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Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.

The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.

The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.

Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

Academic interest in role changes of management accountants (MAs) has increased during the past two decades. Role changes imply identity reconstructions as they do not only require an external legitimacy, but professionals have to internalize a new role script. Thus, this paper aims to contribute to a comprehensive understanding of the ongoing changes concerning MAs by providing an identity perspective.

This paper systematically reviews the literature on the changing role of MAs from an identity perspective, based on a conclusive sample of 64 articles.

This review identified several external factors such as professional associations and educational institutions as well as organizational and individual factors that impact MAs’ identity and act as change drivers. MAs’ identity is linked with their image in the public and within the organization and is challenged by increasing demands, conflicting expectations and technological progress. Hence, the literature sample illustrates a fragmented and contradictory picture regarding the changes of MAs’ identities and roles and displays that the idea of a simple movement from one identity to another is misleading. Furthermore, the identity perspective offers new issues for management accounting research, practice and education such as nested identity, multiple or desired identities.

To the best of the authors’ knowledge, this study is the first to review the literature of MAs’ changing identities and roles from an identity perspective. This perspective enables a novel focus on internal views, perceptions and internalized meanings of MAs connected with their role instead of exclusively debating changed external behavior expectations.

This study aims to simulate the dendritic growth in Stokes flow by iteratively coupling a domain and boundary type meshless method.

A preconditioned phase-field model for dendritic solidification of a pure supercooled melt is solved by the strong-form space-time adaptive approach based on dynamic quadtree domain decomposition. The domain-type space discretisation relies on monomial augmented polyharmonic splines interpolation. The forward Euler scheme is used for time evolution. The boundary-type meshless method solves the Stokes flow around the dendrite based on the collocation of the moving and fixed flow boundaries with the regularised Stokes flow fundamental solution. Both approaches are iteratively coupled at the moving solid–liquid interface. The solution procedure ensures computationally efficient and accurate calculations. The novel approach is numerically implemented for a 2D case.

The solution procedure reflects the advantages of both meshless methods. Domain one is not sensitive to the dendrite orientation and boundary one reduces the dimensionality of the flow field solution. The procedure results agree well with the reference results obtained by the classical numerical methods. Directions for selecting the appropriate free parameters which yield the highest accuracy and computational efficiency are presented.

A combination of boundary- and domain-type meshless methods is used to simulate dendritic solidification with the influence of fluid flow efficiently.

Flooding is an emerging problem in Ho Chi Minh City (HCMC), Vietnam, and is fast becoming a major barrier to its ongoing development. While flooding is presently of nuisance value, there is a growing concern that a combination of rapid urban expansion and climate changes will significantly exacerbate the problem. There has been a trend of population being rapidly accommodated in new urban areas, which are considered highly vulnerable to floods, while the development strategy by the local government still attracts more property investments into the three new districts on the right side of Saigon River. This paper aims to discuss the increase in the number of residences vulnerable to flooding, to underline the need for more appropriate future spatial development. For the vision, an application of compact and resilient theories to strategic planning and management of this city is proposed to reduce vulnerability. This paper also highlights the need to better understand growing vulnerability to floods related to urban expansion over low-lying former wetlands and the more important role of planning spatial development accompanied with transportation investment which can contribute to flooding resilience.

This research uses combined-methods geographical information system (GIS) analysis based on secondary data of flood records, population distributions, property development (with the details of 270 housing projects compiled as part of this research) and flooding simulation. This allows an integrated approach to the theories of urban resilience and compactness to discuss the implication of spatial planning and management in relevance to flooding vulnerability.

The flooding situation in HCMC is an evidence of inappropriate urban expansion leading to increase in flooding vulnerability. Although climate change impacts are obvious, the rapid population growth and associated accommodation development are believed to be the key cause which has not been solved. It was found that the three new emerging districts (District 2, 9 and ThuDuc) are highly vulnerable to floods, but the local government still implements the plan for attracted investments in housing without an integrated flooding management. This is also in line with the development pattern of many coastal cities in Southeast Asia, as economic development can be seen as a driving factor.

The data of property development are diversified from different sources which have been compiled by this research from the basic map of housing investments from a governmental body, the Department of Construction. The number of projects was limited to 270 per over 500 projects, but this still sufficiently supports the evidence of increasing accommodation in new development districts.

