. Furthermore, various preliminary tests were conducted to assess the data for cross-sectional dependence, slope heterogeneity, heteroscedasticity, and autocorrelation. Additionally, the Westerlund cointegration test was employed to evaluate the potential long-term equilibrium relationships among the variables. The findings reveal that the influence of independent variables on the dependent variable, which in this case is the level of inclusive growth (ING), differs significantly among three distinct income categories: lower-middle-income countries (LMYCs), upper-middle-income countries (UMYCs), and high-income countries (HYCs). Notably, the results underscore the overall statistical significance and robust fit of the regression model for all three income groups. One of the primary contribu-tions of this research is the provision of empirical evidence concerning the role played by fishery and aquaculture production in fostering inclu-sive growth in the Asian context. This research also highlights the trade-offs between economic development and environmental sustainability in terms of trade openness, agriculture, forestry and fishing, ecological footprint, and renewable energy utilization. Enhancing inclusive growth in Asia requires improving fishery and aquaculture management, diversifying economic activities, reducing ecological footprint, and increasing renewable energy utilization. The paper suggests some future work directions for extending the analysis to other regions and indicators, as well as incorporating dynamic panel data models and causality tests. The paper also suggests some policy implications for fostering inclusive growth in Asia through regional cooperation, capacity building, technology transfer and green financing.