HCMC needs neater strategies for planning and management of spatial development to minimize the areas vulnerable to floods: creating more compact spaces in the central areas (Zone 1) protected by the current flooding management system, and offering more resilient spaces for new development areas (Zone 2), by improving the resilience of transportation system. Nevertheless, a similar combination of compact spaces and resilient spaces in emerging districts could also be incorporated into the existing developments, and sustainable drainage systems or underground water storage in buildings could also be included in the design to compensate for the former wetlands lost.

This paper highlights the need to better understand growing vulnerability to floods related to urban expansion over low-lying former wetlands and emphasizes the more important role of planning spatial development accompanied with transportation investment which can contribute to flooding resilience. Coastal cities in southeast countries need to utilize the former-land, whereas feasibility of new land for urban expansion needs to be thoroughly considered under risk of natural disasters.

A combination of compact spaces with improved urban resilience is an alternative approach to decrease the flooding risk beyond that of traditional resistant systems and underlines the increasingly important role of urban planning and management to combat the future impacts of floods.

This paper aims to develop a model that supports public organisations in making informed strategic decisions as to which public services are most suitable to be improved through co-creation. Thus, it first identifies the features that make public services (un)suitable for co-creation and then applies this knowledge to develop a multi-criteria decision support model for the assessment of their co-creation readiness.

The decision support model is the result of design science research. While its structure is determined by a qualitative multi-criteria decision analysis, its substance builds on a content analysis of Web of Science papers and over a dozen empirical case studies.

The model is comprised of 13 criteria clustered into two groups: service readiness criteria from the perspective of service users and service readiness criteria from the perspective of a public organisation.

The model attributes rely on a limited number of empirical cases and references from the literature review. The model was tested by only one public organisation on four of its services.

The paper shifts the research focus from organisational properties and capacity, as the key co-creation drivers and barriers, to features of public services as additional factors that affect the prospect of co-creation. Thus, it makes a pioneering step towards the conceptualisation of the idea of “service readiness for co-creation” and the development of a practical instrument that supports co-creation in the public sector.

Based on an extensive literature review, this chapter outlines key developments in global health and research during the last century with focus on the emergence of violence and child maltreatment as international public health priorities. Violence has been known to humans for millennia, but only in the late 1990s was it recognised as a global public health issue. Every year, an estimated 1 billion children are exposed to trauma, loss, abuse and neglect. Child maltreatment takes a social and economic toll on countries. Research initiated in 1985 found child maltreatment to be associated with increased disease, disability and premature death in adult survivors. The global availability of data on child maltreatment is, however, sporadic with low validity and reliability. Few global experts have consulted and involved the survivors of child maltreatment, as the experts by experience, in their attempts to provide a more comprehensive picture of reality. Youth and adult survivors of child maltreatment are often traumatised by the experience, and it is important to use trauma-informed approaches to prevent re-traumatisation. Participatory and inclusive research on child maltreatment is only in its infancy. There is a need for more inclusive research, designed by survivors for survivors, hereby strengthening local capacity building and informing policymakers from the bottom up. This chapter reviews lessons learnt and provides recommendations for how to enhance the participation and inclusion of the experts by experience in research on child maltreatment.

This paper adds to the literature on societal grand challenges by shifting the focus away from business firms and other formal organizations as key actors in addressing such challenges toward the inherent organizing capacity that lies in the use of language itself. More specifically, we focus on the organizing capacities of metaphor-based communication, seeking to ascertain which qualities of metaphors enable them to co-orient collective action toward tackling grand challenges. In addressing this question, we develop an analytical framework based on two qualities of metaphorical communication that can provide such co-orientation: a metaphor’s (a) vividness and (b) responsible actionability. We illustrate the usefulness of this framework by assessing selected metaphors used in the public discourse to make sense of and organize collective responses to the Covid-19 pandemic, including the flu metaphor/analogy, the war metaphor, and the combined metaphor of “the hammer and the dance.” Our paper contributes to extant research by providing a means to assess the co-orienting potential of metaphors in bridging varied interpretations. In so doing, our framework can pave the way toward more responsible use of metaphorical communication in tackling society’s grand challenges.